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IEEE Advanced information Technology, Electronic and Automation control Conference (IAEAC)

作者： Yixuan Wang Wei Li School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China Wuhan China

Alzheimer’s disease (AD) is the most prevalent form of dementia, and early diagnosis is crucial for delaying and treating AD. Resting-state functional magnetic resonance imaging (rs-fMRI), a widely used medical imaging technique, offers rich temporal and spatial data, which has led researchers to explore various feature extraction methods based on rs-fMRI images for AD identification. However, the related work still suffers from insufficient utilization of temporal and spatial information which leads to unsatisfactory early diagnosis. In this study, we propose a dual-branch fusion model to extract spatial-temporal features from rs-fMRI images. Our proposed model can extract temporal features at different levels. We developed a Class Activation Sequence (CAS) branch, which is a structure that emphasizes the function of each temporal node throughout the whole time series. Additionally, we created a time-domain local branch for local feature extraction. Further, we designed a fusion module for the model to describe temporal contextual relationships and fuse features at various levels. We tested the performance of the model on the ADNI dataset, and the experimental results show that compared with other algorithms, the dual-branch fusion model achieves higher classification accuracy on several classification tasks including early diagnosis, which proves the advantage of the dual-branch fusion model in temporal and spatial feature extraction for rs-fMRI images, and our work also provides a foundation for the temporal domain characterization of rs-fMRI images.

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Dual-mode predictive control of a rotor suspension system

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Science China(information Sciences) 2020年第1期63卷 178-189页

作者： Yue WU Gui-Ping REN Hai-Tao ZHANG Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and Technology State Key Laboratory of Digital Manufacturing Equipments and Technology

Rotor active magnetic bearing(rotor-AMB) systems are frequently used to alleviate vibrations for various applications such as in national defense, manufacturing industries, IC production, and aerospace engineering. One obstacle to improve machining efficiency and quality is the open-loop instability of rotorAMB systems during the machining process. We built a closed-loop processing platform using a spindle rotor installed with AMBs and thereby developed a rotor-AMB suspension system embedded with a dual-mode predictive controller(DMPC). The performance of the system is thus substantially improved. In the proposed DMPC, both model-based prediction and receding horizon optimization are utilized to guarantee the closedloop stability of the rotor-AMB suspension systems with input constraints. Finally, the effectiveness and superiority of the proposed method are examined through substantial levitation experiments on a machining platform with installed AMBs.

关键词： dual-mode predictive control system identification optimization control model predictive control suspension control

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Polyp Segmentation Neural Network with Simple Encoding Structure and Reverse Attention Module 13

Polyp Segmentation Neural Network with Simple Encoding Struc...

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13th International Conference on information Technology in Medicine and Education, ITME 2023

作者： Wang, Jie Li, Zuoyong Xu, Haiping Cheng, Xuesong Teng, Shenghua College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China Fujian Key Laboratory of Medical Big Data Engineering Fujian Provincial Hospital Fuzhou350001 China College of Mathematics and Data Science Minjiang University Fuzhou350121 China

ISBN: (纸本)9798350319156

Colorectal cancer is a common malignancy. In colonoscopy images, computer-assisted polyp segmentation helps doctors diagnose and treat disorders more precisely. In recent years, some methods based on deep convolutional neural networks have made significant breakthroughs in this task. However, only a few algorithms explicitly consider the effect of light intensity on segmentation performance, which results in algorithms needing to be more robust for complex samples. This paper proposes a neural network combining a simple encoding structure with a reverse attention module. Specifically, the model uses HarDNet68 as the backbone network, adds a camouflage identification module (CIM) to enhance the capture of polyp details on the underlying features, and finally utilizes adaptive feature fusion with the reverse attention module (RAM). The proposed model effectively addresses the challenge of poor segmentation quality in colonoscopy images caused by inconsistent illumination. Extensive experiments on five datasets show that the proposed model performs well in polyp segmentation. © 2023 IEEE.

关键词： Diseases

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A Gaze-Guided Human Locomotion Intention Recognition and Volitional control Method for Knee-Ankle Prostheses

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Chen, Xinxing Chen, Zhaokai Wang, Yuxuan Yin, Shucong Chen, Chuheng Huang, Jian Fu, Chenglong Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Hubei Key Laboratory of Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Wuhan430074 China Southern University of Science and Technology Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Shenzhen518055 China Shanghai Jiaotong University Shanghai China

This paper presents a novel gaze-guided volitional control method for knee-ankle prostheses, designed to enhance the precision and intuitiveness of prosthetic control in complex locomotion tasks. The method utilizes an eye-tracking system coupled with an RGBD camera to estimate 3D gaze points, which are then used to construct a gaze attention map. This map serves as the basis for predicting human locomotion intentions, enabling the prosthesis to accurately step on the intended foot placement. Experimental validation was conducted with both a healthy subject and an amputee subject, focusing on level-ground walking and stair ascent tasks. The results demonstrated that the prosthesis could achieve precise foot placement, with a mean error of 1.22 cm ± 3.14 cm for the healthy subject and 3.40 cm ± 0.66 cm for the amputee subject during level-ground walking. In stair ascent tasks, the system successfully adapted to various gait patterns, with the prosthetic joint angles closely matching natural human movements. The proposed method also effectively reduced the root mean square error (RMSE) of 3D gaze estimation to 2.70 cm, 2.63 cm, and 1.97 cm along the x, y, and z axes, respectively. These findings indicate that the gaze-guided volitional control method not only enhances the functionality of knee-ankle prostheses but also offers a more intuitive interface for users, aligning prosthetic actions with the user’s visual focus and intentions. © 2016 IEEE.

关键词： Knee prostheses

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Boarding Pass Positioning with Jointly Multi-channel Segmentation and Perspective Transformation Correction 4th

Boarding Pass Positioning with Jointly Multi-channel Segmen...

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Wu, Jiahui Li, Zuoyong Keikhosrokiani, Pantea Cai, Yuanzheng College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer Sciences Universiti Sains Malaysia Penang Minden11800 Malaysia

ISBN: (纸本)9783031200953

To solve the problem of boarding pass positioning with image tilt and sticking, we proposed a jointly boarding pass positioning and correction algorithm using multi-channel color components. First, a local threshold segmentation is performed on red and blue components in RGB color space to determine the general area of the boarding pass. Then, edge detection is performed on the blue component and pixel value standardization statistics on the three components to obtain partial edges and internal information of the boarding pass to refine the binary image. Next, the watershed algorithm is used to achieve precise positioning of the boarding pass. Finally, the segmentation and the boarding pass are completely rectified through perspective transformation. The results of segmentation and correction of boarding passes in actual scenarios show that the proposed algorithm can complete segmentation and correction under complex conditions such as boarding pass images sticking to each other, uneven illumination, and large-angle tilt. The average time spent on the test set is 0.049s, and the segmentation and correction success rate is 95.16 %. The proposed algorithm has high application value. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Binary images

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PCB Defect Detection Method Based on Improved RetinaNet 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Xu, Yusheng Cao, Xinrong Hu, Rong Keikhosrokiani, Pantea Li, Zuoyong College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia

ISBN: (数字)9783031200991

ISBN: (纸本)9783031200984

In the electronic industry product quality control, PCB defect detection is a crucial part, which has the characteristics of small defect size and high similarity. The existing defect detection methods are still not good enough for detecting small target defects;therefore, the algorithm in this paper proposes an improved algorithm for PCB defect detection based on the RetinaNet model. The ResNet-D residual structure and efficient channel focus module are introduced in the model backbone network to enhance its feature extraction capability and achieve the purpose of improving the detection accuracy. At the same time, the method replaces the original multi-step learning decay strategy with a cosine annealing scheduling learning strategy, which optimizes the training process of the model. Finally, the performance of the method is verified on the publicly available PCB defect dataset from the Open laboratory of intelligent Robotics, Peking University. The experimental results show that the algorithm improves the mAP value by 3.2% compared with the original algorithm, while the fastest detection speed reaches 36.9 FPS, which can effectively improve the defect detection performance of PCB. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Defects

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Yajun Li, Ru Li, Aimin Fei, Rong Guo, Xie Wu, Fang-Xiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9798350337488

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions. © 2023 IEEE.

关键词： interactive inference network noncoding RNA piRNA-mRNA interactions positive-unlabeled learning

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STMAD:A Simple Multimodal Anomaly Detection for Thyroid Medical information 17

STMAD:A Simple Multimodal Anomaly Detection for Thyroid Medi...

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17th International Congress on image and Signal processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Xu, Wentong Fu, Chao Yu, Jing Si, Caifeng Li, Zuoyong Li, Chaochao Fan, Haoyi School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China First Affiliated Hospital of Zhengzhou University Department of Ultrasound Henan Zhengzhou450052 China School of Computer and Big Data Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China

ISBN: (纸本)9798331507398

Anomaly detection has been widely explored by training an out-of-distribution detector with only normal data. Medical image analysis, as a common tool, faces the challenge of how to further enhance the detection performance for thyroid cancer, especially with the application of various deep learning methods in the thyroid-related field. In this paper, we validate the effectiveness of sonographic feature descriptions (textual information) for thyroid cancer diagnosis. Building on this, we propose a simple multimodal self-supervised framework that leverages both textual and image modalities for thyroid cancer anomaly detection. Results on a private dataset demonstrate the effectiveness and superiority of our proposed method compared to advanced methods. © 2024 IEEE.

关键词： Self-supervised learning

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Efficient Reinforcement Learning for 3D LiDAR Navigation of Mobile Robot 41

Efficient Reinforcement Learning for 3D LiDAR Navigation of ...

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第41届中国控制会议

作者： Yu Zhai Zhe Liu Yanzi Miao Hesheng Wang China University of Mining and Technology MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai Engineering Research Center of Intelligent Control and ManagementShanghai Jiao Tong University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Developing an efficient automatic navigation system for mobile robots is challenging in the strange scenarios where robots can only observe the environment of the surrounding limited *** other distributed automatic navigation systems exist,they often require extracting semantic information to calculate navigation action,which requires extra modules to provide perceptual information and is not *** propose an end-to-end automatic navigation system based on the reinforcement learning *** particular,the raw 3 D LiDAR data is used to directly map an efficient navigation *** design a novel dense reward function to handle the reward sparsity issue and provide a graphical representation method to enable the efficient feature learning from the raw 3 D LiDAR data in our navigation *** addition,an imitation learning based policy initialization is introduced before the subsequent reinforcement learning,which increases the learning efficiency and,in the meantime,still encouraging the robot to explore all the potential states to achieve advanced performance than the imitated *** navigation model is trained in the Webots environment and the experimental results show that our model has efficient and flexible navigation performance in complex *** importantly,trained model can be easily extended to unfamiliar environments.

关键词： End-to-end Navigation Mobile robot Reinforcement Learning 3D LiDAR

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Development prospects of metal-based two-dimensional nanomaterials in lithium-sulfur batteries

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Chinese Chemical Letters 2023年第1期34卷 102-113页

作者： Yuxue Mo Liling Liao Dongyang Li Rongwu Pan Yanhong Deng Yanliang Tan Haiqing Zhou College of Physics and Electronic Engineering Hengyang Normal UniversityHengyang 421002China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan ProvinceDepartment of Physics and Synergetic Innovation Center for Quantum Effects and ApplicationsHunan Normal UniversityChangsha 410081China Hunan Joint International Laboratory of Advanced Materials and Technology for Clean Energy Changsha 410082China Hunan Shengli High-tech Energy Technology Co. Ltd.Yongzhou 425000China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang 421002China

A lithium-sulfur(Li-S)system is an important candidate for future lithium-ion system due to its low cost and high specific theoretical capacity(1675 m Ah/g,2600 Wh/kg),which is greatly hindered by the poor conductivity of sulfur,large volume change and dissolution of lithium ***-dimensional(2D)materials with monolayers or few-layers usually have peculiar structures and physical/chemical properties,which can resolve the critical issues in Li-S ***,the metal-based 2D nanomaterials,including ferrum,cobalt or other metal-based composites with various anions,can provide high conductivity,large surface area and abundant reaction sites for restraining the diffusion for lithium *** this mini-review,we will present an overview of recent developments on metal-based 2D nanomaterials with various anions as the electrode materials for Li-S *** the main bottleneck for the Li-S system is the shuttle of polysulfides,emphasis is placed on the structure and components,physical/chemical interaction and interaction mechanisms of the 2D ***,the challenges and prospects of metal-based 2D nanomaterials for Li-S batteries are discussed and proposed.

关键词： Lithium-sulfur battery Two-dimensional materials Cathode materials Anode materials Anode protection

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