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2022 IEEE International Conference on Unmanned Systems, ICUS 2022

作者： Zhang, Junjie Yang, Jingxin Bian, Yougang Cui, Qingjia Qin, Xiaohui Hu, Manjiang Shen, Da Gao, Hongbo Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha China Institute of Hunan University Wuxi Intelligent Control Research Wuxi China University of Science and Technology of China The Department of Automation Hefei China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

A self-triggered distributed model predictive control (DMPC) approach is proposed in this paper for the cooperative diving of a multi-autonomous underwater vehicle (AUV) system. In order to realize cooperative diving of the multi-AUV system, we design a basic DMPC mechanism with a stability condition which is easy to be realized. Then, a self-triggering mechanism is introduced into the basic DMPC approach which saves communication and computation resources. In the self-triggered mechanism, the maximum time instant that is inside the terminal region decides the triggering interval. The triggering interval is decided after solving the optimization problem, therefore helps reduce the computation burden at each control loop. Lastly, numerical examples are provided to verify the effectiveness of the proposed method. © 2022 IEEE.

关键词： Autonomous underwater vehicles

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Temporal Attention Based TCN-BIGRU Model for Energy Time Series Forecasting

Temporal Attention Based TCN-BIGRU Model for Energy Time Ser...

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2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering, CSAIEE 2021

作者： Li, Liang Hu, Min Ren, Fuji Xu, Haijun Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine HeFei China Graduate School of Advanced Technology Science University of Tokushima Tokushima Japan

ISBN: (纸本)9781665422048

Over the years, energy time series forecasting has been widely studied and has played an important role in various fields, such as electric energy forecasting, solar energy forecasting, etc. In energy time series forecasting, it is crucial to building forecasting models for long series in order to obtain accurate forecasting results. Since the use of long series can cause the accuracy of the model to decrease. In this paper, we propose a deep learning model (TCNTA-BiGRU) based on a bi-directional gated cyclic unit (BiGRU) with a temporal attention mechanism to address the problem of accuracy degradation in long sequence tasks. First, in order to capture long-term dependencies, this paper divide the dataset and input it into a temporal convolutional network (TCN) to transform long sequences into multiple short sequences, which not only solves the problem that to cause gradient explosion or disappearance when processing long sequences, but also reduces the spatial complexity. Then, BiGRU is used to learn historical and future information and capture more short-term dependencies. Moreover, in order to enhance the model's ability to focus on data periodicity, a temporal attention mechanism is introduced. Additionally the autoregressive module is used to increase the linear fitting ability of the model. The model proposed in this paper is applied to the Electricity and Solar Energy datasets and the results show a better performance relate to existing deep learning models. © 2021 IEEE.

关键词： Solar energy

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Wireless power transmission system with CC and CV output based on hybrid LCC-S/P topology

Wireless power transmission system with CC and CV output bas...

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Electronics and Communication, Network and Computer Technology (ECNCT), International Conference on

作者： Kun Li Zheng Gong Jiaheng Meng Qing Liu Tianjin Key Laboratory of Film Electronic and Communication Device Tianjin University of Technology Tianjin China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin China

Wireless Power Transfer (WPT) technology provides a convenient and efficient power supply solution for mobile devices. The utilization of constant current (CC) and constant voltage (CV) control in WPT systems proves valuable for charging battery applications. This paper presents a novel WPT system with CC and CV output characteristics, employing a switching topology. Specifically, the LCC-S and LCC-P topologies are applied and rigorously simulated. The proposed MCR-WPT system’s output characteristics in both CC and CV modes are meticulously validated using Saber software. The simulation results distinctly demonstrate the system’s capability to achieve CC and CV outputs within a varying load resistance range of 5Ω to 30Ω, thus conclusively establishing the efficacy of the topology switching technology.

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Optimal Tracking Performance of Networked Control Systems Under DoS and its Applications

Optimal Tracking Performance of Networked Control Systems Un...

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Chinese Automation Congress (CAC)

作者： Xinyu Ren Bo Li Feng Zhao Xiangyong Chen Xianhe Zhang Xiaowei Jiang Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Wuhan China School of Finance Anhui University of Finance and Economics Bengbu China Key Laboratory of Complex systems and Intelligent Computing Universities of Shandong Linyi University Linyi China Key Laboratory of Complex systems and Intelligent Computing School of Automation and Electrical Engineering Universities of Shandong Linyi University Linyi China School of Electrical Engineering and Automation Hubei Normal University Huangshi China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This paper investigates the optimal tracking performance (OTP) of multiple-input multiple-output (MIMO) discrete networked control systems (NCSs) under the influence of Denial of Service (DoS) attack, channel noise, and bandwidth constraints. The channel noise is assumed to be additive Gaussian white noise (AWGN). Due to the randomness of DoS attack, a Bernoulli distribution is used for DoS modeling. Utilizing frequency domain analysis methods, coprime factorization, all-pass decomposition, and Youla parameterization, an explicit expression for the optimal tracking performance (OTP) of the system under the two-degree-of-freedom (TDOF) controller is derived. The analysis indicates that non-minimum phase (NMP) zeros, unstable poles (UPs), DoS attack, and network constraints affect the OTP of NCSs. Finally, the theoretical results are validated through simulations of MIMO NCS and vehicle control system.

关键词： Automation Networked control systems Frequency-domain analysis Bandwidth Interference White noise Cyber-physical systems Poles and zeros MIMO communication Multi-agent systems

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Fusing SAR Images and Social Media Data Through Domain Adaptation and Land Cover Information: A Case of 2017 Houston Flood Event

Fusing SAR Images and Social Media Data Through Domain Adapt...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Zhenjie Liu Yong Deng Jun Li Javier Plaza Antonio Plaza Hubei Key Laboratory of Intelligent Geo-Information Processing and School of Computer Science China University of Geosciences Wuhan China Changjiang Institute of Survey Planning Design and Research Wuhan China Hyperspectral Computing Laboratory University of Extremadura Caceres Spain

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Global climate change leads to increasing frequency and severity of urban flood disasters, which restricts human sustainable development. So far, many studies have explored the potential of fusing remote sensing images and social media data for monitoring urban flood disasters. However, the SAR flooded characteristics of different land cover in complex urban environment are generally ignored. In this paper, we develop a new method for the fusion of heterogeneous SAR images and social media data by using domain adaptation and land cover information. The 2017 Houston flood event is taken as a case study for evaluation. According to our experiments, compared with traditional methods of optimal transport and geographic optimal transport, the proposed method can align the geo-tagged tweets with location uncertainty to nearby flooded areas of low-, medium-, and high-intensity developed areas with more remarkable performance.

关键词： Uncertainty Social networking (online) Disasters Urban areas Land surface Radar polarimetry Floods

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Simulation of Nuclear Reactor Accident Scenarios using Physics-Informed Neural Networks and Transfer-Learning 4

Simulation of Nuclear Reactor Accident Scenarios using Physi...

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4th International Conference on Electronic Information Engineering and Computer Science, EIECS 2024

作者： Yu, Yang Xie, Yufei Wang, Wenlin Wu, Guohua Lan, Haitao Lin, Enbo An, Ping Sun, Zibin Zhang, Haichuan Wu, Yixian National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China Nuclear Power Institute of China Science and Technology on Reactor System Design Technology Laboratory Chengdu China School of Automation Wuhan University of Technology Wuhan China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Institute of Automotive Engineers Hubei University of Automotive Technology Shiyan China Nuclear Power Institute of China Chengdu China

ISBN: (纸本)9798331531409

In a Loss of Coolant Accident (LOCA), reactor core temperatures can rise rapidly, leading to potential fuel damage and radioactive material release. This research presents a groundbreaking method that combines the power of Monte Carlo Sampling and Physics-Informed Neural Networks (PINNs) to simulate and effectively address the challenging Loss of Coolant Accidents (LOCA) in nuclear reactors. In the event of a LOCA, reactor core temperatures can soar rapidly, posing a significant threat to fuel integrity and potentially leading to the release of radioactive materials. By leveraging the strengths of both Monte Carlo Sampling and PINNs, this approach aims to provide a comprehensive and accurate simulation framework for assessing and mitigating the consequences of such accidents. The method yields high prediction accuracy (MAE: 0.033, RMSE: 0.098, R2: 0.814) and demonstrates robustness through transfer learning, maintaining strong performance (MAE: 0.064, RMSE: 0.163, R2: 0.735). © 2024 IEEE.

关键词： Coolants

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Development of completion tool pressure test control system 10

Development of completion tool pressure test control system

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2023 10th International Conference on advanced Manufacturing Technology and Materials Engineering, AMTME 2023

作者： Sun, Zhiyu Zhao, Lianyu Wang, Chenglin Zhao, Xiaolei Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China Offshore Oil Engineering Co. Ltd. Tianjin300461 China

In oil exploitation, the completion tool is the key point to ensure the effective development of oil wells, and it must undergo strict pressure testing. Due to the high test pressure, there are potential safety hazards in the manual operation process. To improve safety and test efficiency, the automatic precision test system is developed. The detection system must realize the pressure detection process, provide feedback on the key data, give the corresponding curve reference, and generate the detection report. Through the PID algorithm, the precise control of the motor is realized. The system safety is guaranteed, and the detection accuracy is improved. The test results show that the completion tool pressure test control system can greatly improve detection efficiency. It can also fully reflect the entire detection process, accurately calculate some key parameters such as pressure drop, and obtain accurate results. The detection error is kept within 0.5%, ensuring the reliability of the detection process. © Published under licence by IOP Publishing Ltd.

关键词： Control systems

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Exploration and Practice of Blended Teaching in the Course of Engineering Drawing Empowered by Knowledge Graph

Exploration and Practice of Blended Teaching in the Course o...

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International Conference on Educational and Information Technology (ICEIT)

作者： Yahong Xue Haozhi Mu Lijun Xue Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechatronical System National Demonstration Center for Experimental Mechanical and Electrical Engineering Education (Tianjin University of Technology) Tianjin University of Technology Tianjin China

ISBN: (数字)9798350372663

ISBN: (纸本)9798350372670

Under the current blended learning mode, students' ability to process, organize, and integrate knowledge is insufficient, and the ability to construct a complete knowledge system for autonomous learning is slightly weak. Taking the national first-class undergraduate course “Engineering Drawing” at Tianjin University of Technology as an example, this study explores and practices the teaching of online and offline hybrid courses based on knowledge graphs, and conducts a comprehensive effectiveness analysis. Practice has proven that the hybrid teaching empowered by knowledge graphs enables visualization of knowledge points and analysis of learning situations, allows for planned learning paths, intelligent resource recommendations, and can achieve targeted integration of data and teaching of key and difficult issues. It has reference significance for the construction of an open, personalized, and precise intelligent education system.

关键词： Federated learning Engineering drawings Education Knowledge based systems Data visualization Knowledge graphs Hybrid learning

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Dual-axial self-attention network for text classification

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Science China(Information Sciences) 2021年第12期64卷 80-90页

作者： Xiaochuan ZHANG Xipeng QIU Jianmin PANG Fudong LIU Xingwei LI State Key Laboratory of Mathematical Engineering and Advanced Computing Shanghai Key Laboratory of Intelligent Information Processing Fudan University School of Computer Science Fudan University

Text classification is an important task in natural language processing and numerous studies aim to improve the accuracy and efficiency of text classification models. In this study, we propose an effective and efficient text classification model which is based on self-attention solely. The recently proposed multidimensional self-attention significantly improved the performance of self-attention. However, existing models suffer from two major limitations:(1) the previous multi-dimensional self-attention models are quite timeconsuming;(2) the dependencies of elements along the feature axis are not taken into account. To overcome these problems, in this paper, a much more computational efficient multi-dimensional self-attention model is proposed, and two parallel self-attention modules, called dual-axial self-attention, are applied to capture rich dependencies along the feature axis as well as the text axis. A text classification model is then *** experimental results on eight representative datasets show that the proposed text classification model can obtain state-of-the-art results and the proposed self-attention outperforms conventional self-attention models.

关键词： text classification dual-axial self-attention feature-axial dependency

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Research on Fault Diagnosis Method Based on Structural Causal Model in Tennessee Eastman Process 7th

Research on Fault Diagnosis Method Based on Structural Causa...

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7th International Conference on computing, Control and Industrial Engineering, CCIE 2023

作者： Pu, Haoyuan Liu, Jie Chen, Zhi Yang, Xiaohua Ren, Changan Xu, Zhuoran Jian, Yifan School of Computer Science University of South China Hengyang China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan Hengyang China CNNC Key Laboratory on High Trusted Computing Hengyang China School of Nuclear Science and Technology University of South China Hengyang China

ISBN: (纸本)9789819927296

In the practical application of fault diagnosis in large-scale chemical systems, due to the danger of faults, the machine learning diagnosis methods used in current research often face the problem of scarcity of fault samples. This makes it difficult for the model to be trained effectively, thus affecting the fault detection rate of the model. Therefore, for the fault diagnosis of chemical system with small sample data, this paper proposes a diagnostic method that uses structural causal model to further combine the diagnostic results of the two machine learning methods, thereby improving global diagnostic performance. With the help of powerful system description ability of structural causal model, we can establish a causal graph for diagnostic process of the chemical system, and accurately construct the corresponding structural equations for the various diagnostic results of the two machine learning methods to combine them, thus improve the fault detection rate. The global results make up for the shortcomings of single machine learning method in small sample chemical system fault diagnosis. The verification on the chemical system simulation platform Tennessee Eastman process shows that, compared with the diagnosis results of the selected Gaussian Naive Bayes method and the K-nearest neighbour method, the results obtained by our method are effectively improved in fault detection rate of each fault in TEP. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Failure analysis

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