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Multi-Time-Scale Variational Mode Decomposition-Based Robust Fault Diagnosis of Railway Point Machines Under Multiple Noises

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Chinese Journal of Electronics 2024年第3期33卷 814-822页

作者： Junqi LIU Tao WEN Guo XIE Yuan CAO Clive Roberts Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology The School of Electronic and Information Engineering Beijing Jiaotong University Birmingham Centre for Railway Research and Education University of Birmingham

The fault diagnosis of railway point machines(RPMs) has attracted the attention of engineers and *** have studies considered diverse noises along the *** fulfill this aspect,a multi-time-scale variational mode decomposition(MTSVMD) is proposed in this paper to realize the accurate and robust fault diagnosis of RPMs under multiple *** decomposes condition monitoring signals after coarse-grained processing in varying *** this manner,the information contained in the signal components at multiple time scales can construct a more abundant feature space than at a single *** the experimental validation,a random position,random type,random number,and random length(4R) noise-adding algorithm helps to verify the robustness of the *** adequate experimental results demoristrate the superiority of the proposed MTSVMD-based fault diagnosis.

关键词： Railway point machines Fault diagnosis Multi-time-scale Variational mode decomposition

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Learning Knowledge Evolution with Time Duration from Finer-Time-Granularity Temporal Knowledge Graph

Learning Knowledge Evolution with Time Duration from Finer-T...

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2023 China Automation Congress, CAC 2023

作者： Du, Shaochong Chen, Kang Yang, Shijie Huo, Hong Fang, Tao Shanghai Jiao Tong University Department of Automation Shanghai China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai China Shanghai Engineering Research Centre of Intelligent Control and Management Shanghai China

ISBN: (纸本)9798350303759

How to represent the temporal information corresponding to each fact in temporal knowledge graphs (TKGs) effectively is always challenging. Most existing representation learning methods usually map the timelines of knowledges into multiple points and ignore the underlying continuous time property and periodic characteristics behind those dense timestamps in finer-time granularity temporal knowledge graphs. A novel Knowledge Evolution with Time Duration (KETD) model is proposed for knowledge representation of temporal knowledge graphs with finer-time granularities. It represents the entity embedding as a time-varying nonlinear function to find the specific continuous time property from dense timestamps. To the best of our knowledge, it is the first attempt to learn embeddings of time durations and combines them into embeddings of entities and relations to predict potential periodic facts. It also uses the temporal point process to capture the impact of historical facts on the current fact. The experiments on self-built finer-time granularity TKG of Satellite-to-Earth Communication (STEC) and two public datasets have demonstrated the superiority of KETD compared to some baseline approaches. © 2023 IEEE.

关键词： Knowledge graph

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An Integrated Scheduling Algorithm of Satellite Observation and Data Download Based on Multi-Agent Deep Reinforcement Learning

An Integrated Scheduling Algorithm of Satellite Observation ...

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2023 China Automation Congress, CAC 2023

作者： Chen, Kang Du, Shaochong Huo, Hong Fang, Tao Shanghai Jiao Tong University Department of Automation Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Shanghai Engineering Research Centre of Intelligent Control and Management Shanghai China

ISBN: (纸本)9798350303759

The earth observation satellite scheduling has always been critical for the maximum use of limited satellite resources, which basically includes scheduling ground target observation and observation data downloading. Due to the complexity of the mathematical model and its NP-hardness, current research usually splits it into two problems to simplify the model. In this paper, we propose a model of the integrated scheduling problem of satellite observation and data downloading (ISP-SODD) based on the decentralized partially observable Markov decision process (Dec-POMDP), treating each satellite as an agent with autonomous decision-making capability. A multi-agent deep reinforcement learning-based (MADRL-based) algorithm is proposed to solve it. A series of simulation experiments of different scales validate the effectiveness of the method and its advantages compared with the traditional heuristic algorithms. To the best of our knowledge, this study is the first attempt to apply multi-agent reinforcement learning to a multi-satellite scheduling scenario with downloading considerations. © 2023 IEEE.

关键词： Reinforcement learning

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SSD object detection algorithm based on knowledge map

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International Journal of Wireless and Mobile Computing 2024年第2期27卷 152-160页

作者： Huang, Li Wang, Xiaofeng Lu, Jianhua Hu, Wei Zhang, Changrong College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China Key Laboratory of Metallurgical Equipment and Control Technology of the Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Precision Manufacturing Research Institute Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China

With the pervasive integration of artificial intelligence into all aspects of human life, talent emerges as a primary resource. Upon analysing the current state of talent training in higher education institutions, issues such as dispersed knowledge points, overlapping content, a singular practice approach and ineffective evaluation have been identified. In response to these challenges, this paper proposes a multidisciplinary and comprehensive practical teaching methodology grounded in the knowledge graph framework. It delves into diverse paths for practical teaching and assessment, including aspects like teaching objectives, problem decomposition, resource integration, implementation methods and performance evaluation. The practical application of the SSD algorithm in researching service robot indoor object detection serves as an illustrative example. Employing the holistic practical teaching approach facilitated by the knowledge graph, this model guides students in acquiring object detection expertise, thereby enhancing their comprehensive development. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Object detection

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On-Demand Earth system Data Cubes

On-Demand Earth System Data Cubes

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： David Montero César Aybar Chaonan Ji Guido Kraemer Maximilian Söchting Khalil Teber Miguel D. Mahecha Remote Sensing Centre for Earth System Research (RSC4Earth) Leipzig University Institute of Earth System Science & Remote Sensing Leipzig University German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Image Processing Laboratory Universitat de València Image and Signal Processing Group Leipzig University Department of Remote Sensing Helmholtz Centre for Environmental Research (UFZ)

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Advancements in Earth system science have seen a surge in diverse datasets. Earth system Data Cubes (ESDCs) have been introduced to efficiently handle this influx of high-dimensional data. ESDCs offer a structured, intuitive framework for data analysis, organising information within spatio-temporal grids. The structured nature of ESDCs unlocks significant opportunities for Artificial Intelligence (AI) applications. By providing well-organised data, ESDCs are ideally suited for a wide range of sophisticated AI-driven tasks. An automated framework for creating AI-focused ESDCs with minimal user input could significantly accelerate the generation of task-specific training data. Here we introduce cubo, an open-source Python tool designed for easy generation of AI-focused ESDCs. Utilising collections in SpatioTemporal Asset Catalogs (STAC) that are stored as Cloud Optimised GeoTIFFs (COGs), cubo efficiently creates ESDCs, requiring only central coordinates, spatial resolution, edge size, and time range.

关键词： Earth Instruments Training data Geoscience and remote sensing Spatiotemporal phenomena Sensors Task analysis

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Self-Organized Criticality in C. Elegans Neural Network 42

Self-Organized Criticality in C. Elegans Neural Network

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42nd Chinese control Conference, CCC 2023

作者： Gu, Xiao Liu, Jian Yuan, Ye Zhao, Peng Xin, Kuankuan Fang, Tao Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Shanghai Engineering Research Centre of Intelligent Control and Management Shanghai200240 China Institute of Machine Intelligence University of Shanghai for Science and Technology Shanghai200093 China Queensland Brain Institute The University of Queensland BrisbaneQLD4072 Australia

ISBN: (纸本)9789887581543

The self-organized criticality is a fundamental property for organisms to perform normal functional activities. Extensive large-scale biological neural network researches have found the existence of self-organized criticality through avalanche phenomenon, which is typical characteristic of self-organized criticality. However, such research method is not suitable for small-scale biological neural network, because number of neurons in small-scale biological neural network is not large enough to support the emergence of avalanche phenomenon. It has been demonstrated that branching parameter can also be used to describe the propagation of activity in the neural network. Here, in this paper, the C. elegans neural network composed of 302 neurons is selected to investigate the existence of self-organized criticality using branching parameter. Based on the obtained connectome of C. elegans, a directed graph model is constructed to simulate the gradient potential of neurons and the activity of C. elegans, then, the existence of self-organized criticality in the neural network of C. elegans is proved by using branching parameter method. Meanwhile, the result is further confirmed by analyzing the calcium activity data of C. elegans obtained from biological experiments. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Calcium

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An Integrated Scheduling Algorithm of Satellite Observation and Data Download Based on Multi-Agent Deep Reinforcement Learning

An Integrated Scheduling Algorithm of Satellite Observation ...

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Chinese Automation Congress (CAC)

作者： Kang Chen Shaochong Du Hong Huo Tao Fang Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Shanghai Engineering Research Centre of Intelligent Control and Management Shanghai China

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Learning Knowledge Evolution with Time Duration from Finer-Time-Granularity Temporal Knowledge Graph

Learning Knowledge Evolution with Time Duration from Finer-T...

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Chinese Automation Congress (CAC)

作者： Shaochong Du Kang Chen Shijie Yang Hong Huo Tao Fang Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Shanghai Engineering Research Centre of Intelligent Control and Management Shanghai China

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Self-organized Criticality in C. elegans Neural Network

Self-organized Criticality in C. elegans Neural Network

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第42届中国控制会议

作者： Xiao Gu Jian Liu Ye Yuan Peng Zhao Kuankuan Xin Tao Fang Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Engineering Research Centre of Intelligent Control and Management Institute of Machine Intelligence University of Shanghai for Science and Technology Queensland Brain Institute The University of Queensland

The self-organized criticality is a fundamental property for organisms to perform normal functional *** large-scale biological neural network researches have found the existence of self-organized criticality through avalanche phenomenon, which is typical characteristic of self-organized criticality. However, such research method is not suitable for small-scale biological neural network, because number of neurons in small-scale biological neural network is not large enough to support the emergence of avalanche phenomenon. It has been demonstrated that branching parameter can also be used to describe the propagation of activity in the neural network. Here, in this paper, the C. elegans neural network composed of 302 neurons is selected to investigate the existence of self-organized criticality using branching parameter. Based on the obtained connectome of C. elegans, a directed graph model is constructed to simulate the gradient potential of neurons and the activity of C. elegans, then, the existence of self-organized criticality in the neural network of C. elegans is proved by using branching parameter method. Meanwhile, the result is further confirmed by analyzing the calcium activity data of C. elegans obtained from biological experiments.

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Formal Description and Verification of Mixed Right of Way Scenario for Guided Transport system Based on Vehicle-to-vehicle Communication

Formal Description and Verification of Mixed Right of Way Sc...

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2021 International Conference on Computer Application in Transportation Engineering, CATE 2021

作者： Zhu, Tianwen Cao, Yuan Mo, Xiaofan School of Electronics and Information Engineering Beijing Jiaotong University Beijing China National Engineering Research Centre of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China Railway Signal Monitoring System Research Direction Standards and Metrology Research Institute China Academy of Railway Sciences Beijing China

GTS (Guided Transport system) is an innovative new mode of urban transportation, which integrates the characteristics of rail transit into urban public transportation. It is a transportation system that meets the needs of low-cost and high-speed construction. According to the actual requirements of GTS, this paper proposes a GTS train control system based on vehicle-to-vehicle communication, and studies its mixed right of way scenario. Using UPPAAL to establish the formalized model of its typical operation scenario and verify the GTS train control system based on vehicle-to-vehicle communication whether it meets the requirements of functionality, performance and security. © Published under licence by IOP Publishing Ltd.

关键词： Vehicle to vehicle communications

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