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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance engineering, IncoME-V 2023, International conference on the Efficiency and Performance engineering Network, TEPEN 2023

作者： Wang, Nan Wang, Hongjun Zhang, Zhuangzhuang Chen, Baisheng School of Mechanic and Electric Engineering BISTU Beijing100192 China Key Laboratory of Modern Measurement and Control Technology BISTU Beijing100192 China

ISBN: (纸本)9783031494123

Research on fault diagnosis of rotating machinery is crucial as it serves as a core component. In order to diagnose spindle system fault types quickly and accurately, Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Long Short-Term Memory (LSTM) fault diagnosis method is presented. Firstly, collect the fault vibration, then ICEEMDAN is used for vibration signals to extract seven times domain features and four frequency domain features. Finally, the time domain and frequency domain features’ parameters are input into the LSTM network for fault classification. The method is verified for the practical industrial CNC machine center spindle system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Empirical mode decomposition

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Spatiotemporal mode-locking and dissipative solitons in multimode fiber lasers

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Light(Science & Applications) 2023年第11期12卷 2309-2324页

作者： Bo Cao Chenxin Gao Kewei Liu Xiaosheng Xiao Changxi Yang Chengying Bao State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision InstrumentsTsinghua UniversityBeijing 100084China State Key Laboratory of Information Photonics and Optical Communications School of Electronic EngineeringBeijing University of Posts and TelecommunicationsBeijing 100876China

Multimode fiber(MMF)lasers are emerging as a remarkable testbed to study nonlinear spatiotemporal physics with potential applications spanning from high energy pulse generation,precision measurement to nonlinear *** underlying mechanism for the generation of ultrashort pulses,which can be understood as a spatiotempoal dissipative soliton(STDS),in the nonlinear multimode resonators is the spatiotemporal mode-locking(STML)with simultaneous synchronization of temporal and spatial *** this review,we first introduce the general principles of STML,with an emphasize on the STML dynamics with large intermode ***,we present the recent progress of STML,including measurement techniques for STML,exotic nonlinear dynamics of STDS,and mode field engineering in MMF *** conclude by outlining some perspectives that may advance STML in the near future.

关键词： dissipative nonlinear locking

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Observation and research of deep underground multi-physical fields—Huainan–848 m deep experiment

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Science China Earth Sciences 2023年第1期66卷 54-70页

作者： Yun WANG Yaxin YANG Heping SUN Chengliang XIE Qisheng ZHANG Xiaoming CUI Chang CHEN Yongsheng HE Qiangqiang MIAO Chaomin MU Lianghui GUO Jiwen TENG School of Geophysics and Information Technology China University of GeosciencesBeijing 100083China School of Geophysics and Measurement-control Technology East China University of TechnologyNanchang 330013China Innovation Academy for Precision Measurement Science and Technology Chinese Academy of SciencesWuhan 430077China Institution of Engineering Protection IDEAMSPLALuoyang 471023China State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines Anhui University of Science and TechnologyHuainan 232001China Institute of Geochemistry Chinese Academy of SciencesGuiyang 550081China

Compared with the surface,the deep environment has the advantages of allowing“super-quiet and ultra-clean”-geophysical field observation with low vibration noise and little electromagnetic interference,which are conducive to the realization of long-term and high-precision observation of multi-physical fields,thus enabling the solution of a series of geoscience *** the Panyidong Coal Mine,where there are extensive underground tunnels at the depth of 848 m below sea level,we carried out the first deep-underground geophysical observations,including radioactivity,gravity,magnetic,magnetotelluric,background vibration and six-component seismic *** concluded from these measurements that(1)the background of deep subsurface gravity noise in the long-period frequency band less than 2 Hz is nearly two orders of magnitude weaker than that in the surface observation environment;(2)the underground electric field is obviously weaker than the surface electric field,and the relatively high frequency of the underground field,greater than 1 Hz,is more than two orders of magnitude weaker than that of the surface electric field;the east-west magnetic field underground is approximately the same as that at the surface;the relatively high-frequency north-south magnetic field underground,below 10 Hz,is at least one order of magnitude lower than that at the surface,showing that the underground has a clean electromagnetic environment;(3)in addition to the highfrequency and single-frequency noises introduced by underground human activities,the deep underground space has a significantly lower background vibration noise than the surface,which is very beneficial to the detection of weak earthquake and gravity signals;and(4)the underground roadway support system built with ferromagnetic material interferes the geomagnetic *** also found that for deep observation in the“ultra-quiet and ultra-clean”environment,the existing geophysical equipment and observation technology hav

关键词： Multi-physical fields Radioactivity Gravity Geomagnetic Electromagnetic Earthquake Observations deep underground

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Intelligent Elevator Safety Supervision System Combined with Battery Car Identification 22

Intelligent Elevator Safety Supervision System Combined with...

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Proceedings of the 2022 4th International Conference on Video, Signal and Image Processing

作者： Shibo Guo Kang Zhang Zhiyan Zhou Xinyu Zhou Weiyu Gong Zhenghao Wang Kaiyue Zhu Haibin Wu Aili Wang School of Measurement-Control Technology and Communications Engineering Harbin University of Science and Technology China

ISBN: (纸本)9781450397810

In recent years, elevator safety accidents occur frequently, and relevant news has aroused people's strong attention to elevator safety. Among them, electric vehicle explosion accident hot spot is the highest. In this paper, according to the elevator safety hazard source (including illegal vehicle such as storage battery into, passengers trapped, robbed or distress due to illness, the elevator is high concentration flammable gases or burning smoke) design storage battery detection module, voice call module, combustible gas monitoring module for joint operations, monitoring, alarm, the outside world communication as one of the complete system. The system uses Arduino Mega 2560 as the main control chip, ESP32-CAM wireless camera module uses the improved YOLOv4 model for real-time analysis and processing of illegal vehicles, LD3320 speech recognition module and SYN6288 Chinese speech synthesis chip to achieve the voice call function. MQ-2 gas sensitive smoke sensor and LCD1602 LIQUID crystal display module to achieve gas monitoring function, HC-05 Bluetooth module to achieve communication function feedback alarm information to the elevator monitoring and management center, if necessary, can automatically call the alarm phone. The experiment shows that the detection confidence of battery car in elevator environment can reach 0.75, the recognition accuracy of voice call in quiet environment is 96%, and the recognition accuracy of voice call in noise environment is 90%. The alarm can be realized by combustible gas monitoring. The system can monitor the hidden danger in the elevator in real time, solve the problems such as the elevator supervision is weak, the emergency rescue is not timely, improve the efficiency of solving the elevator safety problems.

关键词： voice call combustible gas monitoring battery car identification ASPP-YOLOv4

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The control System of Intelligent Drug Delivery Car Based on Vision Guidance 7

The Control System of Intelligent Drug Delivery Car Based on...

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7th Asian Conference on Artificial Intelligence technology, ACAIT 2023

作者： Wang, Gang Ding, Simin Jing, Shufan Jilin Communications Polytechnic School of Mechanical Engineering Changchun China Jilin Institute of Chemical Technology School of Information And Control Engineering Changchun China

ISBN: (纸本)9798350359145

In response to the "contactless delivery"problem in hospitals, the control system of intelligent drug delivery car was designed based on vision guidance. The system takes raspberry pie as the control core and completes font recognition through high-definition camera;It determines the position and direction of the car through the OpenMV scanning marker line to complete the tracking and obstacle avoidance;It detects whether the drug is put down through the HX711 pressure sensor;It completes human-computer interaction through Bluetooth communication;It combines PID algorithm to realize closed loop control of speed, and controls the moving and changing direction of the car through DC motor module. The test results show that the system has high stability and good robustness, which has certain reference value for the intelligence of public medical service industry. © 2023 IEEE.

关键词： controlled drug delivery

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Channel pruning method driven by similarity of feature extraction capability

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Soft Computing 2025年第2期29卷 1207-1226页

作者： Sun, Chuanmeng Chen, Jiaxin Li, Yong Wang, Yu Ma, Tiehua State Key Laboratory of Dynamic Measurement Technology North University of China Shanxi Taiyuan030051 China School of Electrical and Control Engineering North University of China Shanxi Taiyuan030051 China State Key Laboratory of Coal Mine Disaster Dynamics and Control Chongqing University Chongqing400044 China

Channel pruning is a method to compress convolutional neural networks, which can significantly reduce the number of model parameters and the computational amount. Current methods that focus on the internal parameters of a model and feature mapping information rely on artificially set a priori criteria or reflect filter attributes by partial feature mapping, which lack the ability to analyze and discriminate the channel feature extraction and ignore the basic reasons for the similarity of the channels. This study developed a pruning method based on similar structural features of channels, called SSF. This method focuses on analysing the ability to extract similar features between channels and exploring the characteristics of channels producing similar feature mapping. First, adaptive threshold coding was introduced to numerically transform the channel characteristics into structural features, and channels with similar coding results could generate highly similar feature mapping. Secondly, the spatial distance was calculated for the structural features matrix to obtain the similarity between channels. Moreover, in order to keep rich channel classes in the pruned network, different class cuts were made on the basis of similarity to randomly remove some of the channels. Thirdly, considering the differences in the overall similarity of different layers, this study determined the appropriate pruning ratio for different layers on the basis of the channel dispersion degree reflected by the similarity. Finally, extensive experiments were conducted on image classification tasks, and the experimental results demonstrated the superiority of the SSF method over many existing techniques. On ILSVRC-2012, the SSF method reduced the floating-point operations (FLOPs) of the ResNet-50 model by 57.70% while reducing the Top-1 accuracy only by 1.01%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Research on a transmission line fault detection method based on improved YOLOv7-Tiny 5

Research on a transmission line fault detection method based...

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5th International Conference on Video, Signal and Image Processing, VSIP 2023

作者： Zhang, Zhen Wang, Qingyan Chen, Qingguo Liang, Xintao School Of Measurement And Control Technology And Telecommunications Engineering Harbin University Of Science And Technology Harbin150080 China School Of Electrical And Electronic Engineering Harbin University Of Science And Technology Harbin150080 China

ISBN: (纸本)9798400709272

In response to the challenges faced by most fault detection algorithms for transmission power lines, such as detecting small targets, issues of missed detections and false negatives, a large number of model parameters, and high computational requirements that hinder deployment on unmanned aerial vehicles, this study proposes a novel model called YOLOvS. YOLOvS addresses these challenges by incorporating lightweight models, introducing improved attention mechanisms, and utilizing an enhanced CIoU to achieve fast and accurate detection of typical faults in transmission power lines captured by aerial imagery. Experimental validation confirmed that the YOLOvS algorithm improves the detection accuracy of small targets while maintaining overall detection performance. Furthermore, it achieved a reduction of 21% in model size compared to YOLOv7-Tiny and a 20% decrease in computational requirements. These advancements significantly contribute to lowering the hardware costs associated with deploying the algorithm on unmanned aerial vehicles. © 2023 ACM.

关键词： Fault detection

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Combined Classification of Hyperspectral and LiDAR Data based on Dual-Channel Cross-Transformer 5

Combined Classification of Hyperspectral and LiDAR Data base...

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5th International Conference on Video, Signal and Image Processing, VSIP 2023

作者： Zhou, Binbin Wang, Qingyan Zhang, Junping Wang, Yujing School Of: Measurement-Control And Communication Engineering Harbin University Of Science And Technology Harbin150080 China School Of: Electronics And Information Engineering Harbin Institute Of Technology Harbin150001 China

ISBN: (纸本)9798400709272

In the face of complex scenes, single-modal dominant classification tasks encounter limitations in performance due to insufficient information. On the other hand, joint classification of multimodal remote sensing data faces challenges such as data sample differences and lack of correlation in physical features between modalities, which can impact classification performance. To fully integrate the heterogeneous information of multimodal data and improve classification performance, we propose a dual-channel cross-transformer feature fusion extraction network. The framework leverages self-attention mechanisms to aggregate features within each modality, and the feature fusion module based on cross-modal attention fully considers the complementary information between modalities. Classification tasks are performed using the fused spatial-spectral features obtained from the joint representation of modalities. Extensive experiments conducted on the Houston and MUUFL datasets demonstrate the effectiveness of the proposed model compared to existing methods. © 2023 ACM.

关键词： Classification (of information)

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Coexisting fast–slow dendritic traveling waves in a 3D-array electric field coupled neuronal network

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Chinese Physics B 2024年第6期33卷 614-626页

作者：魏熙乐任泽宇卢梅丽樊亚琴常思远 The Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information EngineeringTianjin UniversityTianjin 300072China School of Information Technology Engineering Tianjin University of Technology and EducationTianjin 300074China

Coexistence of fast and slow traveling waves without synaptic transmission has been found in hhhippocampal tissues,which is closely related to both normal brain activity and abnormal neural activity such as epileptic discharge. However, the propagation mechanism behind this coexistence phenomenon remains unclear. In this paper, a three-dimensional electric field coupled hippocampal neural network is established to investigate generation of coexisting spontaneous fast and slow traveling waves. This model captures two types of dendritic traveling waves propagating in both transverse and longitude directions: the N-methyl-D-aspartate(NMDA)-dependent wave with a speed of about 0.1 m/s and the Ca-dependent wave with a speed of about 0.009 m/s. These traveling waves are synaptic-independent and could be conducted only by the electric fields generated by neighboring neurons, which are basically consistent with the in vitro data measured experiments. It is also found that the slow Ca wave could trigger generation of fast NMDA waves in the propagation path of slow waves whereas fast NMDA waves cannot affect the propagation of slow Ca waves. These results suggest that dendritic Ca waves could acted as the source of the coexistence fast and slow waves. Furthermore, we also confirm the impact of cellular spacing heterogeneity on the onset of coexisting fast and slow waves. The local region with decreasing distances among neighbor neurons is more liable to promote the onset of spontaneous slow waves which, as sources, excite propagation of fast waves. These modeling studies provide possible biophysical mechanisms underlying the neural dynamics of spontaneous traveling waves in brain tissues.

关键词： hippocampal network epileptiform dendritic oscillation traveling wave electric field coupling

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Online Learning-Based Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite-Terrestrial Networks

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China communications 2024年第3期21卷 230-246页

作者： Tong Minglei Li Song Han Wanjiang Wang Xiaoxiang School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing ***,the network state information is uncertain or *** deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this *** problem of minimizing the average sum task completion delay of all IoT devices over all time periods is *** decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier *** results validate that the proposed scheme performs better than other baseline schemes.

关键词： computing resource allocation mobile edge computing satellite-terrestrial networks task offloading decision

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