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2022 Applied Optics and Photonics China: Advanced Laser technology and Applications, AOPC 2022

作者： Jiang, Yanze Sun, Jianfeng Ding, Yuanxue Jiang, Peng Zhang, Hailong Li, Sining Zhou, Shuaijun National Key Laboratory of Science and Technology on Tunable Laser Institute of OptoElectronic Harbin Institute of Technology Heilongjiang Harbin150001 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China Beijing Aerospace Automatic Control Institute Beijing100854 China

ISBN: (纸本)9781510662223

Geiger mode Avalanche Photo Diode (Gm-APD) array lidar is a lidar that can perform single-photon detection. It offers a wide range of applications due to its low power consumption, small size, and extended detecting distance. There haven't been many research on this detector's target classification because of its late development and small detector array. The classification technique based on the Gm-APD array lidar point cloud is the focus of this paper's research: Firstly, the Gm-APD array lidar is utilized to perform imaging tests on four targets from various angles in order to create a target classification *** that, several data preprocessing methods were chosen and implemented based on the characteristics of the obtained data, such as filling in missing values, performing range image and intensity image interpolation, using the principle of keyhole imaging to convert the range image to point cloud data, realizing the information fusion of distance image and intensity image, and using multiple point cloud data enhancement methods. Finally, the point cloud classification networks PointNet and PointNet++ are trained on point cloud data with varying levels of preprocessing, the results are compared and analyzed, and the impact of different preprocessing methods on the classification accuracy of the two networks is determined. Inferences were made and experiments were carried out to verify the inferences. The data set preprocessing method with the highest classification accuracy of the two networks is discovered, laying the groundwork for future Gm-APD lidar target classification and detection research. © 2023 SPIE.

关键词： Optical radar

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Research on Hybrid Online Brain-Computer Interface System Based on MI-mVEP 5

Research on Hybrid Online Brain-Computer Interface System Ba...

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5th Asia Symposium on Image Processing, ASIP 2023

作者： Zhuan, Zhikun Bian, Yan He, Xinglin Yuan, Peng Geng, Liqing Li, Junjie Sun, Miao Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin China

ISBN: (纸本)9798350323429

In this study, a hybrid online brain-computer interface (BCI) system was established to address the issue of low recognition accuracy and limited imagery tasks in motor imagery (MI) based on BCI systems. This hybrid system utilized both MI and motion-onset visual evoked potential (mVEP). After simultaneously acquiring MI and mVEP potentials and electroencephalogram (EEG) signal preprocessing, the filter bank common spatial pattern (FBCSP) algorithm was used to extract MI and mVEP features. These features were then employed to construct training set feature vectors. The training set feature vectors of MI and mVEP signals were classified using the backpropagation (BP) neural network and support vector machine (SVM) algorithms, respectively. Based on the established classification model, single test data were recognized online, and the results were fed back to subjects in real-time via block moving routes. The presented system can realize five different block moving paths, with faster moving effects achieved when the system induces MI and mVEP potential simultaneously along the lower left or lower right diagonal path. The experimental results showed that the average classification accuracies of the lower left and lower right diagonal moving effects are up to 84.53% and 84.60%, respectively. These results indicate that this study effectively controls the fast-moving blocks in the online BCI system of MI-mVEP, providing a theoretical basis and experimental support for the research of a hybrid online BCI system based on MI-mVEP. In summary, the proposed system has demonstrated improved performance through the combination of simultaneous MI and mVEP potential utilization and efficient classification algorithms, with promising future applications in BCI research. © 2023 IEEE.

关键词： Brain computer interface

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Design of Rail Surface Defect Detection System Based on LabVIEW Machine Vision 6

Design of Rail Surface Defect Detection System Based on LabV...

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6th IEEE Information technology, Networking, Electronic and Automation control Conference, ITNEC 2023

作者： Han, Han Tian, Liguo Li, Meng Cui, Xudong Shang, Chunhu Hou, Shidong Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin China

ISBN: (纸本)9781665460033

A method of the surface defect detection for rail workpiece is proposed in this paper. Based on the principle of machine vision inspection technology and LabVIEW software program, the surface quality of rail workpiece can be achieved. The image collected by CMOS color industrial camera is processed and analyzed by image preprocessing, image threshold segmentation, morphological analysis, feature location and pattern matching. The graphical display interface is designed by LabVIEW software programming, and the real-time display of rail defects is carried out. This experiment shows that the system can quickly and efficiently diagnose the two major defects of rail surface crack and scratch. It is easier to realize the intellectualization of defect surface detection and achieve the requirements of rail workpiece surface defect detection in the rail production. © 2023 IEEE.

关键词： Computer vision

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A Novel Stochastic Event-Based Feedback Watermark for Replay Attack Detection 13th

A Novel Stochastic Event-Based Feedback Watermark for Repla...

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13th International Conference on Sensor Systems and Software, S-Cube 2022

作者： Zhao, Xudong Wang, Xinyu Xu, Fengde Wang, Yuhan Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Dalian University of Technology Dalian116024 China

ISBN: (纸本)9783031348983

The security of cyber physical systems (CPSs) is the premise of resource sharing in modern industry, which has attracted considerable attention of researchers. Many effective methods have been proposed to defend CPSs, among which physical watermark is one prevailing approach which can enhance the replay attack detection capability of CPSs. In order to make the watermarks work more effective, a novel stochastic event-based feedback physical watermark is proposed in this paper to detect replay attacks. We formulate first the problem taking into account the Kalman filter, the linear quadratic Gaussian (LQG) optimal controller and the χ2 detector. Then, we characterize the LQG performance loss and the probability of adding a physical watermark in two different scenarios: the system operates with and without replay attacks. It is proved that the probability of adding a watermark signal will increase when replay attacks exist. Furthermore, we discuss the performance of the χ2 detector under the framework of our approach. Finally, numerical simulations are verified the theoretical results. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Feedback

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Multi-Time-Scale Variational Mode Decomposition-Based Robust Fault Diagnosis of Railway Point Machines Under Multiple Noises

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Chinese Journal of Electronics 2024年第3期33卷 814-822页

作者： Junqi LIU Tao WEN Guo XIE Yuan CAO Clive Roberts Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology The School of Electronic and Information Engineering Beijing Jiaotong University Birmingham Centre for Railway Research and Education University of Birmingham

The fault diagnosis of railway point machines(RPMs) has attracted the attention of engineers and *** have studies considered diverse noises along the *** fulfill this aspect,a multi-time-scale variational mode decomposition(MTSVMD) is proposed in this paper to realize the accurate and robust fault diagnosis of RPMs under multiple *** decomposes condition monitoring signals after coarse-grained processing in varying *** this manner,the information contained in the signal components at multiple time scales can construct a more abundant feature space than at a single *** the experimental validation,a random position,random type,random number,and random length(4R) noise-adding algorithm helps to verify the robustness of the *** adequate experimental results demoristrate the superiority of the proposed MTSVMD-based fault diagnosis.

关键词： Railway point machines Fault diagnosis Multi-time-scale Variational mode decomposition

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Fine-Grained Classification of Wild Fungi Based on Attention Residual Mechanism 3

Fine-Grained Classification of Wild Fungi Based on Attention...

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3rd International Conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2023

作者： Wen, Shuoyi Yang, Li Duan, Hailong Zhang, Tingting Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjing China

ISBN: (纸本)9781643684444

Identifying wild mushroom species are an important way to prevent poisoning from consuming toxic wild mushrooms. Therefore, an attention-based method for fine-grained classification of wild mushrooms is proposed, in which an attention mechanism is incorporated and a new network model structure is constructed in combination with a residual module. Firstly, nine diseased wild mushroom image samples were collected, and in order to make the model have better generalization ability, the images were pre-processed so that the number of samples reached 7200, and the experimental results showed that the improved deep residual network model achieved about 99.12% accuracy in classifying and recognizing the established wild mushroom database, and the improved neural network had a great improvement in classification accuracy. © 2023 The Authors.

关键词： Classification (of information)

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Improved CBAM Rock Image Classification Based on Xception 3

Improved CBAM Rock Image Classification Based on Xception

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3rd International Conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2023

作者： Zhang, Tingting Ren, Shuyan Duan, Hailong Wen, Shuoyi Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjing China

ISBN: (纸本)9781643684444

To address the problems of traditional machine learning algorithms in rock recognition which are time-consuming, labor-intensive and low accuracy, a migration learning method using network weights for feature learning based on Xception network structure and incorporating an improved convolutional attention module (CBAM) to strengthen the important features of rocks, as well as adding a feature map perturbation structure to enhance the generalization ability of the model is proposed. Six types of manually collected rock sample images from five provinces, including Fujian and Zhejiang, are selected to construct the rock dataset and conduct experiments, and the learning rate is set at 0.001. Experiments show that the precision of the proposed method is 98.9% with 0.05 loss, which is higher than other mainstream recognition models with higher recognition accuracy and lower loss. © 2023 The Authors.

关键词： Rocks

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Characteristics Analysis and Classification of Foot Autonomic Movements Based on EEG Signals 5

Characteristics Analysis and Classification of Foot Autonomi...

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5th Asia Symposium on Image Processing, ASIP 2023

作者： He, Lin Meng, Tongning Zhuan, Zhikun Bian, Yan Zhang, Zhiwen Li, Junjie Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin China

ISBN: (纸本)9798350323429

To improve the effectiveness of rehabilitation for stroke patients, active training can be used to treat and recover patients' foot dyskinesia. Recognizing the characteristics of different foot motion states is an important part of active rehabilitation for stroke patients. In this study, EEG signals were collected during three different foot actions (foot at rest, 15 ° stretched and 45° stretched) to conduct a classification study on the EEG signals of different voluntary movements of the right foot. Features of autonomous action EEG signals were extracted and support vector machine (SVM) and random forest (RF) were both used for classification. Brain topographic maps and time-frequency maps were utilized to display the characteristics of EEG data. The ground averaged time-frequency map and brain topographic map revealed that the ERD (Event-Related Desynchronization) characteristics of the stretched feet at 45 ° were more obvious than those of the stretched feet at 15°, while the ERD characteristics of the stretched feet at 15° were more obvious than those in the resting state. The classification results showed that the RF algorithm achieved higher classification accuracy (81.89%) compared to the SVM algorithm (74.93%) among the three different action states. The RF classification algorithm was found to be more suitable for the classification of different foot movements with a classification result that was 6.97% higher than that of SVM. Moreover, 90% of the subjects using the RF algorithm achieved an accuracy score of more than 75%. These findings highlight the potential of identifying different voluntary movements of the foot by classifying the characteristics of EEG signals and using them as a basis for the rehabilitation effect evaluation of stroke patients. © 2023 IEEE.

关键词： Support vector machines

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Risk Assessment of Stage Machinery Based on Entropy Weight and Fuzzy Inference Method

Risk Assessment of Stage Machinery Based on Entropy Weight a...

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2023 International Conference on Culture-Oriented Science and technology, CoST 2023

作者： Wang, Huiqin Yang, Qingqing Li, Zhen Wang, Huiqi Ministry of Culture and Tourism Key Laboratory of Acoustic Visual Technology and Intelligent Control System Beijing China

ISBN: (纸本)9798350357998

It is of great significance to find out the hidden hazards of the stage machinery in advance and formulate the corresponding protective measures to improve the safety of the stage performance. A comprehensive method that integrates entropy weights and the fuzzy inference is proposed. Firstly, the risk factors are identified through Failure Modes and Effects Analysis (FMEA) method, with the information obtained by field survey and research. Then, the entropy weight method and Fault Tree Analysis (FTA) methods are adopted to analyze the risk factors so as to obtain the probability of the hazardous events. Lastly, the risk level of different event is obtained by the fuzzy inference method. The simulation result proves that the model is reasonable, objective, and feasible. © 2023 IEEE.

关键词： Fuzzy inference

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Multilayer core–shell structured FeNi_(3)@C with enhanced interfacial polarization for microwave absorbers

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Nano Research 2025年第2期18卷 833-842页

作者： Shaohan Guo Junying Zhang Chengyou Lin Jin Ge Song Bi Zhi-Ling Hou College of Mathematics and Physics Beijing University of Chemical TechnologyBeijing 100029China School of Physics and Optoelectronic Engineering Beijing University of TechnologyBeijing 100124China National Key Laboratory of Electromagnetic Information Control and Effects Shenyang Aircraft Design and Research InstituteShenyang 110035China 501 Department Xi’an Research Institute of High-TechXi’an 710025China

Magnetoelectric composites can achieve magneto-electric synergy to optimize impedance ***,it is challenging to enhance the broadband absorption performance for thin thickness ***,FeNi_(3)@C composite with magnetic alloy core and N-doped macroporous multilevel layered carbon shell was constructed by hierarchical interface *** uniform multilevel layered carbon shell could induce interfacial polarization and multiple scattering,and further endows enhanced electromagnetic attenuation and optimized impedance *** ultra-thin broadband absorber with an effective absorption bandwidth of 7.4 GHz is achieved at a thickness of only 1.6 ***,full-band absorption from 2 to 18 GHz at a thickness of 9.8 mm is realized through metastructure *** work provides an alternative strategy to prepare coreshell structured magnetoelectric composites for ultra-thin and broadband absorbers.

关键词： covalent organic frameworks(COFs) FeNi_(3)@C core-shell structure interfacial polarization impedance matching

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