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Memristor-Based Circuit of Sequential Associative Memory with Memory Interactions and Stimulus Similarity Effect

IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Xiong, Dongdong Wang, Xiaoping Jiang, Yiming Yang, Chao Jiang, Man Lai, Jingang Zeng, Zhigang Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China Hubei Key Laboratory of Braininspired Intelligent Systems Wuhan430074 China Huazhong University of Science and Technology Institute of Artificial Intelligence School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China Guangxi University of Science and Technology School of Computer Science and Technology Liuzhou545006 China Huazhong University of Science and Technology Department of Physiology School of Basic Medicine and Tongji Medical College Wuhan430030 China

Most proposed memristor-based circuits of associative memory consider various mechanisms in only one associative memory. Few works on circuit design of sequential associative memory have been reported. In this paper, a memristor-based circuit of sequential associative memory with memory interactions and stimulus similarity effect is designed. Two associative memories, the prior associative memory (PAM) and the later associative memory (LAM), are formed successively. The PAM modifies the rate of the formation of the LAM, and the LAM, in turn, affects the strength of the PAM, which are two interactions in sequential associative memory, called transfer and retroaction. In addition, the magnitudes of transfer and retroaction are determined by the similarity of the conditioned stimuli. The above functions are realized by the ring input processing module, memory module, transfer module and retroaction module. It has been identified in circuit analysis that the proposed circuit is power efficient and has good robustness. Furthermore, the proposed circuit has promising applications for fire rescue robots. © 2016 IEEE.

关键词： Associative storage

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Multi-objective Evolutionary Algorithm Based Graph Neural Network Architecture Search 11th

Multi-objective Evolutionary Algorithm Based Graph Neural Ne...

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11th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2024

作者： He, Lianyi Liu, Xiaobo Xiang, Hongbo Wang, Guangjun School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of Geosciences Wuhan430074 China

ISBN: (纸本)9789819647521

Graph Neural Networks (GNN) has become a powerful graph data processing method, which has been widely used in node classification, link prediction, and other graph analysis tasks. Due to the diversity and complexity of graph structures and information propagation, as well as the handling of heterogeneous graphs, etc., the design of GNNs presents many challenges. The existing methods have high classification accuracy, but their structures are very complex. In this paper, a graph neural network architecture search framework named MO-GNN, which is based on multi-objective evolutionary algorithm is proposed. Furthermore, a weight sharing strategy MO-GNN-WS is proposed to reduce the resource consumption caused by weight training of different architectures. To verify the performance of the proposed algorithm, experiments on four popular graph datasets are used for transduction and induction tasks. The experimental results show that the MO-GNN-WS algorithm outperforms the most advanced neural network architecture search methods in terms of classification accuracy and resource consumption. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data Sharing

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Spiking Neural Network for Intra-cortical Brain Signal Decoding

arXiv

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arXiv 2025年

作者： Yang, Song Fu, Haotian Zhang, Herui Zhang, Peng Li, Wei Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Brain-inspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China Department of Biomedical Engineering College of Life Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Decoding brain signals accurately and efficiently is crucial for intra-cortical brain-computer interfaces. Traditional decoding approaches based on neural activity vector features suffer from low accuracy, whereas deep learning based approaches have high computational cost. To improve both the decoding accuracy and efficiency, this paper proposes a spiking neural network (SNN) for effective and energy-efficient intra-cortical brain signal decoding. We also propose a feature fusion approach, which integrates the manually extracted neural activity vector features with those extracted by a deep neural network, to further improve the decoding accuracy. Experiments in decoding motor-related intra-cortical brain signals of two rhesus macaques demonstrated that our SNN model achieved higher accuracy than traditional artificial neural networks;more importantly, it was tens or hundreds of times more efficient. The SNN model is very suitable for high precision and low power applications like intracortical brain-computer interfaces. © 2025, CC BY.

关键词： Deep neural networks

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A Review of Machine Tool Motorized Spindle Variable Pressure Preload Technology Research

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Recent Patents on Engineering 2025年第5期19卷

作者： Dai, Ye Deng, Hao Pan, Gaofeng Chen, Xinda Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of Education School of Mechanical and Power Engineering Harbin University of Science and Technology Harbin150080 China School of College of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin150080 China

High-precision spindles are a vital study topic because of the growing demand for high-speed, high-precision, reliable, and long-lasting machine tools. Bearing preload is the primary means of improving the accuracy of the spindle rotor system. This patent study aims to summarize the research results, systematically analyze and evaluate the machine tool motorized spindle variable pressure preload technology, and provide a scientific basis for the practical application of the motor spindle variable pressure preload technology. This study is an in-depth analysis of the basic principles of the electric spindle preloading technology, the study of its influence mechanism on the thermal deformation, stiffness, slewing error, and life of the spindle system, and a discussion of the current shortcomings in the field of spindle preloading to provide a reference for the subsequent optimization. In the spindle system, the performance of rolling bearings and the design of bearing preload have an important impact on the spindle's accuracy and stability. The traditional preload method has yet to meet the requirements of modern high-speed machine tools. Real-time controllable preload has become the new direction of development, but the current preload technology still has certain limitations and needs to be further improved. In the future, the spindle bearing variable pressure preload technology will be more efficient and intelligent. © 2025 Bentham Science Publishers.

关键词： Machine tools

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Multimodal Image Matching based on Frequency-domain Information of Local Energy Response

arXiv

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arXiv 2025年

作者： Yang, Meng Chen, Jun Gong, Wenping Wei, Longsheng Tian, Xin School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Faculty of Engineering China University of Geosciences Wuhan430074 China Electronic Information School Wuhan University Wuhan430072 China

Complicated nonlinear intensity differences, nonlinear local geometric distortions, noises and rotation transformation are main challenges in multimodal image matching. In order to solve these problems, we propose a method based on Frequency-domain Information of Local Energy Response called FILER. The core of FILER is the local energy response model based on frequency-domain information, which can overcome the effect of nonlinear intensity differences. To improve the robustness to local nonlinear geometric distortions and noises, we design a new edge structure enhanced feature detector and convolutional feature weighted descriptor, respectively. In addition, FILER overcomes the sensitivity of the frequency-domain information to the rotation angle and achieves rotation invariance. Extensive experiments multimodal image pairs show that FILER outperforms other state-of-the-art algorithms and has good robustness and *** Codes 68U10 (Primary), 68T45, 68Wxx (Secondary) Copyright © 2025, The Authors. All rights reserved.

关键词： Mathematical transformations

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Safe Reinforcement Learning for Social Motor Coordination in Two Dimensional Space

Safe Reinforcement Learning for Social Motor Coordination in...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Fan Yang Yuening Huang Chao Zhai Hehong Zhang China University of Geosciences Wuhan Campus School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Rice University George R. Brown School of Engineering and Computing Houston Texas United States Fuzhou University School of Computer and Data Science Fuzhou China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This paper presents an interactive motion control method based on reinforcement learning, designed to assist children with autism who have social motor impairments through a mirror game intervention. The virtual teacher uses the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize its actions, guiding the participant to follow a Lissajous trajectory. To ensure safety, a motion-correction mechanism was developed, which automatically adjusts actions when the predicted trajectory surpasses predefined safety boundaries. The reward function considers both the distance between the virtual teacher and the target trajectory, as well as the distance between the virtual teacher and the participant, with dynamic adjustments applied by the motion-correction mechanism. Experimental results demonstrate that the virtual teacher effectively guides the participant towards the target trajectory while adhering to safety constraints.

关键词： Autism Filters Reinforcement learning Games Variable speed drives Motors Trajectory Safety Mirrors Motion control

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Unleashing Correlation and Continuity for Hyperspectral Reconstruction from RGB Images

arXiv

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arXiv 2025年

作者： Feng, Fuxiang Cong, Runmin Wei, Shoushui Zhang, Yipeng Li, Jun Kwong, Sam Zhang, Wei The School of Control Science and Engineering Shandong University Jinan250061 China The Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China The University of California Los Angeles United States The Hubei Key Laboratory of Intelligent Geo-Information Processing The School of Computer Science China University of Geo-sciences Wuhan430078 China The Lingnan University Hong Kong

Reconstructing Hyperspectral Images (HSI) from RGB images can yield high spatial resolution HSI at a lower cost, demonstrating significant application potential. This paper reveals that local correlation and global continuity of the spectral characteristics are crucial for HSI reconstruction tasks. Therefore, we fully explore these inter-spectral relationships and propose a Correlation and Continuity Network (CCNet) for HSI reconstruction from RGB images. For the correlation of local spectrum, we introduce the Group-wise Spectral Correlation Modeling (GrSCM) module, which efficiently establishes spectral band similarity within a localized range. For the continuity of global spectrum, we design the Neighborhood-wise Spectral Continuity Modeling (NeSCM) module, which employs memory units to recursively model the progressive variation characteristics at the global level. In order to explore the inherent complementarity of these two modules, we design the Patch-wise Adaptive Fusion (PAF) module to efficiently integrate global continuity features into the spectral features in a patch-wise adaptive manner. These innovations enhance the quality of reconstructed HSI. We perform comprehensive comparison and ablation experiments on the mainstream datasets NTIRE2022 and NTIRE2020 for the spectral reconstruction task. Compared to the current advanced spectral reconstruction algorithms, our designed algorithm achieves State-Of-The-Art (SOTA) performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Multimodal Deep Learning Fusing Clinical and Radiomics Scores for Prediction of Early-Stage Lung Adenocarcinoma Lymph Node Metastasis

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Academic Radiology 2025年第5期32卷 2977-2989页

作者： Xia, Chengcheng Zuo, Minjing Lin, Ze Deng, Libin Rao, Yulian Chen, Wenxiang Chen, Jinqin Yao, Weirong Hu, Min School of Public Health Jiangxi Medical College Nanchang University Nanchang 330006 China Jiangxi Provincial Key Laboratory of Disease Prevention and Public Health Nanchang University Nanchang 330006 China Department of Radiology The Second Affiliated Hospital Jiangxi Medical College Nanchang University Nanchang 330006 China Intelligent Medical Imaging of Jiangxi Key Laboratory Nanchang 330006 China Department of Radiology Hubei Provincial Hospital of Traditional Chinese Medicine Wuhan 430022 China Affiliated Hospital of Hubei University of Chinese Medicine Wuhan 430022 China Wanli District Center for Disease Control and Prevention of Nanchang Nanchang 330004 China Department of Oncology Jiangxi Provincial People's Hospital The First Affiliated Hospital of Nanchang Medical College Nanchang China Jiangxi Medical College Nanchang University Nanchang China

Rationale and Objectives: To develop and validate a multimodal deep learning (DL) model based on computed tomography (CT) images and clinical knowledge to predict lymph node metastasis (LNM) in early lung adenocarcinoma. Materials and Methods: A total of 724 pathologically confirmed early invasive lung adenocarcinoma patients were retrospectively included from two centers. Clinical and CT semantic features of the patients were collected, and 3D radiomics features were extracted from nonenhanced CT images. We proposed a multimodal feature fusion DL network based on the InceptionResNetV2 architecture, which can effectively extract and integrate image and clinical knowledge to predict LNM. Results: A total of 524 lung adenocarcinoma patients from Center 1 were randomly divided into training (n=418) and internal validation (n=106) sets in a 4:1 ratio, while 200 lung adenocarcinoma patients from Center 2 served as the independent test set. Among the 16 collected clinical and imaging features, 8 were selected: gender, serum carcinoembryonic antigen, cytokeratin 19 fragment antigen 21–1, neuron-specific enolase, tumor size, location, density, and centrality. From the 1595 extracted radiomics features, six key features were identified. The CS-RS-DL fusion model achieved the highest area under the receiver operating characteristic curve in both the internal validation set (0.877) and the independent test set (0.906) compared to other models. The Delong test results for the independent test set indicated that the CS-RS-DL model significantly outperformed the clinical model (0.844), radiomics model (0.850), CS-RS model (0.872), single DL model (0.848), and the CS-DL model (0.875) (all P<0.05). Additionally, the CS-RS-DL model exhibited the highest sensitivity (0.941) and average precision (0.642). Conclusion: The knowledge derived from clinical, radiomics, and DL is complementary in predicting LNM in lung adenocarcinoma. The integration of clinical and radiomics scores through

关键词： Deep learning Feature fusion Lung adenocarcinoma Lymph node metastasis Radiomics

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Path Planning for Distribution Network Robots with Adaptive Simulated Annealing-Enhanced Artificial Potential Field Method

Path Planning for Distribution Network Robots with Adaptive ...

引用

IEEE International Conference on Industrial Technology (ICIT)

作者： Baichao Zhang Xin Chen Jinge Chen Jinbin Li Xiaokai Wang Xu Jian School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China State Grid Hubei Electric Power Co. Ltd. Electric Power Science Research Institute Wuhan P. R. China Hubei Fangyuan Dongli Electric Power Science Research Co. Ltd Wuhan P. R. China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

In this paper, we propose a hybrid algorithm that combines an improved Artificial Potential Field (APF) method with the Simulated Annealing (SA) algorithm for path planning of an electric power operation robot manipulator in complex distribution grid environments. To address the unreachable target issue inherent in traditional APF, we introduce a distance regulation factor to optimize the repulsive function. This modification allows the manipulator to smoothly approach the target point as it nears, while the repulsion from obstacles gradually decreases. Additionally, to overcome the limitations of the traditional SA algorithm, such as its tendency to get trapped in local minimum solutions and its inefficiency in complex environments, we propose an adaptive temperature rise strategy. This strategy increases the temperature, enhancing the probability of escaping local optimal solutions. When the APF algorithm becomes trapped in a local optimum, the improved SA algorithm is applied to escape the local minimum. Once the local optimum is avoided, the algorithm switches back to APF to continue the path planning process. Simulation results demonstrate that the proposed improved APF-SA algorithm adapts effectively to various complex environments, achieving shorter planning times and higher success rates compared to traditional APF and SA algorithms. It successfully resolves the unreachable target and local minimum problems associated with APF. Finally, the feasibility of the proposed APF-SA fusion algorithm is validated through experiments conducted on an electric power operation robot experimental platform for distribution grid applications.

关键词： Electric potential Adaptation models Force Distribution networks Simulated annealing Manipulators Path planning Planning Collision avoidance Robots

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Sim2Real Learning With Domain Randomization for Autonomous Guidewire Navigation in Robotic-Assisted Endovascular Procedures

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 13842-13854页

作者： Yao, Tianliang Wang, Haoyu Lu, Bo Ge, Jiajia Pei, Zhiqiang Kowarschik, Markus Sun, Lining Seneviratne, Lakmal Qi, Peng Tongji University College of Electronics and Information Engineering Shanghai Institute of Intelligent Science and Technology Department of Control Science and Engineering Shanghai200092 China Shanghai University of Engineering Science School of Electronic and Electrical Engineering Shanghai201620 China Soochow University Robotics and Microsystems Center School of Mechanical and Electrical Engineering Jiangsu Suzhou215131 China Advanced Therapies Siemens Healthineers Ltd. Shanghai201318 China University of Shanghai for Science and Technology Oriental Pan-Vascular Devices Innovation College Shanghai200093 China Bavaria Forchheim91301 Germany Khalifa University KUCARS Department of Mechanical and Nuclear Engineering Abu Dhabi United Arab Emirates Tongji University College of Electronics and Information Engineering Shanghai Institute of Intelligent Science and Technology State Key Laboratory of Cardiovascular Diseases and Medical Innovation Center Shanghai East Hospital Department of Control Science and Engineering School of Medicine Shanghai200092 China

Over the past decade, significant advancements have been made in the research and industrialization of robotic systems for endovascular procedures, yet their clinical application remains relatively limited. Physicians commonly report that these robots lack certain intelligent assistive capabilities during procedures. There has been increasing interest and attempts to apply learning-centered algorithms to the training and enhancement of surgical robot skills. This paper proposes an autonomous navigation algorithm for interventional guidewires that is initially trained solely in a virtual simulation environment and subsequently deployed to a real-world robot. Experimental results demonstrate the feasibility of this approach for real-world applications. The proposed approach can help physicians reduce the learning curve for guidewire manipulation and elevate the robot to a higher level of autonomous operation, thereby breaking through the current bottleneck in the level of intelligence for clinical applications of interventional robots. It also holds promise for bringing intelligent transformation to future interventional procedures. © 2004-2012 IEEE.

关键词： Adversarial machine learning

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