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2021 6th International Conference on Information Science, Computer Technology and Transportation, ISCTT 2021

作者： Mu, Jinshuang Gao, Zhanjun Zhou, Fengyuan Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Shandong Jinan250061 China

ISBN: (纸本)9783800757282

In operation and maintenance, State Grid has accumulated a large amount of historical equipment defect text data. For problems such as difficulty in text data mining. This paper established a text information extraction model at first. Extract important information such as equipment defect phenomena and defect locations from historical defect data. Based on it, establishing convolutional neural network (CNN) to achieve defect level classification. By comparing classification effect before and after information extraction, results indicate that text information extraction model can improve accuracy of defect level classification model. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Data mining

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Music emotion prediction based on multidimensional feature fusion 3

Music emotion prediction based on multidimensional feature f...

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3rd IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2023

作者： Zhou, Xuanye Su, Zhibin Ren, Hui Communication University of China School of Information and Communication Engineering Beijing China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism Beijing China

ISBN: (纸本)9781665490788

Audience sentiment analysis is a popular research task in Music Information Retrieval (MIR). In this work, we aim to lean the prediction model of fine-grained music emotion for intelligent editing and retrieval. To satisfy the properties of time-series of music, the RNN-based model of BILSTM was selected as the bone. In particular, the traditional features and deep features extracted from the CNN architecture of spectrogram was fused with attention mechanism to enhance the recognition ability for different emotions. The ablation experiment has proved the necessary of the multi-dimensional features. Finally, the contrasting experiments on recent algorithms demonstrate that our proposed method for emotion prediction is effective, which is also suitable for small data sets. © 2023 IEEE.

关键词： Forecasting

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Distributed Tracking control of Nonlinear Multi-agent systems Against False Data Injection Attacks 41

Distributed Tracking Control of Nonlinear Multi-agent System...

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第41届中国控制会议

作者： Yanhui Zhang Jian Sun Gang Wang Yong Xu the School of Automation and the Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology the Beijing Institute of Technology Chongqing Innovation Center IEEE

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,the distributed resilient tracking control problem is investigated for nonlinear multi-agent systems with false data injection(FDI) attacks and unknown *** FDI attacks are considered in communication network between ***,the neighbor's information received by the agent may be wrong due to the attacks,which will affect tracking consensus ***,the approximate property of fuzzy logic systems are utilized to estimate unknown nonlinear *** backstepping framework,we design a distributed resilient control scheme under asymmetric *** asymmetric saturation nonlinearity,an auxiliary system is constructed to simplify designed progress of the *** on Lyapunov stability theory,it is proved that all signals in the closed-loop system are semi-globally uniformly bounded,and the followers' outputs are driven to a neighborhood near the output of ***,simulation results are presented to illustrate the effectiveness of the proposed method.

关键词： Adaptive resilient control nonlinear MASs FDI attacks asymmetric saturation

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Wind and PV Power Ramp Events Prediction Based on Long Short-Term Memory Network

Wind and PV Power Ramp Events Prediction Based on Long Short...

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2023 IEEE Sustainable Power and Energy Conference, iSPEC 2023

作者： Wang, Zengwei Li, Zhendong Liu, Yutian Ma, Huan The Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan China The State Grid Shandong Electric Power Research Institute Jinan China

ISBN: (纸本)9798350322699

With the increasing penetration rate of renewable generation, the challenges arising from its randomness and intermittent nature are becoming more pronounced. In particular, wind and photovoltaic power ramp events pose a serious threat to the secure and stable operation of power systems, and even lead to frequency instability and load shedding. Based on the long short-term memory neural networks (LSTM), this paper proposes a novel renewable power ramp events (RPREs) forecasting approach. First, filter the key tie-lines. Second, the LSTM network was driven by the known time series of wind, PV, load, and tie-line power to forecast the tie-line power for the next hour, thereby determining the adjustable capacity of the tie-line. Finally, the regulatory capability of the grid is considered to judge the occurrence of a power ramping event. Case study demonstrates that the proposed method can effectively predict power ramping events and meets the requirements of online assessment. © 2023 IEEE.

关键词： LSTM photovoltaic power ramping events forecasting wind power

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Real-time Semantic Segmentation for Aggregating Long-Range Information and Region Understanding 41

Real-time Semantic Segmentation for Aggregating Long-Range I...

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第41届中国控制会议

作者： Yifan Chen Liping Yan Yuanqing Xia Bo Xiao Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology School of Artificial Intelligence Beijing University of Posts and Telecommunications

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the improvement of hardware performance,semantic segmentation based on convolutional neural network has achieved wide concern for its' advantage of high ***,in practical application scenarios,such as autonomous driving,what we care about is not only the segmentation accuracy,but also the ability of the model to process information in real-time on edge *** this paper,a lightweight and efficient real-time semantic segmentation network was proposed,which is based on a two-way structure,aggregates detailed spatial information and high-level semantic information,integrates long-range dependencies and region-level *** proposed algorithm achieves good *** conducted experiments based on an NVIDIA Geforce RTX 2080 SUPER graphics card and achieved 73.8% accuracy on Cityscapes dataset with a speed of 211 FPS,and 72.8% accuracy on Camvid dataset.

关键词： Semantic Segmentation Real-time Semantic Segmentation Deep Learning Bilateral Neural Network

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Electromagnetic Transient Modeling and Real-time Simulation Based on State-space Equations 4

Electromagnetic Transient Modeling and Real-time Simulation ...

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4th International Conference on New Energy and Power Engineering, ICNEPE 2024

作者： Guo, Tianyu Hu, Binjiang Luo, Hui Tu, Liang Zhao, Yanjun Hu, YuFeng China Southern Power Grid State Key Laboratory of Hvdc Electric Power Research Institute Guangdong China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangdong China China Southern Power Grid Csg Key Laboratory for Power System Simulation Electric Power Research Institute Guangdong China China Southern Power Grid Co. Ltd China

ISBN: (纸本)9798331516086

Due to the different output characteristics of different components of the power system, the simulation results of its output deviate from the actual situation. Therefore, the electromagnetic transient modeling and real-time simulation based on the state space equation are proposed. From the perspective of rectifier, DC transmission line, inverter and control system, the composition of the state space model of the complete power system and the output characteristics of each part in the operation process are analyzed. In the test results, the design method can achieve a stable fit between the real-time simulation results of voltage waveform containing two VSC modules and the actual situation. © 2024 IEEE.

关键词： Electromagnetic transients

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CDP-GAN:Near-Infrared and Visible Image Fusion Via Color Distribution Preserved GAN

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IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1698-1701页

作者： Jun Chen Kangle Wu Yang Yu Linbo Luo the School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex SystemsEngineering Research Center of Intelligent Technology for Geo-ExplorationMinistry of EducationChina University of GeosciencesWuhan 430074China the Shanghai Institute of Technical Physics Key Laboratory of Infrared System Detecting and Imaging TechnologyChinese Academy of SciencesShanghai 200083China the School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan 430074China

Dear Editor,This letter is concerned with dealing with the great discrepancy between near-infrared(NIR)and visible(VS)image fusion via color distribution preserved generative adversarial network(CDP-GAN).Different from the global discriminator in prior GAN,conflict of preserving NIR details and VS color is resolved by introducing an attention guidance mechanism into the ***。

关键词： details dealing preserving

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GNSS/IMU Integrated Navigation Methods with NLOS Mitigation 24

GNSS/IMU Integrated Navigation Methods with NLOS Mitigation

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24th COTA International Conference of Transportation Professionals: Resilient, intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Zuo, Yang Duan, Xuting Xia, Haiying Zhou, Jianshan School of Transportation Science and Engineering Beihang Univ. Beijing China State Key Lab of Intelligent Transportation System Beijing China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Beijing China Zhongguancun Laboratory Beijing China Key Laboratory of Operation Safety Technology on Transport Vehicles Ministry of Transport Beijing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

With the development of computer technology, intelligent vehicles have become a popular orientation at present. This paper provides a solution for the traditional GNSS/IMU integrated navigation to mitigate the influence of non-line-of-sight (NLOS) environments and achieve high-precision navigation. The LOS/NLOS switching is treated as a transition in a Markov chain, and the DIMM filtering is applied to obtain GNSS-filtered data free from the influence of NLOS. GNSS/IMU integrated navigation is loose coupling in that the method combines IMU information with processed GNSS measurement using the ESKF (Error-State Kalman Filter) to achieve accurate target tracking and positioning by utilizing the state difference as the state variable. Simulation results demonstrate that the proposed method significantly improves tracking and positioning accuracy by effectively mitigating NLOS effects compared to traditional methods. This research provides an effective solution for real-world autonomous vehicle navigation systems and optimizes the city driving experience. © ASCE.

关键词： Markov chains

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First attempt of barrier functions for Caputo's fractional-order nonlinear dynamical systems

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Science China(Information Sciences) 2023年第7期66卷 313-314页

作者： Zheren ZHU Pengfei HUANG Xinmin ZHANG Yi CHAI Zhihuan SONG College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology School of Automation Chongqing University Key Laboratory of Complex System Safety and Control(Chongqing University) Ministry of Education

Dear editor, In the past two decades, scholars have begun to use system dynamical models and state motions to study the safety of a system to realize the analysis, diagnosis, and control of system safety. This safety ...

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Pipeline Anomaly Detection Based on KPCA and Cosine Distance Prototypical Network 26

Pipeline Anomaly Detection Based on KPCA and Cosine Distance...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Yi, Kang Liu, Xin Hu, Wenkai Li, Yupeng School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9798331521950

Accurately and promptly detecting the pipeline anomaly is crucial to the safe operation of pipeline systems, while a difficulty lies in that many existing methods require massive data for training models. However, pipelines are running under normal state for the most of the time, and labeled pipeline anomaly data is usually scarce. Among the commonly used sensors, vibration sensors are widely utilized in pipeline detection because of their advantages such as easy installation and high sensitivity. However, the vibration signal shows non-stationary characteristics when anomalies occur, and are contaminated by noises, making it difficult to represent the actual state with features extracted from either the time or frequency domain. Accordingly, this paper proposes a pipeline anomaly detection method based on the KPCA (kernel principal component analysis) and cosine distance prototypical network. First, features are extracted from original signals;then, the feature dimension is reduced by KPCA;last, the cosine distance is introduced to the prototypical network for anomaly detection. The effectiveness of the proposed method is demonstrated by case studies involving experimental data. © 2025 IEEE.

关键词： Frequency domain analysis

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