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

作者： Wang, Yongjing Wen, Chenying Zhu, Zheren Zhang, Xinming Song, Zhihuan Zhu, Zugang Fan, Xueer Zhejiang University The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Hangzhou Underground Pipeline Development Co. Ltd Hangzhou310064 China

ISBN: (纸本)9798350361674

Underground pipe galleries are constructed beneath urban areas, providing pathways for the installation of various municipal pipelines. These galleries help protect pipelines and efficiently utilize underground space, serving as a significant facilitator for the construction of smart cities. Due to the confined space inside the galleries and the simultaneous presence of multiple pipelines, there is a potential safety hazard of fire incidents. To ensure the safe operation of pipelines, online monitoring and timely warning of fire incidents are valuable tasks. Current traditional computer vision and machine learning approaches for object detection often experience slow response time. Therefore the You Only Look Once (YOLO) series due to its fast response time, has become a popular method in real-time detection. Nonetheless, YOLOv8 still suffer from information fusion problems. In response to this issue, this paper proposed YOLOH algorithm, which improves YOLOv8 by employing the Gather-and-Distribute (GD) mechanism for intelligent online monitoring of fire incidents in underground pipe galleries. Finally, the effectiveness of YOLOH was validated using a combination of publicly available fire datasets and simulated fire datasets in underground pipe galleries. The YOLOH attains a 63.2 mAP, surpassing the YOLOv8-N baseline by +2.15 mAP. © 2024 IEEE.

关键词： Fires

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Mechanism and Methods for Fault Recovery of Grid-Forming Inverter by Graphical Illustration

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IEEE Transactions on Power Systems 2025年第2期40卷 1973-1976页

作者： Yang, Jialun Ye, Hua Wang, Yantuan Liu, Yutian Li, Yujun Du, Zhengchun Shandong Research Institute of Industrial Technology Jinan250061 China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan250061 China Xi'an Jiaotong University Department of Electrical Engineering Xi'an710049 China

The existing priority-based current limiting control (CLC) for grid-forming (GFM) inverters may lead to failures in fault recovery, including being locked in CLC and mode oscillation between CLC and constant voltage control (CVC). To resolve these problems, this letter first reveals the fault recovery mechanism of droop controlled GFM inverters by illustrating the conditions of three fault recovery states in the phasor diagram. Then, two methods including determination of the optimal range of current angle and upper limit of saturation block are presented to guarantee successful fault recovery of GFM inverters. The correctness of theoretical analyses is verified and validated by multiple experimental tests. © 1969-2012 IEEE.

关键词： Fault current limiters

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Object-Guided and Motion-Refined Attention Network for Video Anomaly Detection

Object-Guided and Motion-Refined Attention Network for Video...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Zhou, Wenhao Li, Yingxuan Zhao, Chunhui College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Video anomaly detection is challenging due to the lack of abnormal videos and ambiguity of anomaly definition. Context information is important to identify anomalous events and can be reflected from the salient objects and related background. In this paper, we propose a new multi-level attention network consisting of an Object-Guided Attention Module (OGAM) and a Motion-Refined Attention Module (MRAM) to fully exploit context by leveraging both frame-level and object-level semantics. Specifically, OGAM highlights features of salient objects on frame features for future frame prediction. MRAM further leverages global positions and optical flow of objects to improve the prediction loss. By multi-level attention, objects are highlighted in the predicted frame and necessary context information is preserved at both feature level and pixel level to identify anomalies. Experiments demonstrate our method achieves state-of-the-art performance on benchmark datasets including Avenue, UCSD Ped2 and ShanghaiTech, specifically obtaining a frame-level AUC of 92.6% on Avenue. © 2022 IEEE.

关键词： Anomaly detection

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Distributed Kalman filtering over sensor networks with fading measurements and random link failures

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Journal of the Franklin Institute 2023年第4期360卷 2457-2475页

作者： Zhu, Mingyan Sui, Tianju Wang, Rui School of Control Science and Engineering Dalian University of Technology Dalian116024 China School of Aeronautics and Astronautics Dalian University of Technology Dalian116024 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China Key Laboratory of Advanced Technology for Aerospace Vehicles Liaoning Province Dalian116024 China

This paper investigates the distributed state estimation problem for a linear time-invariant system characterized by fading measurements and random link failures. We assume that the fading effect of the measurements occurs slowly. Additionally, communication failures between sensors can affect the state estimation performance. To this end, we propose a Kalman filtering algorithm composed of a structural data fusion stage and a signal date fusion stage. The number of communications can be decreased by executing signal data fusion when a global estimate is required. Then, we investigate the stability conditions for the proposed distributed approach. Furthermore, we analyze the mismatch between the estimation generated by the proposed distributed algorithm and that obtained by the centralized Kalman filter. Lastly, numerical results verify the feasibility of the proposed distributed method. © 2023 The Franklin Institute

关键词： State estimation

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NO reduction by carbon-based gases based on coal gasification fractions:NO reduction intermediate identification and kinetic analysis

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Frontiers of Environmental Science & engineering 2025年第3期19卷 175-189页

作者： Xiang Zhang Jian Tian Tanghui Hu Guangyong Yue Xianlong Liu Wen Zhou Xiaohong Liu Xinye Wang Ministry-of-Education Key Laboratory for the Green Preparation and Application of Functional Materials Hubei Engineering Research Center for Green Industrialization Technology of Industrial WasteSchool of Materials Science and EngineeringHubei UniversityWuhan 430062China Hubei Huda Tianshu New Energy Materials Industrial Research and Design Institute Co. Ltd.Wuhan 430062China Wuhan Tianshu Dust Removal Equipment Co. Ltd.Wuhan 430062China Engineering Laboratory for Energy System Process Conversion&Emission Control Technology of Jiangsu Province School of Energy and Mechanical EngineeringNanjing Normal UniversityNanjing 210042China

Combining pulverized coal gasifiers with cement kiln production is promising for application in low-cost and efficient NO *** paper presents a pulverized coal gasifier catalytic denitration technique and investigates the homogeneous reduction(by CO and CH_(4))and heterogeneous catalytic reduction(by coke,CaO,MgO,and Fe_(2)O_(3))of NO.A combination of Chemkin simulations and fixed-bed experiments is used to elucidate the reaction pathways and key intermediates of NO reduction by carbon-based *** addition,the activation energies for different catalyst combinations were analyzed via reaction *** results demonstrate that the presence of small amounts of O_(2) inhibits NO reduction by CO but promotes NO reduction by CH_(4).The NCO·radical is essential for the NO reduction process,and the generation of this radical depends on the CH_(4) cleavage intermediate and O·*** and Fe_(2)O_(3) exhibit more significant catalytic effects on NO reduction by carbon-based gases than the other catalysts *** presence of a small amount of O_(2) in the reacting gas mixtures facilitates the NO reduction *** activation energy is reduced to 1.02 kJ/mol,and the NO conversion reaches 99.80%when the catalyst is C+CaO+MgO+Fe_(2)O_(3) and the gas composition is CO+CH_(4)+O_(2).This work provides theoretical support and data recommendations for the use of pulverized coal gasifiers for the denitrification of cement kilns.

关键词： Carbon-based gas Catalytic denitration Coke Kinetic analysis Reaction mechanism

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Face Inpainting Based on Gan Multi-Dimensional Consistency Guidance

SSRN

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SSRN 2024年

作者： Li, Yaqian Zhan, Xin Li, Haibin Zhang, Wenming Hebei Provincial Key Laboratory of Industrial Computer Control Engineering Yanshan University Qinhuangdao066004 China

A B S T R A C TThe averageness of low-frequency features and the unreliability of high-frequency features result in identity deviations in the inpainted faces. To address this problem, a two-stage generative model is proposed, which includes structural prior guidance and multidimensional consistency guidance. Firstly, a parsing inpainting module was designed to predict structural information. Subsequently, a multidimensional information guidance module was constructed, which, based on semantic consistency guidance, extracts low-frequency confidence information of the inpainted image to suppress the averageness of facial inpainting. Frequency-domain information is extracted from the inpainted image, combined with a facial recognition network, and the frequency-domain identity feature consistency loss is calculated to aid in the inpainting of high-frequency information, including identity features, thus achieving identity consistency in facial inpainting. © 2024, The Authors. All rights reserved.

关键词： Face recognition

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Dynamic event-triggered formation control of second-order nonholonomic systems

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Journal of Systems engineering and Electronics 2023年第2期34卷 501-514页

作者： WANG Xiaoyu SUN Sijia XIAO Feng YU Mei School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China

In this paper,the formation control problem of secondorder nonholonomic mobile robot systems is investigated in a dynamic event-triggered ***-triggered control protocols combined with persistent excitation(PE)conditions are *** event-detecting processes,an inactive time is introduced after each sampling instant,which can ensure a positive minimum sampling *** increase the flexibility of the event-triggered scheme,internal dynamic variables are included in event-triggering ***,the dynamic event-triggered scheme plays an important role in increasing the lengths of time intervals between any two consecutive *** addition,event-triggered control protocols without forward and angular velocities are also presented based on approximate-differentiation(low-pass)*** asymptotic convergence results are given based on a nested Matrosov theorem and artificial sampling methods.

关键词： nonholonomic system dynamic event-triggered control consensus-based formation

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Analyzing the Correlation between Student Learning Behaviors and Academic Performance in Blended Learning Environments

Analyzing the Correlation between Student Learning Behaviors...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Ying Chen, Kang Xu, Zhezhuang Lin, Ye Zeng, Qingbo College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9798350368604

Blended learning integrates online and face-to-face (FTF) teaching methods to offer students in higher education a flexible and customized educational experience. Despite its significant advantages in promoting academic achievement and increasing student participation, blended learning presents challenges for educators due to difficulties in collecting and analyzing learning behavior data. Existing research mainly focuses on single learning modes, and data collection methods primarily rely on interviews and questionnaires, which can be subjective. To address these challenges, this study developed a mobile software called EasyClass, integrated with an online Small Private Online Course(SPOC) platform, to automatically record student behaviors in a blended learning environment. Obtaining data from an undergraduate blended learning course, statistical methods are used to analyze the correlation between student behaviors and final grades, as well as to identify factors affecting grades. The results of multivariate linear regression models confirmed the feasibility and effectiveness of blended instruction. This study provides a new approach to identifying and disseminating effective data collection strategies in blended learning, enabling educators and educational institutions to better understand and assess student behaviors, and to further explore their impact on final academic achievement. © 2024 IEEE.

关键词： Contrastive Learning

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Operational Reliability Modeling and Assessment of Battery Energy Storage Based on Lithium-ion Battery Lifetime Degradation

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Journal of Modern Power Systems and Clean Energy 2022年第6期10卷 1738-1749页

作者： Lin Cheng Yuxiang Wan Yanglin Zhou David Wenzhong Gao State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijingChina Department of Electrical and Computer Engineering University of DenverDenverUSA IEEE

Battery energy storage(BES)systems can effectively meet the diversified needs of power system dispatching and assist in renewable energy *** reliability of energy storage is essential to ensure the operational safety of the power ***,BES systems are composed of battery *** suggests that BES performance depends not only on the configuration but also on the operating state over different lifetime *** lack of safety and reliability is the main bottleneck preventing widespread applications of BES ***,a reliability assessment algorithm and a weak-link analytical method for BES systems are proposed while considering battery lifetime ***,a novel lithium-ion battery model is proposed to identify the degradation rate of solid electrolyte interphase film formation and capacity *** impacts of different operating conditions are considered in stress factor ***,a reliability assessment algorithm for a BES system is introduced based on a universal generating *** innovative weak-link analytical method based on the reliability importance index is proposed that combines the evaluation results of state-oriented and state-change-oriented indexes through an entropy weight *** model,algorithm,indexes,and the usefulness are demonstrated in case studies based on aging test data and actual bus operating *** results demonstrate the effects of the battery status and working conditions on BES ***-link analysis is also used to assist BES systems in avoiding short-board batteries to achieve long lifetimes and efficient operation.

关键词： Battery energy storage lifetime degradation operational reliability capacity plummeting weak-link analysis

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Semi-supervised learning with GAN for Semantic Segmentation of Wood Defect image

Semi-supervised learning with GAN for Semantic Segmentation ...

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2024 China Automation Congress, CAC 2024

作者： Zheng, Kunxin Zheng, Song Xu, Zhezhuang Lin, Ye Zhu, Yuhang College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9798350368604

Automatic defect detection on wood surface is essential for ensuring product quality. Semantic segmentation methods have shown outstanding performance in wood defect detection. However, it is costly to acquire correct pixel-level labels of wood surface defects due to the complicated characteristics. To mitigate the cost of acquiring high-quality datasets, this study proposes a semi-supervised defect detection approach based on generative adversarial networks, which contains a generator and a discriminator. The generator is utilized to generate pixel-level segmentation results of wood defect images. To improve the segmentation performance, the discriminator learns against the generator by judging the goodness of the prediction results and generates supervised signals for the prediction results of unlabeled images. With this learning model, the proposed method can enhance segmentation performance using unlabeled images and alleviate the burden of data annotation. We validate the effectiveness of the proposed approach on a collected dataset of wood defects. Experimental results demonstrate the remarkable performance of our method even when utilizing only a small amount of annotated data. © 2024 IEEE.

关键词： Generative adversarial networks

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