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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Shao, Mingmei Liu, Yanyan Tang, Jiaze Liu, Huanyu Li, Junbao Faculty of Computing Harbin Institute of Technology Harbin150080 China National Key Laboratory of Electromagnetic Space Security Tianjin300308 China Electrical Measurement Technology and Intelligent Control Institute School of Instrumentation Science and Engineering Harbin Institute of Technology Harbin150080 China

ISBN: (纸本)9789819700677

This paper presents an approach for robust target detection and localization in the context of UAV autonomous landing with partially occluded targets. Since the performance of the autonomous landing system largely depends on the accurate detection and positioning of the landing target, it is very important to deal with the local occlusion caused by obstacles or environmental factors. In order to improve the overall performance of the system, we propose a fault-tolerant target detection method and target completion strategy. The proposed target detection method combines image processing technology with feature extraction algorithm, which can recognize and locate the landing target even in the case of occlusion. The target completion strategy aims to recover the missing part of the occluded target and make the detection and location algorithm execute more reliably. We perform experiments on image data sets with different horizontal object occlusion to evaluate the performance of the proposed method. The results show that our method successfully detects and locates the landing target under different occlusion levels, and significantly improves the performance of UAV autonomous landing system in the real scene. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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Data Augmentation Integrated with Feature-Enhanced Convolutional Neural Network for Imbalanced Fault Diagnosis in Rolling Bearings 13

Data Augmentation Integrated with Feature-Enhanced Convoluti...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Wang, Li Qu, Qilin Wang, Yanwei Wong, David Shan-Hill Zheng, Ying School of Artificial Intelligence and Automation Huazhong University of Science and Technology China -Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China School of Mechanical & Electrical Engineering Wuhan Institute of Technology 693 Chuxiong Road Wuhan430073 China National Tsing-Hua University Department of Chemical Engineering Hsinchu30071 Taiwan

ISBN: (纸本)9798350361674

Imbalance in fault samples is a key problem with most rolling bearing datasets in real industrial processes, limiting the performance of fault detection. In addition, existing fault diagnosis methods for rolling bearings do not adopt a modular division between the initial layer and the classification layer. This lack of modularization inhibits the full activation of the network's feature extraction capabilities and impedes subsequent model improvements. In this paper, a novel fault diagnosis method based on data augmentation integrated with a feature-enhanced convolutional neural network (DA-FECNN) is proposed. First, Variational Autoencoder-Generative Adversarial Network (VAE-GAN) is utilized to expand the number of fault samples and improve the diversity of few-shot. Second, a uniform feature-enhanced structure is designed with a feature extraction module, a multi-scale classification module, and a reconstruction module. The network's ability on feature extraction is improved by focusing on high quality features. Thereby, accurate fault classification is realized simultaneously. Experiments on real world data show the effectiveness of the proposed method on fault diagnosis task. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on Rolling Bearing Fault Diagnosis Based on Classification Generative Adversarial Network

Research on Rolling Bearing Fault Diagnosis Based on Classif...

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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： Kang, Ma Hongjun, Wang School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing100192 China Beijing International Science and Technology Cooperation Base for Intelligent Sensing and Control of High-End Equipment Beijing100192 China Beijing Key Laboratory of Measurement and Control of Electromechanical System Beijing100192 China

ISBN: (纸本)9783031494123

In practical engineering, it is usually difficult to label monitoring data, and the fault diagnosis accuracy is not high in strong noise environment. To solve the above problems, this paper uses Cwt-CatGAN method to process signals and diagnose faults. In this method, CWT is first used to convert one-dimensional original vibration signals into two-dimensional time–frequency graphs as the input of classification generation admixture network. Then, the CatGAN model is obtained through the adversarial training process, and fake samples similar to the extraction distribution of CWT are generated. Finally, the input samples are clustered into certain categories. In addition, the performance of the Cwt-CatGAN method is verified by using the classical rotating machine data set, the experiment is compared with the unsupervised fault diagnosis model CatAAE. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Failure analysis

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Monitoring and Early Warning System for Bolt Looseness of the Electric Power Tower Based on Capacitance Sensor and LoRa Network 42

Monitoring and Early Warning System for Bolt Looseness of th...

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42nd Chinese control Conference, CCC 2023

作者： Zhao, Liang Chen, Changxin Zhang, Qianru North University of China State Key Laboratory of Dynamic Measurement Technology Taiyuan030051 China School of Electrical and Control Engineering North University of China Taiyuan030051 China

ISBN: (纸本)9789887581543

In order to solve the problem of the electric power tower collapsed caused by bolt loosening, a monitoring and early warning system for bolt loosening of the electric power tower based on LoRa network is designed in this manuscript. A non-contact measurement method and height-dividing area management for bolt is used, and its rationality is verified by simulation with Ansoft Maxwell or experiment. The results show that the capacitance of the bolt looseness test module can change obviously when the bolt or nut rotates 10 degrees, and the error of the height measurement module is 6.4%. The LoRa network can realize remote monitoring, so a monitoring system designed can meet the requirements of remote on-line monitoring of bolts to reduce accidents by bolt looseness. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Capacitance

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MoB-NetV2-RVFL: Diagnosis of Coronavirus (COVID-19) from CT images via a Deep Learning Architecture

MoB-NetV2-RVFL: Diagnosis of Coronavirus (COVID-19) from CT ...

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2024 Jiangsu Annual Conference on Automation, JACA 2024

作者： Tang, Xiaoyu Chai, Lin Song, Chengze School of Automation Southeast University Jiangsu Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Jiangsu Nanjing210096 China School of Sino-french Engineering Institute Nanjing University of Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9781837242689

Computer-aided diagnosis has emerged as a crucial trend in modern medicine. Recently, CT-based automatic detection algorithms for lung infectious diseases suffer from issues of low accuracy and complex architecture. In this paper, three MoB-NetV2 models (MoB-NetV2-SNN, MoB-NetV2-ELM, and MoB-NetV2-RVFL) for the detection of COVID-19 in CTs are proposed. the MoB-NetV2 model is proposed by inserting batch normalization layers between bottlenecks in MobileNetV2 and substituting the fully connected layer with the Net-1 model (SNN, ELM, RVFL), i.e. MoB-NetV2-SNN, MoB-NetV2-ELM, and MoB-NetV2-RVFL. After comparison, it is found that the proposed MoB-NetV2-RVFL achieves an average accuracy of 93.42% on a private dataset comprising of 2392 images, while the precision is reached at 97.33%. Furthermore, it is demonstrated that our MoB-NetV2-RVFL outperforms other existing algorithms in the literature. High classification accuracy and precision and low computing amount are achieved, thereby enhancing the potential application of the model in the field of medical image-aided diagnosis of COVID-19 virus. © The Institution of engineering & technology 2024.

关键词： COVID-19

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Electrocoagulation coupled with electrooxidation for the simultaneous treatment of multiple pollutants in contaminated sediments

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Journal of Environmental Sciences 2023年第2期124卷 89-97页

作者： Qingjun Zeng Yifan Zhang Pingshan Chen Yuting He Congli Yi Chunhua Feng Fourth Harbor Engineering Institute Co. Ltd.China Communications Construction Co.Ltd.Guangzhou 510230China Key Laboratory of Environmental Protection&Safety of Communication Foundation Engineering China Communications Construction Co.Ltd.Guangzhou 510230China The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters Ministry of EducationSchool of Environment and EnergySouth China University of TechnologyGuangzhou 510006China

In situ and simultaneous remediation of a variety of pollutants in sediments remains a *** this study,we report that the combination of electrocoagulation(EC)and electrooxidation(EO)is efficient in the immobilization of phosphorus and heavymetals and in the oxidation of ammonium and toxic *** integratedmixed metal oxide(MMO)/Fe anode system allowed the facile removal of ammonium and phosphorus in the overlying water(99% of 10 mg/L NH_(4)^(+)-N and 95% of 10 mg/L P disappeared in 15 and 30 min,respectively).Compared with the controls of the single Fe anode and single MMO anode systems,the dual MMO/Fe anode system significantly improved the removal of phenanthrene and promoted the transition of Pb and Cu from the mobile species to the immobile *** concentrations of Pb and Cu in the toxicity characteristic leaching procedure extracts were reduced by 99%and 97% after an 8 hr *** tests with four real polluted samples indicated that substantial proportions of acid-soluble fraction Pb and Cu were reduced(30%-31% for Pb and 16%–23% for Cu),and the amounts of total organic carbon and NH_(4)^(+)-N decreased by 56%–71% and 32%–63%,*** was proposed that the in situ electrogenerated Fe(II)at the Fe anode and the active oxygen/chlorine species at the MMO anode are conducive to outstanding performance in the co-treatment of multiple *** results suggest that the EC/EO method is a powerful technology for the in situ remediation of sediments contaminated with different pollutants.

关键词： In situ sediment remediation Electrocoagulation Electrooxidation Stabilization of heavy metals Ammonia oxidation Removal of polycyclic aromatic hydrocarbons

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Research on frequency control strategy of combined wind- energy storage system 2

Research on frequency control strategy of combined wind- ene...

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2nd International Conference on Smart Grids and Energy Systems, SGES 2023

作者： Jiang, Zhihong Liu, Guiyuan Song, Kangnan Li, Han Yang, Hao Wu, Han Changchun Institute of Technology School of Electrical Engineering and Information Technology Changchun China National Local Joint Engineering Research Center for Smart Distribution Grid Measurement and Control With Safety Operation Technology Changchun China International Joint Research Center for New Energy Power Generation and Network Control Technology of Jilin Province Changchun China

ISBN: (纸本)9798350371659

Under the high penetration rate of wind power, the power system puts forward technical requirements for the frequency regulation capability of wind farms. Considering that the frequency response capability of the double-fed induction generator (DFIG) is insufficient, it is proposed to combine energy storage (ES) with wind power for system frequency regulation. Based on the frequency characteristic models of the wind farm and the ES system, this paper establishes a prediction model for the over speed control of the wind farm and a prediction model for the electrochemical energy storage system, and optimizes the active power output of the wind turbine and the energy storage system. The strategy is based on the optimal control of the wind turbine output power and ES power variation, and on this basis, an optimal control model considering the ES state of charge (SOC) and power is designed. The simulation results show that the proposed control strategy can not only effectively suppress grid frequency deviation can also coordinate the ES state of charge and frequency control. © 2023 IEEE.

关键词： Model predictive control

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Intelligent Clustering Based Active Power Allocation Strategy for Wind Turbines 2

Intelligent Clustering Based Active Power Allocation Strateg...

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2nd International Conference on Smart Grids and Energy Systems, SGES 2023

作者： Jiang, Zhihong Song, Kangnan Liu, Guiyuan Wu, Han Li, Han Yang, Hao Changchun Institute of Technology School of Electrical Engineering and Information Technology Changchun China National Local Joint Engineering Research Center for Smart Distribution Grid Measurement and Control With Safety Operation Technology Changchun China International Joint Research Center for New Energy Power Generation and Network Control Technology of Jilin Province Changchun China

ISBN: (纸本)9798350371659

Aiming at the problem of uneven distribution of active power of each unit in a wind farm, an artificial intelligence clustering method based active power distribution strategy of wind turbine is proposed. Firstly, according to the actual operation data of the wind farm, the Pearson correlation coefficient is used to select the clustering data. Then, the Gaussian mixture model is used for clustering experiments. Finally, according to the clustering results, the distribution method of weight proportional coefficient is adopted to evenly distribute power to all units. The simulation results show that the proposed clustering method has a good evaluation score, and the power allocation strategy based on this can fully meet the scheduling criterias. All units can realize the smooth and orderly power rise and fall in the safe operation state, and avoid the problem of frequent start-stop and overload operation of some units caused by uneven power distribution. © 2023 IEEE.

关键词： Cluster analysis

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Combined Classification of Hyperspectral and LiDAR Data based on Dual-Channel Cross-Transformer 23

Combined Classification of Hyperspectral and LiDAR Data base...

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Proceedings of the 2023 5th International Conference on Video, Signal and Image Processing

作者： Binbin Zhou Qingyan Wang Junping Zhang Yujing Wang School of Measurement-Control and Communication Engineering Harbin University of Science and Technology China School of Electronics and Information Engineering Harbin Institute of Technology 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.

关键词： Hyperspectral image LiDAR data cross modality feature fusion transformer

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Multi-Object Detection and Classification in Construction Sites Based on YOLOv5 23

Multi-Object Detection and Classification in Construction Si...

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Proceedings of the 2023 5th International Conference on Video, Signal and Image Processing

作者： Xintao Liang Tianqi Jiang Qiang Fu Qingyan Wang School of Measurement-Control and Communication Engineering Harbin University of Science and Technology China School of Electronics and Information Engineering Harbin Institute of Technology China

ISBN: (纸本)9798400709272

A thorough analysis of the framework structure of the YOLO algorithm is conducted, and based on the YOLOv5 algorithm, rapid detection and classification of extracted features are implemented. Addressing the issue of multi-object detection and classification in engineering sites, this study utilizes the YOLOv5 algorithm for object detection in engineering scenarios, constructs the ResNet50 network, and achieves training and recognition of categories for engineering vehicles, whether washed or unwashed. Additionally, the YOLOv5 algorithm is employed to detect whether construction personnel are wearing safety helmets and reflective clothing, enabling fast and accurate entity detection and classification at construction sites.

关键词： Deep Learning Object Detection ResNet50 YOLOv5

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