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A method for bubble volume calculating in vertical two-phase flow

Journal of Physics: Conference Series 2009年第1期147卷

作者： H Y Wang F Dong Tianjin Key Laboratory of Process Measurement and Control School of Electrical Engineering & Automation Tianjin University Tianjin 300072 CHINA

The movement of bubble is a basic subject in gas-liquid two-phase flow research. A method for calculating bubble volume which is one of the most important characters in bubble motion research was proposed. A suit of visualized experimental device was designed and set up. Single bubble rising in stagnant liquid in a rectangular tank was studied using the high-speed video system. Bubbles generated by four orifice with different diameter (1mm, 2mm, 3mm, 4mm) were recorded respectively. Sequences of recorded high-speed images were processed by digital image processing method, such as image noise remove, binary image transform, bubble filling, and so on. then, Several parameters could be obtained from the processed image. Bubble area, equivalent diameter, bubble velocity, bubble acceleration are all indispensable in bubble volume calculating. In order to get the force balance equation, forces that work on bubble along vertical direction, including drag force, virtual mass force, buoyancy, gravity and liquid thrust, were analyzed. Finally, the bubble volume formula could be derived from the force balance equation and bubble parameters. Examples were given to shown the computing process and results. Comparison of the bubble volume calculated by geomettic method and the present method have shown the superiority of the proposed method in this paper.

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Bionic Geomagnetic Navigation for Autonomous Underwater Vehicle with Temporal Attention-based Data-Driven Dead Reckoning

TechRxiv

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TechRxiv 2023年

作者： Yang, Songnan Zhang, Xiaohui Liu, Ding Bai, Wenqi Liu, Fan The School of Automation and Information Engineering Xi’an University of Technology Shaanxi 710048 China The Institute of Advanced Navigation and Electromagnetics Xi’an University of Technology Shaanxi 710048 China The State Key Laboratory of Eco-Hydraulics in the Northwest Arid Region of China Xi’an University of Technology Shaanxi 710048 China The Shaanxi Key Laboratory of Complex System Control with the Intelligent Information Processing Xi’an University of Technology Shaanxi 710048 China

Numerous studies have demonstrated that numerous animal species are capable of goal-directed navigation using environmental information for dead reckoning. The stable magnetic field of the earth provides important information for the migration of animals over long distances. Inspired by the goal-directed navigation of animals, a novel Geomagnetic Navigation with Temporal Attention-based Data-Driven Dead Reckoning (Attention-DR) method for Autonomous Underwater Vehicles (AUV) is presented in this article, which only utilizes the inclination angle (I) and the declination angle (D) of the Geomagnetic Field (GF) for underwater navigation without any prior knowledge of the geographical location or geographic map. This article proposes a Temporal Attention-based Long Short-Term Memory (TA-LSTM) neural network by combining history, GF information, and location in time series to achieve the optimization heading angle of underwater dead reckoning. Due to its ability to adjust the utilization weights of local and global temporal information in the path, TA-LSTM possesses the capability to continually update neural network model parameters through a data-driven process during the navigation process. This enables the Attention-DR to achieve efficient navigation in regions without prior magnetic maps and overcome navigation failures caused by magnetic field anomalies. The simulation outcomes confirm the efficiency, precision, and practicability of the proposed algorithm. © 2023, CC BY.

关键词： Navigation

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Privacy-Preserving Brain-Computer Interfaces: A Systematic Review

arXiv

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arXiv 2024年

作者： Xia, Kun Duch, Wlodzislaw Sun, Yu Xu, Kedi Fang, Weili Luo, Hanbin Zhang, Yi Sang, Dong Xu, Xiaodong Wang, Fei-Yue Wu, Dongrui The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China The Department of Informatics School of Physics Astronomy and Informatics Neurocognitive Laboratory Center for Modern Interdisciplinary Technologies Nicolaus Copernicus University Torun Poland The Ministry of Education Key Laboratory for Biomedical Engineering Zhejiang University Hangzhou China The Zhejiang University School of Medicine National Clinical Research Center for Child Health Hangzhou China Department of Biomedical Engineering Zhejiang University Hangzhou China The School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China The College of Public Administration Huazhong University of Science and Technology Wuhan430074 China The NetsUnion Clearing Corporation Beijing100045 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100049 China

A brain-computer interface (BCI) establishes a direct communication pathway between the human brain and a computer. It has been widely used in medical diagnosis, rehabilitation, education, entertainment, etc. Most research so far focuses on making BCIs more accurate and reliable, but much less attention has been paid to their privacy. Developing a commercial BCI system usually requires close collaborations among multiple organizations, e.g., hospitals, universities, and/or companies. Input data in BCIs, e.g., electroencephalogram (EEG), contain rich privacy information, and the developed machine learning model is usually proprietary. Data and model transmission among different parties may incur significant privacy threats, and hence privacy protection in BCIs must be considered. Unfortunately, there does not exist any contemporary and comprehensive review on privacy-preserving BCIs. This paper fills this gap, by describing potential privacy threats and protection strategies in BCIs. It also points out several challenges and future research directions in developing privacy-preserving BCIs. © 2024, CC BY.

关键词： Differential privacy

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Finite-time Switching Synchronization of Four Real Chaotic systems with Combination Mode

Finite-time Switching Synchronization of Four Real Chaotic S...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Weiqi Qin Yanfeng Wang Tianze Li Ming Guo Xiangyong Chen Xinru Tong Affiliated Hospital of Shandong Medical College Linyi P. R. China School of Engineering Huzhou University Huzhou P. R. China School of Automation and Electrical Engineering Linyi University Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi P. R. China College of Mathematics and Systems Science Xinjiang University Urumqi P. R. China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

This paper mainly studies the problem of finite-time switching synchronization for four real chaotic systems with combination mode. Firstly, we analyze multi-switching synchronization behavior in signal switching transmission, and the definition of finite-time combination synchronization is given by choosing the preset switching rules. And then, a novel finite time controller is designed to realize fast synchronization, and the sufficient conditions are given. Finally, numerical examples verify the effectiveness of the proposed control scheme.

关键词： Synchronization Switching systems Chaotic communication control system synthesis

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Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach

arXiv

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arXiv 2022年

作者： Li, Yuanzheng He, Shangyang Li, Yang Ge, Leijiao Lou, Suhua Zeng, Zhigang School of Artificial Intelligence and Automation Key Laboratory on Image Information Processing and Intelligent Control of Ministry of Education Huazhong University of Science and Technology Wuhan430074 China China-Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering Northeast Electric Power University Jilin 132012 China The Key Laboratory of Smart Grid of Ministry of Education Tianjin University Tianjin 300072 China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China

The electric vehicle (EV) and electric vehicle charging station (EVCS) have been widely deployed with the development of large-scale transportation electrifications. However, since charging behaviors of EVs show large uncertainties, the forecasting of EVCS charging power is non-trivial. This paper tackles this issue by proposing a reinforcement learning assisted deep learning framework for the probabilistic EVCS charging power forecasting to capture its uncertainties. Since the EVCS charging power data are not standard time-series data like electricity load, they are first converted to the time-series format. On this basis, one of the most popular deep learning models, the long short-term memory (LSTM) is used and trained to obtain the point forecast of EVCS charging power. To further capture the forecast uncertainty, a Markov decision process (MDP) is employed to model the change of LSTM cell states, which is solved by our proposed adaptive exploration proximal policy optimization (AePPO) algorithm based on reinforcement learning. Finally, experiments are carried out on the real EVCSs charging data from Caltech, and Jet Propulsion laboratory, USA, respectively. The results and comparative analysis verify the effectiveness and outperformance of our proposed framework. Copyright © 2022, The Authors. All rights reserved.

关键词： Markov processes

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Prescriptive Manufacturing in Society 5.0: Human Autonomous Organizations and “On-Demand” Smart Services

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IFAC-PapersOnLine 2024年第3期58卷 139-144页

作者： Xiao Wang Jun Huang Jing Yang Xiaoxing Wang Fei-Yue Wang Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei 230601 China Macau University of Science and Technology Macau 999078 China School of artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Beijing SANBODY Technology Company Ltd Beijing 214000 China Qingdao Academy of Intelligent Industries Qingdao 266109 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

Society 5.0 addresses human behaviors and their consequences in social management, while emphasizing the integration of Cyber-Physical-Social Spaces to achieve a highly connected and circulated society. Prompted by the various social needs expressed on social media platforms, Prescriptive Manufacturing (PM) emerges as a proactive pre-manufacturing paradigm. This paper delves into PM within the context of Society 5.0. It highlights the development of Human Autonomous Organizations (HAOs) and the delivery of “on-demand” smart services, which are customized to meet both individual and broader societal demands. The social sensing framework of social needs across different social media platforms that enables the identification and anticipation of consumer demands in real-time is illustrated. This data-driven insight leverages artificial intelligence and robotics to facilitate customized design and on-demand production, aligning product creation with real user requirements. Furthermore, we discuss the implementation of human-centered smart services in PM, such as virtual try-on, virtual fitting, personalized retrofit, user-friendly scheduling, and proximity logistics, which not only enhance consumer experience but also optimize resource efficiency By examining the symbiosis between technological advancements and social need fulfillment, this study sheds light on how PM and HAOs can foster a more responsive, sustainable, and inclusive economy in society, ultimately contributing to a higher quality of life and sustainable development goals of Society 5.0.

关键词： Social Needs Sensing Human Autonomous Organization Customized Design personalized fine-tuning prescriptive manufacturing Human-Centric Smart Services

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Design of User Plane in 5G Core Network Based on SDN

Design of User Plane in 5G Core Network Based on SDN

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IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)

作者： Ping Wang Zhan Xu Ming Zhao Zhao Liu Yi Gong School of Automation Beijing Information Science and Technology University Beijing China Ministry of Education Key Laboratory of Modern Measurement & Control Technology Beijing Information Science and Technology University Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Department of Electronic Engineering Tsinghua University Beijing China

With the rapid increase in demand for the fifth-generation mobile communication technology (5G) in the BusinesstoBusiness (ToB) market, future factories, especially those involving a large number of Internet of Things (IoT) devices, will prioritize deploying private networks to achieve prioritized scheduling, resource allocation, and security control over the entire network. The User Plane Function (UPF) is an important network element in the 5G core network. In practical deployments, UPF typically uses dedicated hardware devices, which are not only expensive but also have a tightly coupled architecture that imposes certain limitations on future network upgrades and scalability. To achieve flexible deployment of Network Private Network (NPN), we implemented the 5G core network architecture using Software Defined Networking (SDN) with Programming Protocol-Independent Packet Processors (P4) programmable switch. Subsequently, we conducted the connectivity test of the core network and analyzed the maximum achievable user capacity. Considering that the introduction of SDN may bring new security risks to private networks, we have designed a solution to mitigate SDN data plane Distributed Denial of Service (DDoS) attacks and have proven its effectiveness through practical experiments.

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Fractional Sliding Mode control for Nonlinear Aerospace systems

引用

IOP Conference Series: Materials Science and engineering 2020年第1期831卷

作者： Shiqi Zheng Bingyun Liang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China

This paper focuses on the study of fractional order terminal sliding mode control for nonlinear aerospace systems. Firstly, a novel fractional order integral terminal sliding mode control (FO-I-TSMC) method is proposed for the control of first order nonlinear system. FO-I-TSMC has three attractive advantages: i) Non-singular control law; ii) Elimination of the reaching phase; iii) Calculable finite convergence time. Furthermore, theory analysis is presented to reveal the potential advantages of the FO-I-TSMC method over its integer order counterparts. Secondly, a novel fractional order derivative integral-TSMC (FO-DI-TSMC) method is presented to deal with second order nonlinear system. Finally, FO-DI-TSMC is extended to deal with a general class of higher order control system. Simulation results are given to verify the effectiveness of the proposed methods.

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How Simulation Helps Autonomous Driving: A Survey of Sim2real, Digital Twins, and Parallel Intelligence

arXiv

引用

arXiv 2023年

作者： Hu, Xuemin Li, Shen Huang, Tingyu Tang, Bo Huai, Rouxing Chen, Long The School of Artificial Intelligence Hubei University Hubei Wuhan430062 China The Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Beijing Huairou Academy of Parallel Sensing Beijing101499 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100864 China The Waytous Inc. Beijing100083 China

Safety and cost are two important concerns for the development of autonomous driving technologies. From the academic research to commercial applications of autonomous driving vehicles, sufficient simulation and real world testing are required. In general, a large scale of testing in simulation environment is conducted and then the learned driving knowledge is transferred to the real world, so how to adapt driving knowledge learned in simulation to reality becomes a critical issue. However, the virtual simulation world differs from the real world in many aspects such as lighting, textures, vehicle dynamics, and agents' behaviors, etc., which makes it difficult to bridge the gap between the virtual and real worlds. This gap is commonly referred to as the reality gap (RG). In recent years, researchers have explored various approaches to address the reality gap issue, which can be broadly classified into three categories: transferring knowledge from simulation to reality (sim2real), learning in digital twins (DTs), and learning by parallel intelligence (PI) technologies. In this paper, we consider the solutions through the sim2real, DTs, and PI technologies, and review important applications and innovations in the field of autonomous driving. Meanwhile, we show the state-of-the-arts from the views of algorithms, models, and simulators, and elaborate the development process from sim2real to DTs and PI. The presentation also illustrates the far-reaching effects and challenges in the development of sim2real, DTs, and PI in autonomous driving. Copyright © 2023, The Authors. All rights reserved.

关键词： Textures

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Research on Trajectory Planning and Motion control of 7-DoF Sawyer Manipulator

Research on Trajectory Planning and Motion Control of 7-DoF ...

引用

Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Kun Luo Gang Xiong Yuxiao Wang Bin Hu Sheng Liu Shichao Chen The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijng China School of Artificial Intelligence University of Chinese Academy of Sciences Beijng China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Beijng China Qingdao Academy of Intelligent Industries Chinese Academy of Sciences Beijng China

Regarding human-machine collaborative task, generated trajectories of a robot manipulator should be able to avoid joint limits and singularities, but the 6 Degrees of Freedom(DoF) or lower DoF manipulators encounter difficulties in many schemes. In this paper, research is implemented on the methods of trajectory planning and motion control on the newly human-machine collaborative 7-DoF Sawyer manipulator. We try to pay more attention to these approaches and find a better combination between trajectory planners and motion controllers for Sawyer manipulator. First of all, comparison is proposed between the methods about trajectory planning in task space and joint space, drawing a conclusion that the latter has the advantage of quickness and smoothness on trajectories in real time. Next, we choose to plan trajectory in the joint space by interpolating positions and orientations. Three approaches, i.e., trapezoidal velocity trajectory, cubic polynomial trajectory and basic spline trajectory, are applied to interpolate positions. Experimental results show that it results in the smoothest trajectory using basic spline trajectory planning. We also compare linear and nonlinear orientation interpolations to address the angle wrapping issue, which leads to difficulties in smooth orientation interpolation between several waypoints. Combining polynomial trajectory planning methods with the proportional-derivative (PD) motion controller and the computed-torque motion controller, we focus on the performance of the two different combinations. It is found that trajectories with computed-torque controller have shorter path with a congruous convergence on joint position, and more accurate trajectories with less overshoots in position than others.

关键词： Interpolation Trajectory planning Collaboration Aerospace electronics Manipulators Trajectory Task analysis

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