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An open dataset for intelligent recognition and classification of abnormal condition in longwall mining

Coal International 2024年第4期272卷 22-35页

作者： Yang, Wenjuan Zhang, Xuhui Ma, Bing Wang, Yanqun Wu, Yujia Yan, Jianxing Liu, Yongwei Zhang, Chao Wan, Jicheng Wang, Yue Huang, Mengyao Li, Yuyang Zhao, Dian School of Mechanical Engineering Xi’an University of Science and Technology No.58 Mid-Yanta Road Xi’an710054 China Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control No.58 Yanta Road Xi’an710054 China MARCO automatic control system development Co. LTD No.20 Fenghui South Road Xi’an710054 China

The underground coal mine production of the fully mechanised mining face exists many problems, such as poor operating environment, high accident rate and so on. Recently, the intelligent autonomous coal mining is gradually replacing the traditional mining process. The artificial intelligence technology is an active research area and is expected to identify and warn the underground abnormal conditions for intelligent longwall mining. It is inseparable from the construction of datasets, but the downhole dataset is still blank at present. This work develops an image dataset of underground longwall mining face (DsLMF+), which consists of 138004 images with annotation 6 categories of mine personnel, hydraulic support guard plate, large coal, towline, miners’ behaviour, and mine safety helmet. All the labels of dataset are publicly available in YOLO format and COCO format. The availability and accuracy of the datasets were reviewed by experts in coal mine field. The dataset is open access and aims to support further research and advancement of the intelligent identification and classification of abnormal conditions for underground mining. © 2024 Tradelink Publications Ltd.. All rights reserved.

关键词： Longwall mining

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Hybrid Group Consensus of Multi-agent systems with Time-delays and Additive Noises 41

Hybrid Group Consensus of Multi-agent Systems with Time-dela...

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第41届中国控制会议

作者： Chuanjian Li Xiaofeng Zong School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The hybrid group consensus of multi-agent systems that consist of two groups in environments with time-delays and additive noises is studied in this *** hybrid group consensus implies that the agents in one group achieve strong consensus and the agents in another group achieve weak consensus.A new type of control protocol is proposed to achieve the following hybrid group consensus behavior:the agents in the first group and in the second group achieve strong consensus and weak consensus,*** sufficient conditions are obtained for the hybrid group consensus problem in both mean square and almost sure ***,a simulation example is given to illustrate the feasibility of the theoretical results.

关键词： multi-agent system hybrid group consensus time-delays additive noises

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Defect Analysis of Power Equipment Based on Information Extraction Model 6

Defect Analysis of Power Equipment Based on Information Extr...

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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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A Novel Thermal Analysis Method for Tubular PM Linear Motors Based on Transfer Learning

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7379-7388页

作者： Wu, Tao Dai, Peipei Xue, Gang Guo, Youguang Lei, Gang Zhu, Jianguo Wang, Yifei China University of Geosciences School of Automation 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 University of Technology Sydney School of Electrical and Data Engineering UltimoNSW2007 Australia The University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Thermal modeling and analysis are critical for permanent magnet linear synchronous motors (PMLSMs), particularly in multiphysical analysis and motor design optimization. This article proposes a new method for thermal steady state modeling and analysis based on transfer learning (TL), which combines the thermal mechanism model with the data-driven model to achieve high-precision temperature analysis with small number of finite element analysis (FEA) samples. First, an equivalent thermal circuit (ETC) model is established for PMLSMs based on the structural parameters and heat transfer principles. Second, a hybrid model based on TL is developed: the model is pretrained by a large sample set generated from the ETC in the source field (mechanism analytical model), and then, the trained model is transferred to the target field (FEA data-driven model). The parameters of the neural network in the target field are optimized by a few FEA sample sets with higher precision to improve the prediction accuracy of the target model. Experimental results demonstrate that the proposed method significantly boosts the performance of the deep neural network (DNN) model, particularly when training sets are small. The root mean square error (RMSE) of the DNN model decreases by 0.901 in the 5:5 (training/testing) dataset and 1.726 in the 2:8 dataset;and the corresponding R^{2} and maximal absolute error of motor performances are significantly decreased. The smaller the proportion of the training set, the better performance of the hybrid modeling will achieve. Furthermore, a temperature rise experiment conducted on a prototype motor validates the efficacy and precision of the proposed hybrid modeling methodology. © 2015 IEEE.

关键词： Permanent magnets

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Polar-segmented vector optical field and its anomalous focal properties

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Optics Express 2025年第10期33卷 21030-21039页

作者： Gao, Xu-Zhen Song, Xin-Yu Chen, Jing-Rui Wang, Dan Xu, Shi-Tong Niu, Mingsheng Cao, Hongzhong Ma, Rende Pan, Yue School of Physics and Physical Engineering Qufu Normal University Qufu273165 China Shandong Provincial Key Laboratory of Laser Polarization and Information Technology Qufu Normal University Qufu273165 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education College of Physics and Optoelectronics Taiyuan University of Technology Shanxi Taiyuan030024 China

In recent years, study of spatially structured light has become an attractive and promising area, providing more ways to explore unusual and anomalous effects in optics. Here, we propose a kind of polar-segmented vector optical field (VOF), and demonstrate anomalous focal properties. Due to the appearance of the longitudinal component in tight focusing processes, the transverse energy flow and transverse spin angular momentum (SAM) are achieved, which can be applied in areas such as manipulating particles, as the transverse energy can transport the particles and the SAM can spin the particles in more degrees. Overall, the proposed polar-segmented VOF and the corresponding anomalous focal properties are crucial for advancing theories and technologies in optics, underscoring its importance in both fundamental research and practical applications. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Focusing

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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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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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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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