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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Yafei Fan Jun Liu Jian Wang Kecheng Zhou Jun Wang School of Computer Science and Technology Shenyang University of Chemical Technology Key Laboratory of Industrial Intelligence Technology on Chemical Process Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

In terms of surface defect detection of glazed tiles, there are problems such as false detection and insufficient ability to identify small target defects due to the rich texture on the surface of glazed tiles. In response to these challenges, this article has compiled a dataset of glazed ceramic tiles and proposed an improved algorithm ECANet-FPG based on Faster R-CNN. Due to the similarity in texture and defects on the surface of ceramic tiles, this algorithm introduces the Efficient Channel Attention (ECA) module into the feature extraction network of Faster R-CNN to enhance the ability to extract deep features. At the same time, a Feature Pyramid Grids (FPG) structure is adopted to achieve efficient fusion of multi-scale features, in order to improve the recognition accuracy of small target defects. The experimental results show that the improved algorithm achieves higher detection accuracy and efficiency than the baseline model, demonstrating its effectiveness and practicality in detecting surface defects on glazed ceramic tiles.

关键词： Accuracy Target recognition Tiles Object detection Feature extraction Robustness Ceramics Surface texture Surface treatment Defect detection

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Gaussian Mixture Model and Double-Weighted Deep Neural Networks for Data Augmentation Soft Sensing

Gaussian Mixture Model and Double-Weighted Deep Neural Netwo...

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Data Driven control and Learning systems (DDCLS)

作者： Xiaoyu Jiang Le Yao Zeyu Yang Zhihuan Song Bingbing Shen State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou Zhejiang PR China School of Mathematics Hangzhou Normal University Hangzhou P. R. China Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou University Huzhou China

In practice, data-driven soft sensors often face data shortages in modeling. Data augmentation technology has offered a feasible solution for this problem in recent years. However, how to better use virtual data for data augmentation is still an open topic. In this paper, a novel data augmentation soft sensing method is proposed. It uses Gaussian mixture models (GMM) to generate virtual data for the training dataset, and developed a double-weighted neural network (dwDNN) for weighted regression modeling. On top of that, the Bayesian optimization algorithm is applied to the weight selection of dwDNN to further enhance the efficiency and effectiveness of GMM -dwDNN on virtual data. In the end, a real industrial case is used to illustrate the superiority of the proposed approach in soft sensing.

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Enhancing cyber-Resiliency of DER-based Smart Grid: A Survey

arXiv

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arXiv 2023年

作者： Liu, Mengxiang Fei, Teng Zhang, Zhenyong Ge, Pudong Sun, Mingyang Deng, Ruilong Cheng, Peng Chen, Jiming The Department of Electrical and Electronic Engineering Imperial College London London United Kingdom The College of Control Science and Engineering Zhejiang University Hangzhou China The State Key Laboratory of Industrial Control Technology The College of Control Science and Engineering Zhejiang University Hangzhou310027 China The Huzhou Institute of Industrial Control Technology Huzhou313000 China

The rapid development of information and communications technology has enabled the use of digital-controlled and software-driven distributed energy resources (DERs) to improve the flexibility and efficiency of power supply, and support grid operations. However, this evolution also exposes geographically-dispersed DERs to cyber threats, including hardware and software vulnerabilities, communication issues, and personnel errors, etc. Therefore, enhancing the cyber-resiliency of DER-based smart grid - the ability to survive successful cyber intrusions - is becoming increasingly vital and has garnered significant attention from both industry and academia. In this survey, we aim to provide a comprehensive review regarding the cyber-resiliency enhancement (CRE) developments of the DER-based smart grid, present a holistic CRE framework, and thoroughly discuss the research directions of the next-generation CRE methods. Firstly, an integrated threat modeling method is tailored for the hierarchical DER-based smart grid with special emphasises on vulnerability identification and impact analysis. Then, the defense-in-depth strategies encompassing prevention, detection, mitigation, and recovery are comprehensively surveyed, systematically classified, and rigorously summarized. A holistic CRE framework is subsequently proposed to incorporate the five key resiliency enablers. Finally, challenges and future directions are discussed in details. The overall aim of this survey is to illustrates the recent development of CRE methods and motivate further efforts to improve the cyber-resiliency of DER-based smart grid. Copyright © 2023, The Authors. All rights reserved.

关键词： Smart power grids

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Power Load Forecasting Based on COA-Bi-LSTM Method 2

Power Load Forecasting Based on COA-Bi-LSTM Method

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2nd International Conference on Electrical Engineering and control Science, IC2ECS 2022

作者： Chen, Gang Zhang, Dian Xiang, Wei Guan, Zhenqi Huang, Jing Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Complex Systems and Bionic Control Nanan District Chongqing China Economic and Technology Research Institute State Grid Chongqing Electric Power Company Chongqing China

ISBN: (纸本)9798350399165

A power load forecasting method based on Coyote optimization Algorithm-Bidirectional Long short-Term Memory Neural Network (COA-Bi-LSTM) model was proposed in this study to solve the problem of inaccurate power load prediction. Aiming at the problem of artificial selection of neural network hyperparameters leading to inaccurate prediction, COA was used to iteratively optimize the three hyperparameters of Bi-LSTM: learning rate, iteration times and number of hidden units. Two groups of historical load data with different characteristics in January and August 2018 were selected and compared with the proposed method using BP neural network, LSTM and Bi-LSTM, respectively. The simulation results show that the optimized prediction effect is better. The Mean Absolute Percentage Error (MAPE) in the data prediction results in January is reduced by 45.46%, 28.57%, and 24.05% respectively. Meanwhile, the MAPE in the data prediction results in August are respectively reduced by 73.71%, 44.04%, 17.57%, verifying the effectiveness of the method. © 2022 IEEE.

关键词： Long short-term memory

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High-precision Calibration of Camera and IMU on Manipulator for Bio-inspired Robotic System

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Journal of Bionic Engineering 2022年第2期19卷 299-313页

作者： Yinlong Zhang Wei Liang Sichao Zhang Xudong Yuan Xiaofang Xia Jindong Tan Zhibo Pang State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China The School of Computer Science and Technology Xidian UniversityXi’an 710071China Department of Mechanical Aerospace and Biomedical EngineeringUniversity of TennesseeKonxville 37996USA Department of Automation Technology ABB Corporate Research Sweden72178 VästerasSweden

Inspired by box jellyfish that has distributed and complementary perceptive system,we seek to equip manipulator with a camera and an Inertial Measurement Unit(IMU)to perceive ego motion and surrounding unstructured *** robot perception,a reliable and high-precision calibration between camera,IMU and manipulator is a critical *** paper introduces a novel calibration ***,we seek to correlate the spatial relationship between the sensing units and manipulator in a joint ***,the manipulator moving trajectory is elaborately designed in a spiral pattern that enables full excitations on yaw-pitch-roll rotations and x-y-z translations in a repeatable and consistent *** calibration has been evaluated on our collected visual inertial-manipulator *** systematic comparisons and analysis indicate the consistency,precision and effectiveness of our proposed calibration method.

关键词： Bio-inspired robotic system Monocular camera IMU Manipulator Calibration

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Comprehensive Gear Shift Schedule Design for 2-AMT Electric Vehicles Based on Sparrow Search Algorithm

Comprehensive Gear Shift Schedule Design for 2-AMT Electric ...

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Chinese control Conference (CCC)

作者： Tianyi Wang Liling Ma Junzheng Wang State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing P.R. China

With the aim of 2-AMT electric vehicles, a comprehensive shift schedule that considers both power and economy is proposed. First, the objective function of the comprehensive shift schedule is constructed, which is the weighted sum of vehicle acceleration time and vehicle power consumption per unit distance. Second, Sparrow Search Algorithm is used to solve the objective function and obtain the comprehensive shift schedule. Finally, AVL Cruise simulation software is used to simulate vehicle dynamics and economy under an NEDC. The results show that the comprehensive shift schedule is similar to the optimal power shift schedule in terms of dynamic performance, and the economy is optimized by 3%. The feasibility of the comprehensive shift schedule is verified.

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Viral Tools for Neural Circuit Tracing

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Neuroscience Bulletin 2022年第12期38卷 1508-1518页

作者： Qing Liu Yang Wu Huadong Wang Fan Jia Fuqiang Xu The Brain Cognition and Brain Disease Institute(BCBDI) Shenzhen Key Laboratory of Viral Vectors for BiomedicineShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research InstitutionsNMPA Key Laboratory for Research and Evaluation of Viral Vector Technology in Cell and Gene Therapy Medicinal ProductsShenzhen Key Laboratory of Quality Control Technology for Virus-Based TherapeuticsGuangdong Provincial Medical Products AdministrationShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics Key Laboratory of Magnetic Resonance in Biological SystemsWuhan Center for Magnetic ResonanceInnovation Academy for Precision Measurement Science and TechnologyChinese Academy of SciencesWuhan 430071China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200031China University of Chinese Academy of Sciences Beijing 100049China

Neural circuits provide an anatomical basis for functional ***,dissecting the structure of neural circuits is essential to understanding how the brain *** neurotropic viruses are important tools for neural circuit tracing with many advantages over non-viral tracers:they allow for anterograde,retrograde,and trans-synaptic delivery of tracers in a cell type-specific,circuit-selective *** this review,we summarize the recent developments in the viral tools for neural circuit tracing,discuss the key principles of using viral tools in neuroscience research,and highlight innovations for developing and optimizing viral tools for neural circuit tracing across diverse animal species,including nonhuman primates.

关键词： Neural circuit Viral tool Anterograde Retrograde Trans-synaptic

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Multi-scale discriminative regions attention network for fine-grained vehicle classification

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第2期6卷 164-177页

作者： Rong, Wen-Zhong Han, Jin Cai, Ying-Hao Liu, Gen College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Fine-grained vehicle classification is a challenging task in computer vision due to the low intra-class variance. Some methods have been developed to improve the accuracy of fine-grained vehicle classification by improving the ability of discriminative features extraction, but there is still room for further improvement in the localization accuracy of vehicle discriminative regions. Based on deep convolutional neural networks, we focus on finding a more efficient structure that pays more attention to the discriminative image regions to enhance the ability of fine-grained vehicle classification. We propose a novel Multi-Scale Discriminative Regions Attention Network (MS-DRAN), which extracts feature maps through ResNet-50 backbone network and generates multi-scale feature maps by a Feature Pyramid Network (FPN). Then, MS-DRAN generates discriminative regions attention maps on the multi-scale feature maps. The attention maps from shallow layer perform pixel-level multiplication with the feature maps from deeper layer. By this way, the network gradually extracts a more discriminating feature maps for classification. We also design a multi-task loss function to associate the classification results for each scale and optimize the network parameter in training. Lastly, we validate the MS-DRAN on Stanford Cars-196 dataset and CompCars dataset and achieves 94.3% and 98.1% in accuracy, respectively. ©2021 ISSN 2414-8105 (Online).

关键词： Classification (of information)

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Electromagnetic Performance and Vibration Analysis of a Special-shaped External-rotor Machine 18

Electromagnetic Performance and Vibration Analysis of a Spec...

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18th IEEE Conference on industrial Electronics and Applications, ICIEA 2023

作者： Dong, Ziwei Yan, Liang Lu, Zhang Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Beihang University Science and Technology on Aircraft Control Laboratory Beijing100191 China Tianmushan Laboratory Xixi Octagon City Yuhang District Hangzhou310023 China Beihang University Ningbo Institute of Technology Ningbo315800 China Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou China

ISBN: (纸本)9798350312201

Permanent magnet synchronous machines have advantages such as small size, light weight, high efficiency, etc. It is more and more widely used in robots, electromagnetic propulsion, electric vehicles and so on. High power density and high torque density are urgent need for those applications. The electromagnetic design of machines mostly adopt multi-pole magnet patterns to reduce the stator yoke size. It helps to decrease the mass of the machine with the same power and improve the torque density. As the number of poles grows larger, the frequency and the core loss of per unit volume will also increase with the same speed, which can aggravate the heat problem of the machine. It is easy to generate the low-order vibration with high flux density. Therefore, the performance of a special-shaped external-rotor machine and comparisons with the traditional machine are analyzed in this paper. Numerical simulation is conduced to validated that the special-shaped magnet pattern can reduce the torque ripple, improve the torque output and power density. From the two-dimensional Fourier analysis of the radial electromagnetic force, it is found that the low-order amplitude of the special-shaped machine is greatly reduced, so the vibration can be avoided. Subsequently, the vibration simulation is carried out to indicate that the magnet pattern can satisfy low level in vibration and noise of the machine. © 2023 IEEE.

关键词： Vibration analysis

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Multi-Task Learning-Enabled Automatic Vessel Draft Reading for Intelligent Maritime Surveillance

arXiv

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arXiv 2023年

作者： Qu, Jingxiang Liu, Ryan Wen Zhao, Chenjie Guo, Yu Xu, Sendren Sheng-Dong Zhu, Fenghua Lv, Yisheng The Hubei Key Laboratory of Inland Shipping Technology School of Navigation Wuhan University of Technology Wuhan430063 China The State Key Laboratory of Maritime Technology and Safety Wuhan430063 China The Automation and Control Center The Graduate Institute of Automation and Control National Taiwan University of Science and Technology Taipei106335 Taiwan The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

The accurate and efficient vessel draft reading (VDR) is an important component of intelligent maritime surveillance, which could be exploited to assist in judging whether the vessel is normally loaded or overloaded. The computer vision technique with an excellent price-to-performance ratio has become a popular medium to estimate vessel draft depth. However, the traditional estimation methods easily suffer from several limitations, such as sensitivity to low-quality images, high computational cost, etc. In this work, we propose a multi-task learning-enabled computational method (termed MTL-VDR) for generating highly reliable VDR. In particular, our MTL-VDR mainly consists of four components, i.e., draft mark detection, draft scale recognition, vessel/water segmentation, and final draft depth estimation. We first construct a benchmark dataset related to draft mark detection and employ a powerful and efficient convolutional neural network to accurately perform the detection task. The multi-task learning method is then proposed for simultaneous draft scale recognition and vessel/water segmentation. To obtain more robust VDR under complex conditions (e.g., damaged and stained scales, etc.), the accurate draft scales are generated by an automatic correction method, which is presented based on the spatial distribution rules of draft scales. Finally, an adaptive computational method is exploited to yield an accurate and robust draft depth. Extensive experiments have been implemented on the realistic dataset to compare our MTL-VDR with state-of-the-art methods. The results have demonstrated its superior performance in terms of accuracy, robustness, and efficiency. The computational speed exceeds 40 FPS, which satisfies the requirements of real-time maritime surveillance to guarantee vessel traffic safety. Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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