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Chinese Automation Congress (CAC)

作者： Xuejin Wu Yanghu Hu Yali Wu Department of Information and Control Engineering Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China State Key Laboratory of Metal Extrusion and Forging Equipment Technology China National Heavy Machinery Research Institute Co. Ltd Xi'an China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

The scarcity of fault samples presents a significant challenge for fault diagnosis in industrial processes. Conventional methods often fail to deliver satisfactory results when dealing with small sample sizes. In addressing the issue of small sample fault diagnosis in industrial processes, this paper utilizes the Variational Mode Reconstruction(VMR) algorithm to screen the best components decomposed by the Variational Mode Decomposition(VMD) through the envelope entropy then random weighting them and signal reconstruction to achieve data enhancement, to enrich the sample size and to prevent the model overfitting phenomenon due to the scarcity of samples. Meanwhile, this paper integrates Convolutional Neural Network (CNN) and Long Short-term Memory Neural Network (LSTM) to propose a network model of spatio-temporal feature fusion for feature extraction and classification. The experiments demonstrate that this model not only enhances data diversity but also ensures consistency before and after enhancement, effectively mitigating overfitting. Furthermore, it guarantees high recognition and generalization rates in fault diagnosis.

关键词： Fault diagnosis Training Neural networks Rolling bearings Feature extraction Data models Signal reconstruction Convolutional neural networks Long short term memory Overfitting

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An Optimized One-Stage Detector for Autonomous PV Defect Detection

An Optimized One-Stage Detector for Autonomous PV Defect Det...

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International Conference on Autonomous Unmanned systems, ICAUS 2021

作者： Huidi, Xiao Jiajia, Zhao Lihui, Wang Qiucheng, Shen Nongjie, Ma Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology Ministry of Education School of Instrument Science and Engineering Southeast University Nanjing210096 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China School of Mechanical and Electronic Control Engineering Beijing Jiaotong University Beijing China

ISBN: (纸本)9789811694912

To realize the defect detection of Photovoltaic (PV) modules based on infrared images, a one-stage detector based on FPT and loss function optimization is proposed. Firstly, ResNet50 is selected as the backbone network for feature extraction, combining with the idea of migration learning to strengthen its feature extraction ability. Besides, replacing the traditional feature pyramid with FPT, using Transformer structure help realize the interaction of the same position between different pyramid layers as well as different positions of the same layer, improving the feature fusion capability of the model. Finally, to alleviate the problem of suppression of non-optimal predictions by NMS strategy, a bipartite graph matching algorithm based on loss function minimization is designed to enhance the learning of the distribution of ground truth targets by the network. The dataset is expanded by Mosaic data enhancement method for model training, so as to achieve infrared image-based PV defect detection. Results show that the improved one-stage target detection network has a promising detection effect on PV module defects under infrared images, with the average accuracy improving by 2.20% compared with the original algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Defects

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Anti-occlusion Object Tracking Algorithm using Response Template Fusion and Combined Features

Anti-occlusion Object Tracking Algorithm using Response Temp...

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第35届中国控制与决策会议

作者： Jing Wang Yuan Wei Yisong Liu Weichao Huang Ming Hu Faculty of Printing Packaging Engineerring And Digital Media Technology Xi'an University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

The kernel correlation filtering algorithm cannot judge the occlusion by itself when the object is occluded byforeign objects,and the tracking performance is greatly *** at this problem,this paper proposes an anti-occlusion object tracking algorithm that fuses response templates and combined *** peak sidelobe ratio(PSR) is used to determine whether the object is *** the object is partially occluded,a combined feature representation object model is introduced to enhance robustness,and a multi-response template with anti-occlusion performance is constructed to improve the performance of the object when it is occluded by foreign *** problem of reducing the available information,thereby improving algorithm *** in the OTB-100 data set can prove that the success rate and accuracy of the algorithm have competitive results compared with the comparison algorithms.

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Early Forest Fire Recognition Method Based on C-GhostNet Network 5

Early Forest Fire Recognition Method Based on C-GhostNet Net...

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5th International Symposium on Autonomous systems, ISAS 2022

作者： Li, Mengna Zhang, Youmin Mu, Lingxia Xin, Jing Yu, Ziquan Jiao, Shangbin Liu, Han Xie, Guo Yi, Yingmin Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Shaanxi Xi'an710048 China Concordia University Industrial and Aerospace Engineering Department of Mechanical MontrealQCH3G 1M8 Canada Nanjing University of Aeronautics and Astronautics College of Automation Engineering Nanjing211106 China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an Shaanxi 710048 China

ISBN: (纸本)9781665487085

Early forest fire image recognition plays an important role in timely fire fighting. This paper proposes an early forest fire recognition method based on C-GhostNet network. The C-GhostNet network is an improved version of GhostNet, which mainly consists of a series of c-ghost bottlenecks, each c-ghost bottleneck mainly consists of two c-ghost modules. Through the concat operation and channel shuffle, the c-ghost module establishes the relationship between the input data and the output features generated by the original ghost module, so that GhostNet network has the ability to capture contextual features. The experimental results show that the C-GhostNet network achieves better recognition performance than GhostNet without introducing additional computational load, and the running speed reaches about 20FPS, which basically realizes real-time recognition. In addition, like GhostN et, the C-GhostNet network is also a lightweight network. © 2022 IEEE.

关键词： Image recognition

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A Track Association Algorithm Based on Transitive Closure Fuzzy Clustering

A Track Association Algorithm Based on Transitive Closure Fu...

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International Conference on Guidance, Navigation and control, ICGNC 2020

作者： Huang, Yinghao Zhang, Kaihua Wang, Jing Cai, Yunze Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Shanghai Academy of Spaceflight Technology Shanghai201100 China

ISBN: (纸本)9789811581540

Aiming at track association before and after multi-target track interruption, a track associating method on the basis of transitive closure fuzzy clustering is proposed. Firstly, the method converts the traditional track fuzzy similarity matrix into a fuzzy equivalent matrix by squaring method, and split sub-blocks of the equivalent matrix. Then the method calculates final track correlation matrix based on correlating threshold function related to track interruption duration, and obtains correct track association by the correlation matrix. The simulation proves that, the proposed method improves association accuracy comparing with other association algorithms based on fuzzy theory, and presents a more significant advantage in the case that targets maneuver and tracks cross. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy clustering

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Chaos with Gaussian invariant distribution by quantum-noise random phase feedback

arXiv

引用

arXiv 2023年

作者： Guo, Yanqiang Li, Haifeng Wang, Yingqi Meng, Xiangyu Zhao, Tong Guo, Xiaomin Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education College of Physics Taiyuan University of Technology Taiyuan030024 China State Key Laboratory of Cryptology Beijing100878 China

We experimentally present a random phase feedback based on quantum noise to generate a chaotic laser with Gaussian invariant distribution. The quantum noise from vacuum fluctuations is acquired by balanced homodyne detection and injected into a phase modulator to form a random phase feedback. An optical switch using high-speed intensity modulator is employed to reset the chaotic states repeatedly and the time evolutions of intensity statistical distributions of the chaotic states stemming from the initial noise are measured. By the quantum-noise random phase feedback, the transient intensity distributions of the chaotic outputs are improved from asymmetric invariant distributions to Gaussian invariant distributions, and the Gaussian invariant distribution indicates a randomly perturbed dynamical transition from microscopic initial noise to macroscopic stochastic fluctuation. The effects of phase feedback bandwidth and modulation depth on the invariant distributions are investigated experimentally. The chaotic time-delay signature and mean permutation entropy are suppressed to 0.036 and enhanced to 0.999 using the random phase feedback, respectively. The high-quality chaotic laser with Gaussian invariant distribution can be a desired random source for ultrafast random number generation and secure communication. Copyright © 2023, The Authors. All rights reserved.

关键词： Quantum noise

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High-speed photon correlation monitoring of amplified quantum noise by chaos using deep-learning balanced homodyne detection

arXiv

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arXiv 2023年

作者： Guo, Yanqiang Hu, Zinan Zhang, Jianchao Zhu, Chenyu Guo, Xiaomin Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education College of Physics Taiyuan University of Technology Taiyuan030024 China State Key Laboratory of Cryptology Beijing100878 China

Precision experimental determination of photon correlation requires the massive amounts of data and extensive measurement time. We present a technique to monitor second-order photon correlation g(2)(0) of amplified quantum noise based on wideband balanced homodyne detection and deep-learning acceleration. The quantum noise is effectively amplified by an injection of weak chaotic laser and the g(2)(0) of the amplified quantum noise is measured with a real-time sample rate of 1.4 GHz. We also exploit a photon correlation convolutional neural network accelerating correlation data using a few quadrature fluctuations to perform a parallel processing of the g(2)(0) for various chaos injection intensities and effective bandwidths. The deep-learning method accelerates the g(2)(0) experimental acquisition with a high accuracy, estimating 6107 sets of photon correlation data with a mean square error of 0.002 in 22 seconds and achieving a three orders of magnitude acceleration in data acquisition time. This technique contributes to a high-speed and precision coherence evaluation of entropy source in secure communication and quantum imaging. Copyright © 2023, The Authors. All rights reserved.

关键词： Quantum noise

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Study on 6D Pose Estimation system of Occlusion Targets for the Spherical Amphibious Robot based on Neural Network 20

Study on 6D Pose Estimation System of Occlusion Targets for ...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Du, Chaofeng Guo, Jian Guo, Shuxiang Fu, Qiang Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory BinshuiXidao Extension 391 Tianjin300384 China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd. No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen518100 China School of Life Science Beijing Institute of Technology Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology No.5 Zhongguancun South Street Beijing100081 China

ISBN: (纸本)9798350320831

The amphibious robot needs to accurately estimate the 6D pose of the target in tasks such as target tracking, docking with the recovery module, and target grasping. The current research on target 6D pose estimation is mainly applied to unoccluded targets, but when the target is occluded, the target's pose cannot be accurately identified. Compared with other algorithms, the PVNet algorithm shows better robustness when target is occluded, but the accuracy is still low. To improve the accuracy of the PVNet algorithm, this paper adds the confidence score prediction of the prediction vector at the last layer of the PVNet network, and designs a vector confidence score loss function to train the network. Before generating the hypothetical keypoints, the pixels whose confidence score is lower than the set threshold are screened out, so that the generated hypothetical 2D keypoints are closer to the true 2D keypoints. Finally, the method in this paper is compared with the Tekin, PoseCNN, Oberweger and Pvnet algorithm, and demonstrate the superiority of the proposed method. © 2023 IEEE.

关键词： Semantics

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Time-attenuating Twin Delayed DDPG Reinforcement Learning for Trajectory Tracking control of Quadrotors

arXiv

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arXiv 2023年

作者： Deng, Boyuan Sun, Jian Li, Zhuo Wang, Gang School of Automation Beijing Institute of Technology Beijing100081 China Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing100081 China

Continuous trajectory tracking control of quadrotors is complicated when considering noise from the environment. Due to the difficulty in modeling the environmental dynamics, tracking methodologies based on conventional control theory, such as model predictive control, have limitations on tracking accuracy and response time. We propose a Time-attenuating Twin Delayed DDPG, a model-free algorithm that is robust to noise, to better handle the trajectory tracking task. A deep reinforcement learning framework is constructed, where a time decay strategy is designed to avoid trapping into local optima. The experimental results show that the tracking error is significantly small, and the operation time is one-tenth of that of a traditional algorithm. The OpenAI Mujoco tool is used to verify the proposed algorithm, and the simulation results show that, the proposed method can significantly improve the training efficiency and effectively improve the accuracy and convergence stability. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Attribute Analysis and Modeling Research on Color Harmony of Three-Color Combination

Attribute Analysis and Modeling Research on Color Harmony of...

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Culture-Oriented Science and technology (CoST), International Conference on

作者： Jiapeng Pu Shuang Wang Hui Ren State Key Laboratory of Media Convergence and Communication Communication University of China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Communication University of China Beijing Key Laboratory of Modern Entertainment Technology Communication University of China Beijing China

Color harmony is the result of objective color matching and human subjective feeling. This paper aims to study the subjective and objective factors of color harmony and carry out mathematical models between them. Firstly, we constructed a color data set commonly used three-color combinations. Secondly, we analyzed the subjective and objective factors affecting color harmony, extracted the 36-dimensional objective physical features, and divided subjective factors into direct psychological factors and indirect psychological factors. Then, based on color harmony and its subjective factors, a total of 33 subjects evaluated 150 three-color combinations on a five-level scale. Finally, the mathematical models of color harmony were constructed by machine learning algorithms. The research shows that color harmony is not only affected by objective factors, but also by subjective factors, and the influence of subjective factors is greater. In addition, the multiple linear regression algorithm plays best on color harmony predicting, which can better explain the color harmony mechanism.

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