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A Memristive Spiking Neural Network Circuit for Bio-inspired Navigation Based on Spatial Cognitive Mechanisms

IEEE Transactions on Biomedical Circuits and Systems 2024年第3期19卷 686-698页

作者： Chen, Zhanfei Wang, Xiaoping Wang, Zilu Yang, Chao Huang, Tingwen Lai, Jingang Zeng, Zhigang School of Mechanical Engineering and Automation Shenzhen518055 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation China Hubei Key Laboratory of Brain-inspired Intelligent Systems 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 Shenzhen University of Advanced Technology School of Computer Science and Control Engineering Shenzhen518055 China

Cognitive navigation,a high-level and crucial function for organisms' survival in nature,enables autonomous exploration and navigation within the environment. However,most existing works for bio-inspired navigation are implemented with non-neuromorphic computing. This work proposes a bio-inspired memristive spiking neural network (SNN) circuit for goal-oriented navigation,capable of online decision-making through reward-based learning. The circuit comprises three primary modules. The place cell module encodes the agent's spatial position in real-time through Poisson spiking;the action cell module determines the direction of subsequent movement;and the reward-based learning module provides a bio-inspired learning method adaptive to delayed and sparse rewards. To facilitate practical application,the entire SNN is quantized and deployed on a real memristive hardware platform,achieving about a 21× reduction in energy consumption compared to a typical digital acceleration system in the forward computing phase. This work offers an implementation idea of neuromorphic solution for robotic navigation application in low-power scenarios. © 2007-2012 IEEE.

关键词： intelligent robots

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Lower limb motor imagery EEG signals classification based on 1D-CNN-LSTM algorithm

Lower limb motor imagery EEG signals classification based on...

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2024 International Conference on Future of Medicine and Biological Information Engineering, MBIE 2024

作者： Han, Chunxiao Hu, Yue Ren, Hui Chen, Wenjin Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin University of Technology and Education Tianjin300222 China

ISBN: (纸本)9781510682825

With the continuous development of the brain-computer interface (BCI) technology, the lower limb rehabilitation system based on Motor Imagery (MI) has graduallybecome a researchhotspot in the field of *** recognize the lower limbs MI, this paper designed an experimental paradigmfor MIlower limb MI and used the 1D-CNN-LSTMdeep learning algorithm to classify lower limb movement features from MI EEG signals. Compared with classical machine learning algorithms, the results showed that 1D-CNN-LSTM has relatively higher accuracy. Meanwhile, the paper built a real-time lower limb rehabilitation system based on the 1D-CNN-LSTM algorithm, which verifies the effectiveness and feasibility of the algorithm. The system provides an advanced and effective solution for brain-computer interfaces based on MI. © 2024 SPIE.

关键词： Image reconstruction

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Data-Driven Group Formation control of Cyber-Physical Systems via Distributed Cloud Computing

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 341-350页

作者： Ren, Hongru Long, Yinren Li, Hongyi Huang, Tingwen Guangdong University of Technology School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

This paper investigates the group formation control problem for cyber-physical systems (CPSs) with random communication constraints. The distributed cloud computing system is constructed to divide agents into groups and establish communication between agents. A data-driven predictive control strategy is proposed by combining networked predictive control and model-free adaptive control method. The desired group formation control performance can be achieved and the three-channel random communication constraints of CPSs are actively compensated. Thisstrategy does not require the system model and relies solely on the system's I/O data for adaptive learning. Further analyses concludes the conditions for simultaneous reach stability and group formation of the closed-loop CPSs using the data-driven predictive control strategy. The effectiveness of the proposed strategy is validated by simulation results. © 2023 IEEE.

关键词： Distributed cloud

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Enhanced fully convolutional network based on external attention for low-dose CT denoising 24

Enhanced fully convolutional network based on external atten...

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6th International Conference on control and Computer Vision, ICCCV 2024

作者： Zhang, Haining Dong, Jian Tianjin Key Laboratory of information sensing and intelligent control Tianjin University of Technology and Education Tianjin300222 China

ISBN: (纸本)9798400718045

In recent decades, researchers have concentrated their efforts on developing techniques to denoise low-dose, sparse-angle CT images. Currently, mainstream low-dose CT methods include traditional iterative reconstruction algorithms and deep learning algorithms. However, these methods still exhibit transition smoothing, noise, and artifact residue. Inspired by the idea of deep learning, we propose an improved fully convolutional neural network using external attention. The network incorporates a novel external attention module, and use perceptual loss as the loss function which is used to construct a fully convolutional model with dense connections. This model fuses extracted feature information to achieve image denoising. Following training, the proposed EA-FCN demonstrated superior performance on actual clinical CT images. Furthermore, our method achieved favorable subjective and objective evaluations in terms of noise and artifact suppression. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Convolutional neural networks

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Two-stage stochastic planning for distribution networks considering uncertainties of electric vehicles and distributed generators 3

Two-stage stochastic planning for distribution networks cons...

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2024 3rd International Conference on Smart Grid and Green Energy, ICSGGE 2024

作者： Zheng, Huhu Ye, Jianhua Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin University of Technology and Education Tianjin300222 China

A significant quantity of electric vehicles and distributed generator (DG) sources are connected to the distribution network (DN), resulting in the formation of a new power-transportation network (PTN). This integration introduces source-load uncertainty to the system. This research presents a stochastic planning model for PTN that focuses on making decisions on charging station locations. Initially, a model for the distribution network is created, which incorporates energy storage devices, demand response devices, and DG devices. The transportation network utilizes a P-median siting determination model. Ultimately, a two-stage stochastic planning approach is employed to address system uncertainty. The primary goal is to minimize both the investment cost and operation cost of the system while also considering several common situations. The efficacy of the proposed approach has been proven using a combined system consisting of a 33-node DN and a 25-node TN. © Published under licence by IOP Publishing Ltd.

关键词： Stochastic programming

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Multi-Agent Reinforcement Learning with Region Division and Leader-Follower Communication

Multi-Agent Reinforcement Learning with Region Division and ...

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2024 China Automation Congress, CAC 2024

作者： Wang, Chao Xu, Xiang Shenzhen Key Laboratory of Control Theory and Intelligent Systems School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9798350368604

The integration of multi-agent reinforcement learning (MARL) into complex systems has paved new ways for collaborative problem-solving. However, traditional approaches to MARL frequently encounter the challenge of achieving efficient communication among agents, essential for coordinated action. This paper introduces a region division and leader-follower(RDLF) communication algrithm with the MARL framework. RDLF divides the environment into several regions, each managed by a leader agent that coordinates the actions of follower agents and handling inter-region communication. This hierarchical structure reduces unnecessary communication, enhancing learning efficiency. Experimental results in multi-particle environments demonstrate RDLF’s superiority over existing MARL algorithms, especially with increasing agent numbers. RDLF effectively addresses scalability and communication challenges in large-scale multi-agent systems, providing a robust foundation for its application in complex and dynamic environment. © 2024 IEEE.

关键词： Adversarial machine learning

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Polyphonic sound event localization and detection based on Multiple Attention Fusion ResNet

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Mathematical Biosciences and Engineering 2024年第2期21卷 2004-2023页

作者： Zhang, Shouming Zhang, Yaling Liao, Yixiao Pang, Kunkun Wan, Zhiyong Zhou, Songbin Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Institute of Intelligent Manufacturing Guangdong Academy of Science Guangdong Key Laboratory of Modern Control Technology Guangzhou510030 China

Sound event localization and detection have been applied in various fields. Due to the polyphony and noise interference, it becomes challenging to accurately predict the sound event and their occurrence locations. Aiming at this problem, we propose a Multiple Attention Fusion ResNet, which uses ResNet34 as the base network. Given the situation that the sound duration is not fixed, and there are multiple polyphonic and noise, we introduce the Gated Channel Transform to enhance the residual basic block. This enables the model to capture contextual information, evaluate channel weights, and reduce the interference caused by polyphony and noise. Furthermore, Split Attention is introduced to the model for capturing cross-channel information, which enhances the ability to distinguish the polyphony. Finally, Coordinate Attention is introduced to the model so that the model can focus on both the channel information and spatial location information of sound events. Experiments were conducted on two different datasets, TAU-NIGENS Spatial Sound Events 2020, and TAU-NIGENS Spatial Sound Events 2021. The results demonstrate that the proposed model significantly outperforms state-of-the-art methods under multiple polyphonic and noise-directional interference environments and it achieves competitive performance under a single polyphonic environment. ©2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License.

关键词： Deep learning

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Distributed Resilient Fusion Filtering for Nonlinear Systems with Random Sensor Delays:Optimized Algorithm Design and Boundedness Analysis

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Journal of Systems Science & Complexity 2023年第4期36卷 1423-1442页

作者： HU Jun HU Zhibin DONG Hongli LIU Hongjian School of Automation Harbin University of Science and TechnologyHarbin 150080China Department of Mathematics Harbin University of Science and TechnologyHarbin 150080China Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and TechnologyHarbin 150080China Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum UniversityDaqing 163318China School of Mathematics-Physics and Finance Anhui Polytechnic UniversityWuhu 241000China

This paper is concerned with the distributed resilient fusion filtering(DRFF)problem for a class of time-varying multi-sensor nonlinear stochastic systems(MNSSs)with random sensor delays(RSDs).The phenomenon of the RSDs is modeled by a set of random variables with certain statistical *** addition,the nonlinear function is handled via Taylor expansion in order to deal with the nonlinear fusion filtering *** aim of the addressed issue is to propose a DRFF scheme for MNSSs such that,for both RSDs and estimator gain perturbations,certain upper bounds of estimation error covariance(EEC)are given and locally minimized at every sample *** the light of the obtained local filters,a new DRFF algorithm is developed via the matrix-weighted fusion ***,a sufficient condition is presented,which can guarantee that the local upper bound of the EEC is ***,a numerical example is provided,which can show the usefulness of the developed DRFF approach.

关键词： Distributed resilient fusion filtering matrix-weighted fusion nonlinear time-varying systems random sensor delays

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Cooperative Game Theory Based Coordinated Scheduling of Two-Machine Flow-Shop and Transportation

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Journal of Systems Science & Complexity 2023年第6期36卷 2415-2433页

作者： SUN Wenjuan GONG Hua LIU Peng School of Management Shenyang University of TechnologyShenyang110870China Liaoning Key Laboratory of Intelligent Optimization and Control for Ordnance Industry School of ScienceShenyang Ligong UniversityShenyang110159China

A cooperative game theoretical approach is taken to production and transportation coordinated scheduling problems of two-machine flow-shop(TFS-PTCS problems)with an interstage *** authors assume that there is an initial scheduling order for processing jobs on the *** cooperative sequencing game models associated with TFS-PTCS problems are established with jobs as players and the maximal cost savings of a coalition as its *** properties of cooperative games under two different types of admissible rearrangements are *** TFS-PTCS problems with identical processing time,it is proved that,the corresponding games areσ_(0)-component additive and convex under one admissible *** Shapley value gives a core allocation,and is provided in a computable *** the other admissible rearrangement,the games neither need to beσ_(0)-component additive nor convex,and an allocation rule of modified Shapley value is *** properties of the cooperative games are analysed by a counterexample for general problems.

关键词： Cooperative sequencing games core allocation production and transportation coordinated scheduling Shapley value two-machine flow-shop

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Turbulent drag reduction by sector-shaped counter-flow dielectric barrier discharge plasma actuator

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Chinese Physics B 2025年第2期34卷 362-370页

作者： Borui Zheng Shaojie Qi Minghao Yu Jianbo Zhang Linwu Wang Dongliang Bian School of Automation and Information Engineering Xi’an University of TechnologyXi’an 710048China School of Mechanical and Precision Instrument Engineering Xi’an University of TechnologyXi’an 710048China Science and Technology on Plasma Dynamics Laboratory Air Force Engineering UniversityXi’an 710038China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an 710048China Shaanxi University Key Laboratory of Photonic Power Devices and Discharge Regulation Xi’an 710048China

The primary objective in aircraft transportation is to minimize turbulent drag, thereby conserving energy and reducing emissions. We propose a sector-shaped counter-flow dielectric barrier discharge plasma actuator, which leverages jet synthesis for drag reduction. A drag control experiment was conducted in a low-speed wind tunnel with a controlled flow velocity of 9.6 m/s(Re = 1.445 × 10^(4)). This study investigated the effects of varying pulse frequencies and actuation voltages on the turbulent boundary layer. Using a hot-wire measurement system, we analyzed the pulsating and time-averaged velocity distributions within the boundary layer to evaluate the streamwise turbulent drag reduction. The results show that the local TDR decreases as the pulse frequency increases, reaching a maximum reduction of approximately 20.97% at a pulse frequency of 50 Hz. In addition, as the actuation voltage increases, the friction coefficient decreases, increasing the drag reduction rate. The maximum drag reduction of approximately 33.34% is achieved at an actuation voltage of 10 kV.

关键词： plasma flow control turbulent boundary layer turbulent drag reduction

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