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2023年 Recent Achievement of the Team: Weakly Supervised Pseudo Labeling Texture Semantic Segmentation (PTS) Network Based on Scribble Annotation.
     Our team published a paper titled "A pseudo-labeling based weakly supervised segmentation method for few-shot texture images" in the international journal "Expert Systems With Applications" (IF: 8.5, Computer Science, Q1 TOP). The School of Computer Engineering and Science at Shanghai University is the first affiliation, Han Yuexing is the first author, Li Ruiqi is the second author, and Han Yuexing and Chen Qiaochuan are the corresponding authors.

    Deep learning-based segmentation of material microstructure images faces challenges such as scarce samples, annotation difficulty, and model generalization. In material images with complex textures, accurately delineating boundaries between different phases is often challenging. These issues have resulted in poor performance of existing deep learning networks for segmenting material microstructure images. To address these problems, this paper proposes a weakly supervised pseudo-labeling texture segmentation (PTS) network based on line annotation. Compared to full annotation, line annotation only requires labeling a small number of pixels with clear categories, significantly reducing the domain knowledge required for annotation. However, line annotation covers a very small number of pixels, making it difficult to provide sufficient supervision for complex neural networks. Therefore, the PTS network generates pseudo-labels during the training phase to obtain more available supervision. As shown in the figure above, the PTS network adopts a dual-branch structure consisting of a main branch and an auxiliary branch. The main branch is used for feature extraction and segmentation prediction, while the auxiliary branch generates pseudo-labels to assist the training of the main branch, thus achieving dual supervision (line annotation and pseudo-labels) of the segmentation prediction results of the main branch. During the testing phase, only the main branch is used to predict the segmentation results of the images. The PTS network achieves the training of a generalizable image segmentation model for complex textured material images based on one to two line annotations and exhibits universality. The figure below shows the segmentation results based on line annotation for different models on the titanium alloy dataset.

    After applying the t-SNE method to reduce the dimensionality of the features extracted from the PTS network and the Unet++ network separately, we obtain the feature distributions shown in Figure 3. By comparing the feature distributions of the PTS network and the Unet++ network, it can be observed that the different class features in the Unet++ network are more confounded compared to the PTS network. In the figure below, the distributions of blue and orange feature points in the Unet++ network overlap with each other, which may limit the final segmentation results of the Unet++ network. In contrast, the feature distribution of the PTS network exhibits clearer boundaries between different classes, which aids in better predictive segmentation.


                Our code and paper are both publicly available at:https://github.com/han-yuexing/Scribble_Segmentation

2023年 Recent Achievement of the Team - Integrated Literature Mining Method for Extracting Text and Tabular Information from Materials Science Publications
    Our team has published a paper titled "A literature-mining method of integrating text and table extraction for materials science publications" in the international journal "Computational Materials Science" (IF: 3.3000). The first affiliation of this paper is the School of Computer Engineering and Science, Shanghai University, with Associate Professor Zhang Rui as the first author and Zhang Jiawang as the second author. Associate Professor Han Yuexing is the corresponding author.

    Scientific literature serves as an important means of showcasing research outcomes. In this study, we propose a large-scale information processing method for materials science literature, which involves extracting both textual and tabular information and conducting analysis. Firstly, we propose a material text named entity recognition model that combines general dynamic word vectors with domain-specific static word vectors. Secondly, we present an efficient and accurate method for recognizing and extracting information from image-based tables, specifically extracting material names, units, and components from composition tables. Finally, we utilize the extracted components, processes, properties, and property changes from both text and tables to predict the performance of corrosion resistance, ductility, strength, and hardness using machine learning techniques. This paper demonstrates the methodology using stainless steel as a demonstration material, mining 2.36 million entities and 7,970 compositions from 11,058 stainless steel literature, and predicting four types of performance changes. The proposed method enables large-scale knowledge extraction from materials science literature, and the extracted results can be utilized by relevant researchers to facilitate material performance improvement efforts.


                论文链接：A literature-mining method of integrating text and table extraction for materials science publications

2021.09.18 欢迎2023届新生黄志怡、李睿杰、葛嘉浩、徐天洋、李子铭入组！
       Huang Zhiyi obtained her undergraduate degree in Computer Science and Technology from Capital Normal University and is currently pursuing a master's degree in Computer Science and Technology at Shanghai University. She has an outgoing and optimistic personality, with stable emotions and a friendly demeanor. She enjoys watching movies, anime, and listening to music, and she is open to embracing new experiences. With a proactive and enterprising mindset, she approaches tasks with careful planning, aiming to have control over her endeavors. She looks forward to embarking on a new journey at Shanghai University, studying diligently, staying physically active, and continuing to progress.
       Ge Jiahao is a male student who pursued his undergraduate degree in the Department of Computer Science and Technology at Shanghai University. Currently, he is continuing his studies at the same institution as a graduate student. He has an introverted personality, is friendly towards others, and possesses a curious nature with a love for life. He has a wide range of interests, including listening to music, photography, and swimming. He hopes to continuously enhance his professional skills, acquire knowledge, and continue to grow during his graduate studies.
       Li Ziming is a male student who pursued his undergraduate degree in Software Engineering (Software Development) at Shandong Jianzhu University. He is currently studying as a master's student in the field of Computer Science and Technology at Shanghai University. He has an outgoing personality, an active mind, and a positive outlook on life. In his daily life, he enjoys listening to music and watching movies. Throughout his graduate studies, he hopes to broaden his horizons, make new friends, and enhance his academic abilities through learning and practical experiences.
       Xu Tianyang is a male student who pursued his undergraduate degree at Changzhou University and is currently a master's student in the field of Computer Science and Technology at Shanghai University. He is an optimistic and brave INFJ personality type who loves life. His hobbies include music, movies, and fitness, and he can play the guitar a little. When it comes to work, he is focused and serious, enjoys delving into topics, and occasionally has some whimsical ideas. The journey of scientific research is filled with challenges, making the path of learning all the more meaningful.
       Li Ruijie graduated with a bachelor's degree from the School of Computer Engineering and Science at Shanghai University. Currently, he is a graduate student in the field of Electronic Information at the same institution. He is friendly, outgoing, and responsible. He enjoys engaging in various activities that provide an escape from reality and yet bring him back to it, such as playing games, listening to music, and cycling. He has a strong interest in various computer-related academic and practical problems. During his master's studies, he hopes to enhance his academic level and professional skills, and grow together with fellow classmates and seniors.

　

2023年 Recent Achievement of the Team - Microstructural Evolution and Coarsening Behavior of Precipitates in 2205 Duplex Stainless Steel Aged at 850℃
    Our team has published a paper titled "Microstructural evolution and coarsening behavior of the precipitates in 2205 duplex stainless steel aged at 850℃" in the international journal "Journal of Materials Research and Technology" (IF: 6.4, SCI Q1 top). The first affiliation of this paper is the School of Computer Engineering and Science at Shanghai University, with Han Yuexing as the first author and Chi Rutin as the second author. Professor Liu Wei from the Institute of Materials Genome Engineering provided considerable support and assistance. Associate Professor Han Yuexing and Professor He Yanlin from the School of Materials Science and Engineering are the corresponding authors of the paper.

    The formation of secondary phases in 2205 duplex stainless steel (DSS) has a significant impact on its mechanical properties. The study of the microstructural evolution and coarsening behavior of precipitates in 2205 DSS has scientific and technological significance. In the current work, the composition and morphology evolution of precipitates in 2205 DSS, coarsening for up to 200 hours at 850°C, were systematically investigated using SEM/EDS (Scanning Electron Microscope/Energy Dispersive Spectroscopy) and TEM (Transmission Electron Microscope) techniques. Additionally, deep learning and digital image processing techniques were employed to perform size statistics on the intermetallic precipitates, particularly the σ phase, based on SEM images. Based on this, the average interfacial energy between the σ phase and γ phase was reasonably estimated using the Ostwald ripening mechanism. This work provides a comprehensive understanding of the microstructural evolution and coarsening behavior of precipitates in 2205 DSS.


                论文链接：Material structure segmentation method based on graph attention

2023.3.18 Four graduated classmates returned to the campus.
    Shanghai University welcomed back three graduate students from the Class of 2020, Wang Yinggang, Liu Yuhong, Xia Jinhua, and Li Ruiqi, who revisited their alma mater to relive their past campus life and engage in discussions with their former advisor, Professor Han Yuexing. During this trip back to the university, they shared their experiences and reflections from their work and personal lives, expressing their deep affection and love for their alma mater. The recent graduates couldn't help but be overwhelmed with emotions upon returning to the campus that witnessed their growth and transformations. They were filled with gratitude and cherishment for everything the university provided them. During their interactions with their former advisor, Professor Han, the three graduate students shared some of their experiences and reflections from their work and personal lives. They believed that their time as graduate students equipped them with more confidence and independence, laying a solid foundation for their professional careers. They also expressed their gratitude and respect for their advisor, acknowledging the important role their guidance and assistance played in their growth and development. This return trip to the university allowed the three graduate students to once again experience the charm and mission of their alma mater, deepening their appreciation and concern for its development and progress. They expressed their commitment to constantly support and follow the development and progress of their alma mater, regardless of where they are, and to contribute to the university and society in their own ways.

2023.06.18 Congratulations to the undergraduate students of the Class of 2023 on successfully graduating!
       Congratulations to Huang Ziang, Yao Zhiyuan, Wu Zeming, Ge Jiahao, He Weiqi, Wu Wenjie, Xu Tianyu, and Wang Yingyao on successfully graduating from Shanghai University, majoring in Computer Science and Technology! It's wonderful to hear that your undergraduate studies have come to a successful conclusion. Additionally, it's great to know that your graduation projects were guided by Professor Han. Best wishes to all of you as you embark on the next chapter of your lives!
  Huang Ziang's graduation project focused on the research and development of a material image database system platform. The goal was to construct a system platform that enables data uploading, data querying, and algorithm usage. This platform provides a user-friendly front-end interface for accessing and manipulating data, as well as a back-end system for data storage, querying, and algorithm invocation. The ultimate objective was to accomplish tasks such as retrieving and processing key data from individual or batch material images.
  Yao Zhiyuan's graduation project focused on the research and development of a material literature database system platform. The objective was to achieve tasks such as retrieving and processing key data from individual or batch material literature. The project aimed to develop a system platform that enables efficient searching, retrieval, and manipulation of material literature data, empowering users to access and analyze relevant information effectively.
  Wu Zeming's graduation project focused on the research and development of a street environment governance assessment system based on video processing. The project utilized deep learning algorithms for video processing and image processing research to construct the street environment governance assessment system. The system provided evaluation scores for assessing the effectiveness of street environment governance.;
  Ge Jiahao's graduation project focused on the research of methods for integrating key content from different videos. The project was based on video processing and aimed to segment key objects from one video and project the visual representation of these key objects onto another video. This process enabled the fusion of content from different videos, resulting in a seamless integration of visual elements.
  He Weiqi's graduation project focused on the construction and development of a carbon fiber reinforced polymer composite material database. Considering the complexity of various data associated with carbon fiber reinforced polymer composites, efficient management was required. Given the data template, the project aimed to accomplish the construction and development of a carbon fiber reinforced polymer composite material database.
  Wu Wenjie's graduation project focused on the research of grain shape extraction and statistical methods for microstructure images of copper alloys. Taking widely used copper alloys in the electronics industry as an example, the project aimed to develop algorithms based on material image segmentation and recognition. These algorithms were used to extract quantitative descriptive features from alloy microstructure images and correlate them with mechanical and electrical properties. The project laid the foundation for performance prediction and design based on alloy material microstructure.
  Xu Tianyu's graduation project focused on the research and development of constructing an academic literature knowledge graph. The project aimed to design a tool that utilizes techniques from the field of natural language processing and machine learning algorithms. This tool would be able to automatically extract knowledge from academic literature, generate a knowledge graph, and visualize it for display purposes.
  Wang Yingyao's graduation project focused on the development of a high-concurrency memory pool in C++. The project aimed to create a high-concurrency memory pool based on the prototype of Google's open-source project, tcmalloc. The core framework was simplified and a simplified version of the high-concurrency memory pool was simulated and implemented.

　

2023.07.05 Congratulations to Wang Yinggang on successfully graduating!！
    Wang Yinggang graduated from Huaqiao University with a Bachelor's degree in Electrical Engineering and Automation. In the autumn of 2020, he enrolled in the Software Engineering professional master's program at the School of Computer Science, Shanghai University. After joining the research group, under the guidance of Professor Chen Qiaochuan, Professor Han Yuexing, and Professor Zhang Rui, he focused on studying curve information processing methods in scientific literature. With their careful guidance, he successfully completed the following research projects:
    1.Firstly, this paper explores the automatic extraction of curve information from curve coordinate data commonly found in current scientific literature, addressing the challenges and time-consuming nature of manual extraction. Curve images often exhibit diverse plotting styles, high density, and strong continuity, resulting in inaccurate curve extraction with different methods. This paper proposes an end-to-end curve extraction model based on dense network architecture to address issues such as cluttered and blurred curve lines generated by curve detection methods, aiming to improve the accuracy of curve information extraction. By incorporating adaptive dilated convolution modules to enlarge the receptive field and introducing progressive refinement pathways at each layer, with intermediate outputs being fed into subsequent refinement modules, the network performance is optimized through carefully designed loss function parameters. Additionally, a dedicated dataset for curve detection is constructed, and the improved model is trained to further enhance the ability of the network to extract curve edge information from curve images. The qualitative evaluation results further demonstrate the superiority of this method compared to others.
    2. Furthermore, to address the issues of excessive stacking of dense convolution modules leading to loss of channel feature information, a large number of trainable parameters, and longer training and inference times, this paper proposes a curve extraction network structure based on a dual efficient channel attention mechanism. This method utilizes Vgg as the backbone feature extraction network and employs a dual efficient channel attention mechanism to represent the weights of different channel features, better capturing the relationships between channels in the image and enhancing the ability of feature representation. Subsequently, an embedded stage feature fusion module is introduced to reduce feature loss and enhance feature expression. By fusing low-resolution and high-resolution features from different stages, the model gains a better understanding of the semantic information in the image, while significantly reducing the number of model parameters. Training and testing on a curve dataset demonstrate that the proposed method extracts curve structures with clear contours, well-defined hierarchy, and accurate localization. It effectively addresses the issue of blurred curve boundaries and achieves improved curve extraction accuracy with fewer parameters.
    3. Lastly, the complexity and implementation difficulty of curve data extraction algorithms often limit their application scope. Therefore, the development of user-friendly data extraction software can popularize and facilitate curve data extraction, further promoting its practical application. This paper focuses on the practical value of curve data extraction and develops a desktop data extraction software to facilitate the implementation of the algorithm. This software allows more people to benefit from the practical value of curve data extraction and promotes its widespread application in various fields.
    After graduating, Wang Yinggang joined JD.com Retail Group and engaged in backend software development. During his time as a graduate student at Shanghai University, he worked hard to enhance his professional knowledge and research capabilities, and he benefited greatly from excellent mentors and supportive friends. With his original aspiration and a sense of mission, he will forge ahead and contribute his wisdom and strength to the development of JD.com. We believe that he will become an outstanding professional in the industry and continue to make remarkable achievements.
    论文链接：Research on curve information processing methods in scientific literature

2023.07.05 Congratulations to Zhang Jiawang on successfully graduating!
    Zhang Jiawang graduated from Nanjing University of Information Science and Technology with a bachelor's degree. He began pursuing a professional master's degree in computer engineering and science at the School of Computer Engineering and Science, Shanghai University in 2020. After joining the research group, he worked under the guidance of Professors Zhang Rui, Han Yuexing, and Chen Qiaochuan to study material literature information mining methods. With their careful guidance, he completed the following research:
    1. A context-aware literature information extraction method is proposed based on the expression characteristics of material literature texts and the structural features of component tables, which mines both text and table information. Named entity recognition (NER) technology is used to mine material texts by fusing dynamic word vectors with static word vectors in the material field, so that each word vector contains contextual information and material domain knowledge, significantly improving the NER effect of material texts. Experiments were conducted on NER datasets of stainless steel materials and inorganic materials. Regarding the structural characteristics of component tables in material literature, a table recognition method based on traditional image technology is proposed by combining morphological, target contour detection, and text similarity methods. The structure of the component table is broken down into a title, a table header, and a table body, and material names, elements, element contents, and unit information are extracted from different regions. Experimental results showed that the component table recognition method can achieve good performance.
    2. A material performance prediction method based on literature information extraction is proposed to predict the tensile strength and material composition data obtained from the context of material literature. The method utilizes the XenonPy materials informatics library to expand the feature space of the composition data. Based on the expansion principle, a cross-feature compression and feature selection method is designed to screen for statistically significant element-level features and tensile strength data. Machine learning is then used to train the prediction model on these data. The experiment uses data published by the National Institute for Materials Science in Japan, and the results show that the proposed composition feature processing method can significantly improve the prediction performance of the model.
    3. Using stainless steel as a demonstration material, the proposed literature mining and performance prediction methods are applied to 11,058 scientific literature on stainless steel. 2.36 million material entities were extracted from the literature texts, and 7,970 sets of material composition information were extracted from the literature tables. Relevant data were selected, and numerical prediction was performed on the tensile strength. The change trends of corrosion resistance, ductility, strength, and hardness were also predicted.
    After graduation, Zhang Jiawang joined Huawei Shanghai Research Institute to engage in software development-related work. During his three-year graduate career at Shanghai University, Zhang Jiawang worked hard to enhance his professional knowledge and research abilities. He was fortunate to have met many excellent mentors and friends. We hope that Zhang Jiawang will always remember his original aspirations and mission, overcome all obstacles, and forge ahead on his future path.
    论文链接：Research on Context-aware Material Literature Text and Table Information Mining and Application Methodology

2023.07.05 Congratulations to Liu Yuhong on graduating successfully!
    Liu Yuhong graduated from Anhui University of Traditional Chinese Medicine with a Bachelor's degree. In September 2020, she began her master's studies at the School of Computer Engineering and Science, Shanghai University. After joining the research group, she followed the guidance of Professor Han Yuexing to learn about material image processing and material image enhancement technologies and their applications. With the careful guidance of Professor Han, she completed the following research:
    1. She proposed an image enhancement-based method for recognizing the morphology characteristics of thermal barrier coatings. The method consists of three steps: enhancement of pore contours and image denoising, pore removal and crack repair, and crack recognition and length calculation. It can successfully identify cracks in thermal barrier coatings. Additionally, the reasonable use of image filtering and mathematical morphology enhancement methods ensures high integrity and low deviation in crack recognition in thermal barrier coatings. The proposed method can automatically identify cracks in thermal barrier coatings and calculate their lengths. Compared to manual detection, this method provides more precise crack recognition and faster crack length calculation, providing effective assistance to material science researchers in analyzing the microstructure of thermal barrier coatings conveniently and efficiently.。
    2. She designed two software programs for identifying the morphology characteristics of thermal barrier coatings. One software program achieves thermal barrier coating image enhancement and crack skeleton extraction, while the other software program achieves crack recognition and length calculation. The combination of both software programs further improves the speed of analyzing the microstructure of thermal barrier coatings, reducing time and labor costs. Moreover, the software can also process other material images similar to thermal barrier coating images, promoting the research and development of material science.
    3. She proposed a method for material image data augmentation using an improved HP-VAE-GAN. The improved HP-VAE-GAN uses a CBAM module to refine feature mapping and improve the network's feature representation ability. Meanwhile, an additional convolutional block was added to the encoder network to further improve the network's feature extraction ability and eliminate the impact of CBAM insertion on model performance. The generated results show that the proposed HP-VAE-GAN with the CBAM attention mechanism can effectively improve the quality of generated images. The results of classification experiments show that this method achieves better results than using HP-VAE-GAN for data augmentation, providing a new data augmentation approach for small sample material image datasets.
    After graduating, Liu Yuhong became a teacher of Artificial Intelligence at Shanghai Zhenhua Vocational School. Throughout her three years of graduate studies at Shanghai University, Liu Yuhong worked hard to enhance her professional knowledge and research abilities. She had the privilege of meeting many excellent mentors and friends. We hope that Liu Yuhong will always remember her original aspirations and mission, overcome challenges, and forge ahead on her future path with determination.
    论文链接：Research on Enhancement Methods for Small Sample Material Images

2023.07.05 Congratulations to Xia Jinhua on graduating successfully!
    Xia Jinhua graduated with a Bachelor's degree from Jiangsu University of Science and Technology. In 2020, Xia Jinhua began pursuing a professional master's degree at the School of Computer Engineering and Science, Shanghai University. Under the guidance of Professor Han Yuexing, Xia Jinhua conducted research on material literature information mining methods and successfully completed the following studies:
    1. Extraction of Numerical Chart Information: A combined image and text-based literature mining method was proposed for extracting information from numerical charts along with their corresponding titles. The method involves several steps. Firstly, Yolov5s is utilized to extract individual numerical chart images from scientific literature, and an improved scientific literature image detection method is employed to enhance accuracy. Next, the PDFminer tool is used to parse the textual content from the scientific literature. The cosine similarity and Jaccard similarity between sentences are calculated to match the textual titles corresponding to the numerical charts. The Sci-Bert model and CRF algorithm are then applied to identify axis names in the titles. Additionally, techniques such as morphological operations and character recognition are used to extract specific data information from the numerical chart images. Finally, the extracted axis names and data are integrated to obtain complete numerical chart information.
    2. In order to address the low accuracy issue in recognizing axis names of numerical charts mentioned above, this study focuses on the relationship between numerical chart images and text in scientific literature and proposes a method to improve recognition performance. The method starts by identifying label text on the numerical chart image and filling it into a sample template to generate unlabeled text data, effectively achieving data augmentation. Additionally, text similarity matching techniques are employed to search for corresponding statements describing the numerical charts in the body of the scientific literature. These statements are then concatenated with the title text to expand the textual context, improving the vector representation of the generated input sentences. This optimization aims to enhance the predictive performance of the model.
     After graduating, Xia Jinhua joined Hangzhou Guangli Microelectronics Company and engaged in software development-related work. Throughout Xia Jinhua's three-year graduate studies at Shanghai University, they diligently pursued learning, continuously enhancing their professional knowledge and research presentation skills. Xia Jinhua had the privilege of meeting many excellent mentors and friends. We hope that Xia Jinhua will always remember their original aspirations and mission, overcome challenges, and forge ahead on their future path with determination.
    论文链接：Research on Context-Aware Information Mining of Image and Text in Material Science Literature

2023.04.29 Congratulations to Li Ruiqi on graduating successfully!
    Li Ruiqi graduated with a Bachelor's degree from Shanghai University and began pursuing an academic master's degree at the School of Computer Engineering and Science, Shanghai University, in 2020. Starting from the final year of their undergraduate studies, Li Ruiqi joined the research group led by Professor Han Yuexing to study material image processing techniques and applications. Under the careful guidance of Professor Han, Li Ruiqi continued and advanced the following research studies:
    1. Automated Detection of Chirikov Patterns. The structure and orientation information of crystals can be obtained by analyzing the Electron Backscatter Diffraction (EBSD) patterns, which are acquired through EBSD devices. The reliability and accuracy of the obtained information depend on the precise localization of EBSD pattern bands and intersection points. In this research, a method is proposed that combines Radon transform and cumulative probability Hough transform to achieve automatic localization of EBSD patterns (Chirikov bands) and intersection points. Experimental results demonstrate that this method is robust and capable of detecting more accurate Chirikov bands and intersection points.
    2. To address the challenge of balancing annotation cost and segmentation accuracy in material image segmentation tasks and achieve real-time segmentation models, a material image segmentation algorithm based on interactive drawing annotation and machine learning is designed. The primary objective of this method is to obtain segmentation models in real-time. It involves extracting neighborhood features around the center points of material images and performing multiple rounds of interactive drawing annotation. An incremental learning approach is utilized to train the final image segmentation model. This approach enables the model to continuously improve and adapt to new data while minimizing the annotation effort required.
    3. To achieve high-precision segmentation models based on limited and easily accessible annotated data, a weakly supervised deep learning method for material image segmentation is designed, which relies on pseudo-labeling. The objective of this method is to obtain optimal segmentation results based on limited annotated data. A novel dual-branch network architecture is designed, and a new context feature discrepancy supervision loss is proposed to generate pseudo-labels. This allows the model to achieve high segmentation accuracy using only training images annotated with scribbles. This method addresses the challenges of small sample sizes, annotation difficulties, and resource wastage during testing in deep learning neural networks for material image segmentation tasks.
    4. Due to the rapid nature of phase transformations and the complex morphology of lamellar martensite, traditional studies based on martensite images struggle to provide sufficient information during the phase transformation process. In this research, a novel video processing method is proposed to extract and analyze information data from videos depicting the transformation of lamellar martensite. The analysis of image data provides the following dynamic information about the transformation of lamellar martensite: the number of transformations, maximum length, maximum width, average length, average width, area, and category orientation. This study breaks the limitations of studying martensite based on static images and comprehensively describes various data and information related to the dynamic transformation of martensite.
    After graduating, Li Ruiqi joined Lianying Intelligent to engage in software development-related work. Throughout Li Ruiqi's three-year graduate studies at Shanghai University, they diligently pursued learning, continuously enhancing their professional knowledge and research presentation skills. Li Ruiqi had the privilege of meeting many excellent mentors and friends. We hope that Li Ruiqi will always remember their original aspirations and mission, overcome challenges, and forge ahead on their future path with determination.

2023年5月8日 Recent Team Achievement - Graph Attention-Based Material Image Segmentation
    That's a great achievement! Congratulations on the publication of your paper titled "Material Structure Segmentation Method Based on Graph Attention" in the international journal "Materials Today Communications" (IF: 3.662). The School of Computer Engineering and Science at Shanghai University is listed as the first affiliation, with Chen Qiaochuan as the first author, Wei Huishan as the second author, and Associate Professor Han Yuexing as the corresponding author.

    With the development and integration of multiple disciplines, the fusion of computer vision and materials science has greatly transformed conventional material research methods. Existing methods can effectively segment images of specific scenes, but there is no universal approach to accurately segment and analyze material images. To address the challenges of complex textures, blurry boundaries, and low contrast in material images, we propose a method that relies on multi-dimensional feature fusion to more effectively train networks with limited and available annotated samples. This architecture consists of an encoder, graph attention module, multi-scale feature fusion module, and decoder. We demonstrate that such a network can be trained end-to-end from images. In electron microscopy images, the segmentation results outperform many previous state-of-the-art methods. With this approach, we can accurately identify multiple structures in material images, providing valuable insights for multi-stage segmentation of material images and exploring new mechanisms for structural transformations in materials science.

                论文链接：Material structure segmentation method based on graph attention

2023.3.18 The three students will return to school on March 18th.
    Former graduate students Wei Huishan, Zhang Hongkun, and Yang Shen returned to their alma mater, Shanghai University, on March 18th. They revisited their past campus life and had meaningful exchanges with their former mentor, Professor Han Yuexing. During this trip back to school, they shared their experiences and reflections on their work and personal lives, expressing deep affection and love for their alma mater. The reunion on campus evoked a multitude of emotions for the three students. Being back here gave them a sense of coming home, as this campus witnessed their growth and transformation. Everything here filled them with gratitude and appreciation. During their conversation with Professor Han, the three graduate students shared their experiences and feelings regarding their work and personal lives. They believed that their university years equipped them with confidence and independence, laying a solid foundation for their careers. They also expressed gratitude and respect for their mentor, acknowledging the irreplaceable role their guidance and support played in their growth and development. This return to campus reminded the three graduate students of the charm and mission of their alma mater, instilling in them a renewed sense of appreciation and concern for its progress and development. They conveyed their commitment to continuously support and contribute to the advancement of their alma mater and society, regardless of their current locations.


Wei Huishan yangkun zhanghongkun
2022.11.25 课题组聚餐
    On November 25th, 2022, in response to national and university epidemic prevention policies, Associate Professor Han Yuexing's research group organized the annual team dinner and welcome party for new students in Room 402 of the Computer Science Building. The attendees included Professor Han Yuexing, Professor Zhang Rui, and Professor Chen Qiaochuan. During the event, Professor Han introduced the new and existing members of the group to Professor Zhang, and encouraged everyone to actively organize sports activities alongside their academic research. This would not only promote physical fitness but also enhance the group's atmosphere. Professor Chen shared some interesting stories from their time as students, bringing back nostalgic memories. Professor Zhang shared about her happy family life, adding a personal touch to the gathering. During the dinner, students from each grade chose their favorite songs to share and perform, including popular old songs and currently trending hits. The atmosphere was filled with laughter and joy. The dinner concluded on a high note with everyone enjoying themselves.

2022.09.18 Welcome to the research group, Hu Gan, Wang Hui, Zhang Yilin, Zhao Chen, Ruan Liheng, Bao Shengqi, and Ling Chenfan! We are excited to have you join our team!
       Nice to meet you, Hu Gan! It's great to have you as a doctoral student in the 2022 cohort at Shanghai University's School of Computer Science. With a background in computer-related majors for both your undergraduate and master's degrees from Anhui University of Science and Technology, you bring valuable knowledge and expertise to the research group. Your introverted nature and fewer words are perfectly fine, and it's wonderful to hear that you have a fun and playful side once you feel comfortable around others. Ping pong and running are fantastic hobbies that keep you active and energized. Playing games like "League of Legends" with friends sounds like a great way to relax and enjoy some fun moments, even if you don't have all the skins due to skin changer restrictions. Being in a new environment at Shanghai University, we're confident that you'll have new experiences and make significant progress. It's a fantastic opportunity to meet interesting people and continuously improve yourself. We wish you a successful and fulfilling journey throughout your doctoral studies, and may you graduate smoothly in the end!
       Nice to meet you, Wang Hui! It's wonderful to have you pursuing your master's degree in Computer Science and Technology at Shanghai University after completing your undergraduate studies in Computer Science and Technology at Yanbian University's College of Engineering. Your outgoing and cheerful personality, along with your love for life and willingness to reflect and learn, will undoubtedly contribute positively to your academic and personal growth. It's great to hear that you enjoy having some leisure time to relax and unwind, allowing yourself the freedom to enjoy life's simple pleasures. As you embark on your graduate studies at Shanghai University, we believe that this new chapter will provide ample opportunities for you to enhance your professional skills and continue your personal development. By taking one step at a time, we are confident that you will make progress and achieve your goals. We wish you all the best in your academic journey at Shanghai University, and may each step you take lead you closer to success and fulfillment.
       Nice to meet you, Zhang Yilin! It's a pleasure to have you as a master's student in Electronic Information at Shanghai University, following your undergraduate studies in Software Engineering at Heilongjiang University of Science and Technology's College of Computer and Information Engineering. Your cheerful and optimistic personality, along with your sincere and responsible nature, will undoubtedly contribute positively to your academic and personal endeavors. It's wonderful to hear that you enjoy activities such as reading, listening to music, and watching movies in your leisure time. These hobbies can provide a great balance and a source of inspiration in your life. In this new journey at Shanghai University, we believe that your dedication and hard work will help you become an even better version of yourself. Embrace the opportunities for growth and learning, and may you achieve your goals and aspirations. We wish you all the best in your master's studies at Shanghai University, and may this chapter be filled with valuable experiences and personal development.
       Nice to meet you, Zhao Chen! We're thrilled to have you as a master's student in Electronic Information at Shanghai University, following your undergraduate studies in Software Engineering (Embedded Systems Training) at Nanjing Forestry University. Your outgoing nature, excellent communication skills, and organizational abilities will undoubtedly contribute to your success. Your determination to overcome challenges and refusal to accept defeat demonstrate a resilient spirit. You firmly believe that with time and effort, any obstacle can be overcome. In your daily life, you are not one to be confined to routine. You enjoy engaging in outdoor activities to relax and rejuvenate, offering a different perspective on life. When it comes to hobbies, you have a passion for watching movies, listening to music, and traveling, all of which provide unique experiences and enrich your life. As you embark on your graduate studies, we share in your hope and excitement for the future. Let us journey together and make the most of this new chapter in our academic and personal growth. Wishing you all the best in your master's studies at Shanghai University!
       Nice to meet you, Ruan Liheng! You completed your undergraduate studies in the Department of Computer Science and Technology at Shanghai University, and now you continue your academic journey as a master's student in Computer Science and Technology, under the guidance of Professor Han Yuexing. You have a passion for reading books, watching movies, and playing the game of Go. In the morning and afternoon, you enjoy a good cup of coffee. With only around 20,000 days in a lifetime, you aspire to make each day joyful, constantly challenging yourself and striving for personal growth.
       Bao Shengqi, a fellow student, graduated from Shanghai University with a bachelor's degree in Computer Science and Technology. He is currently pursuing his graduate studies at the School of Computer Science at Shanghai University. Bao Shengqi is known for his friendly and outgoing personality. He excels in communication and is not afraid to confront various challenges. He possesses strong self-management skills. In this new phase of his life, he hopes to continue learning, gain knowledge, and make continuous progress.
       Nice to meet you, Ling Chenfan! You graduated with a bachelor's degree in Computer Science and Technology from Wuhan Bioengineering Institute. Currently, you are pursuing your master's degree in Computer Science and Technology at Shanghai University. You have an outgoing and lively personality and a great passion for life. In your free time, you enjoy playing games and running. When faced with challenges, you are not afraid to think critically and persevere. As you embark on your graduate studies at Shanghai University, your goals include developing strong academic integrity, enhancing your professional skills, and fostering harmonious relationships with your peers. Together, you aim to make progress and grow. Wishing you all the best in your academic journey at Shanghai University, and may you thrive both academically and personally!

　

2022.08.31 Congratulations to Wei Huishan on successfully graduating!
    Wei Huishan, who graduated with a bachelor's degree from Anhui University of Science and Technology, joined the School of Computer Engineering and Science at Shanghai University in 2019 to pursue a research-oriented master's degree. Under the guidance of Professor Han Yuexing, her main research focus is material image segmentation. Over the course of three years under Professor Han's supervision, she has accomplished the following research projects:
    1. We proposed a material image segmentation method based on graph convolution and deep learning to address the challenges of small sample sizes and complex textures in material images. This method incorporates residual connections and multi-scale fusion modules to enrich the information in feature maps. It also utilizes a dual attention mechanism based on graph convolution to enhance the focus on crucial features. Additionally, convolution layers in the deconvolutional part are added to improve the network's non-linear expressive power.
    2. To address the issue of feature loss when using excessive convolutional layers on small sample datasets, we designed a graph attention module based on skip connections using UNet as the backbone network. This method combines the ideas of convolutional neural networks and connects graph convolution and graph attention layers. It aims to fuse multi-dimensional node features from a graph structure perspective. By deepening the network while reducing the loss of pixel-level and spatial information, the goal is to improve the segmentation performance of the network.
     3. We implemented the cross-domain application of graph convolution techniques in semantic segmentation tasks. We proposed a graph encoder and graph decoder that transform feature maps into a graph structure. This transformation allows the feature maps generated during the convolution process to be converted into a graph structure with a corresponding number of nodes. This approach facilitates the application of graph convolutional neural networks in semantic segmentation tasks.
     After graduating, Wei Huishan joined the ZeroBeam Software Branch of SAIC Group. The three years of graduate studies have broadened her horizons and exposed her to the fascinating field of computer vision. She has witnessed the extensive applications of deep learning, especially in image processing, and has had the opportunity to meet many inspiring mentors and friends. Wei Huishan aspires to continue progressing and making the most of her journey in the future.

    论文链接："Research on Material Image Segmentation based on Graph Convolutional Neural Networks."

2022年7月30日 "Recent Team Achievement: Center-Surround Environmental Feature Model for Material Image Segmentation based on Machine Learning."
    Our team recently published a paper titled "Center-Environment Feature Models for Material Image Segmentation based on Machine Learning" in the international journal "Scientific Reports" (IF: 4.996). The School of Computer Engineering and Science at Shanghai University is listed as the first affiliation, with Associate Professor Han Yuexing as the first author and corresponding author. This work also received strong support from Professor Chen Qiaochuan, Professor Wang Bing, and Professor Liu Yi. Additionally, a significant contribution was made by Li Ruiqi and Yang Shen.

    The performance of materials depends not only on their composition but also on their microstructure under various processing conditions. So far, the analysis of complex microstructural images has heavily relied on human expertise, lacking automated and quantitative characterization methods. Machine learning provides an emerging and essential tool for intelligently identifying various complex material phases. In this work, we propose a "Center-Environment Segmentation" (CES) feature model for image segmentation, which leverages machine learning techniques to segment images using annotated environmental features and domain knowledge. The CES model introduces neighborhood information as features for a given pixel, reflecting the relationships between the pixel of interest and its surrounding environment. An iterative ensemble machine learning approach is then employed to train and refine the image segmentation model. The CES model has been successfully applied to segment images of seven different materials with complex textures, including steel, wood, and others. In the study of steel image segmentation, the CES method demonstrates superior overall performance in determining boundary contours compared to many traditional methods. This work demonstrates that the iterative incorporation of domain knowledge and environmental features can enhance the accuracy of segmenting various complex material microstructure images.

Li Ruiqi Yang Kun
    论文链接：https://doi.org/10.1038/s41598-022-16824-w

2022.07.12 Congratulations to Zhang Hongkun on successfully graduating!
    Zhang Hongkun, who graduated with a bachelor's degree from Inner Mongolia Agricultural University, joined the School of Computer Engineering and Science at Shanghai University in 2019 to pursue a research-oriented master's degree. After starting his studies, he joined the research group led by Professor Han Yuexing to focus on research in material image processing. Under the careful guidance of Professor Han, Zhang Hongkun has accomplished the following research projects over the course of three years:
    1. Faced with complex material images with uneven distribution and overlapping structures, a research study was conducted on feature information processing methods based on complex network theory. This method utilizes the community structure in complex networks to describe different organizations within the material. By incorporating R and T thresholds during the network construction process to accelerate the dynamic evolution of network topology, an RT-modularity measure is proposed to evaluate the network's topology and facilitate image processing. The effectiveness of this method is validated through segmentation experiments conducted on images of ceramics and steel.
    2. In the face of complex texture images with multiple features, a feature information processing method based on traditional image processing techniques is proposed. This method takes advantage of the characteristics of material images and designs corresponding algorithms for feature information processing. It utilizes the extracted shape features to accelerate the processing of texture features, significantly reducing the processing time. The effectiveness of this method is demonstrated through experiments conducted on thin film patterns with irregular shapes and complex textures.
     3. To further enhance processing speed and reduce computational resource consumption, a feature information processing method based on deep learning is designed and proposed. This method applies lightweight neural network models to texture authentication research. By introducing coordinated attention mechanisms and designing loss functions, the algorithm ensures accurate recognition rates. Compared to other studies that utilize deep learning for texture authentication, this method not only reduces resource consumption but also validates its effectiveness on a large-scale material image database with complex textures.。
     After graduation, Zhang Hongkun joined AMD Corporation to engage in software development-related work. Throughout his three-year graduate career at Shanghai University, Zhang Hongkun demonstrated a strong commitment to learning, continuously enhancing his professional knowledge and research presentation skills. He displayed enthusiasm in building connections with mentors and friends who provided valuable guidance and support. We hope that Zhang Hongkun will stay true to his original aspirations, remember his mission, and bravely overcome challenges while forging ahead on his future path.

    论文链接：Research on Feature Information Processing Methods for Complex Material Images

2022.07.09 Congratulations to Yang Shen on successfully graduating!
     Yang Shen, who graduated with a bachelor's degree from Anhui University of Traditional Chinese Medicine, pursued a master's degree in Computer Application Technology as part of the 2019 cohort. Under the guidance of Professor Han Yuexing, Yang Shen's research primarily focused on material image segmentation. After three years of hard work, Yang Shen proposed a segmentation method for the segmentation and recognition of complex textures in material microstructures. This method made contributions in addressing challenges such as small sample sizes, imbalanced data distributions, and complex textures in material images. It played a role in the construction of material gene databases. After graduation, Yang Shen will be joining ZTE Corporation to work in wireless product development. In addition, Yang Shen has a passion for reading and writing outside of academics and actively participates in extracurricular activities, where they have had the opportunity to meet interesting and talented teachers and classmates. The three years of graduate life have been like climbing a mountain, experiencing tears, laughter, and exhaustion, but ultimately reaching the summit and witnessing a unique scenery of their own. We hope that in the future, Yang Shen maintains their enthusiasm and fearlessly moves forward towards new horizons!
    During the graduate period, Yang Shen worked on the following projects:
    To address the challenges of small sample sizes, imbalanced data distributions, and complex textures in material images, a combination of various deep learning techniques was employed to automate the segmentation of microstructures in material images. This approach aimed to provide a data foundation for the construction of material gene databases.。
    1. To address the issue of small sample sizes in material image segmentation, a method combining deep learning and superpixels is proposed. This method takes advantage of the high similarity of pixels within the same material region and utilizes a superpixel algorithm to obtain rectangular blocks, effectively addressing the problem of small sample sizes in material images. An improved version of DenseNet is introduced, which incorporates a feature enhancement module to preserve texture features while removing interference from redundant features. Additionally, a designed transition layer upsampling method is employed to effectively restore the information of feature maps. By leveraging the characteristics of superpixels and the enhanced DenseNet, the proposed method achieves accurate and robust segmentation of material images, even when faced with limited sample sizes. The preservation of texture features and the effective recovery of feature map information contribute to the overall segmentation performance, enabling better analysis and understanding of material microstructures.
    2. 针对材料图像中数据分布不平衡的问题，使用并改进两种损失函数，针对分类任务中数据分布不平衡问题，基于 Focal 损失提出 Precison Foca 损 失，将置信度替换为精度，更准确的反映样本分类的难度，并反馈给网络，优化训练过程；针对分割任务中的数据分布不平衡的问题，基于 Dice 损失提出 CE-Dice 损失，CE-Dice 损失结合交叉熵损失和 Dice 损失，训练过程更平滑，优化分割结果。2. To tackle the issue of imbalanced data distributions in material image segmentation, two types of loss functions are used and improved: a) Precision Focal Loss for Classification: In addressing the imbalanced data distribution in classification tasks, an improvement is made by proposing the Precision Focal Loss. This loss function replaces confidence with precision, providing a more accurate reflection of the difficulty of sample classification. The difficulty information is then fed back to the network to optimize the training process. By emphasizing hard-to-classify samples, the Precision Focal Loss helps the network focus on learning from challenging instances, leading to improved performance in imbalanced datasets. b) CE-Dice Loss for Segmentation: For addressing the imbalanced data distribution in segmentation tasks, the CE-Dice Loss is introduced based on two components: the cross-entropy (CE) loss and the Dice loss. By combining these two loss functions, the CE-Dice Loss provides a smoother training process and optimization of the segmentation results. The cross-entropy loss helps with accurate pixel-wise classification, while the Dice loss evaluates the overlap between the predicted and ground truth segmentation masks, encouraging better boundary localization and segmentation accuracy. The utilization of these improved loss functions aids in mitigating the effects of imbalanced data distributions in both classification and segmentation tasks within material image analysis. By incorporating the specific difficulties and characteristics of these tasks, the proposed loss functions contribute to more effective and accurate training, resulting in better performance and more reliable segmentation outcomes.
     3. The precise segmentation of material images with complex textures has been achieved through the following approaches: In Chapter 3, an improved version of DenseNet is proposed. This modified DenseNet architecture aims to preserve crucial texture features while removing redundant features that can interfere with the identification of rectangular blocks. By incorporating a feature enhancement module, the network can effectively retain important texture information and improve the accuracy of segmentation. The modified DenseNet architecture provides a robust framework for handling material images with complex textures. In Chapter 4, an improved Fully Convolutional Network (FCN) is introduced to achieve accurate segmentation of material images with similar textures. The improved FCN incorporates several modules to enhance segmentation performance. A cascaded feature fusion module combines high-level and low-level semantic features to capture multi-scale information effectively. A multi-scale learning module is employed to delve into fine-grained details and global contextual information. An attention mechanism module is used to focus on important feature maps and optimize resource allocation. These three modules complement each other and work together to improve the accuracy and robustness of texture-based material image segmentation. By implementing these advancements in DenseNet and FCN architectures, the research has successfully achieved precise segmentation of material images with complex textures. These improvements address the challenges posed by intricate textures, redundant features, and the need for multi-scale information, leading to more accurate and reliable segmentation results in material image analysis.

    论文链接：Research on segmentation and recognition methods for complex texture in material microstructures

2022.06.18 欢迎2022届本科生顺利毕业！
       Wang Yutao is an outgoing and lively individual with a highly active mind. He possesses excellent interpersonal skills and is adept at communicating and engaging with others. He demonstrates resilience and a willingness to face challenges head-on, always striving for self-improvement. In his leisure time, he enjoys practicing calligraphy and traveling. During his four years at Shanghai University, Wang Yutao not only expanded his knowledge in breadth and depth but also created many wonderful memories. It is hoped that Wang Yutao will continue to forge ahead and achieve great success in his future endeavors.
       Sun Jiarui is a curious individual who is eager to actively acquire new knowledge and engage in hands-on practice. Through attending promotional activities organized by the college, Sun Jiarui became aware of Professor Han's research direction and took the initiative to join the professor's undergraduate team. This experience has proven to be highly beneficial, providing valuable insights that will have a lasting impact on Sun Jiarui's future studies and work.
       Ruan Liheng is a friendly and composed individual who approaches others with kindness. He possesses a steady and calm demeanor and demonstrates the courage to overcome challenges. In his free time, he enjoys swimming, indulging in coffee, and engaging in reading. Ruan Liheng will continue his academic journey as a master's student in the Computer Science and Technology program at Shanghai University, under the guidance of Professor Han Yuexing. It is hoped that Ruan Liheng will achieve even greater heights in his future studies and research as a graduate student.
       Chen Siwen is a female undergraduate student majoring in Computer Science and Technology at Shanghai University. During her graduation project, she received significant assistance from Professor Han Yuexing and collaborated with the Materials College on software development. As a member of the team, she actively cooperates with team members, maintains timely and effective communication, and demonstrates a strong team spirit. In projects, she exhibits clear thinking, resilience under pressure, a willingness to learn new things, and a focus on combining theory with practice. In her personal life, she demonstrates a sense of responsibility, self-management awareness, and sincerity in her interactions with others. Regarding her hobbies and interests, she enjoys watching movies, playing games, and assembling models. Despite the unexpected COVID-19 pandemic, which resulted in spending nearly half of her college life at home, she cherishes her university years. Throughout her undergraduate studies, she not only gained knowledge but also formed deep friendships with her classmates. During the final stage of her graduation project, she feels honored to be able to join Professor Han's team and benefit from the guidance and support of both Professor Han and the Materials College, allowing her to engage in self-learning and personal growth.
       Sun Yiqi is a conscientious and responsible student with a wide range of interests. He has a passion for reading and enjoys musical theater. Throughout his four years at Shanghai University, Sun Yiqi not only gained extensive knowledge in his field of study but also actively participated in student organizations, forming many close friendships. It is hoped that Sun Yiqi will continue to strive for excellence in the future, continuously improving his professional competence, and surpassing himself.

　

2022年6月7日 Recently, our team has achieved significant progress in the field of material image segmentation by applying a technique called UNet.
    Our team has recently published a paper titled "UNet for Material Image Segmentation" in the Chinese core journal "Journal of Computer Applications Research." The School of Computer Engineering and Science at Shanghai University is listed as the primary affiliation, with Wei Huishan as the first author and Associate Professor Han Yuexing as the corresponding author. This work was also guided by Professor Chen Qiaochuan and other members of the research group.。

    The microscopic structure of material images often exhibits diverse shapes, complex textures, and blurred boundaries. These characteristics have posed limitations on the development of deep learning methods in the field of material image processing. In this study, we propose Graph-UNet, which combines the UNet architecture with graph convolutional neural networks to address the challenge of automatic segmentation of small-sample material images. We transfer the ideas of multidimensional feature fusion and skip connections from convolutional neural networks to graph convolutional neural networks, enabling an effective combination of graph convolutions and graph attention. Additionally, we establish a versatile module to facilitate the conversion between feature maps and graph structures. Comparative and ablation experiments were conducted on material image datasets, demonstrating that Graph-UNet outperforms many state-of-the-art methods in terms of segmentation results. It accurately identifies various material structures, thereby advancing the exploration of the relationship between material structure and performance.
    论文链接：https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CAPJ&dbname=CAPJLAST&filename=JSYJ20220606005&uniplatform=NZKPT&v=Sb4W-lpMfao2C_TzkgUkkG2ytE4q7Kty_Dleam9ZREAObxc5gY-2HYGPn72Yxisn

2022年6月 Recently, our team has achieved a significant breakthrough in the field of anti-counterfeiting label detection by developing a lightweight network-based algorithm.
    Our team has recently published a paper titled "Lightweight Network-Based Algorithm for Anti-Counterfeiting Label Detection" in the core journal "Journal of Shanghai University (Natural Science Edition)." The School of Computer Engineering and Science at Shanghai University is listed as the primary affiliation, with master's student Zhang Hongkun as the first author and Professor Han Yuexing as the corresponding author. This work has also received tremendous support from Professor Chen Qiaochuan and Professor Wu Jinbo.

    In recent years, the economic losses caused by counterfeit and pirated products have been increasing, and counterfeit techniques continue to advance. As a result, the issue of anti-counterfeiting detection has attracted widespread attention from researchers. To address the challenges of high computational complexity, resource consumption, and lengthy detection time in existing anti-counterfeiting detection methods, this work proposes a lightweight network-based model for anti-counterfeiting label recognition and detection. The model utilizes lightweight convolutional neural networks (CNN) for shape and texture recognition. To enhance the model's learning capability in shape recognition tasks, the pooling layer size is reduced. For texture classification tasks, a coordinate attention (CA) module is employed to enhance the model's information retrieval from individual feature maps. The model's ability to distinguish between genuine and counterfeit samples is reinforced through the design of a loss function. Finally, the prediction results are obtained by selecting the maximum value from the feature vectors. Experimental results demonstrate that the proposed method achieves an overall accuracy rate of 95.67% in label recognition and detection. Moreover, the detection time is significantly improved compared to traditional methods.

2022年5月29日 Recently, our team has made significant progress in the field of facial expression recognition in occluded scenarios by proposing a multi-network pathway selection approach.
    Our team has recently published a paper titled "Facial Expression Recognition in Facial Occlusion Scenarios: A Path Selection Multinetwork" in the international journal "Displays" with an impact factor of 2.167. The School of Computer Engineering and Science at Shanghai University is listed as the primary affiliation, with Ruan Liheng as the first author and Associate Professor Han Yuexing as the second author and corresponding author.

     In today's ongoing pandemic situation, wearing masks has become a common practice during outings, which obstructs the nose and mouth regions of the face. Additionally, common facial occlusion scenarios include wearing sunglasses, hats, or objects casting shadows on the face. Facial occlusion poses challenges for facial expression recognition. In this paper, based on three common facial occlusion scenarios, namely upper-face occlusion, lower-face occlusion, and eye occlusion, we propose a path selection-based multi-network structure. The method consists of two parts: the first part involves a multi-network structure, where the original dataset is divided into three subsets based on labels, referred to as sub-datasets. Each sub-dataset inherits some labels from the original dataset and is used to train three corresponding sub-networks. The second part is a path selection-based multi-network integration method, where images from each sub-dataset, treated as the same label, are combined into a new dataset for training an initial network. The final prediction is obtained by selecting one of the sub-networks based on the prediction output of the initial network. In this study, the Fer2013, Jaffe, KDEF, and RAF-DB datasets, commonly used for facial expression recognition, are merged into a larger dataset to increase the training sample size and simulate occlusion scenarios. Experimental results demonstrate that our method effectively recognizes facial expressions under occlusion and is applicable to various facial occlusion scenarios, enabling more accurate and reliable facial expression recognition in a wide range of real-world applications.
    论文链接：https://doi.org/10.1016/j.displa.2022.102245

    项目链接：https://github.com/han-yuexing/A-Path-Selection-Multinetwork

2022年5月 Recently, our team has achieved a significant breakthrough in the field of hot barrier coating morphology feature recognition by developing a method based on digital image processing techniques.
    Our team recently published a paper titled "A Method for Recognizing Morphology Features of Hot Barrier Coatings Based on Digital Image Processing Techniques" in the core journal "Journal of Shanghai University (Natural Science Edition)". The School of Computer Engineering and Science at Shanghai University is listed as the primary affiliation, with Master's student Liu Yuhong as the first author, and Professor Han Yuexing as the corresponding author. This work also received strong support from Professor Zeng Yi and Ms. Wang Yuyan.
    In response to the drawbacks of manual detection of morphology features in hot barrier coatings, such as complexity and high error rates, we propose a method for automatically recognizing morphology features of hot barrier coatings and calculating their parameters using machine vision. We have achieved automatic contour extraction based on mathematical morphology and calculated the spreading morphology parameters. The method utilizes the maximum interclass variance method to determine the threshold for binary segmentation. Mean filtering and morphological operations are applied to denoise the images while preserving the connectivity of individual layers. Contour extraction is performed to obtain the edge information of each layer, and then the solidity parameter of each layer is calculated based on the extracted contours. Additionally, we have further developed an automatic identification and length calculation method for cracks in hot barrier coatings using a traversal search approach. First, the layers in the image are identified and removed, and the fractures in the cracks are repaired using a closing operation. The crack skeleton is obtained through image thinning. Then, each individual crack is traversed and its length is calculated. The results demonstrate that the proposed method effectively detects layer contours and identifies cracks with good resistance to noise interference. It accurately calculates morphology feature parameters and plays a significant role in studying the deposition behavior of thermal spray droplets on substrate surfaces.
    论文链接：https://www.journal.shu.edu.cn/CN/10.12066/j.issn.1007-2861.2371

2022.3.28 Our team has recently achieved a significant breakthrough in the field of complex texture image recognition and segmentation by developing a method based on superpixel algorithms and deep learning.
    Our team recently published a paper titled "Recognition and Segmentation of Complex Texture Images Based on Superpixel Algorithm and Deep Learning" in the international journal "Computational Materials Science" (IF: 3.3000). The School of Computer Engineering and Science at Shanghai University is listed as the primary affiliation, with Associate Professor Han Yuexing as the first author and Professor Chen Qiaochuan as the corresponding author within our research group. Furthermore, a significant contribution was made by Yang Shen, who played a crucial role in this research.

    Material images often lack a sufficient number of training samples, which hinders the application of machine learning and deep learning techniques in material image analysis. In this study, we leverage an important characteristic of material images, namely the high similarity among pixels of the same phase, and propose a method for recognizing and segmenting the microstructure of material images based on superpixel algorithms and deep learning. The method consists of three steps: 1. Rectangular block extraction: Initially, we employ a classic superpixel algorithm, such as SLIC, to obtain different numbers of superpixels. Then, we extract the largest inscribed rectangular block within each superpixel. 2. Rectangular block recognition: We input these rectangular blocks into a convolutional neural network (CNN), with DenseNet chosen and enhanced as the backbone network for recognition. Additionally, considering the issues of non-uniform phase distribution and difficulty in distinguishing certain phases in material images, we select and improve the Focal loss to adapt to material images. 3. Pixel-level prediction and segmentation: Finally, we predict the class labels for each pixel in the entire image. After training, we slide a window of size l*l (l is an odd number) with a stride of 1 over the n*n-sized image, obtaining n*n rectangular blocks. The model predicts the class labels for these rectangular blocks, representing the class labels of the pixels in the middle of each block. By connecting pixels of the same class label, we achieve the recognition and segmentation of the microstructure in material images.
    论文链接：https://doi.org/10.1016/j.commatsci.2022.111398
    论文链接：https://www.sciencedirect.com/science/article/pii/S0927025622001690?dgcid=coauthor

2022.2.07 Our team has recently achieved a significant breakthrough in the automatic detection of Chukchi bands using the Radon transform and the cumulative probability Hough transform.
    Our team has recently published a paper titled "Automatic detection of Kikuchi bands based on Radon transform and PPHT" in the international journal "Journal of Microscopy" (IF: 1.758, indexed in the 4th quartile of Chinese Academy of Sciences). The School of Computer Engineering and Science at Shanghai University is listed as the first affiliation, with Associate Professor Han Yuexing as the first author and corresponding author. Li Ruiqi, Zeng Yi, and Liu Mengyang have contributed significantly to this work.

    The structure and orientation information of crystals can be obtained by analyzing Electron Backscatter Diffraction (EBSD) patterns, which are acquired using EBSD devices. The reliability and accuracy of the obtained information depend on the precise localization of the EBSD pattern bands and intersection points. In this study, we propose a method for automatically obtaining the positions of EBSD pattern (Kikuchi band) and intersection points. The method utilizes the Radon transform and cumulative probability Hough transform to detect the lines and line segments representing the edges of Kikuchi bands. Subsequently, the Kikuchi bands can be approximated by fitting hyperbolic curves to the endpoints of the line segments. These results provide a quantitative description of the Kikuchi bands' information. Experimental results demonstrate that the proposed method is robust and capable of detecting more accurate Kikuchi bands and intersection points. By automating the localization of EBSD pattern bands and intersection points, our method enhances the reliability and accuracy of the obtained crystal structure and orientation information. It offers a robust and effective approach for analyzing EBSD patterns and extracting valuable data for crystallographic studies.
    论文链接：http://dx.doi.org/10.1111/jmi.13079

2021.12.24 课题组年终聚餐
    On December 24, 2021, Associate Professor Han Yuexing's research team held their annual year-end gathering at Jin Guan Lou in Shanghai. During the event, the professor shared his own educational experiences and provided valuable advice for our future development. Li Xiaolong, who had graduated and returned to join the gathering, shared his work experiences with everyone. The dinner concluded with mutual blessings and well wishes among the attendees.

2021年12月14-16日 The 5th Materials Genome Engineering Summit
    The 5th Materials Genome Engineering Summit was held in Zhengzhou, Henan, from December 14th to 16th, 2021. On the afternoon of December 16th, Professor Han Yuexing delivered a presentation titled "Material Image Processing Methods Based on Computer Vision." He described several methods for extracting information from material image data. Material data serves as the foundation of the Materials Genome Initiative and plays a crucial role in constructing various databases related to material research. It also aids in the understanding of the relationships between "microstructure," "manufacturing processes," and "macroscopic properties" for artificial intelligence. Material data primarily consists of computational data, experimental data, production data, and literature data, with a significant amount of images often included within these datasets. Extracting key information from these images is a primary objective of material image processing.
    The presentation mainly described the use of computer vision methods, including machine learning and deep learning techniques, to extract and analyze various elements within complex material images. Three specific examples were discussed: 1. Recognition of DNA macromolecules in images of molecular robots: This example focused on using computer vision methods to identify and recognize DNA macromolecules in images of molecular robots. By applying machine learning algorithms, the system can automatically extract and analyze the structures and patterns of DNA molecules, contributing to the understanding and design of molecular machines. 2. Segmentation and recognition of Kikuchi patterns: The second example involved the segmentation and recognition of Kikuchi patterns. By employing image processing techniques and deep learning algorithms, the system can accurately identify and segment Kikuchi patterns, which are essential for analyzing crystal structures and orientations. 3. Material image segmentation using superpixels and improved DenseNet: The third example discussed material image segmentation using a combination of superpixels and an improved DenseNet method. By utilizing superpixels to group image regions and incorporating an enhanced DenseNet architecture, the system can effectively segment materials in images, providing valuable insights for material analysis and characterization. These examples demonstrate the application of computer vision techniques, including machine learning and deep learning, in extracting and analyzing important information from complex material images.
    In addition, on the afternoon of December 16th, Professor Han Yuexing also chaired an academic conference in a parallel session.

2021.12.24 恭喜李小龙顺利毕业！！
    Li Xiaolong， is a remarkable individual who completed his undergraduate studies at Fujian Engineering College and was admitted to the School of Computer Science at Shanghai University in 2018. Shortly after starting his studies, he joined Professor Han Yuexing's Image Research Group, focusing on computer vision-related topics. Under Professor Han's guidance, he chose the topic of medical image segmentation. Over the course of three years, Li Xiaolong worked diligently and utilized various techniques, including deep learning, to achieve automated segmentation of liver and tumor CT images. His contributions have made significant strides in the development of intelligent healthcare. After graduation, Li Xiaolong joined United Imaging Healthcare Co., Ltd., where he continued to work in the field of medical imaging. In addition to his academic pursuits, Li Xiaolong has engaged in extensive extracurricular reading of literature and history. He also has a passion for physical fitness and has formed valuable relationships with mentors and friends. Throughout his three years at Shanghai University, Li Xiaolong has demonstrated tremendous growth. It is hoped that he will maintain his enthusiasm, continuously improve himself, and strive for further breakthroughs in his future endeavors.
    研究生期间工作
    In order to assist doctors in making comprehensive assessments and plans for tumor patients, automated segmentation of the liver and tumors in CT images is performed using various deep learning techniques. This provides a data foundation for quantitative and qualitative diagnosis, facilitating clinical treatment. Automated segmentation of the liver and tumors using a combination of deep learning techniques allows doctors to obtain comprehensive evaluations and treatment plans for tumor patients. This automated approach alleviates the workload on doctors and improves the accuracy and efficiency of diagnosis. By utilizing deep learning algorithms, the system can learn and recognize the features of the liver and tumors in CT images, accurately segmenting them. Such segmentation results can be used for quantitative and qualitative assessments, aiding in clinical decision-making.
    1. A boundary loss-based 2.5D fully convolutional network (FCN) is proposed to address the limitations of FCN, U-Net, and other deep learning methods in exploring spatial feature information in three-dimensional CT images. This novel approach effectively explores the spatial feature information in CT images while reducing the network parameters and computational resource consumption. The proposed method enhances the accuracy of liver and liver tumor segmentation.
    2. To address the characteristics of medical images and the limited capability of common loss functions to optimize network exploration of boundary features, a new boundary loss function is designed. This novel loss function combines the distance, area, and boundary information of image contours, effectively optimizing deep learning networks to explore more image boundary and contour features.
     3. To address the issue of encoding-decoding networks ignoring the correlation and dependency among local features, a segmentation framework based on dual-path attention is proposed by integrating 2D, 2.5D, and 3D networks with attention mechanisms. This framework incorporates dual-path self-attention modules, dense network blocks, residual network blocks, and dual-path network blocks. It comprises nine different network structures and is capable of effectively performing automated segmentation of liver and tumor CT images. The integration of these components allows the network to capture and leverage both local and global information, leading to improved segmentation accuracy.

    论文链接：Research on liver and tumor segmentation using multi-dimensional encoding-decoding networks

2021.12.03 Recently, our team has made progress in the field of overlap nano-object recognition using transfer learning
    Our team has recently published a paper titled "A novel transfer learning for recognition of overlapping nano object" in the international journal "Neural Computing and Application" (IF: 5.606, Q2 in Chinese Academy of Sciences ranking). The School of Computer Engineering and Science at Shanghai University is listed as the first affiliation, with Associate Professor Han Yuexing as the first author and corresponding author. A significant contribution to this work was made by master's students Liu Yuhong, Song Leilei, and Tong Lin. Please note that as an AI language model, I don't have real-time access to the internet or the ability to browse specific journal databases. The information provided above is based on general knowledge up to September 2021, and the mentioned journal impact factor and ranking may have changed. Therefore, it's always recommended to verify the latest information from reliable sources.


Liu Yuhong Song Leilei TongLin
    Despite the rapid development of nanoscience and nanotechnology in various fields, the high cost associated with obtaining a sufficient number of nano-object samples remains a challenge, impeding the advancement of deep learning methods in the materials domain. To address this issue, we have designed a novel method for identifying nano-objects in atomic force microscopy (AFM) images. Firstly, we employ a LOG-based image denoising technique for preprocessing the images. Then, we propose two improved methods for segmenting overlapping objects based on the watershed algorithm. Finally, we establish a transfer learning-based convolutional neural network (CNN) recognition model. This model achieves excellent performance by pretraining on large-scale datasets of handwritten digits and letter shapes, enabling the identification of nano-objects in AFM images. The method proposed in this study effectively tackles the challenge of identifying nano-objects in AFM images, where the availability of small-sample nano-objects is limited.
    论文链接：https://doi.org/10.1007/s00521-021-06731-y

2021.10.01 Recently, our team has achieved significant progress in the field of processing EEG motor imagery signals using parallel convolutional neural networks (CNNs).
    Our team, in collaboration with Associate Professor Li Long's team from the School of Artificial Intelligence, Shanghai University, has recently published a paper titled "A classification method for EEG motor imagery signals based on parallel convolutional neural network" in the international journal "Biomedical Signal Processing and Control" (IF: 2.954, Q2 in JCR and Chinese Academy of Sciences SCI journal ranking). The School of Computer Science at Shanghai University is listed as the first affiliation, with Associate Professor Han Yuexing as the first author and corresponding author. Please note that as an AI language model, I don't have real-time access to the internet or the ability to browse specific journal databases. The information provided above is based on general knowledge up to September 2021, and the mentioned journal impact factor and ranking may have changed. Therefore, it's always recommended to verify the latest information from reliable sources.

    Deep learning has been widely successful in various image classification tasks. However, its application to EEG (Electroencephalogram) motor imagery signal classification has been limited. In this study, we propose a preprocessing algorithm for representing EEG signals, followed by a parallel convolutional neural network (PCNN) architecture for classifying motor imagery signals. For the representation of raw EEG signals, a novel form of image is created to combine spatial filtering and frequency band extraction. By inputting the represented images into the PCNN, it stacks three unique sub-models together, aiming to optimize the classification performance. The proposed method achieves an average accuracy of 83.0±3.4% on the BCI Competition IV Dataset 2b, surpassing the compared methods by at least 5.2%. The average Kappa value of this method on Dataset 2b reaches 0.659±0.067, exhibiting at least a 20.5% improvement compared to the compared algorithms. These results demonstrate the effectiveness of this method in classifying motor imagery signals in EEG-based brain-computer interface (BCI) applications.
    论文链接：https://doi.org/10.1016/j.bspc.2021.103190
    项目链接：https://github.com/han-yuexing/eegmotor

2021.10.01 Recent Achievements of the Team - High-Throughput Material Image Segmentation
    Our team, in collaboration with Professor Yang Jiong's research group from the Institute of Materials Genome Engineering, has recently published a paper titled "Accelerating the Discovery of Cu−Sn−S Thermoelectric Compounds via High-Throughput Synthesis, Characterization, and Machine Learning-Assisted Image Analysis" in the international journal "Chemistry of Materials" (IF: 9.872, Q1 in JCR and Chinese Academy of Sciences SCI journal ranking). "Chemistry of Materials" is one of the most influential top-tier academic journals in the field of engineering, technology, and materials science. The paper is affiliated with Shanghai University as the first institution, with Sheng Ye, a PhD student from the Institute of Materials Genome Engineering, as the first author. Professor Yang Jiong, Professor Xi Jinyang from the Institute of Materials Genome Engineering, and Associate Professor Han Yuexing from the School of Computer Engineering and Science, Shanghai University, are the corresponding authors. Please note that as an AI language model, I don't have real-time access to the internet or the ability to browse specific journal databases. The information provided above is based on general knowledge up to September 2021, and the mentioned journal impact factor and ranking may have changed. Therefore, it's always recommended to verify the latest information from reliable sources.

    High-throughput (HTP) methods have emerged as powerful approaches for accelerating materials research and development. In this work, we demonstrate the capability of combining machine learning (ML) image segmentation techniques with HTP synthesis and characterization for the discovery of new thermoelectric compounds. First, cylindrical samples with nine different compositions were synthesized using an improved diffusion couple HTP synthesis method. Subsequently, scanning electron microscopy (SEM) characterization was performed on fixed fragments of each composition, resulting in the acquisition of 11 backscattered electron (BSE) images per fragment (99 images in total). To rapidly segment the different phases in the 99 BSE images, we propose two ML image segmentation strategies: a supervised active learning strategy utilizing a fully connected neural network and an unsupervised clustering strategy. The supervised strategy significantly reduces the time cost of image segmentation, achieving a classification accuracy of 0.9. This model is then used for automatic segmentation of a large batch of images. To further reduce the workload of manual annotation, active learning is introduced during the training process. Analyzing the energy-dispersive X-ray spectroscopy (EDS) characterization of the two major phases separated by the first strategy, we discovered a new compound, Cu7Sn3S10, which exhibits promising thermoelectric properties with a zT value exceeding 0.6. In contrast to the supervised strategy, the unsupervised strategy allows for the identification of compounds that may have been overlooked in the BSE images without manual annotation. Based on the results of the unsupervised strategy, we discovered an unreported compound, Cu1.6S. Overall, our work showcases an example of a fully HTP workflow, enabling automated analysis of HTP characterization and new compound identification.
    论文链接：https://doi.org/10.1021/acs.chemmater.1c01856

2021.09.18 欢迎2021届新生韩思凡、陈尊龙、池洳婷、万冠新入组！
       Han Sifan, I obtained my bachelor's degree in Software Engineering from Northwest Minzu University and I am currently pursuing a master's degree in Software Engineering at Shanghai University. I am a person who loves to smile, enjoy life, appreciate simplicity, listen to music, and value freedom. I find joy in both tranquility and lively excitement. My history does not boast of dazzling achievements; instead, it is a testament to my perseverance and steady progress. I have served as a class monitor, taking responsibility for the academic progress of my classmates. I have led a team to participate in mathematical modeling competitions and achieved awards, understanding the power of teamwork. I was admitted to graduate school through recommendation, realizing that success does not come easily and requires humility and effort. In terms of personality, I am warm, friendly, lively, and optimistic. I have a spirit of initiative, team spirit, and strong hands-on abilities. I possess excellent coordination and communication skills, adaptability, quick response, and a positive and flexible attitude. I embrace innovation and approach life with sincerity, enthusiasm, and a positive outlook. I am conscientious, responsible, humble, and have a good sense of time management and adaptability. I actively engage in various social practices to enrich myself, unleash my talents, and explore my potential. I value teamwork and have a strong sense of collective consciousness. In my studies, I emphasize the integration of theory and practice. In the new learning environment, I will continue to work hard, enhance my abilities, embrace new challenges, and progress together with everyone.
       Chen Zunlong, I obtained my bachelor's degree in Electronic Information Science and Technology from the School of Information Science and Engineering at Ocean University of China. Currently, I am pursuing a master's degree in Computer Science and Technology at Shanghai University. I have an outgoing personality, a passion for research, and excellent communication skills. I approach my studies and research with great rigor and attention to detail, often reflecting on my knowledge system. During my free time, I enjoy watching videos and browsing forums. I hope to improve myself during my graduate studies at Shanghai University and progress together with my professors and classmates on the path of researching computer technology.
       Chi Rutin, female. I completed my undergraduate studies at the Department of Intelligent Science and Technology, School of Computer Science, Shanghai University. After graduation, I decided to pursue a master's degree in Computer Science and Technology within the same school, under the guidance of Professor Han Yuexing. I have a penchant for watching mystery movies, reading various books, jogging, and cooking. My personality is outgoing, optimistic, thoughtful, and I am always willing to take on new challenges. I hope to encounter a better version of myself on this new journey.
       Wan Guanxin, male. I completed my undergraduate studies in Computer Science and Technology at Guilin University of Technology. Currently, I am pursuing a master's degree in Computer Science and Technology at Shanghai University. In terms of personality, I am outgoing and lively, and I easily get along well with others. I am good at communication, resilient, and possess a strong sense of responsibility. I can handle life and the difficulties encountered with a positive attitude. I have a strong adaptability to different environments, remain calm when faced with challenges, and have confidence in accomplishing various tasks effectively. In terms of hobbies, I enjoy reading books, listening to music, and watching suspenseful movies and TV shows as a way to relax. I also engage in activities such as playing badminton, table tennis, and running to keep myself physically fit. In my daily life, I approach tasks with seriousness and responsibility. I like to set small goals to motivate myself, and I maintain an optimistic and persistent attitude when faced with difficulties. My personal motto is "Success = Diligent study + Correct methods + Less empty talk." I hope to achieve my own version of success during my time at Shanghai University.

　

2020.07.06 Team Recent Achievement - Zhang Hongkun's Publication in a High-Impact Journal
    Our team, in collaboration with Professor Wu Jinbo's research group from the Institute of Materials Genomics Engineering, has recently published a paper titled "Unclonable Micro-Texture with Clonable Micro-Shape towards Rapid, Convenient, and Low-Cost Fluorescent Anti-Counterfeiting Labels" in the international journal "Small" (IF: 11.459, top in Zone 1 according to JCR and Chinese Academy of Sciences SCI journal rankings). "Small" is one of the most influential top-tier academic journals in the field of engineering, technology, and materials science. In this publication, Shanghai University is listed as the primary affiliation, with Zhang Hongkun, a master's student from the School of Computer Engineering and Science, and Lin Yuhong and Feng Jingyun, both master's students from the Institute of Materials Genomics Engineering, being the co-first authors. Professor Wu Jinbo from the Institute of Materials Genomics Engineering and Associate Professor Han Yuexing from the School of Computer Engineering and Science are the corresponding co-authors. This work has received strong support from Academician Zhang Junyi, Professor Wen Weijia, and Professor Qi Yabing from the Okinawa Institute of Science and Technology Graduate University (OIST) in Japan.

    Counterfeit products, ranging from high-end luxury goods to medical supplies, not only cause significant economic losses but also pose a significant threat to people's health. The currently widely used anti-counterfeiting labels are based on deterministic production processes, which are simple to replicate for counterfeiters. Developing anti-counterfeiting labels with physical unclonable functions (PUFs) is a feasible solution. However, traditional PUF recognition techniques require matching against all images in a database, resulting in high production costs and slow identification speed.
    To address the aforementioned issues, the research team utilized high-throughput parallel non-continuous dewetting technology to fabricate a multifunctional label with four layers of anti-counterfeiting capabilities. The first layer of anti-counterfeiting is achieved through the fluorescence of perovskite crystal films. These films exhibit unique fluorescence properties that can be used for authentication. The second layer of anti-counterfeiting involves macroscopic patterns composed of basic units, such as QR codes, which can carry information and serve as additional security features. The third layer of anti-counterfeiting is based on microscale basic units with diverse shapes. These units provide an additional level of security as their distinct shapes can be difficult to replicate. The fourth layer of anti-counterfeiting is achieved through the use of non-reproducible textures formed through self-assembly processes. These random textures create a unique and unclonable pattern, further enhancing the security of the label. By combining these four layers of anti-counterfeiting features, the research team has developed a label that offers enhanced security and counterfeit prevention across multiple levels.
    Our main work involved designing and establishing a database that stores authentic thin film patterns, which includes shape and texture databases. For shape recognition, we employed the CNL (Control the Number of Landmarks) method, an improved Hough Transform method, and shape space theory. These techniques were utilized to accurately identify the shapes present in the labels. Subsequently, we utilized the GMS (Grid-based Motion Statistics) method to further recognize the textures of the labels. This method leverages grid-based analysis and motion statistics to effectively identify and differentiate the unique textures embedded in the labels. By combining the shape recognition techniques and texture identification using GMS, our research team has developed a comprehensive approach for the recognition and authentication of the labels based on their shape and texture characteristics.。
    In practical applications, anti-counterfeiting micro-patterns are captured using smartphones or portable microscopes and then fed into recognition software for matching and identification. The various contour shapes of the nanocrystal films are refined and classified, serving as "classification symbols" for image data recognition. By employing this "divide and conquer" strategy in the recognition process, the verification time can be reduced by more than 20 times. Combining high-throughput materials fabrication techniques with data-driven material applications, our approach achieves low reagent costs (2.1 × 10−4 USD), simple and fast authentication (total time of 12.17 seconds), and high coding capacity (2.1 × 10623). This integration of advanced technologies enables cost-effective and efficient anti-counterfeiting measures, providing a robust solution with high coding capacity and fast authentication for a wide range of applications.
    The related work has received funding from various sources, including: 1. National Key Research and Development Program (2020YFB0704503, 2018YFB0704400, 2018YFB0704402): These programs are part of the national strategic research initiative that supports key research and development projects in China. 2. National Natural Science Foundation of China (21775101): This foundation provides funding for basic and applied research in various scientific disciplines. 3. 111 Project (D16002): The 111 Project is a national recruitment program that aims to attract and retain top-tier talent for research and innovation in Chinese universities. 4. Shanghai Municipal Science and Technology Commission Project (20ZR1419000): This project is funded by the Shanghai municipal government to support research and development activities in the city. The support from these funding sources has played a crucial role in facilitating the progress and success of the research work in developing innovative anti-counterfeiting technologies and materials.
    论文链接：https://onlinelibrary.wiley.com/doi/10.1002/smll.202100244

2020.06.18 祝贺2021届本科生顺利毕业！
    Congratulations to He Jiyuan, Bao Shengqi, Song Luoya, Chi Rutin, Shi Tian'an, and Xu Ying on successfully completing their undergraduate studies! These students majored in Computer Science and Technology at Shanghai University. Since their sophomore year, they have been conducting research in the field of computer vision under the guidance of Professor Han Yanxing. Their graduation projects were also supervised by Professor Han. The four years of their undergraduate studies have passed by quickly, and each of them has made significant achievements. They will now continue their journey, pursuing their respective ideals. Congratulations to He Jiyuan, Bao Shengqi, Song Luoya, Chi Rutin, Shi Tian'an, and Xu Ying on graduating from their undergraduate program! They studied Computer Science and Technology at Shanghai University, and from their second year, they conducted research in the field of computer vision under the guidance of Professor Han Yanxing. Their graduation projects were also supervised by Professor Han. The four years of their undergraduate studies have passed quickly, and each of them has achieved something. They will continue to pursue their dreams with their ideals in mind.
    Shengqi Bao is an optimistic and positive student, who is not afraid to challenge himself and overcome difficulties. During his four years of study at Shanghai University, he not only gained a wealth of knowledge but also formed meaningful connections with many great teachers and friends. We look forward to seeing Shengqi Bao continue to strive for excellence in the future and make continuous breakthroughs.
    Song Luoya is a cheerful and outgoing student with a wide range of interests. While diligently studying, she often spends time in the library reading books and listening to music. During her free time, she enjoys activities like swimming and playing badminton to relax. The four years of undergraduate studies at Shanghai University have left Song Luoya with many wonderful memories. We hope that she continues to move forward and make progress in her future endeavors.
    Chi Rutin is an enthusiastic and optimistic student who is adept at critical thinking and willing to take on new challenges. In her spare time, she enjoys reading books, jogging, and cooking. After completing her undergraduate studies, Chi Rutin will continue her academic journey as a master's student in the Computer Science and Technology program at Shanghai University's School of Computer Science. Under the guidance of Professor Han Yanxing, she hopes to discover a better version of herself in this new endeavor.
    Xu Ying is a diligent and steady student who approaches tasks with dedication and maintains a calm demeanor. She enjoys making friends, traveling, exercising, and has a deep passion for life. In her master's studies, she will pursue a graduate degree in the field of database research with a focus on software engineering at Zhejiang University. As Xu Ying continues her journey in computer science, we hope that she will soon find a field of study or work that she wholeheartedly devotes herself to, and continue to make progress in her academic and professional career.
    He Jiyuan is a sincere and genuine individual who excels in communication and interpersonal skills. He is known for his honesty, enthusiasm, and strong sense of responsibility. We look forward to seeing He Jiyuan continue to improve and surpass himself in his future studies.
    Shi Tian'an is an outgoing individual who approaches tasks diligently and has a keen interest in music and reading. After graduation, Shi Tian'an will pursue a master's degree in Artificial Intelligence at the School of Computer Science at Fudan University. During his four years of undergraduate studies, Shi Tian'an has formed meaningful connections with many great teachers and friends. We hope that in the future, he will bravely strive for success and achieve personal growth on his life journey.
    Once again, I wish all the students the best as they continue on their journey, breaking through challenges and forging ahead! I also hope that each of you will strive relentlessly to achieve your lofty life goals, seize the present moment, and seize your own lives. Through your unwavering efforts, may you create a bright future that belongs to both yourselves and our country. With determination, confidence, perseverance, and patience, may these four "hearts" always accompany you! May you use your wisdom to create a brilliant life of your own.

2021.05.14 获得优秀班导师
    I am honored to receive recognition as the Outstanding Class Advisor in the first and second editions in 2019 and 2020, respectively. I attended the award ceremony on May 14, 2021. I will continue to strive for excellence in the future because the responsibilities of a teacher encompass both teaching and research, with other tasks serving as auxiliary to these two main aspects.

2020.12.31 The journey ahead is long, but only through hard work can we achieve success! Wishing you all a Happy New Year and may you forge ahead fearlessly in 2021, achieving even greater accomplishments!
    Looking back at the past year, the research group led by Han Yanxing has achieved remarkable results in scientific research, with numerous successful projects and a record number of high-quality papers accepted. The group has always been guided by the goal of serving the country, staying abreast of cutting-edge research, and striving for academic excellence. This would not have been possible without the dedicated efforts of each member of the research group and the strong support from the university and department. As we look towards the future, the road ahead may be long, but the research group will continue to fear no challenges, explore new academic frontiers, and each member will walk hand in hand, striving together to reach new heights in research. It is through facing difficulties that true courage is revealed, and it is through honing our skills that we can achieve success!
2020.12.26 Ma Ke attended the 2020 Academic Annual Conference of the Shanghai Institute of Materials Genomic Engineering.
    Constructing the Relationship between Geometric Characteristics of Magnesium Alloy Grains and Yield Strength Based on Machine Learning
    The Hall-Petch relationship provides a quantitative description of the relationship between grain size and yield strength in polycrystalline materials. However, there are various statistical methods for determining grain size, such as intercept method, matching method, and grid method. Additionally, concepts of grain size can be represented by parameters such as equivalent circle diameter and grain perimeter. Investigating the geometric characteristics of grains that have the highest correlation with yield strength in polycrystalline materials requires image and big data processing. This study combines image processing and machine learning techniques to construct the relationship between geometric characteristics of magnesium alloy grains and yield strength.

2020.9.23 Welcome to the team, Wang Lu, Li Ruiqi, Liu Yuhong, and Xia Jinhua, who are the new members of the 2020 intake!
       Wang Lu is a female student who completed her undergraduate studies in Mathematics and Applied Mathematics at Shanxi Datong University. She pursued her master's degree in Dynamic Systems and Computational Mathematics at Kunming University of Science and Technology, focusing on digital image processing, specifically image denoising. Currently, she is a Ph.D. student in Computer Science and Technology at Shanghai University, specializing in material image recognition. Wang Lu has an outgoing and lively personality, with a positive and optimistic attitude. She is adventurous and enjoys exploring new things. In her free time, she likes photography, reading, and trying different cuisines. She believes that "a small step can lead to a great distance" and hopes to become a better version of herself during her four years at Shanghai University.
       Li Ruiqi is a male student who completed his undergraduate studies in Computer Science and Technology at the School of Computer Engineering and Science, Shanghai University. Currently, he is pursuing a master's degree in Computer Science and Technology at Shanghai University. He is adept at critical thinking and possesses a cooperative spirit. During his graduate studies, Li Ruiqi is under the guidance of Professor Han Yanxing, focusing on research areas such as material image analysis and few-shot learning. He warmly welcomes and looks forward to exploring various computer technologies and information together with teachers, classmates, and colleagues, fostering continuous progress through mutual exchange and collaboration.
       Liu Yuhong pursued her undergraduate studies in Computer Science and Technology at Anhui University of Traditional Chinese Medicine. Currently, she is a master's student in Computer Science and Technology at Shanghai University. She has an outgoing and enthusiastic personality, with a passion for life. Liu Yuhong enjoys listening to music, reading books, and reciting poetry. She is particularly interested in detective and suspense dramas, rap music, and has a love for poetry, history, as well as a fascination with food, archaeology, and historical documentaries. Music and words have always provided her with strength during difficult and confusing times, allowing her to perceive the future through the lens of poetry and history. One phrase that constantly motivates her is "Not a coder, but a thinker." She hopes to derive nourishment from life and create wonderful memories during her time at Shanghai University.
       Xia jinhua, you completed your undergraduate studies in Software Engineering at Jiangsu University of Science and Technology. Currently, you are a master's student in Electronic Information, specializing in professional software. You have an outgoing and active personality, always willing to try new things, and actively seek innovative solutions. You are practical, proactive, and possess good communication skills, as well as a strong sense of teamwork. In terms of hobbies, you enjoy sports, particularly playing basketball. You also indulge in watching anime during your free time. During your undergraduate years, you actively participated in various collective activities, fostering unity among your classmates. You have a strong team spirit and have collaborated with your peers on various design and internship projects. Additionally, you have participated in sports events such as the university sports meet and the basketball tournament organized by your college. You have also explored multiple interests to develop your well-rounded qualities and gain diverse experiences.

　

2020.9.12 Back-to-School Gathering & Song Leilei's Teacher Appreciation Dinner
Sometimes, parting is a way to reunite in a better way, and leaving is a means to return as the best version of oneself.

　

2020.7.7 Congratulations to Zeng Renbei, Fan Guoxiang, Fu Yuhao, Li Ruiqi, Liu Mengyang, and Wei Aijia on successfully completing their undergraduate studies! May they fear no challenges and overcome obstacles on their journey ahead, using their hands to create a brilliant life of their own.

　

2020.7.1 Congratulations to Song Leilei on successfully graduating! As the saying goes, "Heaven rewards diligent efforts." In this world, no one becomes a genius without hard work. May you continue to work diligently day and night, and may you achieve success at an early stage!
研究生期间工作：
    To analyze and solve typical small-sample problems in materials, the following approaches can be used to handle and analyze three different types of small-sample data. Targeted solutions for small-sample problems can be developed using relevant theoretical knowledge of machine learning, deep learning, and complex networks.
    1. A machine learning-based method for crystal structure recognition has been proposed to address data related to crystal structures. This method involves augmenting the sample data and defining feature representations and feature selection based on the distribution characteristics of atoms in space. Finally, machine learning techniques are utilized to achieve crystal structure recognition.
    2. A transfer learning-based recognition method is proposed for Atomic Force Microscopy (AFM) image data. This method involves preprocessing the images to remove noise and improving the watershed segmentation technique for segmenting overlapping objects. Finally, transfer learning is utilized to recognize the target objects.
    3. A complex network theory-based image segmentation method is proposed for microscopic structure images of ceramics. This method generates a set of nodes in network space based on the pixel value distribution of the image. It then defines the similarity between nodes and generates a network topology structure. Finally, it optimizes the network topology structure and performs image segmentation.

毕业论文： Research on segmentation and recognition methods based on small-sample data in materials.

　
2019.12.14 Year-end dinner gathering.