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2nd International Conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Pei, Wenliang Hua, Changchun Cui, Hailong Zhao, Guanglei Ma, Zhuang The Institute of Electrical Engineering The Key Lab of Intelligent Rehabilitation and Neuroregulation in Hebei Province Key Laboratory of Intelligent Control and Neural Information Processing Ministry of Education Yanshan University Hebei China Tangshan University Hebei China

ISBN: (纸本)9798400710681

This paper solves the event-triggered robust consensus problem of disturbed multi-agent systems (MASs) in fully distributed fashion. First, a distributed extended state observer (DESO) is developed for appropriately estimating system states and external disturbances. Then, by utilising these estimates, a fully distributed event-triggering control protocol, that composes of an adaptive distributed controller to avoid using global information while maintaining desired consensus property, and a novel hybrid dynamic event-triggering mechanism (ETM) to save communication resources, is put forward for the MASs. It is notable that an auxiliary differential equation is incorporated in the triggering condition, with which, positive minimum interevent times (MIETs) are available to ease the hardware implementation, while being with adaptive coefficients, the proposed hybrid dynamic ETM can be independent of any global information. Finally, the obtained main results are testified via a numerical example. © 2024 Copyright held by the owner/author(s).

关键词： State estimation

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Concentric Circles Nested Array Design Method for Acoustic Imaging Based on Differential Co-Array Model

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IEEE Sensors Journal 2025年第10期25卷 18106-18114页

作者： Xie, Zhiyuan Yang, Yan Zhang, Junyan Zhu, Wenzhao Bai, Zonglong North China Electric Power University Department of Electrical and Communication Engineering Baoding071003 China Hebei Provincial Key Laboratory of Power Internet of Things Baoding071003 China Yanshan University Key Laboratory of Intelligent Control and Neural Information Processing Education Ministry of China Qinhuangdao066004 China

Acoustic imaging intuitively illustrates the locations of sound sources and has a wide range of applications, including fault detection. However, this technology requires a substantial number of microphones, which increases the complexity and cost of the data acquisition system. To reduce the number of microphones while maintaining the performance of acoustic imaging, this paper introduces a concentric circular nested array (CCNA) design method. By extending the array using a differential co-array model, a virtual uniform concentric circular array is achieved. This virtual array preserves the advantages of the circular array structure while minimizing the number of required microphones. The performance of the proposed array is validated through simulation experiments. The simulation results demonstrate that the CCNA offers higher resolution, a greater dynamic range (DR), and improved positioning accuracy in acoustic imaging compared to other arrays. Finally, this paper compares the acoustic imaging results of the CCNA with those of the uniform circular array (UCA) through experiments. The experimental results reveal that the positioning accuracy of the proposed CCNA surpasses that of the UCA, thereby confirming the superiority of the proposed design. © 2001-2012 IEEE.

关键词： Acoustic imaging

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High-Performance wearable flexible humidity sensor based on plasma-treated graphene oxide for medical monitoring and non-contact sensing

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Applied Surface Science 2025年 702卷

作者： He, Bin Xia, Wenjie Deng, Yifan Wu, Jinxin Guo, Zikai Wang, Zhiyuan Zhang, Boyu Feng, Yi State Key Laboratory of Intelligent Power Distribution Equipment and System Hebei University of Technology Tianjin300401 China Hebei Key Laboratory of Bioelectromagnetics and Neural Engineering Tianjin300401 China Tianjin Key Laboratory of Bioelectricity and Intelligent Health Tianjin300401 China College of Chemical Engineering Hebei University of Technology Tianjin300401 China

Graphene oxide (GO) is a humidity sensitive material commonly used in the manufacture of humidity sensors. However, GO-based humidity sensors still face issues such as low sensitivity, slow humidity response speed and poor stability in practical applications. In this paper, a flexible humidity sensor based on plasma graphene oxide (P-GO) was introduced, which used plasma-modified GO film as the humidity-sensitive layer, laser-induced graphene (LIG) with fork-finger structure as the electrode layer, and polydimethylsiloxane (PDMS) as the flexible substrate. It was found that the surface water contact angle of the P-GO film treated with plasma for 180 s decreased from 50° to 10°, reaching a superhydrophilic state. The sensitivity of the flexible humidity sensor based on P-GO was improved by nearly 10 times higher than that of the GO sensor. The mechanism was that the surface of the P-GO film was rougher and the relative contents of the surface oxygen-containing functional groups C-O and C=O increased by 6.28 % and 8.49 %, respectively. Meanwhile, the P-GO-based flexible humidity sensor was found to be able to perform a stable and sensitive response over a wide relative humidity range of 7 %∼97 %, possessing a response/recovery speed of 15 s/9s and exhibiting high stability over 60 days. In addition, the P-GO-based flexible humidity sensor was applied for breath detection and non-contact detection, and was found to have a large potential for application in the medical monitoring field. © 2025

关键词： Polydimethylsiloxane

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iPro-CSAF: identification of promoters based on convolutional spiking neural networks and spiking attention mechanism

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PeerJ Computer Science 2025年 11卷 e2761-e2761页

作者： Zhou, Qian Meng, Jie Luo, Hao Hebei Key Laboratory of Bioelectromagnetics and Neural Engineering School of Health Sciences and Biomedical Engineering Hebei University of Technology Tianjin China Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health School of Health Sciences and Biomedical Engineering Tianjin China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China Department of Physics School of Science Tianjin University Tianjin China

A promoter is a DNA segment which plays a key role in regulating gene expression. Accurate identification of promoters is significant for understanding the regulatory mechanisms involved in gene expression and genetic disease treatment. Therefore, it is an urgent challenge to develop computational methods for identifying promoters. Most current methods were designed for promoter recognition on few species and required complex feature extraction methods in order to attain high recognition accuracy. Spiking neural networks have inherent recurrence and use spike-based sparse coding. Therefore, they have good property of processing spatio-temporal information and are well suited for learning sequence information. In this study, iPro-CSAF, a convolutional spiking neural network combined with spiking attention mechanism is designed for promoter recognition. The method extracts promoter features by two parallel branches including spiking attention mechanism and a convolutional spiking layer. The promoter recognition of iPro-CSAF is evaluated by exhaustive promoter recognition experiments including both prokaryotic and eukaryotic promoter recognition from seven species. Our results show that iPro- CSAF outperforms promoter recognition methods which used parallel CNN layers, methods which combined CNNs with capsule networks, attention mechanism, LSTM or BiLSTM, and CNNs-based methods which needed priori biological or text feature extraction, while our method has much fewer network parameters. It indicates that iPro-CSAF is an effective computational method with low complexity and good generalization for promoter recognition. © 2025 Zhou et al.

关键词： Convolution

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Enhancing Network Abnormal Detection With NMF-SECNN: Leveraging Deep Feature Learning for High-Precision Traffic Analysis

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IEEE Transactions on Network Science and engineering 2025年第3期12卷 2069-2080页

作者： Yuan, Yazhou Yu, Ning Zheng, Zhuolin Yang, Yong Ma, Kai Liu, Zhixin Chen, Cailian Zhang, Jianmin Yanshan University Institute of Electrical Engineering Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Control and Neural Information Processing of the Ministry of Education of China Qinhuangdao066004 China State Grid Handan Electric Power Supply Company Hebei 056000 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Baosteel Research Institute Automation Department Shanghai200240 China

Detection of abnormalities in industrial network traffic plays a crucial role in maintaining network system security. However, current abnormal detection models suffer from low precision, and extracting deep-level feature information from industrial network traffic is difficult. This leads to the loss of partial feature information during the detection process, thereby affecting detection efficiency. To address this issue, this paper proposes an abnormal traffic detection framework for industrial networks. By employing a Non-negative Matrix Factorization (NMF)-based method for extracting abnormal traffic features and optimizing the NMF decomposition process through constructing label consistency constraints, we facilitate effective feature extraction. Additionally, the Squeeze-and-Excitation attention mechanism is introduced into a Convolutional neural Network (CNN) to construct a classifier that enhances detection precision without increasing complexity, enabling efficient identification of complex network traffic patterns. This results in the NMF-Squeeze-and-Excitation-CNN (NMF-SECNN) model, which combines effective feature extraction capability with a lightweight structural design, achieving superior detection performance in industrial network environments. The proposed method achieves a detection accuracy of 99.4%, representing a 5.6% improvement over baseline methods, and a recall rate of 98.2%, showcasing the model's capability to identify abnormalities across diverse scenarios. Various classification metrics confirm the model's robustness and effectiveness, demonstrating its significant advantages over traditional methods. © 2013 IEEE.

关键词： Non-negative matrix factorization

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Effect of astrocyte on synchronization of thermosensitive neuron–astrocyte minimum system

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Chinese Physics B 2022年第8期31卷 325-336页

作者： Yi-Xuan Shan Hui-Lan Yang Hong-Bin Wang Shuai Zhang Ying Li Gui-Zhi Xu State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of TechnologyTianjin 300130China Hebei Key Laboratory of Bioelectromagnetics and Neural Engineering Hebei University of TechnologyTianjin 300130China

Astrocytes have a regulatory function on the central nervous system(CNS), especially in the temperature-sensitive hippocampal region. In order to explore the thermosensitive dynamic mechanism of astrocytes in the CNS, we establish a neuron-astrocyte minimum system to analyze the synchronization change characteristics based on the Hodgkin-Huxley model, in which a pyramidal cell and an interneuron are connected by an astrocyte. The temperature range is set as 0-40 ℃ to juggle between theoretical calculation and the reality of a brain environment. It is shown that the synchronization of thermosensitive neurons exhibits nonlinear behavior with changes in astrocyte parameters. At a temperature range of0 ℃-18 ℃, the effects of the astrocyte can provide a tremendous influence on neurons in synchronization. We find the existence of a value for inositol triphosphate(IP_(3)) production rate and feedback intensities of astrocytes to neurons, which can ensure the weak synchronization of two neurons. In addition, it is revealed that the regulation of astrocytes to pyramidal cells is more sensitive than that to interneurons. Finally, it is shown that the synchronization and phase transition of neurons depend on the change in Ca^(2+) concentration at the temperature of weak synchronization. The results in this paper provide some enlightenment on the mechanism of cognitive dysfunction and neurological disorders with astrocytes.

关键词： astrocytes synchronization Hodgkin–Huxley model thermosensitive neuron

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Abnormal EEG Microstates in Post-stroke Patients: A Pilot Study

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IEEE Sensors Journal 2025年

作者： Chen, Xiaoling Zhai, Jiashun Cai, Yuhao Zhang, Tengyu Wang, Juan Mao, Gengsheng Cheng, Shengcui Zhang, Yuanyuan Xie, Ping Yanshan University Key Laboratory of Intelligent Control and Neural Information Processing Ministry of Education Hebei Province Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Qinhuangdao Hebei China National Research Center for Rehabilitation Technical Aids Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability Beijing China PLA General Hospital Senior Department of Neurosurgery Beijing China Tianjin University Academy of Medical Engineering and Translational Medicine Tianjin300072 China Capital Medical University Xuanwu Hospital Beijing China

Exploring brain function is crucial for unraveling the pathological mechanism underlying stroke. while most studies focus on brain function emphasize dynamic connections and interactions within or between brain regions, they often ignore the global properties of large-scale network topology. In this study, we analyzed resting-state EEG microstates in stroke patients, calculating key parameters such as mean duration (MD), occurrence (OC), time coverage (TC), and transition probability (TP) across different microstate classes. As a result, we identified four microstate classes (A, B, C, and D) in both healthy subjects and stroke patients. Notably, stroke patients showed significant changes in Class B, C, and D, with increased MD, OC, and TC in Class B and C and decreased MD, OC, and TC in Class D. additionally, stroke patients displayed higher TP between Class B and C than healthy controls. Moreover, we observed a positive correlation between the OC of Class D and clinical Fugl-Meyer scores, suggestion a link between microstate dynamic and motor recovery. This study highlights the connection between specific microstates and brain regions, providing evidence that stroke patients demonstrate increased activity in visual and salience networks, but decreased activity in the dorsal attention network. We speculate these results from changes in the occipital lobe, frontal lobe, parietal lobe, and dorsal anterior cingulate cortex. These findings deepen our understanding of stroke pathophysiology and support further research. © 2001-2012 IEEE.

关键词： Electroencephalography

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FPGA-based small-world spiking neural network with anti-interference ability under external noise

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neural Computing and Applications 2024年第20期36卷 12505-12527页

作者： Guo, Lei Liu, Yongkang Wu, Youxi Xu, Guizhi Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering School of Health Sciences and Biomedical Engineering Hebei University of Technology Tianjin300131 China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300131 China School of Artificial Intelligence Hebei University of Technology Tianjin300401 China

Neuromorphic hardware has become hotspot in the field of brain-like computing due to its advantages. However, the presence of external noise imposes challenges with respect to maintaining normal function of neuromorphic hardware. Biological brains have self-adaptability to external noise, meaning that a brain-like hardware with bio-plausibility can be expected to improve robustness. The purpose of this paper is to implement a highly fitted brain-like hardware with anti-interference ability (AIA) while preserving bio-plausibility. We propose a method of implementing a small-world spiking neural network (SWSNN) with bio-plausibility based on a field-programmable gate array (FPGA), in which the nodes are Izhikevich neuron modules, the edges are synaptic plasticity modules, and the topology is a small-world network. Then, the AIAs of the FPGA-based SNNs with different external noises are evaluated by two anti-interference indices. Further, taking a speech recognition task as the case study, the AIAs of these FPGA-based SNNs are verified in application. Finally, the AIA mechanism of the FPGA-based SNNs is discussed. Our results demonstrate that: (i) In the FPGA-based SWSNN, the FPGA-based Izhikevich neuron modules and the synaptic plasticity modules highly fit to the corresponding simulation results, and the topology conforms to the small-world property of human functional brain networks. (ii) Based on two anti-interference indices, the FPGA-based SWSNN outperforms the FPGA-based SNNs with other topologies, which is further verified by the speech recognition accuracy. (iii) Our discussions hint that the synaptic plasticity is intrinsic factor of the AIA, and the topology is a factor affecting the AIA. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Topology

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Decoding of Motor Imagery based on Multi-scale Residual Network and Multi-head Attention block 43

Decoding of Motor Imagery based on Multi-scale Residual Netw...

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43rd Chinese Control Conference, CCC 2024

作者： Li, Jundi Li, Mengfan Zhang, Song Xu, Zhenghua Xu, Guizhi Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering Tianjin China Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health Tianjin China Hebei University of Technology State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Sciences and Biomedical Engineering Tianjin China

ISBN: (纸本)9789887581581

One of the important parts in motion imagery brain-computer interfaces is EEG decoding, and deep learning has a huge potential to play a crucial role in the EEG end-to-end decoding process. Currently, the challenge for deep learning is to cope with time-varying EEG signals to be able to maintain robust decoding and keep high classification performance. This study proposes a multiscale residual network containing an attention mechanism(MSRANet) to decode four-classified motor imagery. The MSRANet utilises a multi-head attentional mechanism to focus on the important information of EEG features;and applies a multiscale residual block to extract the enriched EEG features as well as employs a shared portion of the block parameters in order to extract the common features. Compared with the five baseline methods, MSRANet has the highest accuracy on BCI Competition IV 2a and 83.02 % in the session-specific task. Thus, the proposed MSRANet can robustly decode time-varying EEG, save the cost of using BCI, and facilitate long-term use. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Brain computer interface

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Analysis of Electric Field Focalization Induced by Transcranial Magneto-Acoustical-Electrical Stimulation Parameters 9th

Analysis of Electric Field Focalization Induced by Transcran...

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9th Frontier Academic Forum of Electrical engineering, FAFEE 2020

作者： Shuai, Zhang Mengdi, Li Yixiao, Wang Junjie, Wang Key Laboratory of Bioelectromagnetic Technology and Neural Engineering of Hebei Province Hebei University of Technology Tianjin300130 China Tianjin Key Laboratory of Bioelectrical and Intelligent Health Tianjin300130 China

ISBN: (纸本)9789813366084

Transcranial magnetic-acoustical-electrical stimulation is a new noninvasive brain modulation method with high spatial resolution and high penetration depth. However, the effect of electric field focalization induced by transcranial magneto-acoustical-electrical stimulation parameters is still unclear. In this paper, the electric field generated by transcranial magnetic-acoustical-electrical stimulation is simulated. The influence of the phased array transducer parameters on the induced electric field distribution is analyzed. The induced electric field distribution caused by different phased array parameters was calculated. The distribution characteristics of the induced electric field are analyzed from the radial and axial directions of the preset focusing point. The simulation results show that induced electric field intensity in the focusing region increases with the length and width of array elements and decreases with the array gap widening. There is a significant negative correlation between the size of focal region and the gap of the array elements. The correlation between phased array parameters and focusing performance is analyzed in this paper, which provides reference and theoretical guidance for research on improving the focusing property of induced electric field. © 2021, Beijing Oriental Sun Cult. Comm. CO Ltd.

关键词： Magnetos

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