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arXiv 2024年

作者： Liu, Peng Fei, Zesong Wang, Xinyi Zhou, Yiqing Zhang, Yan Liu, Fan The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The State Key Laboratory of Processors The Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China

Mobile edge computing (MEC) is powerful to alleviate the heavy computing tasks in integrated sensing and communication (ISAC) systems. In this paper, we investigate joint beamforming and offloading design in a three-tier integrated sensing, communication and computation (ISCC) framework comprising one cloud server, multiple mobile edge servers, and multiple terminals. While executing sensing tasks, the user terminals can optionally offload sensing data to either MEC server or cloud servers. To minimize the execution latency, we jointly optimize the transmit beamforming matrices and offloading decision variables under the constraint of sensing performance. An alternating optimization algorithm based on multidimensional fractional programming is proposed to tackle the non-convex problem. Simulation results demonstrates the superiority of the proposed mechanism in terms of convergence and task execution latency reduction, compared with the state-of-the-art two-tier ISCC framework. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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An Efficient Color and Geometric Feature Fusion Module for 6D Object Pose Estiamtion 12

An Efficient Color and Geometric Feature Fusion Module for 6...

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12th International Conference on CYBER Technology in Automation, Control, and intelligent Systems, CYBER 2022

作者： Li, Jiangeng Liu, Hong Huang, Gao Zuo, Guoyu Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China Faculty of Information Beijing University of Technology Beijing100124 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

6D pose estimation is widely used in robot tasks such as sorting and grasping. RGB-D-based methods have recently attained brilliant success, but they are still susceptible to heavy occlusion. Our critical insight is that color and geometry information in RGBD images are two complementary data, and the crux of the pose estimation problem under occlusion is fully leveraging them. Towards this end, we propose a new color and geometry feature fusion module that can efficiently leverage two complementary data sources from RGB-D images. Unlike prior fusion methods, we conduct a two-stage fusion strategy to do color-depth fusion and local-global fusion successively. Specifically, we fuse the color features extracted from RGB images into the point cloud in the first stage. In the second stage, we extract local and global features from the fused point cloud using an ASSANet-like network and splice them together to obtain the final fusion features. We conducted experiments on the widely used LineMod and YCB-Video datasets, which shows that our method improves the prediction accuracy while reducing the training time. © 2022 IEEE.

关键词： Color

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Efficient Hierarchical Exploration with An Active Subgoal Generation Strategy

Efficient Hierarchical Exploration with An Active Subgoal Ge...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Xu, Xin Zuo, Guoyu Li, Jiangeng Huang, Gao The Faculty of Information Technology Beijing University of Technology Beijing100124 China The Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China Faculty of Information Beijing University of Technology Beijing100124 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China

ISBN: (纸本)9781665481090

Goal-conditioned hierarchical reinforcement learning (HRL) is a promising approach in long-horizon and complex tasks. It has been successfully used by decomposing tasks via subgoals. However, the existing goal-conditioned HRL methods often suffer from training inefficiency in the long-horizon tasks with sparse external rewards. For this problem, this paper proposes a novel framework that can train high-level policy efficiently with an effective sub goal generation method. The key component of the framework is an active subgoal generation strategy which consists of (a) an efficient high-level policy learning mechanism and (b) two measures of novelty and coverage for filtering effective subgoals. We also introduce the HER mechanism to the framework to solve the problem of sparse reward in the environment. Building upon the proposed framework, this paper develops an active hierarchical exploration strategy with an efficient subgoal generation method. We test our method on a variety of continuous control tasks. Experimental results demonstrate that the strategy outperforms the state-of-the-art HRL approaches. © 2022 IEEE.

关键词： Reinforcement learning

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Cooperative Diving of Multi-AUV System with Self-triggered Mechanism

Cooperative Diving of Multi-AUV System with Self-triggered M...

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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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