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42nd Chinese control Conference, CCC 2023

作者： Ma, Song Yu, Xin-Meng Sun, Tao Zhao, Guang Guo, Mei Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China Tsinghua University Department of Automation Beijing100084 China School of Energy and Power Engineering Dalian University of Technology Dalian116024 China Avic Shenyang Engine Design Institute Shenyang110066 China

ISBN: (纸本)9789887581543

In this brief paper, a data-based method on the fault diagnosis in aero-engine transmission systems is developed. Firstly, during the operation of splines, we acquire the acceleration vibration signal. We process the signal into time-frequency images using the short-time Fourier transform. Then, the improved convolutional neural network with channel attention is trained using time-frequency images of multiple fault signals. Finally, we implement fault prediction for spline wear and misalignment faults in aero-engine transmission systems. The result shows that the fault diagnosis using the proposed method has a high level of accuracy. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Failure analysis

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Adaptive Positional Encoding for Bundle-Adjusting Neural Radiance Fields

Adaptive Positional Encoding for Bundle-Adjusting Neural Rad...

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International Conference on computer Vision (ICCV)

作者： Zelin Gao Weichen Dai Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province and School of Computer Science Hangzhou Dianzi University Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province

Neural Radiance Fields have shown great potential to synthesize novel views with only a few discrete image observations of the world. However, the requirement of accurate camera parameters to learn scene representations limits its further application. In this paper, we present adaptive positional encoding (APE) for bundle-adjusting neural radiance fields to reconstruct the neural radiance fields from unknown camera poses (or even intrinsics). Inspired by Fourier series regression, we investigate its relationship with the positional encoding method and therefore propose APE where all frequency bands are trainable. Furthermore, we introduce period-activated multilayer perceptrons (PMLPs) to construct the implicit network for the high-order scene representations and fine-grained gradients during backpropagation. Experimental results on public datasets demonstrate that the proposed method with APE and PMLPs can outperform the state-of-the-art methods in accurate camera poses and high-fidelity view synthesis.

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Bis-benzylisoquinoline alkaloids inhibit flavivirus entry and replication by compromising endolysosomal trafficking and autophagy

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Virologica Sinica 2024年第6期39卷 892-908页

作者： Lihong Huang Lele Liu Junhai Zhu Nanjun Chen Jie Chen Chuen-Fuk Chan Fei Gao Youqin Yin Jiufeng Sun Rongxin Zhang Kehui Zhang Wenbao Qi Jianbo Yue State Key Laboratory for Animal Disease Control and Prevention South China Agricultural UniversityGuangzhou510642China Key Laboratory of Zoonoses Ministry of Agriculture and Rural AffairsSouth China Agricultural UniversityGuangzhou510642China National and Regional Joint Engineering Laboratory for Medicament of Zoonoses Prevention and Control Guangzhou510642China Key Laboratory of Zoonoses Prevention and Control of Guangdong Province GuangzhouChina Department of Computer Science City University of Hong KongHong Kong999077China Department of Biomedical Sciences City University of Hong KongHong Kong999077China Guangdong Workstation for Emerging Infectious Disease Control and Prevention Guangdong Provincial Key Laboratory of Pathogen Detection for Emerging Infectious Disease ResponseGuangdong Provincial Center for Disease Control and PreventionGuangzhou511430China Laboratory of Immunology and Inflammation Institute of Basic Medical Sciences and Department of BiotechnologySchool of Life Sciences and BiopharmaceuticsGuangdong Pharmaceutical UniversityGuangzhou510006China State Key Laboratory of Bioactive Substance and Function of Natural Medicines Institute of Materia MedicaChinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing100730China Beijing Key Laboratory of Active Substances Discovery and Druggability Evaluation Institute of Materia MedicaChinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing100730China City University of Hong Kong Shenzhen Research Institute Shenzhen518057China Division of Natural and Applied Sciences Synear Molecular Biology LabGlobal Health Research CenterDuke Kunshan UniversityKunshan215316China College of Life Sciences Wuhan UniversityWuhan430072China

Flaviviruses,such as dengue virus(DENV),Zika virus(ZIKV),and Japanese encephalitis virus(JEV),represent a substantial public health challenge as there are currently no approved treatments ***,we investigated the antiviral effects of bis-benzylisoquinoline alkaloids(BBAs)on flavivirus *** evaluated five specific BBAs—berbamine,tetrandrine,iso-tetrandrine,fangchinoline,and cepharanthine—and found that they effectively inhibited infections by ZIKV,DENV,or JEV by blocking virus entry and genome replication stages in the flavivirus life ***,we synthesized a fluorophore-conjugated BBA and showed that BBAs targeted endolysosomes,causing lysosomal pH *** studies on inhibiting ZIKV infection by BBAs revealed that these compounds blocked TRPML channels,leading to lysosomal dysfunction and reducing the expression of NCAM1,a key receptor for the entry of ZIKV into cells,thereby decreasing cells susceptibility to ZIKV ***,BBAs inhibited the fusion of autophagosomes and lysosomes,significantly reducing viral RNA ***,our results suggest that BBAs inhibit flavivirus entry and replication by compromising endolysosomal trafficking and autophagy,respectively,underscoring the potential of BBAs as therapeutic agents against flavivirus infections.

关键词： Bis-benzylisoquinoline alkaloids(BBAs) Flavivirus Zika virus(ZIKV) Endolysosomes TRPML channels Autophagy

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Active Power control for Off-Shore Wind Turbines Against FDI Attack

Active Power Control for Off-Shore Wind Turbines Against FDI...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Xuguo Jiao Hao Luo Liuyang Jiang Lin Wang Xin Wang Zuqiang Huang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China School of Information and Control Engineering Qingdao University of Technology Qingdao China Zhejiang Windey Co. Ltd. Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Academy of Sciences Qilu University of Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Civil Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Recently, offshore wind power has rapidly developed due to its low environmental interference and abundant wind resources. However, due to its remote geographical location and high demand for communication infrastructure, off-shore wind turbines (OWTs) are vulnerable to potential threats from false data injection (FDI) attack. FDI attack can interfere with active power control strategies by tampering with the rotor speed data measured by sensors and pose a threat to the stability of the power grid. To ensure that OWTs can accurately track the active power instructions set by wind farm's control center under FDI attack, this paper proposes a rotor speed control (RSC) strategy based on Kalman filtering technology (RSC- KF). This strategy can estimate the rotor speed of the wind turbine in real-time, and then a residual detection and compensation mechanism is designed to effectively detect and correct attacked rotor speed information caused by FDI attack. Further, based on the corrected rotor speed, the RSC method is developed, which can flexibly switch control strategies according to different rotor speed values, ensuring the efficient operation of OWTs under different wind speed conditions. The proposed approach's effectiveness is proved via simulations conducted on the OpenFAST platform.

关键词： Filtering Velocity control Power control Rotors Power system stability Stability analysis Real-time systems Wind turbines Kalman filters Cyberattack

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Weakly Supervised and Semi-Supervised Semantic Segmentation Makes Full Use of a Small Amount of labeled Data

SSRN

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SSRN 2023年

作者： Zhang, Wenming Li, Yaqian Li, Haibin Wang, Zhe Engineering Research Center Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University Qinhuangdao China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China School of Electrical Engineering Yanshan University Qinhuangdao066004 China

Semi-supervised and weakly supervised semantic segmentation are very challenging tasks and have been studied in recent years. The purpose is to reduce the high cost of semantic segmentation label production. In this paper, we propose a weakly supervised assistant supervision strategy (ASS), which introduces more semantic segmentation for the shallow network by introducing additional classification tasks to the classification network, and enhances the fusion of shallow features and deep features. In addition, we propose a multi-scale global correlation module (MSG). This module uses the correlation degree of different scale regions to improve the weight of the nondiscriminative part, which solves the problem of insufficient CAMs activation. In most scenarios, weak supervision and semi-supervision are inseparable. We creatively propose a training strategy that integrates semi-supervision and weak supervision, adding pixel-level supervision in the training process of weak supervision to give full play to the role of pixel-level annotation. Extensive experiments on PASCAL VOC 2012 dataset show that our weakly supervised training strategy achieves the state of the art, and the combination of weakly supervised and semi-supervised training strategy can make more effective use of pixel-level labels. © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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SA-U-Net++: SAR marine floating raft aquaculture identification based on semantic segmentation and ISAR augmentation

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Journal of Applied Remote Sensing 2021年第1期15卷

作者： Wang, Deyi Han, Min Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Ganjingzi District Dalian China Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Ganjingzi District Dalian China

Marine floating raft aquaculture (FRA) monitoring is vital for environment protection and mariculture management. Synthetic aperture radar (SAR) could provide high-quality remote sensing images under all weather conditions compared with the existing optical remote-sensing-based methods. Traditional SAR monitoring methods extract the pixel feature of marine FRA in single patches, which commonly leads to poor generalization. We propose a self-Attention semantic segmentation method based on modified U-Net++ (SA-U-Net++) for FRA segmentation, which could automatically extract semantic feature information, and provide superior performance under complicated scenes. The proposed self-Attention backbone could help to extract more precise features and enhance the overall accuracy. Furthermore, we propose a FRA-ISAR data generation method based on inverse SAR (ISAR) imaging to alleviate the sample shortage problem. We introduce the semantic segmentation method into FRA-SAR segmentation for the first time. The experiments verify the effectiveness and superiority of SAR FRA segmentation based on the proposed SA-U-Net++ model compared with the existed semantic segmentation approaches. © 2021 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Deep learning

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Phase-Based Quantification of Sports Performance Metrics Using a Smart IoT Sensor

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IEEE Internet of Things Journal 2023年第18期10卷 15900-15911页

作者： Chen, Meng Szu, Hui Fang Lin, Hsin Yen Liu, Yifan Chan, Ho-Yin Wang, Yufan Zhao, Yuliang Zhang, Guanglie Yao, Jeffrey Da-Jeng Li, Wen Jung National Tsing Hua University Physical Education Office Hsinchu City300 Taiwan Kla Corporation Tainan City744 Taiwan Michigan State University Department of Electronic and Computer Engineering East LansingMI48824 United States Shanghai Jiao Tong University School of Mechanical Engineering Department of Industrial Engineering and Management Shanghai200240 China Northeastern University at Qinhuangdao Hebei Key Lab. of Micro-Nano Prec. Optical Sensing and Msrmt. Technol. Sch. of Control Engineering Qinhuangdao066004 China City University of Hong Kong Department of Mechanical Engineering Kowloon Tong Hong Kong City University of Hong Kong Shenzhen Research Institute CASŰCityU Joint Laboratory for Robotics Shenzhen518057 China National Tsing Hua University Department of Power Mechanical Engineering Hsinchu City300 Taiwan

Sports performance is often judged based on the results of a series of motions rather than observing and analyzing the detailed sequential motions that lead to the results. Hence, subjective feedback from the coaches is often ineffective in improving player performance. In this work, we custom-built a smart Internet of Things wristband motion sensor to implement data-based sports performance evaluation. A phase-based feature selection method is also proposed to assess the athletes' sequential detailed motion for selected sport activities. To demonstrate the merits of this technology, we quantified the quality of the sequential motions of a specific type of volleyball serve by analyzing 183 samples of motion data obtained from a total of 18 players. The general skill levels (i.e., elite, subelite, and amateur) of the players were identified by machine learning algorithms, with accuracies of up to 95%. Moreover, we adopted biomechanical principles to extract 11 motion-related performance metrics from various phases of the players' serve motion. We identified the distributions of these metrics across different skill levels and found eight key metrics that were highly correlated to the skill level of a players. We suggest that these metric distributions can be used as a reference for providing feedback to the coaches and players, to improve a player's skill in the future. This phased-based analysis method can potentially be applied across many sports to increase the effectiveness of athletes' training. © 2014 IEEE.

关键词： Internet of things

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Wind Speed Prediction Based on ARMA and SVR

Wind Speed Prediction Based on ARMA and SVR

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Data Driven control and Learning Systems (DDCLS)

作者： Xuguo Jiao Daoyuan Zhang Qinmin Yang Zhenyong Zhang Wenfeng Liu School of Information and Control Engineering Qingdao University of Technology Qingdao China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Civil Engineering Qingdao University of Technology Qingdao China

Wind power generation technology is one of the research hotspots of renewable energy nowadays. In order to ensure the stable and reliable operation of wind power generation equipment, wind speed prediction is very important. This paper provides a new idea for the wind speed prediction based on Autoregressive Moving Average (ARMA) and Support Vector Regression (SVR). First, to reasonably divide the original data into multiple historical data with strong correlation as features to predict the future wind speed, the ARMA model is employed and its partial autocorrelation coefficient is calculated. By this means, the input features can be optimally selected and the training set of the prediction model can be constructed. Further, SVR model is used to build the nonlinear relationship between the input features and future wind speed. Finally, through simulation, it proves that this method saves more time than try and error method in selecting input features, and through comparison with Backpropagation Neural Network (BPNN), it proves that this method can achieves higher wind speed prediction accuracy.

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Joint Optimization of Fog Server Workload and Task Completion Time in Fog Computing 8

Joint Optimization of Fog Server Workload and Task Completio...

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8th IEEE International Conference on computer and Communications, ICCC 2022

作者： Ju, Chengen Ma, Yue Yin, Zhenyu Bi, Zhiying Wang, Jie Shenyang Institute of Computing Technology Chinese Academy of Sciences Shengyang China School of Computer Science and Technology University of Chinese Academy of Sciences Shengyang China Liaoning Key Lab. of Domestic Industrial Control Platform Technology on Basic Hardware Software Shengyang China

ISBN: (纸本)9781665450515

Fog computing as a promising technology develops network service quality by bringing resources closer to the end users. Unlike traditional cloud, fog is characterized by sporadic resources availability, geographical distribution and increased flexibility. However, tasks offloading and scheduling mechanisms still meet challenges in fog server workload balancing and task completion time minimizing. For solving these problems, a task scheduling strategy based on a general IoT-Fog analysis model is proposed in this paper. In the strategy, we will study how the end device selects the suitable fog server to process the requests. Based on the strategy, the end devices offload their tasks to specific fog server based on the propagation latency, computation capability of the server, etc. Without loss of generality, we consider the problem in probabilistic manner, such as request generating rate of the end device and the service rate of the fog server. Finally, extensive simulation experiments are done to demonstrate the effectiveness of our proposed strategy. © 2022 IEEE.

关键词： Fog

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Efficient Wideband Adaptive Beamforming With Null Broadening Using MHSA-CNN

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Liu, Fulai Huang, Hai Liu, Ruxin Yang, Jinwei Suo, Luyao Du, Ruiyan Northeastern University at Qinhuangdao Lab of Electromagnetic Environment Cognition and Control Utilization Hebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao066004 China Northeastern University School of Computer Science and Engineering Shenyang110819 China

In the case of interference perturbation, the wideband adaptive beamforming (WAB) weight vector may be mismatched, which leads to the decrease of interference suppression ability. To improve communication quality under the interference position perturbation, this paper presents a multi-head self-attention conventional neural network (MHSA-CNN)-based WAB algorithm with null broadening. In the presented approach, a MHSA-CNN structure is proposed to improve the prediction accuracy of beamforming weight vector in the case of interference perturbation. Specifically, by processing multiple attention heads in parallel to obtain the information of different signal subspaces, MHSA mechanism enables the network to dynamically adjust the attention distribution of signal features and effectively extract the global features of the covariance matrix. Then, based on focused reconstruction and null broadening, an effective neural network training label is used to enhance the ability of suppressing interferences. Finally, the well-trained MHSA-CNN can accurately output the weight vector suitable for WAB with null broadening in real time. Simulation results demonstrate that the proposed algorithm can suppress interferences accurately within the interference perturbation range and enhance the output signal-to-interference-plus-noise ratio while ensuring real-time communication performance. © 2025 IEEE.

关键词： Signal to noise ratio

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