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Gossip-based algorithm for economic dispatch of microgrids integrating isolated and grid-connected modes

Science China(Information Sciences) 2025年第3期68卷 258-271页

作者： Yanmeng ZHANG Yalin ZHANG Zhongxin LIU Zengqiang CHEN College of Artificial Intelligence Nankai University Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation

A gossip-based economic dispatch(ED) algorithm for microgrids is presented in this paper, designed to cope with communication link failures and enable smooth switching of microgrid operation modes. The algorithm is supported by the Push-Pull architecture, which allows its application to direct graphs and relaxes the initial conditions compared to many existing ED algorithms. Under an asynchronous communication network, where only one directed edge is activated at each moment,it has been shown that ED can be achieved with probability 1, provided that the communication graph is strongly ***, in a synchronous communication network, where each directed communication link is activated at each moment with a certain probability, ED is also achieved with probability 1 under the condition that the communication graph is strongly connected. This demonstrates that optimal consensus is reached under randomly switched communication networks as long as the expectation of communication graphs is strongly connected, a condition that is less stringent than the B-strongly connected requirement found in many other studies. The algorithm's use of a non-decreasing variable step size enables a transition from a sub-linear convergence rate, associated with a diminishing step size, to a linear convergence rate. This also lays the groundwork for future improvements in convergence rate through online step size optimization based on the communication topology. Finally,the algorithm's effectiveness and its potential application to anti-collusion are demonstrated through simulations.

关键词： distributed optimization economic dispatch gossip algorithms multi-agent systems consensus algorithm

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From Perception to Action:brain-to-brain Information Transmission of Pigeons

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Journal of Bionic Engineering 2024年第6期21卷 2913-2923页

作者： Lifang Yang Long Yang Haofeng Wang Mengmeng Li Zhigang Shang School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou450001China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou450001China

Along with the flourishing of brain-computer interface technology,the brain-to-brain information transmission between different organisms has received high attention in recent ***,specific information transmission mode and implementation technology need to be further *** this paper,we constructed a brain-to-brain information transmission system between pigeons based on the neural information decoding and electrical stimulation encoding *** system consists of three parts:(1)the“perception pigeon”learns to distinguish different visual stimuli with two discrepant frequencies,(2)the computer decodes the stimuli based on the neural signals recorded from the“perception pigeon”through a frequency identification algorithm(neural information decoding)and encodes them into different kinds of electrical pulses,(3)the“action pigeon”receives the Intracortical Microstimulation(ICMS)and executes corresponding key-pecking actions through discriminative learning(electrical stimulation encoding).The experimental results show that our brain-to-brain system achieves information transmission from perception to action between two pigeons with the average accuracy of about 72%.Our study verifies the feasibility of information transmission between inter-brain based on neural information decoding and ICMS encoding,providing important technical methods and experimental program references for the development of brain-to-brain communication technology.

关键词： Pigeon neural Information Decoding Electrical Stimulation Encoding Intracortical Microstimulation brain-to-brain Information Transmission

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A PI+R Control Scheme Based on Multi-Agent Systems for Economic Dispatch in Isolated BESSs

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IEEE/CAA Journal of Automatica Sinica 2024年第10期11卷 2154-2165页

作者： Yalin Zhang Zhongxin Liu Zengqiang Chen College of Artificial Intelligence Nankai UniversityTianjin 300350 Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai UniversityTianjin 300350China IEEE

Battery energy storage systems(BESSs)are widely used in smart ***,power consumed by inner impedance and the capacity degradation of each battery unit become particularly severe,which has resulted in an increase in operating *** general economic dispatch(ED)algorithm based on marginal cost(MC)consensus is usually a proportional(P)controller,which encounters the defects of slow convergence speed and low control *** order to solve the distributed ED problem of the isolated BESS network with excellent dynamic and steady-state performance,we attempt to design a proportional integral(PI)controller with a reset mechanism(PI+R)to asymptotically promote MC consensus and total power mismatch towards 0 in this *** be frank,the integral term in the PI controller is reset to 0 at an appropriate time when the proportional term undergoes a zero crossing,which accelerates convergence,improves control accuracy,and avoids *** eigenvalues of the system under a PI+R controller is well analyzed,ensuring the regularity of the system and enabling the reset *** ensure supply and demand balance within the isolated BESSs,a centralized reset mechanism is introduced,so that the controller is distributed in a flow set and centralized in a jump *** cope with Zeno behavior and input delay,a dwell time that the system resides in a flow set is *** on this,the system with input delays can be reduced to a time-delay free *** the capacity limitation of the battery,a modified MC scheme with PI+R controller is *** correctness of the designed scheme is verified through relevant simulations.

关键词： Battery energy storage system(BESS) distributed control economic dispatch multi-agent system reset control

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Control of defensive behavior by the nucleus of Darkschewitsch GABAergic neurons

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National Science Review 2024年第4期11卷 70-83页

作者： Huiying Zhao Jinrong Liu Yujin Shao Xiang Feng Binhan Zhao Li Sun Yijun Liu Linghui Zeng Xiao-ming Li Hongbin Yang Shumin Duan Yan-qin Yu Department of Neurology of Second Affiliated Hospital and School of Brain Science and Brain Medicine Zhejiang University School of Medicine Nanhu Brain-Computer Interface Institute Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-Machine Integration State Key Laboratory of Brain-Machine Intelligence Zhejiang University NHC and CAMS Key Laboratory of Medical Neurobiology Zhejiang University Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province School of Medicine Hangzhou City University

The nucleus of Darkschewitsch(ND), mainly composed of GABAergic neurons, is widely recognized as a component of the eye-movement controlling system. However, the functional contribution of ND GABAergic neurons(NDGABA) in animal behavior is largely unknown. Here, we show that NDGABA neurons were selectively activated by different types of fear stimuli, such as predator odor and foot *** and chemogenetic manipulations revealed that NDGABA neurons mediate freezing ***, using circuit-based optogenetic and neuroanatomical tracing methods, we identified an excitatory pathway from the lateral periaqueductal gray(l PAG) to the ND that induces freezing by exciting ND inhibitory outputs to the motor-related gigantocellular reticular nucleus, ventral part(Gi V). Together,these findings indicate the NDGABA population as a novel hub for controlling defensive response by relaying fearful information from the l PAG to Gi V, a mechanism critical for understanding how the freezing behavior is encoded in the mammalian brain.

关键词： nucleus of Darkschewitsch GABAergic population defensive behavior freezing neural circuitry

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Design and Verification of Tuning Platform for Micro-turbojet Aircraft 7

Design and Verification of Tuning Platform for Micro-turboje...

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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Tian, Yu Ouyang, Tianyu Zhang, Jiachen Duan, Feng Nankai University Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Tianjin China

ISBN: (纸本)9798350363173

Micro-turbojet is widely used in civil aviation due to high thrust-to-weight ratio, superior combustion efficiency and low fuel consumption. In order to exert the operation effect of micro-turbojet aircraft, it is very important to adjust the parameters of PID controller. The efficiency and accuracy of parameter tuning tests are determined by the platform used. However, existing platforms are limited to single tests, making the process complex and reliant on the experience of testing personnel. In order to optimize the tuning test of micro-turbojet aircraft, this study designed a new aircraft tuning platform based on BP neural network and fuzzy algorithm. The platform focuses on controlling the steering gear and electric cylinder. It adjusts the multidimensional parameters of the aircraft and determines the thrust of each turbojet engine, serving as the basis for evaluating optimization effects. This ensures improved adaptability and robustness of the aircraft. The feasibility of the parameter adjustment platform is verified by simulation. The tracking error peak value is ±0.088, which realizes the goal of multiple parameter adjustment in a single test. The results of this study offer robust support for advancing micro-turbojet technology. They also establish a solid foundation for extending its application into areas such as unmanned aerial vehicles and small-scale commercial aircraft. © 2024 IEEE.

关键词： Efficiency

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Relationship Between the Long-Term Practice of Tai Chi Stance with neural Patterns of the brain: An Exploratory Study

Relationship Between the Long-Term Practice of Tai Chi Stanc...

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2024 China Automation Congress, CAC 2024

作者： Li, Mengmeng Zhang, Jianbo Wan, Hong School of Electrical and Information Engineering Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350368604

Research has shown that practicing Tai Chi can alleviate brain tension and enhance the activity of the autonomic nervous system in the human body. However, there is limited research on the effects of Tai Chi Stance on brain function. This study aims to analyze the electroencephalogram (EEG) characteristics of long-term Tai Chi Zhuang practitioners and investigate the changes in brain dynamic neural patterns during different stages of Tai Chi Stance, together with the differences between long-term practitioners and a control group. In this study, we first analyzed the changes in EEG spectral characteristics during different stages of Tai Chi Stance practice of long-term practitioners. We found that the alpha (8-13 Hz) power significantly increased during the stance, indicating a state of deep relaxation during the Tai Chi practice. The experimental group also exhibited significant changes in brain functional network parameters in the alpha band during the stance, including increased clustering coefficient and global efficiency. Finally, inter-group differences in EEG characteristic parameters were analyzed between the experimental group and control group. The results showed significant differences in relative power and functional connectivity in the alpha band between the two groups, indicating that long-term Tai Chi Stance practice can effectively enhance brain network efficiency and improve information integration capabilities. This study provides scientific evidence for Tai Chi Stance training to enhance brain health. © 2024 IEEE.

关键词： Electroencephalography

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SEMG-Based Gesture Recognition in Multiple Scenarios Using a Phase Locked Value-Based Deep Learning Method

SEMG-Based Gesture Recognition in Multiple Scenarios Using a...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Xueze Wang, Minjie Chen, Zihua Wang, Lu Wang, Hongbo Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of Ai & Robotics Ministry of Education Shanghai Engineering Research Center of Ai & Robotics Moe Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of Ai & Robotics Institude of Meta-Medical Academy for Engineering & Technology Fudan University Shanghai China

ISBN: (纸本)9798350386226

Surface electromyography (sEMG) signals-based gesture recognition method is widely employed in human-computer interaction task. In this paper, we proposed a phase locked value (PLV)-based feature extraction method for sEMG-based gesture recognition. We conducted validation experiments on public datasets by integrating the previous proposed gesture recognition algorithms, STCN-GR and ConSSL. PLV-STCN-GR and PLV-ConSSL were utilized to test the performance of proposed feature extraction method in intra-session, inter-session and inter-subject scenarios. In general, the proposed PLV-based sEMG decoding framework has achieved good recognition results on public datasets and demonstrated the possibility of its application in practical scenarios. © 2024 IEEE.

关键词： Deep learning

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Motion Sickness Alleviation Based on a Mindfulness brain-computer interface 16

Motion Sickness Alleviation Based on a Mindfulness Brain-Com...

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16th International Conference on Advanced Computational Intelligence, ICACI 2024

作者： Zhu, Jiawei Bao, Xiaoyu Huang, Qiyun Wang, Tao Huang, Li Han, Yupeng Li, Kendi Chen, Di Xu, Kailin Wang, Zijian Jiang, Ya Li, Yuanqing Pazhou Laboratory Dept. Research Center for Brain-Computer Interface Guangzhou China Huizhou Desay SV Automotive Co. Ltd. Huizhou China South China University of Technology Dept. School of Automation Science and Engineering China Pazhou Laboratory Dept. Research Center for Brain-Computer Interface China South China Brain-Computer Interface Technology Co. Ltd. Guangzhou China

ISBN: (纸本)9798350308501

Motion sickness is a common affliction that affects nearly half of the global population and poses challenges to comfortable travel experiences, necessitating diverse intervention strategies. Pharmacological interventions have shown promise but present limitations due to side effects. To date, there is no non- pharmacological alleviation method for motion sickness that has been proven effective in a large population. In this study, we developed a novel mindfulness brain-computer interface (mindfulness BCI) for alleviation of motion sickness. Thirty-one subjects who were susceptible to motion sickness participated in our experiments in a real-world car motion environment. The experimental results demonstrated that the proposed mindfulness BCI can effectively alleviate motion sickness symptoms, where more than 90% of subjects reported the effectiveness of the mindfulness BCI system and a significant reduction of Motion Sickness Susceptibility Questionnaire (MISC) scores was obtained when comparing the BCI-based meditation state with the resting state for the subjects (t30=4.968, P © 2024 IEEE.

关键词： brain computer interface

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Rats Behavior Recognition Based on Improved YOLOv8

Rats Behavior Recognition Based on Improved YOLOv8

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Bai, Zhuoxuan Li, Yifan Ru, Zhihao Li, Sining Li, Wenyu Duan, Feng College of Artificial Intelligence Nankai University Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Tianjin300110 China

ISBN: (纸本)9781665481090

In the domains of neuroscience and pharmacology, rodent models are crucial for deepening our understanding of neurobiological processes. Traditional behavioral recognition methods, however, encounter issues such as high costs and suboptimal accuracy. This research presents a pioneering method that employs Deformable Attention to enhance rat behavior recognition on an observation platform. By integrating Deformable Convolutional Networks (DCNs) into the YOLOv8 algorithm, the approach amplifies detection precision and robustness. We performed experiments utilizing six hours of video data from rats navigating an eight-arm maze, categorizing behaviors including walking, sniffing, barbering, eating and so on. The refined model showed a substantial increase in mean Average Precision (mAP), from 0.776 to 0.990, along with a smoother and more stable loss curve, indicating enhanced predictive performance. This study demonstrates the feasibility of leveraging standard cameras and sophisticated machine learning techniques for effective and precise behavior analysis under limited resource conditions. © 2024 IEEE.

关键词： Rats

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Digital interpretation of Tai Chi basic skills based on surface electromyography feature analysis 4

Digital interpretation of Tai Chi basic skills based on surf...

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4th International Conference on Biomedicine and Bioinformatics Engineering, ICBBE 2024

作者： Li, Mengmeng Gu, Zhiyuan Wan, Hong School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou450001 China

ISBN: (纸本)9781510682443

Basic Tai Chi exercises serve as the foundation and core of Tai Chi practice. A deep understanding of the movement principles and essential characteristics is crucial to ensuring the effectiveness of training. However, practitioners often struggle to grasp the essentials, resulting in minimal progress or even injuries that may lead them to abandon the practice. To achieve a digital interpretation of the movement transitions and force application principles in basic Tai Chi exercises, we introduced surface electromyography (sEMG) detection into basic Tai Chi in this study. Eight Tai Chi experts were invited to construct an expert subject dataset. Then we extracted and analyzed the integral whole electric value and contribution rates of relevant muscles in basic Kai He Gong (KHG) exercise, as well as the dynamic changes on both sides of the body. An improved Teager-Kaiser Energy (TKE) algorithm was used to accurately identify the muscle force sequencing in expert-level and trainee-level basic movement. The results revealed that the cooperated relationship the left and right body sides and the crucial role of the erector spinae in the waist connecting the upper and lower limbs in the KHG exercises, demonstrating the Tai Chi principle of "controlling from the waist". In all, we explored the sEMG activity patterns in basic Tai Chi exercises in this study, enabling a digital interpretation of movement transitions and muscle force application principles. The results could provide references for the training and teaching of basic Tai Chi exercises. © 2024 SPIE.

关键词： Electromyography

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