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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： Qiao, Qi Wang, Hongjun School of Mechanical Electrical Engineering Beijing Information Science and Technology University Beijing100192 China Beijing International Science Cooperation Base of High-End Equipment Intelligent Perception Beijing100192 China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing100192 China

ISBN: (纸本)9783031494208

Aiming at the problems of difficulty in fault data collection and scarcity of fault samples in gas turbine rotor fault diagnosis, a gas turbine rotor fault diagnosis model based on VIT model and transfer learning is proposed. Firstly, the acquired one-dimensional vibration signal is converted into a two-dimensional time–frequency image by continuous wavelet transformation as the input of the VIT model, and the fault diagnosis is realized through its recognition of time–frequency image and feature extraction. Using CRWU bearing dataset to pre-train the model combined with transfer learning, only the weights in the MLP Head structure are migrated by freezing the part before the MLP Head structure and using the gas turbine rotor dataset fault classification, the results show that the fault recognition accuracy reaches 99.5% after transfer learning, and the superiority of the proposed model is proved by comparative experiments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Failure analysis

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LVRT control Strategy for Photovoltaic Grid-Connected Inverter Based on Improved Virtual Impedance

LVRT Control Strategy for Photovoltaic Grid-Connected Invert...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Tiancheng Hu Chun Zhang Wentai Wu Tao Li School of Electrical Engineering Anhui Polytechnic University Key Experiments of High -End Equipment Advanced Perception and Intelligent Control of the Ministry of Education Wuhu China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

The traditional LVRT control strategy does not consider the poor dynamic performance of the system and the increase of current harmonics during the grid fault. Therefore, in order to suppress the grid-connected current harmonics caused by grid voltage disturbance and improve the dynamic performance of the system, this paper studies an improved virtual impedance low voltage ride-through control strategy, which replaces the parasitic resistance on the traditional LCL filter by replacing the virtual impedance, and feeds back the real-time values of capacitance current and inverter current to the control loop through feedforward and feedback respectively, and autonomously adjusts the equivalent damping, so as to suppress the current harmonics during the grid fault and improve the dynamic performance of the system on the basis of cost saving.

关键词： Photovoltaic systems Resistance Low voltage Filtering Power system stability Harmonic analysis Inverters

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Anomaly-Resistant Decentralized State Estimation Under Minimum Error Entropy With Fiducial Points for Wide-Area Power Systems

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 74-87页

作者： Bogang Qu Zidong Wang Bo Shen Hongli Dong Hongjian Liu the College of Automation Engineering Shanghai University of Electric PowerShanghai 200090China IEEE the Department of Computer Science Brunel University LondonUxbridgeMiddiesex UB83PHUK the College of Information Science and Technology Donghua UniversityShanghai 200051 the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China the Artificial Intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast PetroleumDaqing 163318 the Sanya Offshore Oil&Gas Research Institute Northeast Petroleum UniversitySanya 572024China the Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of EducationAnhui Polytechnic UniversityWuhu 2410000 the School of Mathematics and Physics Anhui Polytechnic UniversityWuhu 241000China

This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.

关键词： Decentralized state estimation(SE) measurements with anomalies minimum error entropy unscented Kalman filter wide-area power systems

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Backstepping Sliding Mode control for Flight Attitude of Quadrotor UAVs 8

Backstepping Sliding Mode Control for Flight Attitude of Qua...

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8th International Conference on Electromechanical control Technology and Transportation, ICECTT 2023

作者： Lin, Lin Huang, Yiqing School of Electrical Engineering Anhui Polytechnic University Anhui Wuhu241000 China Anhui Key Laboratory of Electric Drive and Control Anhui Polytechnic University Anhui Wuhu241000 China Key Laboratory of Advanced Perception and Intellgent Control of High-end Equipment Ministry of Education Anhui Wuhu241000 China

ISBN: (纸本)9781510668331

In order to solve the problem of controlling position and attitude of quadrotor UAVs, The work is carried out from its characteristics of non-linearity, strong coupling and underactuation. The system stability problem when the system is disturbed by external interference is included in the system design and a backstepping sliding mode control strategy is proposed. On this basis, the Lyapunov function is designed to prove the stability of the system theoretically. Through simulation and comparative experiments, It shows that the controller has the characteristics of small tracking error, fast response time and anti-interference. The stability of the system is proved by experiments, which verifies the superiority and effectiveness of the backstepping sliding mode algorithm. © 2023 SPIE.

关键词： System stability

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Loop Closure Detection for SLAM Based on Siamese Convolutional Neural Network 3

Loop Closure Detection for SLAM Based on Siamese Convolution...

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2021 3rd International Conference on Robotics, intelligent control and Artificial Intelligence, ICRICA 2021

作者： Li, Xiang Xu, Gang Zhang, Xingyu Yu, Ze Zhu, Zhimin Anhui Key Laboratory of Detection Technology and Energy Saving Devices Anhui Polytechnic University Anhui China Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment Anhui Polytechnic University Anhui China Jiangnan University Wuxi China

Loop closure detection is an indispensable part of the SLAM system, which can reduce the cumulative error of mobile robots in positioning and mapping. This paper proposes a supervised learning method, which combines the Siamese network and the VGG16 network in deep learning to handle loop closure detection. The Siamese network method and the traditional method are contrasted in the New College and City Centre data sets to prove the method's usefulness in this research. The experiment's findings reveal that the method of this paper ensures precision and recall rate while reducing the time necessary to assess similarities compared to the traditional method. It has a better chance of achieving real-time SLAM. © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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Data augmented multi-loss hybrid learning for cross-modality person re-identification

Data augmented multi-loss hybrid learning for cross-modality...

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2022 International Workshop on Automation, control, and Communication Engineering, IWACCE 2022

作者： Yan, Tao Wang, Fengsui Xu, Yue Qian, Yaping School of Electrical Engineering Anhui Polytechnic University Wuhu China Anhui Key Laboratory of Detection Technology and Energy Saving Devices Wuhu China Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment Ministry of Education Wuhu China

ISBN: (纸本)9781510660915

The main challenges are the intra-class differences of person images and the cross-modal differences between visible and infrared images for cross-modal person re-identification. How to reduce the cross-modal differences becomes the key to cross-modal person re-identification. In this paper, we propose a hybrid learning strategy using Cross-Entropy loss and weighted squared triplet loss as identity (ID) loss to solve the intra-modal and inter-modal person identity classification problem, while supervising the network to extract more effective modal shared features to form specific feature descriptors. Besides, for cross-modal person image attributes, a data augmentation method of channel-swapped random erasure is used to improve the robustness of the model to color changes, simulating different degrees of image occlusion, reducing the risk of overfitting and further enriching the image diversity. Experimental results on the public dataset SYSU-MM01 demonstrate the effectiveness of the proposed method, with an average accuracy mAP of 60.08% even in the most difficult full-search single-shot mode. © 2022 SPIE. All rights reserved.

关键词： Image enhancement

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Study on the 3D point cloud semantic segmentation method of fusion semantic edge detection 3

Study on the 3D point cloud semantic segmentation method of ...

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2021 3rd International Conference on Robotics, intelligent control and Artificial Intelligence, ICRICA 2021

作者： Chen, Ling Xu, Gang Fu, Nana Hu, Zhifeng Zheng, Shuzhan Li, Xiang Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment Anhui Polytechnic University Anhui China Jiangnan University Wuxi China Anhui Key Laboratory of Detection Technology and Energy Saving Devices Anhui Polytechnic University Anhui China

With the continuous development of deep learning, semantic segmentation, as the basis of 3D scene understanding, has also been widely used in 3D point clouds. Semantic segmentation based on the point cloud has obvious advantages due to its rich data. Aiming at the problems of unclear segmentation target and unclear edge in point cloud semantic segmentation, a 3D point cloud semantic segmentation algorithm integrating edge detection was proposed. Firstly, complete global semantic features are obtained by the mainstream 3D point cloud semantic segmentation framework. Then, semantic edge detection is used to extract edge semantic features from the point cloud. Finally, the fusion module fuses the semantic features belonging to the same object to make the segmentation target more accurate. In addition, a dual semantic loss function is used to produce semantic segmentation results with better boundaries. Experimental results show that the improved algorithm has better precision than KPConv in S3DIS and ScanNet datasets and better segmentation performance. © Published under licence by IOP Publishing Ltd.

关键词： Semantics

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Adaptive modulation attention-based face super-resolution reconstruction method

Adaptive modulation attention-based face super-resolution re...

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2022 International Workshop on Automation, control, and Communication Engineering, IWACCE 2022

作者： Xie, Zhenglei Wang, Fengsui Wang, Yunlong Xu, Yue School of Electrical Engineering Anhui Polytechnic University Wuhu China Anhui Key Laboratory of Detection Technology and Energy Saving Devices Wuhu China Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment Ministry of Education Wuhu China

ISBN: (纸本)9781510660915

In view of the large deviation of the pixel value of the generated image caused by the abandonment of the BN layer by the previous deep face super-resolution module, and the inaccuracy of the face prior alignment module, an adaptive modulation super-resolution neural network combining the attention mechanism and face alignment is proposed. Firstly, in order to solve the problem of inaccurate face alignment, a specific attention module is used to extract features with low resolution, and the output feature map is aligned with the key point feature map to increase the accuracy of positioning landmarks. Secondly, aiming at the problem that local pixels have maximum values, an adaptive modulation super-division module is proposed to make the reconstructed image more suitable for visual senses. The experimental results show that compared with face super-resolution algorithms such as end-to-end learning facial prior network (FSRNET), facial landmark attention network (PFSR) and deep iterative collaboration network (DIC), better visual effects and performance indicators are achieved. © 2022 SPIE. All rights reserved.

关键词： Deep learning

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Unsupervised person re-identification guided by refinement feature in multi-label distribution sorting learning

Unsupervised person re-identification guided by refinement f...

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2022 International Workshop on Automation, control, and Communication Engineering, IWACCE 2022

作者： Qian, Yaping Wang, Fengsui Xiong, Lei Yan, Tao Xu, Yue School of Electrical Engineering Anhui Polytechnic University Wuhu China Anhui Key Laboratory of Detection Technology and Energy Saving Devices Wuhu China Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment Ministry of Education Wuhu China

ISBN: (纸本)9781510660915

Traditional person re-identification methods are mostly based on supervised state of manually annotated data sets. Although the recognition accuracy is high, the algorithm completely relies on effective annotation information and lacks practicability. Based on the above problems, an unsupervised person re-identification method is proposed, which refines features to guide multi-label distribution sorting learning. Firstly, a single and multi-label classification network structure is constructed to improve the matching accuracy of tags by learning the characteristics of each person and its k nearest neighbor and sharing the same class. Secondly, the refinement feature extraction module is designed and embedded into the original ResNet50 network, and the potential key areas in the person image are adaptively located. While weakening the appearance differences of the features, multi-information mining is carried out to obtain the refined features of the characters and assist the classification network to improve the performance. Thirdly, reliable multi-category labels are screened by punishing the sorting between positive and negative categories of labels in multi-category labels. Finally, the network is supervised by combining multi-label distribution sorting loss and multi-label classification loss. Experiments show that without any annotation information, the recognition accuracy of Market-1501 and DukeMTMC-reID datasets is 58.4% and 42.3%, respectively, and the mAP of Market-1501 dataset is increased by 12.9%. © 2022 SPIE. All rights reserved.

关键词： Sorting

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Research on Lightweight Small Object Detection Method with Fusion Attention Mechanism

Research on Lightweight Small Object Detection Method with F...

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2023 China Automation Congress, CAC 2023

作者： Dai, Haowen Liu, Bingyou Wan, Guoyang Qi, Jingjing Wan, Luanfei School of Electrical Engineering Anhui Polytechnic University Wuhu China Anhui Polytechnic University The Key Lab. of Advanced Perception and Intelligent Control of High-End Equipment Ministry of Education Wuhu China Anhui Dar Intelligent Control System Co. Ltd. Wuhu China Wuhu Institute of Technology Wuhu China

ISBN: (纸本)9798350303759

In the task of small object detection, due to the relatively small size of the objects, complex situations such as occlusion and overlap may arise in high-density scenes. These factors can significantly increase the issues of missed detections and false detections in object detection tasks. To address the aforementioned issues, this paper proposes a lightweight small object detection model, YOLOv5s-MGC. Firstly, we introduce an improved Mobilenetv3 network to replace the backbone network of YOLOv5s, aiming to improve the network's capability for effective feature extraction. Secondly, we incorporate the GSConv module into the feature fusion network to reduce model parameters and strengthen the network's ability in fusing feature information. Finally, we have integrated Convolutional Block Attention Module (CBAM) at the forefront of the detection heads, achieving the fusion of fine-grained features. We assessed our approach on the openly accessible dataset 'SHWD' and contrasted the results with the baseline YOLOv5s and various SOTA object detection models. Precisely, our method attains an accuracy of 91.1%, exceeding the baseline module by 1.6%. Furthermore, the module size is reduced to 1.97MB and it achieves a 52.4 FPS. In comparison to other prevailing algorithms, YOLOv5s-MGC enables efficient and accurate small object detection. © 2023 IEEE.

关键词： Object detection

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