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Influence of contact layer on the sulphur corrosion of copper conductors in power transformers

High Voltage 2022年第1期7卷 176-184页

作者： Lijun Yang Dong Ding Yuan Yuan Sihang Gao Yuncai Lu State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing UniversityChongqingChina College of Material Science and Engineering Chongqing UniversityChongqingChina Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and TelecommunicationsChongqingChina State Grid Jiangsu Electric Power Co. Ltd.Electric Power Research InstituteNanjingChina

Owing to the corrosive sulphides in the mineral insulating oil such as dibenzyl disul-phide(DBDS),copper windings in oil‐immersed transformers may suffer sulphur *** study investigates the influence of different contact conditions on the corrosion of copper conductors in ***‐immersed accelerated corrosion experiment was conducted on copper conductors with various contact materials and different layers of insulating *** surface micromorphology,deposition compo-nent and the potentiodynamic polarization characteristics of copper conductors were *** results show that copper with different contact materials suffer sulphur corrosion;conversely,bare copper rarely *** was also verified that the existence of a particular gap between copper and contact layer is a significant factor in sulphur corrosion by the formation of a concentration cell on the copper *** insu-lating paper wrapped around copper conductors does not increase the corrosion ten-dency of copper;however,it contributes to the formation of a region concentrated with DBDS,Cu^(2+),H^(+)by adsorption and impediment,which plays a key role in facilitating the sulphur corrosion of copper ***,sulphur corrosion can also occur on other metal parts in transformers when there is a particular gap on surfaces other than copper.

关键词： corrosion copper sulphur

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ReaxFF molecular dynamic simulation of primary and secondary reactions involving in sub-bituminous coal pyrolysis for tar production

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Carbon Resources Conversion 2021年第1期4卷 230-238页

作者： Yanan Qian Jin-Hui Zhan Wei Xu Zhennan Han Xiaoxing Liu Guangwen Xu State Key Laboratory of Safety and Control for Chemicals SINOPEC Research Institute of Safety Engineering Co.Ltd.Qingdao 266071China State Key Laboratory of Multiphase Complex System Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China Key Laboratory on Resources Chemicals and Materials of Ministry of Education of China Shenyang University of Chemical TechnologyShenyang 110142China

ReaxFF molecular dynamic simulation combined with experimental verification was performed to understand the overall reaction mechanism,especially the primary and secondary reactions involving in tar formation of sub-bituminous coal *** relationship at atomic level is clarified between bond breakage of functional groups and products generation,revealing that the amount and order in forming each product are subject to the number of corresponding functional groups and their bond energies *** primary breakage of-C-O-and-C-C-bridge-bonds present in initial coal macromolecular generates molecular of heavy tar,whereas heavy tar can be converted into light tar through cracking side chain of aromatic rings and cyclic hydrocarbons at increased pyrolysis *** very high temperatures the cracking of short-chain hydrocarbons and residual atoms connecting to aromatic rings further occurs to generate light tar and *** remaining aromatic-ring fragments of heavy tar are likely cross-linked to form ***,the simultaneous evolution tendency of tar yield and tar quality under different pyrolysis temperatures and heating rates is obtained at molecular *** obtaining high yield and quality of tar,appropriately high temperature as well as suitable heating rate are needed to compromise the high yield of primary tar and high quality of secondarily upgraded products.

关键词： ReaxFF MD simulation Sub-bituminous coal pyrolysis Reaction paths Primary formation Secondary upgrading

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Attentive online sequential extreme learning machine for nonlinear dynamic process monitoring

Attentive online sequential extreme learning machine for non...

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第34届中国过程控制会议

作者： Jingxin Zhang Yuying Cao Hai Zhou Department of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of EngineeringMinistry of Education Department of Automation Southeast University New Energy Research Center China Electric Power Research Institute

This paper provides a new monitoring method based on the attention mechanism and online sequential extreme learning machines(OS-ELM) for nonlinear dynamic process,referred as attentive ***-ELM provides the prediction of outputs and the update of parameters by sequential *** cope with nonlinearity,the attention mechanism is utilized to map data to a high-dimensional feature *** effect of the attention mechanism and the single hidden layer feedforward network trained with OS-ELM work collaboratively in monitoring the real-time process data to detect possible *** addition,fault detection rate and false alarm rate are adopted to evaluate the monitoring *** effectiveness of the proposed method is illustrated by a typically complicated industrial system.

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Attributes Grouping and Mining Hashing for Fine-Grained Image Retrieval

arXiv

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arXiv 2023年

作者： Lu, Xin Chen, Shikun Cao, Yichao Zhou, Xin Lu, Xiaobo School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Nanjing Enbo Technology Co. Ltd. Nanjing China

In recent years, hashing methods have been popular in the large-scale media search for low storage and strong representation capabilities. To describe objects with similar overall appearance but subtle differences, more and more studies focus on hashing-based fine-grained image retrieval. Existing hashing networks usually generate both local and global features through attention guidance on the same deep activation tensor, which limits the diversity of feature representations. To handle this limitation, we substitute convolutional descriptors for attention-guided features and propose an Attributes Grouping and Mining Hashing (AGMH), which groups and embeds the category-specific visual attributes in multiple descriptors to generate a comprehensive feature representation for efficient fine-grained image retrieval. Specifically, an Attention Dispersion Loss (ADL) is designed to force the descriptors to attend to various local regions and capture diverse subtle details. Moreover, we propose a Stepwise Interactive External Attention (SIEA) to mine critical attributes in each descriptor and construct correlations between fine-grained attributes and objects. The attention mechanism is dedicated to learning discrete attributes, which will not cost additional computations in hash codes generation. Finally, the compact binary codes are learned by preserving pairwise similarities. Experimental results demonstrate that AGMH consistently yields the best performance against state-of-the-art methods on fine-grained benchmark datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Hash functions

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Design of an Intelligent Monitoring system for Operating States in a Coal Mine Drilling Process 41

Design of an Intelligent Monitoring System for Operating Sta...

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第41届中国控制会议

作者： Xiao Yang Luefeng Chen Sheng Du Chengda Lu Wangnian Li Min Wu 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 Xi'an Research Institute Co.Ltd. China Coal Technology and Engineering Group

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development trend of intelligent drilling in coal mines,an intelligent monitoring system for operating states in a drilling process is developed to solve the problems of incomplete display of drilling parameters and ineffective *** system realizes the remote monitoring of drilling *** mainly adopts the Fuzzy C-Means clustering algorithm to cluster the drilling *** it combines expert empirical knowledge to classify coal seams into soft,hard and alternating soft and hard coal *** boundary values are calculated according to the boundary ***,three drilling operating states,rated,conservative and over-excited,are judged to provide guidance for the drilling process control.

关键词： Drilling process Intelligent monitoring Fuzzy C-Means clustering Operating states

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Heterogeneous Multi-Robot Cooperative Welding Task Allocation Based on Task-Centered Optimization: An Industrial Case Study

SSRN

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SSRN 2023年

作者： Liu, Wenbo Kuang, Zhian Zhou, Bo Li, Shihua School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Shanghai Friendess Electronic Technology Shanghai201100 China

In this article, the complex task assignment, scheduling and planning of complex heterogeneous multi-robot welding process are studied. For large workpiece welding tasks that involve complex heterogeneous multi-robot task allocation under different internal and external constraints, the traditional multi-robot task allocation (MRTA) framework is extended to a novel and unified heterogeneous multi-robot (H-MRTA) framework, and a hierarchical optimization algorithm is proposed. Firstly, an optimization model based on the H-MRTA framework is established. Taking into account constraints such as accessibility, collision and welding process, the optimization objectives are formulated by combining the total welding time and the task balance between robots. Then, to obtain the optimal solution, a stepwise optimization algorithm is devised to solve the MRTA problem from top to bottom in two layers: working area division problem and multi-robot path planning problem. For the working area division problem, a task-centered optimization model is proposed, which aims to balance the workload among robots, and is solved by branch-and-bound method. For the multi-robot path planning problem, multiple process constraints are considered and the total working time is minimized as the objective function. The effectiveness of the optimization algorithm is verified by a case study on the welding task of a large plate element in a ship hull. compared to two other state-of-the-art algorithms, the proposed optimization algorithm achieves a task allocation scheme with a shorter welding time, a shorter waiting time and a higher welding productivity, which demonstrates the effectiveness and feasibility of the stepwise optimization-based task allocation algorithm for complex heterogeneous multi-robot. © 2023, The Authors. All rights reserved.

关键词： Industrial robots

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Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

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IEEE Transactions on Intelligent Transportation systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

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A comprehensive review of electrochemical hybrid power supply systems and intelligent energy managements for unmanned aerial vehicles in public services

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Energy and AI 2022年第3期9卷 148-171页

作者： Caizhi Zhang Yuqi Qiu Jiawei Chen Yuehua Li Zhitao Liu Yang Liu Jiujun Zhang Chan Siew Hwa College of Mechanical and Vehicle Engineering The State Key Laboratory of Mechanical TransmissionsChongqing Automotive Collaborative Innovation CentreChongqing UniversityChongqing400044China College of Automation Chongqing UniversityKey Laboratory of Complex System Safety and ControlMinistry of EducationChongqing UniversityChongqing400044China School of Mechanical Engineering University of Science and Technology BeijingBeijing100083China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang UniversityHangzhou310027China Institute for Sustainable Energy/College of Sciences Shanghai UniversityShanghai 200444China School of Mechanical and Aerospace Engineering Nanyang Technological University50 Nanyang Avenue639798Singapore Sichuan Energy Internet Research Institute Tsinghua UniversityChengdu 610213Sichuan ProvinceChina

The electric unmanned aerial vehicles (UAVs) are rapidly growing due to their abilities to perform some difficult or dangerous tasks as well as many public services including real-time monitoring, wireless coverage, search and rescue, wildlife surveys, and precision agriculture. However, the electrochemical power supply system of UAV is a critical issue in terms of its energy/power densities and lifetime for service endurance. In this paper, the current power supply systems used in UAVs are comprehensively reviewed and analyzed on the existing power configurations and the energy management systems. It is identified that a single type of electrochemical power source is not enough to support a UAV to achieve a long-haul flight;hence, a hybrid power system architecture is necessary. To make use of the advantages of each type of power source to increase the endurance and achieve good performance of the UAVs, the hybrid systems containing two or three types of power sources (fuel cell,battery, solar cell, and supercapacitor,) have to be developed. In this regard, the selection of an appropriate hybrid power structure with the optimized energy management system is critical for the efficient operation of a UAV. It is found that the data-driven models with artificial intelligence (AI) are promising in intelligent energy management. This paper can provide insights and guidelines for future research and development into the design and fabrication of the advanced UAV power systems.

关键词： Unmanned aerial vehicles(UAVs) Power supply system Fuel cell system Artificial intelligence(AI) Energy management systems

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Streaming-Data-Driven Distributionally Robust Joint Operation of Multi-Microgrids and Off-Site Hydrogen Refueling Stations under Uncertainties 11

Streaming-Data-Driven Distributionally Robust Joint Operatio...

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11th International Conference on Innovative Smart Grid Technologies - Asia, ISGT-Asia 2022

作者： Li, Longyan Ning, Chao Qiu, Haifeng Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China School of Electrical & Electronic Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9798350399660

This paper proposes a novel online-learning-enabled hierarchical distributionally robust energy management framework for multi-microgrids (MMGs) with off-site hydrogen refueling stations (HRSs). The proposed framework integrates day-ahead scheduling, intra-day rescheduling and real-time control. The day-ahead scheduling is implemented via a central energy management system for optimal joint scheduling of the MMG systems and off-site HRSs, then the intra-day rescheduling is carried out to optimize the real-time operation and to generate the reference for local controllers. To adequately characterize the statistical properties of renewable and load uncertainties from data streams, we leverage a Dirichlet process mixture model to construct an online-update ambiguity set, which encodes multimodality and local moment information. Based upon this ambiguity set, an online-learning distributionally robust model predictive control (OL-DRMPC) is developed to enhance the tracking performance of energy storage references and to reduce conservatism while guaranteeing probabilistic operational constraints. Its effectiveness is verified in a case study. © 2022 IEEE.

关键词： Real time control

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A Probabilistic Topology Inference Method for Networked Dynamical system via Single Excitation

A Probabilistic Topology Inference Method for Networked Dyna...

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IEEE Conference on Decision and control

作者： Qing Jiao Yushan Li Jianping He Dept. of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

A growing number of works have investigated inferring the topology of networked dynamical systems from observations, such as to better understand the system behaviour. Despite the tremendous advances, most of them require the observations to be abundant. This paper focuses on inferring the topology by injecting single excitation on a node and collecting several steps of noisy observations. The problem is challenging because the noises cannot be depressed in several observations and are mixed with the injected excitation, making it hard to directly reveal the topology. To practice, we develop a probabilistic method based on the hypothesis test framework. First, we infer the neighbors that are within h-hop of the excited node and derive the accuracy guarantees. Then, we extend the method to infer the exact h-hop neighbors. A computable lower bound for the accuracy probability is established to provide confidence support in the inference procedures. Furthermore, we give the conditions of excitation input to ensure a desired inference probability, which provides guidance for the input design. Numerical simulations are conducted to verify the effectiveness of the proposed method.

关键词： Lower bound Accuracy Network topology Noise Probabilistic logic Numerical simulation Topology Noise measurement Reliability Dynamical systems

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