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SSRN 2024年

作者： Fang, Yan Liu, Xiaodong Lu, Wei Pedrycz, Witold Lang, Qi Yang, Jianhua School of Control Science and Engineering Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian116024 China Department of Electrical and Computer Engineering University of Alberta EdmontonT6R 2V4 AB Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia School of Computer Science and Information Technology Northeast Normal University Changchun130117 China

Knowledge Graph Embedding aims to advance the learning performance of knowledge graphs by deliberating the complexity of hierarchical data structures in a finite-dimensional space. Most traditional Euclidean models struggle to accurately describe and navigate the intricate hierarchical relationships between nodes. The hyperbolic embedding methods, particularly in conjunction with graph neural networks, offer promise in effectively modeling complex hierarchies in a limited-dimensional space. These methods can accommodate more information about nodes under the same conditions but are also more susceptible to interference from complex neighborhood features. This paper introduces HyperGatE, which novelty combines a hyperbolic graph hierarchical neighbor structure based on a gated filter and a hyperbolic isometric rotation model for knowledge graph completion. By analyzing the hierarchical structure of the knowledge base and using a gated mechanism to form multi-layer embedding networks of entity-relational paths, the gated filter selects diverse uneven multi-hop relations within the entity neighborhood and measures them through graph attention networks. Furthermore, the simplified hyperbolic isometric model allows for uncovering hierarchical structures in the data. HyperGatE experimentally surpasses the state-of-the-art Euclidean and hyperbolic space models in high and low dimensions across four well-known data, namely WN18RR, FB15k-237, NELL-995, and Kinship. © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Parallel Planning:A New Motion Planning Framework for Autonomous Driving

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IEEE/CAA Journal of automatica Sinica 2019年第1期6卷 236-246页

作者： Long Chen Xuemin Hu Wei Tian Hong Wang Dongpu Cao Fei-Yue Wang IEEE School of Data and Computer Science Sun Yat-sen University the School of Computer Science and Information Engineering Hubei University Institute of Measurement and Control Systems Karlsruhe Institute of Technology Department of Mechanical and Mechatronics Engineering University of Waterloo the State Key Laboratory of Management and Control for Complex Systems.Institute of Automation Chinese Academy of Sciences the Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as"parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality.A deep planning model which combines a convolutional neural network(CNN) with the Long Short-Term Memory module(LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human ***, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder(VAE) and a generative adversarial network(GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.

关键词： Autonomous driving artificial traffic scene deep learning emergencies motion planning parallel planning

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Problems and Organizational Measures of Implementing and Providing the Distance Electrical Engineering Education at the University Using LMS Moodle in the Conditions of Modern Challenges in Ukraine

Problems and Organizational Measures of Implementing and Pro...

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International Conference on Modern Electrical and Energy systems (MEES)

作者： Gennadii Slavko Serhii Serhiienko Anfisa Shmelova Kateryna Kovalenko Department of Informatics and Higher Mathematics Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Poltava Region Ukraine Department of Systems of Automatic Control and Electric Drive Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Poltava Region Ukraine Department of Fundamental and Branch Juridical Sciences Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Poltava Region Ukraine Department of Translation Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Poltava Region Ukraine

ISBN: (纸本)9798350346848

The problems of implementation and provision of distance learning has been systematized, the possibilities of using and quickly reconfiguring LMS Moodle according to the needs and requirements of extreme military conditions have been investigated, software tools and organizational measures to stabilize the online education system have been considered. Guidance to LMS Moodle administrators and lecturers has been provided, Moodle plugins for creating online virtual laboratory complexes for students of electrical engineering specialties have been proposed.

关键词： Electrical engineering Computer aided instruction Education Energy measurement Software measurement Software tools Electrical engineering education

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Adaptive Smooth Disturbance Observer-Based Fast Finite-Time Attitude Tracking control of a Small Unmanned Helicopter

arXiv

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arXiv 2021年

作者： Wang, Xidong Li, Zhan Yu, Xinghu He, Zhen The Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150001 China Peng Cheng Laboratory Shenzhen518055 China Ningbo Institute of Intelligent Equipment Technology Co. Ltd. Ningbo315201 China The Department of Control Science and Engineering Harbin Institute of Technology Harbin150001 China

In this paper, a novel control scheme, which includes an improved fast finite-time adaptive backstepping controller based on a newly designed adaptive smooth disturbance observer, is presented for the attitude tracking of a small unmanned helicopter system subject to lumped disturbances. First, an adaptive smooth disturbance observer (ASDO) is proposed to approximate the lumped disturbances, which owns the characteristics of smooth output, fast finite-time convergence, and adaptability to disturbances. Then, a finite-time backstepping control protocol is constructed to achieve the attitude tracking objective. By virtue of our newly proposed inequality, a singularity-free virtual control law is designed. To tackle the "explosion of complexity" problem, a fast finite-time command filter (FFTCF) is utilized to estimate the virtual control signals and their derivatives. In addition, an auxiliary dynamic system is introduced to attenuate the impact of the errors caused by FFTCF estimation. Moreover, an adaptive law with σ-modification term is developed to compensate the ASDO approximation error. Theoretical analysis proves that all signals of the closed-loop system are fast finite-time bounded, while the attitude tracking errors fast converge to a small region of the origin in finite time. Finally, two contrastive numerical simulations are carried out to validate the effectiveness and superiority of the designed control scheme. Copyright © 2021, The Authors. All rights reserved.

关键词： Errors

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Testing of Ethernet Function of Domestically-made FPGA Chip and Acquisition Board for the Application of Protective Relaying

Testing of Ethernet Function of Domestically-made FPGA Chip ...

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2021 International Conference on Power System technology, POWERCON 2021

作者： Wang, Bin He, Xiaocai Jiang, Yingjie Lv, Xinquan Zhou, Zhimei Feng, Chen Dong, Xinzhou Tsinghua University Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electric Engineering Beijing China Communication Transmission Center Information Telecommunications Company State Grid Shandong Electeic Power Company Jinan China Chip Design Center Beijing Smartchip Microelectronics Technology CO. Ltd Beijing China Smart Shine Microelectronics Technology CO. Ltd Chip Product Department Beijing China

ISBN: (纸本)9781665407373

In order to solve the problem that the core chips of domestic protective relaying devices generally rely on foreign imported chips and realize the localized replacement of FPGAs, this paper designs the verification peripheral circuits of domestic FPGA chip through the analysis of the structure and test schemes design of domestic FPGA, including board-level verification scheme and data flow control verification scheme. The communication test and random test are carried out to the IP core of domestic FPGA MAC controller. From the test results, it can be seen that the loopback data and the transmitted data in the descriptor are consistent, and the tested domestic FPGA can be competent for its role in the application of protective relaying. © 2021 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Introducing GyroSym: a single-wheel robot

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International Journal of Dynamics and control 2020年第2期8卷 404-417页

作者： Forouhar, Moein Abedin-Nasab, Mohammad H. Liu, Guangjun School of Mechanical Engineering Sharif University of Technology Tehran11365-9567 Iran Department of Biomedical Engineering Rowan University GlassboroNJ08028 United States Systems and Control Lab Ryerson University TorontoONM5B 2K3 Canada

In this paper, GyroSym, a compact highly-stable single-wheel robot is introduced. Symmetrical flywheels have been used as its stabilization mechanism to keep the wheel upright. Thanks to the type of the flywheel used, the dynamic equations of the robot is simplified, the torque needed to tilt the spin axis is reduced, and the space is drastically minimized. Besides, a centrifugal clutch is used to smooth the motion of the robot in forward motion. Two controllers, fuzzy and fuzzy-Padé, were implemented based on their ability to stabilize the robot. Although both prevented the GyroSym from falling over, neither can entirely cancel out the effects of nonlinear behaviors that is present in the response, represented as high-frequency low domain noise. However, fuzzy-Padé demonstrated more efficient functionality, response, and mathematical simplicity which made it a perfect choice for stabilizing the GyroSym. Experimental results of a prototype have validated that of the mathematical analysis and simulations. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： controllers

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An $H_{i}/H_{\infty }$ Optimization Approach to Event-Triggered Fault Detection for Linear Discrete Time systems

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IEEE Transactions on automatic control 2020年第10期65卷 4464-4471页

作者： Maiying Zhong Steven X Ding Donghua Zhou Xiao He College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China Institute for Automatic Control and Complex Systems University of Duisburg-Essen Duisburg Germany Department of Automation Tsinghua University Beijing China

In this article, a new event-triggered H i /H ∞ optimization fault detection (FD) scheme is proposed for linear discrete time systems. A linear discrete time system with varying sampling periods is first presented to describe the dynamics of an event-triggered system. Based on this, an observer-based fault detection filter (FDF) is constructed as an event-triggered residual generator so that the generated residual signal is completely decoupled from the event-triggered transmission error. The design of event-triggered FDF is formulated as a multiobjective optimization problem denoted by H i /H ∞ and an optimal solution can be obtained by recursive calculation of Riccati equations. In addition, an event-triggered residual evaluation strategy is also presented. Comparing with the existing event-triggered FD schemes, the major contribution of this article is to decouple the residual signals from the event-triggered transmission errors completely and, consequently, the FD performance can be improved significantly. Moreover, an optimal tradeoff between the robustness of residual to unknown input and the sensitivity of residual to fault is achieved in the sense of H i /H ∞ optimization, while the design of FDF and event generator can be carried out independently. To demonstrate the effectiveness of the proposed method, a vehicle lateral dynamic system is used as a simulation example.

关键词： Optimization Fault detection Discrete-time systems Generators Robustness Sensitivity Riccati equations

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Direct adaptive fuzzy position controller for an electropneumatic actuator: Design and experimental evaluation

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Mechanical systems and Signal Processing 2021年 147卷

作者： Boubakir, Ahsene Labiod, Salim Boudjema, Fares LAJ Faculty of Science and Technology University of Jijel BP. 98 Ouled Aissa Jijel18000 Algeria LCP Department of Automatic Control National Polytechnic School Avenue Pasteur Hassen Badi BP 182 El-Harrach Algiers Algeria

This paper proposes a direct adaptive fuzzy position controller for an electropneumatic actuator. This control approach is based on the estimation of an ideal inverse dynamic control, which is capable of satisfying the control objectives in terms of tracking desired trajectories and stability. For this purpose, a fuzzy system is used to approximate as best as possible this ideal inverse dynamic control. The adjustable parameters of the used fuzzy system are updated online with a stable adaptation law. This latter is developed in order to minimize the error between the unknown ideal inverse dynamic control and the used fuzzy controller. The general non-affine model of the controlled actuator is used to develop the control law and its adaptation mechanism, meaning that the presented adaptive fuzzy position controller does not rely on a physical model of the pneumatic actuator. The stability of the closed-loop system is performed using a Lyapunov approach. Finally, real-time experimental results are presented to show the performances of the proposed position controller. © 2020 Elsevier Ltd

关键词： Fuzzy systems

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Attack prevention in IoT through hybrid optimization mechanism and deep learning framework

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Measurement: Sensors 2022年 24卷

作者： Nagaraju, Regonda Pentang, Jupeth Toriano Abdufattokhov, Shokhjakhon CosioBorda, Ricardo Fernando Mageswari, N. Uganya, G. Department of Information Technology St.Martin's Engineering College Dhulapally Secunderabad500100 India Western Philippines University Philippines Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Universidad Autónoma del Perú Lima Peru Department of ECE Ashoka Women's Engineering College Andhra Pradesh Kurnool India Department of ECE Saveetha School of Engineering SIMATS Assistant Professor Chennai India

The Internet of Things (IoT) connects schemes, programs, data management, and operations, and as they continuously assist in the corporation, they may be a fresh entryway for cyber-attacks. Presently, illegal downloading and virus attacks pose significant threats to IoT security. These risks may acquire confidential material, causing reputational and financial harm. In this paper hybrid optimization mechanism and deep learning,a frame is used to detect the attack prevention in IoT. To develop a cybersecurity warning system in a huge data set, the cybersecurity warning systems index system is first constructed, then the index factors are picked and measured, and finally, the situation evaluation is *** bio-inspired techniques were used to enhance the productivity of an IDS by lowering the data dimensionality and deleting unnecessary and noisy input. The Grey Wolf Optimization algorithm (GWO) is a developed bio-inspired algorithm that improves the efficacy of the IDS in detecting both regular and abnormal congestion in the network. The smart initialization step integrates the different pre-processing strategies to make sure that informative features are incorporated in the early development stages, has been improved. Researchers pick multi-source material in a big data environment for the identification and verification of index components and present a parallel reduction approach based on the classification significance matrix to decrease data underlying data characteristics. For the simulation of this situation, grey wolf optimization and whale optimization were combined to detect the attack prevention and the deep learning approach was presented. Utilizing system software plagiarism, the TensorFlow deep neural network is intended to classify stolen software. To reduce the noise from the signal and to zoom the significance of each word in the perspective of open-source plagiarism, the tokenization and weighting feature approaches are utilized. Malware spec

关键词： Internet of things

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Load Frequency control of Hybrid Power System Using Marine Predator Algorithm Tuned Cascaded controller

Load Frequency Control of Hybrid Power System Using Marine P...

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International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Muhannnad Inshal Shahzad Muhammad Majid Gulzar Aqsa Shahzad Ahlam Jameel Adnan Shakoor Salman Habib Department of Control & Instrumentation Engineering Dhahran Saudi Arabia Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES) Dhahran Saudi Arabia Comsats Institute of Information Technology Abbottabad Pakistan Department of Electrical Engineering Lahore College for Women University Lahore Pakistan Interdisciplinary Research Center for Biosystems and Machines (IRC-BSM) Dhahran Saudi Arabia Interdisciplinary Research Center for Smart Mobility and Logistics (IRC-SML) King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

ISBN: (数字)9798350375589

ISBN: (纸本)9798350375596

The load frequency control (LFC) problem in multi-area power systems is very common whenFtable RES are added to the power grid because of their irregular behavior. Renewable energy combined with a thermal generator makes a multi-area power system. As renewable sources are not stable, that's why they are attached to conventional sources to complete the load demand. This paper implemented a sophisticated control technique known as the Marine Predator Algorithm (MPA) in conjunction with PID n for LFC of the interconnected power system. By using the MPA control technique, their behavior to locate and capture prey helps to effectively encounter frequency fluctuations in the power system. Different controllers, like GA-PI, FFA-PI, and ABC-PID n , have been analyzed in this study and compared with the proposed technique to show the superiority of the proposed technique.

关键词： Hands Renewable energy sources Accuracy Systematics Sensitivity Generators Power grids Thermal loading Frequency control Convergence

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