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A Parallel Read-Write Circuit With Fast Amplitude-Adaptive Matching Scheme to Memristor Crossbar Array

IEEE Transactions on Circuits and Systems I: Regular Papers 2025年

作者： Jiang, Ningye Jiang, Mingxuan Huang, Haoen Yang, Le Huang, Yi Zeng, Zhigang Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan430074 China Wuhan Institute of Technology School of Electrical and Information Engineering Wuhan430205 China University of Massachusetts Amherst Department of Electrical and Computer Engineering AmherstMA01003 United States

Memristor crossbar array (MCA) is a computing-in-memory (CIM) module for computational acceleration. However, conventional read-write (R-W) circuits for MCA rely heavily on external components and have shortcoming in long writing times. To address these issues, we propose a parallel R-W circuit with a fast amplitude-adaptive matching (AAM) scheme. The fast AAM scheme is designed to accelerate time of writing memristors in MCA by adaptively adjusting the writing voltage to match an optimal amplitude. The architecture of parallel R-W circuit are outlined with the implementation of R-W processes. The design principles of parallel R-W unit, fast AAM unit, and associated control logic are described for analyzing their functions within the circuit. By integrating the parallel R-W circuit with MCA, a computing block is leveraged in a neural network for classification tasks. The experimental results demonstrate that the neural network with multiple computing blocks maintains high accuracy across various datasets (over 80%). Furthermore, the proposed AAM scheme achieves an average 54% reduction in writing time compared to that one without AAM scheme. Compared to the pulse-width scheme, the parallel R-W circuit exhibits an average 3.05× speedup in adjusting a 5×5 MCA, and the speedup efficiency is higher as the increases of MCA size. © 2025 IEEE.

关键词： Memristors

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Enhancing Precision Agriculture Through Human-in-the-Loop Planning and control

Enhancing Precision Agriculture Through Human-in-the-Loop Pl...

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IEEE International Conference on automation science and Engineering (CASE)

作者： Shankar A. Deka Sujet Phodapol Andreu Matoses Gimenez Victor Nan Fernandez-Ayala Rufus Wong Pian Yu Xiao Tan Dimos V. Dimarogonas Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Division of Decision and Control Systems School of EECS Royal Institute of Technology (KTH) Stockholm Sweden Department of Cognitive Robotics Faculty of Mechanical Engineering TU Delft Delft Netherlands Department of Computer Science University of Oxford Oxford UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we introduce a ROS based framework designed for the planning and control of robotic systems within the context of precision agriculture, with an emphasis on human-in-the-loop capabilities. Utilizing Linear Temporal Logic to articulate complex task specifications, our algorithm creates high-level robotic plans that are not only correct by design but also adaptable in real time by human operators. This dual-focus approach ensures that while humans have the flexibility to modify the high-level plan on-the-fly or even take over low-level control of the robots, the system inherently safeguards against any human actions that could potentially breach the predefined task specifications. We demonstrate our algorithm within the dynamic and challenging environment of a real vineyard, where the collaboration between human workers and robots is critical for tasks such as harvesting and pruning, and show the practical applicability and robustness of our software. This work marks a pioneering application of formal methods to complex, real-world agricultural environments.

关键词： Precision agriculture Heuristic algorithms Software algorithms Robustness Human in the loop Software Real-time systems Planning Robots Multi-agent systems

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Reinforcement Learning with Prior Policy Guidance for Motion Planning of Dual-Arm Free-Floating Space Robot

arXiv

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arXiv 2022年

作者： Cao, Yuxue Wang, Shengjie Zheng, Xiang Ma, Wenke Xie, Xinru Liu, Lei Beijing Institute of Control Engineering China Department of Automation Tsinghua University China Department of Computer Science City University of Hong Kong Hong Kong Qian Xuesen Laboratory of Space Technology China

Reinforcement learning methods as a promising technique have achieved superior results in the motion planning of free-floating space robots. However, due to the increase in planning dimension and the intensification of system dynamics coupling, the motion planning of dual-arm free-floating space robots remains an open challenge. In particular, the current study cannot handle the task of capturing a non-cooperative object due to the lack of the pose constraint of the end-effectors. To address the problem, we propose a novel algorithm, EfficientLPT, to facilitate RL-based methods to improve planning accuracy efficiently. Our core contributions are constructing a mixed policy with prior knowledge guidance and introducing k·k∞ to build a more reasonable reward function. Furthermore, our method successfully captures a rotating object with different spinning speeds. Copyright © 2022, The Authors. All rights reserved.

关键词： Motion planning

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T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs

arXiv

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arXiv 2024年

作者： Li, Siyang Wang, Ziwei Luo, Hanbin Ding, Lieyun Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China Henan Key Laboratory of Brain Science and Brain Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China

Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and non-stationarity of EEG signals, such BCIs usually require a subject-specific calibration session before each use, which is time-consuming and user-unfriendly. Transfer learning (TL) has been proposed to shorten or eliminate this calibration, but existing TL approaches mainly consider offline settings, where all unlabeled EEG trials from the new user are available. Methods: This paper proposes Test-Time Information Maximization Ensemble (T-TIME) to accommodate the most challenging online TL scenario, where unlabeled EEG data from the new user arrive in a stream, and immediate classification is performed. T-TIME initializes multiple classifiers from the aligned source data. When an unlabeled test EEG trial arrives, T-TIME first predicts its labels using ensemble learning, and then updates each classifier by conditional entropy minimization and adaptive marginal distribution regularization. Our code is publicized. Results: Extensive experiments on three public motor imagery based BCI datasets demonstrated that T-TIME outperformed about 20 classical and state-of-the-art TL approaches. Significance: To our knowledge, this is the first work on test time adaptation for calibration-free EEG-based BCIs, making plug-and-play BCIs possible. © 2024, CC BY.

关键词： Calibration

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DSTFormer: 3D Human Pose Estimation with a Dual-scale Spatial and Temporal Transformer Network

DSTFormer: 3D Human Pose Estimation with a Dual-scale Spatia...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Shaokun Zhang Xinde Li Chuanfei Hu Jianping Xu Huaping Liu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Nanjing Center for Applied Mathematics Nanjing China Southeast University Shenzhen Research Institute Shenzhen China Science and Technology on Information Systems Engineering Laboratory Nanjing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Recent transformer-based methods for estimating 3D human pose have gained widespread attention, achieving state-of-the-art results. Previous methods have primarily focused on capturing motion patterns of the human body at a single scale or cascading multiple scales, such as joints, bones, and body-parts. However, they are difficult to simultaneously capture spatial-temporal motion patterns of the human body at different scales due to the complex motion patterns. To address this issue, we propose Dual-scale Spatial and Temporal transFormer (DSTFormer), which can concurrently explore the spatial dependencies and temporal motion patterns of human joints and bones. Additionally, we introduce a Gcn-Spatial Transformer Block (GSTB), which introduces Graph Convolutional Networks (GCN) into transformer to enhance the exploitation of local relationships and global information between adjacent joints or bones. Extensive experiments are conducted on the Human3.6M benchmark dataset, and superior results are reported when comparing to other state-of-the-art methods. More remarkably, our model achieves to-date the best published performance, with P1 errors of 37.9 mm and 15.6 mm, respectively.

关键词： Solid modeling Three-dimensional displays Mechatronics Graph convolutional networks Computational modeling Pose estimation Transformers Bones Joints Robots

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Complex Permeability Determination for EE Core Based on Flexible PCB Fixture and Magnetic Flux Path Length Analysis

Complex Permeability Determination for EE Core Based on Flex...

引用

China Power Supply Society Electromagnetic Compatibility Conference (CPEMC)

作者： Huamin Jie Zhenyu Zhao Yongqi Chang Changdong Wang Shiqi Jiang Richard Xian-Ke Gao Kye Yak See School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Electrical and Computer Engineering National Univeristy of Singapore Singapore Singapore Department of Control Science and Engineering Harbin Institute of Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China A*STAR Institute of High Performance Computing Singapore Singapore

ISBN: (数字)9798331509552

ISBN: (纸本)9798331509569

The complex permeability of magnetic cores acts as a vital factor in electromagnetic interference (EMI) filtering choke design and optimization. Many efforts have been reported for toroid cores, but fewer target those with other shapes, such as EE, EI, UU, and UI cores. Owing to their commonly used in planar EMI filters, determining the complex permeability of them is of significance. Targeting EE cores, this paper presents a flexible printed circuit board (PCB)-based fixture adapter tailored designed for performing the measurement, while the corresponding core permeability can be calculated through magnetic flux path length analysis. The experiments validate the effectiveness and accuracy of the proposed methods, spanning a wide frequency range up to 120 MHz.

关键词： Magnetic flux Accuracy Flexible printed circuits Magnetic cores Fixtures Electromagnetic interference Permeability measurement Length measurement Permeability Inductors

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The Research on Sheet Metal Part Recognition Technology Based on Improved Mask R-CNN

The Research on Sheet Metal Part Recognition Technology Base...

引用

Machine Learning and computer Application (ICMLCA), International Conference on

作者： Mingfei Liu Jun Liu Kecheng Zhou Jian Wang Key Laboratory of Industrial Intelligence Technology on Chemical Process School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang Liaoning Province China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

The visual recognition of sheet metal parts is a crucial component of industrial automation sorting, particularly under complex lighting and background interference conditions. To enhance the accuracy of sheet metal part recognition, this paper curates a dataset of sheet metal parts acquired by an intelligent light field image sensor and proposes a visual recognition model, PA-CBAM, based on an improved Mask R-CNN. By improving the original feature pyramid FPN and introducing the Convolutional Block Attention Module (CBAM), the model enhances multi-scale feature fusion and the focus on key features: (1) Bottom up paths and shortcut connections have been added between different layers of the traditional FPN feature pyramid, thereby improving multi-scale object detection capability. (2) CBAM further optimizes the model's channel and spatial attention mechanisms, effectively enhancing the capture of edge details and the suppression of background noise. Experimental results show that the proposed PA-CBAM model significantly improves detection and segmentation performance compared to traditional methods, particularly in terms of edge recognition accuracy for sheet metal parts under complex lighting conditions.

关键词： Visualization Accuracy Image edge detection Metals Lighting Object detection Feature extraction Light fields Optimization Sorting

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Adaptive control of the Structural Balance for a Class of Complex Dynamical Networks

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Journal of Systems science & Complexity 2020年第3期33卷 725-742页

作者： GAO Zilin WANG Yinhe PENG Yi LIU Lizhi CHEN Haoguang Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges UniversityChongqing 404100China School of Computer Science and Engineering Chongqing Three Gorges UniversityChongqing 404100China School of Automation Guangdong University of TechnologyGuangzhou 510006China School of Computer Science Zhongshan Institute of Electronic Science and Technology University of ChinaZhongshan 528402China

In social networks,the structural balance is a state of a group of individuals(nodes)with established mutual relationships(connection relationships)between *** is easy to see that a social network can be described by a complex dynamical network model composed of the nodes subsystem(NS)and the connection relationships subsystem(CS),where the two subsystems are usually coupled with each *** implies that the dynamic changes of nodes'states may cause the structural balance in ***,few papers have discussed the relationship between the structural balance and the specific dynamic changes of the nodes'*** paper proposes a model of complex dynamical networks,and mainly focuses on the dynamic changes of states in NS which can lead to the structural balance in *** is proved that if each state in NS is doing a specific dynamic motion via the controller with the parameter adaptive law,then the CS can track a given structural balance matrix via the effective coupling and the structural balance can be *** a result can be regarded as an explanation of the relationship between the structural balance and the specific dynamic changes of the nodes'***,the simulations verify the effectiveness of the proposed method.

关键词： Complex dynamical networks connection relationships effective coupling structural balance

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3D Human Motion Prediction Based on Graph Convolution Network and Transformer

3D Human Motion Prediction Based on Graph Convolution Networ...

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2021 China automation Congress, CAC 2021

作者： Gao, Chaofei Wang, Tian Zhang, Mengyi Zhu, Aichun Shi, Peng Snoussi, Hichem School of Automation Science and Electrical Engineering Beihang University Beijing China Institute of Artificial Intelligence Beihang University Beijing China College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China School of Computer Science and Technology Nanjing Tech University Nanjing China School of Mathematics and Computer Science Fujian Normal University Fujian China Institute Charles Delaunay-LM2S University of Technology of Troyes Troyes France

ISBN: (纸本)9781665426473

Extracting, recognizing and predicting human actions from image information plays an essential part in the fields of human intention understanding, behavior emergency avoidance and automatic driving. In recent years, with deep learing method developing rapidly, the methods of behavior detection and intention understanding for human actions are also glowing with new vitality. In this paper, based on spatial-temporal synchronous graph convolution network and multi-head self-attention mechanism, a new method of human skeleton action recognition and prediction is proposed. By extracting the spatial features of short-term time series at the same time, we can predict the long-term time series actions, and also we have achieved satisfactory experimental results. Our experiment is based on Human3.6M dataset for training and testing. At the end of the paper, we put forward the limitations of the current research and some future research directions. © 2021 IEEE

关键词： Forecasting

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Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement

Distributed Optimization with Finite Bit Adaptive Quantizati...

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IEEE Conference on Decision and control

作者： Apostolos I. Rikos Wei Jiang Themistoklis Charalambous Karl H. Johansson Artificial Intelligence Thrust of the Information Hub The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong China Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia Cyprus Division of Decision and Control Systems KTH Royal Institute of Technology SE-100 44 Stockholm Sweden

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorithms is essential. In our paper we addresses the challenge of unconstrained distributed optimization. In our scenario each node’s local function exhibits strong convexity with Lipschitz continuous gradients. The exchange of information between nodes occurs through 3-bit bandwidth-limited channels (i.e., nodes exchange messages represented by a only 3 -bits). Our proposed algorithm respects the network’s bandwidth constraints by leveraging zoom-in and zoom-out operations to adjust quantizer parameters dynamically. We show that during our algorithm’s operation nodes are able to converge to the exact optimal solution. Furthermore, we show that our algorithm achieves a linear convergence rate to the optimal solution. We conclude the paper with simulations that highlight our algorithm’s unique characteristics.

关键词： Adaptation models Quantization (signal) Heuristic algorithms Bandwidth Distributed algorithms Optimization Convergence

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