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Memristor-based multilayer neural network with edge learning and weight update

Neurocomputing 2025年

作者： Ningye Jiang Jupeng Xie Mingxuan Jiang Haoen Huang Zhigang Zeng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan 430074 China

Memristor crossbar array (MCA) is a promising computing-in-memory architecture for accelerating vector-matrix multiplication (VMM). However, existing memristor-based circuits primarily perform forward computations without local learning capabilities. To address the issue, a memristor-based multilayer neural network (MNN) with edge learning is proposed, enabling error backpropagation and one-step weight update. In particular, the overall circuit design of a three-layer MNN is outlined, comprising three operational stages: forward computation, backpropagation, and weight update. Subsequently, the schematics of four adopted circuit blocks are detailed. By integrating the memory and weight update blocks with MCA, the proposed three-layer memristor-based MNN exhibits scalability and universality, allowing it to be extended to the deep neural network (DNN) architecture. Circuit simulations validate the effectiveness of the proposed scheme, while offline-online co-simulations demonstrate its strength in compensating for variations and non-ideal characteristics of physical devices. Comparative results further highlight the advantages of the proposed scheme in realizing full-analog edge learning circuits and performing error backpropagation processes efficiently.

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An Approach for National-Scale Accessibility Assessment and Vulnerability Analysis of Integrated Multimodal Transportation Systems

SSRN

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SSRN 2021年

作者： Zhang Ouyang, Min Sun, Wenjing Hong, Liu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Lab. for Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China

A series of ambitious accessibility-oriented policies have been launched in the world, which center around improving accessibility of the integrated multimodal transportation systems including road, airline, and high-speed rail. The multimodal accessibility assessment is one of the "basic" analyses for the design and implementation of these policies. This paper models the integrated road, high-speed rail, airline, and walking system (IRHAWS) as a four-layer network with the consideration of their interdependencies. An algorithm is then developed to accurately assess accessibility based on the four-layer network with the consideration of daily schedule of high-speed trains and flights, where the accessibility between two nodes is defined as the minimum travel time given a departure time. The proposed approach enables the time-varied accessibility assessment of both off-network and on-network areas at a national or global scale, and is applied to map the multimodal accessibility to 363 city centers in China and analyze the optimal travel modes for on-network travels. Based on the proposed accessibility assessment approach, this paper also investigates the vulnerability of the Chinese integrated multimodal transportation system under the extreme storm event recently occurred in Zhengzhou (July 2021). The findings in this work provide insightful suggestions to plan the road, high-speed rail, and airline systems as a whole. © 2021, The Authors. All rights reserved.

关键词： Railroad transportation

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Understanding the Robustness of 3D Object Detection with Bird’s-Eye-View Representations in Autonomous Driving

arXiv

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arXiv 2023年

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Dept. of Comp. Sci. and Tech. Institute for AI THBI Lab BNRist Center Tsinghua University China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province China SAIC Motor AI Lab China Zhongguancun Laboratory China

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird’s-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDAR fusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. Code available at https: //***/zzj403/BEV_Robust. Copyright © 2023, The Authors. All rights reserved.

关键词： Birds

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Scale-free distributed control for the DC MG cluster under DoS attacks: An asynchronous switched strategy

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Automatica 2025年 179卷

作者： Fabin Cheng Jingang Lai School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan 430074 China

This paper studies the voltage restoration problem of the DC Microgrid (MG) cluster with a two-layer communication structure under Denial-of-Service (DoS) attacks. Based on the open multiagent framework, an asynchronous switching strategy is designed to suppress the impact of DoS attacks on the entire MG cluster’s voltage restoration. Meanwhile, we derived the relationship between attack model parameters and communication link switching mode selection through the general model of DoS attacks and asynchronous switching methods based on average dwell time. Moreover, the designed distributed control protocol is scale-free; that is, it does not rely on any information from the two-layer communication structure and the number of distributed generations (DGs), which makes it universal. Through rigorous theoretical proof, it has been proven that in the DC MG cluster with two-layer communication structures, voltage restoration can be achieved regardless of the number and structure of DGs being attacked. Finally, two simulation examples on the MATlab/SimPowerSystem, including a DC MG cluster consisting of 8 DGs and an IEEE 33-node system are used to investigate the scalability and flexibility of the proposed distributed control algorithm.

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Lithium-ion battery state-of-charge estimation using a real-time moving horizon estimation algorithm

Lithium-ion battery state-of-charge estimation using a real-...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Jiayu Yan Song Li Yiming Wan Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan China the 29th Research Institute of China Electronics Technology Group Corporation Chengdu China

ISBN: (纸本)9781665401166

For battery management systems, it is significant to reliably estimate state-of-charge (SOC) from limited measurements in real time. Based on a nonlinear SOC-dependent equivalent circuit model, we propose a real-time moving horizon estimation (MHE) algorithm for SOC estimation. For efficient computation, the sequential quadratic programming strategy is applied, and each quadratic programming (QP) iteration is solved via the structure-exploiting forward and backward recursions. To account for the limited computation time allowed within each sampling interval, the proposed algorithm does not fully solve the receding horizon optimization problem over each sliding window, but performs fixed number of QP iterations. The simulation results show that compared to the existing fully solved MHE applied to SOC estimation, the proposed real-time MHE algorithm significantly saves computation time at the cost of a slight loss of estimation performance.

关键词： Computational modeling Simulation Estimation Real-time systems Time measurement Computational efficiency State of charge

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Research on the detection method of alignment beacon based on the spatial structure feature of train bogies

Research on the detection method of alignment beacon based o...

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Chinese Automation Congress (CAC)

作者： Chong Cao Shaowei Wan Gang Peng Dingxin He Sheng Zhong Zhang Deng Wentao Guo School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan China Beijing Railway Institute of Mechanical & Electrical Engineering Group Co. Ltd. Beijing China Wuhan Lisai Technology Co. Ltd. Wuhan China

The high-speed train inspection robot needs to perform alignment parking and image acquisition at the position of the train bogie, and reliable alignment identification marks are the basis for the inspection robot to achieve highprecision alignment parking. The existing alignment method is mainly based on the way of artificial beacons, in the inspection workshop through manual laying of additional identification marks such as QR codes, magnetic stripes, RFID electronic tags, auxiliary robot alignment parking, but this type of method in beacon maintenance, laying costs and other aspects have certain limitations. Aiming at the problem, this paper proposes a vision-based bogie beacon detection method, which combines the target detection technology with the unique bogie spatial structure feature of the train, and provides reliable identification marks for the alignment and parking of the inspection robot. This method improves the alignment accuracy, reduces the cost of laying and maintaining artificial beacons, and has high application value.

关键词： Shafts Costs Wheels Object detection Inspection Maintenance engineering Feature extraction

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A Visual Kinematics Calibration Method for Manipulator Based on Nonlinear Optimization

A Visual Kinematics Calibration Method for Manipulator Based...

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Chinese Automation Congress (CAC)

作者： Gang Peng Zhihao Wang Shangbin Guan Zhang Deng Wentao Guo Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Beijing Railway Institute of Mechanical & Electrical Engineering Group Co. Ltd. Beijing China Wuhan Lisai Technology Co. Ltd. Wuhan China

The traditional kinematic calibration method for manipulators requires precise three-dimensional measuring instruments to measure the end pose, which is not only expensive due to the high cost of the measuring instruments but also not applicable to all manipulators. Another calibration method uses a camera, but the system error caused by the camera’s parameters affects the calibration accuracy of the kinematics of the robot arm. In order to solve this problem, this paper reduces the dimensionality of the experimental data and redesigns the objective function to be optimized. That is, the three-dimensional positions of the feature points of the calibration plate under each manipulator attitude corresponding to the actual kinematic model and the classic D-H kinematic model are mapped into the pixel coordinate system, and the sum of Euclidean distance errors of the pixel coordinates of the two is used as the objective function to be optimized. And then, nonlinear optimization and parameter compensation are performed. The experimental results show that the pixel deviation of the end pose corresponding to the optimized D-H kinematic model proposed in this paper and the end pose corresponding to the actual kinematic model in the pixel coordinate system is 0.99 pixels. Compared with the 7.9 deviation pixels between the pixel coordinates calculated by the classic D-H kinematic model and the actual pixel coordinates, the deviation is reduced by nearly 7 pixels for an 87% reduction in error. Therefore, the proposed method can effectively avoid system errors caused by camera parameters in visual calibration, can improve the absolute positioning accuracy of the end of the robotic arm, and has good economy and universality.

关键词： Visualization Robot kinematics Instruments Robot vision systems Kinematics Manipulators Cameras

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Fuzzy DNA Strand Displacement: A Strategy to Decrease the Complexity of DNA Network Design

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Angewandte Chemie 2020年第35期132卷 15089-15095页

作者： Wang Z. Hu Y. Pan L. Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology 1037 Luoyu Road Wuhan 430074 China College of Information Science and Technology Shijiazhuang Tiedao University Shijiazhuang 050043 China

Toehold-mediated DNA strand displacement endows DNA nanostructures with dynamic response capability. However, the complexity of sequence design dramatically increases as the size of the DNA network increases. We attribute this problem to the mechanism of toehold-mediated strand displacement, termed exact strand displacement (ESD), in which one input strand corresponds to one specific substrate. In this work, we propose an alternative to toehold-mediated DNA strand displacement, termed fuzzy strand displacement (FSD), in which one-to-many and many-to-one relationships are established between the input strand and the substrate, to reduce the complexity. We have constructed four modules, termed converter, reporter, fuzzy detector, and fuzzy trigger, and demonstrated that a sequence pattern recognition network composed of these modules requires less complex sequence design than an equivalent network based on toehold-mediated DNA strand displacement. © 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

关键词： DNA recognition DNA structures enzymes molecular devices self-assembly

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Grey Wolf Optimization Based Self-Organizing Fuzzy Multi-Objective Evolution Algorithm

Research Square

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Research Square 2021年

作者： Xie, Jialiang Zhang, Shanli Wang, Honghui Wu, Dongrui School of Science Jimei University Xiamen361021 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control China

Two goals of multi-objective evolutionary algorithms are effectively improving its convergence and diversity, and making the Pareto set evenly distributed and close to the real Pareto Front. This paper proposes a grey wolf optimization based self-organizing fuzzy multi-objective evolutionary algorithm. Grey wolf optimization algorithm is used to optimize the initial weights of the self-organizing map network. New neighborhood relationships for individuals are built by self-organizing map, which can maintain the invariance of feature distribution and map the structural information of the current population into Pareto Sets. Based on this neighborhood relationship, this paper uses the fuzzy differential evolution operator, which constructs a fuzzy inference system to dynamically adjust the weighting parameter in the differential operator, to generate a new initial solution, and the polynomial mutation operator to refine them. Boundary processing is then conducted. Experiments on fifteen test problems were conducted to verify its effectiveness. Results show that the convergence and diversity of the proposed algorithm are better than several state-of-the-art multi-objective evolutionary algorithms. © 2021, CC BY.

关键词： Self organizing maps

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A Pushing-Grasping Collaborative Method Based on Deep Q-Network Algorithm in Dual Viewpoints

Research Square

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Research Square 2021年

作者： Peng, Gang Liao, Jinhu Guan, Shangbin Yang, Jin Li, Xinde School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education China School of Automation South East University Nanjing China

In the field of intelligent manufacturing, robot grasping and sorting is an important content. However, in the traditional 2D camera-based robotic arm grasping method, the grasping efficiency is low and the grasping accuracy is low when facing the scene of stacking and occlusion. Insufficiency and other issues, a dual perspective-based deep reinforcement learning promotion and capture method is proposed. In this case, a novel method of pushing-grasping collaborative based on the deep Q-network in dual viewpoints is proposed in this paper. This method adopts an improved deep Q-network algorithm, with an RGB-D camera to obtain the information of objects’ RGB images and point clouds from two viewpoints, and combines the pushing and grasping actions, so that the trained manipulator can make the scenes better for grasping, so that it can perform well in more complicated grasping scenes. What’s more, we improved the reward function of the deep Q-network and propose the piecewise reward function to speed up the convergence of the deep Q-network. We trained different models and tried different methods in the V-REP simulation environment, and it drew a conclusion that the method proposed in this paper converges quickly and the success rate of grasping objects in unstructured scenes raises up to 83.5%. Besides, it shows the generalization ability and well performance when novel objects appear in the scenes that the manipulator has never grasped before. © 2021, CC BY.

关键词： Manipulators

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