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2021 International Conference on System science and engineering, ICSSE 2021

作者： Dinh, Kien Cuong Dao, Ngoc Sang Le, Hai Dang Le, Hoang Lam Cam, Tu Duong Thi Tran, Duc Thien Ho Chi Minh City University of Technology and Education Robotic and Intelligent Control Lab Faculty of Electrical and Electronics Engineering Ho Chi Minh Viet Nam Ho Chi Minh City University of Technology and Education Faculty of Electrical and Electronics Engineering Ho Chi Minh Viet Nam School of Engineering Department of Electrical and Computer Science WashingtonDC20064 United States

ISBN: (纸本)9781665448482

This paper presents a research about kinematics, dynamics for a robotic closed-chain mechanism. The mechanical structure, kinematics, and Lagrange formulation for a dynamic affair of the parallel robot 4-DOF are described. A MATLAB simulation model of the robot is built and utilized for each stage of identification. Besides, comparing the output response with the input signal will support in determining the error factor of the parallel robot. Results show that the movement equations of the robot are really complex and have certain constraints in the calculation;therefore, a combination of different method is implemented for the parallel robot algorithm. © 2021 IEEE.

关键词： Inverse kinematics

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TranDRL: A Transformer-Driven Deep Reinforcement Learning Enabled Prescriptive Maintenance Framework

arXiv

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arXiv 2023年

作者： Zhao, Yang Yang, Jiaxi Wang, Wenbo Yang, Helin Niyato, Dusit 2 Fusionopolis Way Innovis #08-04 Singapore138634 Singapore Shenzhen Institute for Advanced Study School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Mechanical and Electrical Engineering Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology Kunming650050 China Department of Information and Communication Engineering Xiamen University Xiamen361005 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

Industrial systems require reliable predictive maintenance strategies to enhance operational efficiency and reduce downtime. Existing studies rely on heuristic models which may struggle to capture complex temporal dependencies. This paper introduces an integrated framework that leverages the capabilities of the Transformer and Deep Reinforcement Learning (DRL) algorithms to optimize system maintenance actions. Our approach employs the Transformer model to effectively capture complex temporal patterns in IoT sensor data, thus accurately predicting the Remaining Useful Life (RUL) of equipment. Additionally, the DRL component of our framework provides cost-effective and timely maintenance recommendations. Numerous experiments conducted on the NASA C-MPASS dataset demonstrate that our approach has a performance similar to the ground-truth results and could be obviously better than the baseline methods in terms of RUL prediction accuracy as the time cycle increases. Additionally, experimental results demonstrate the effectiveness of optimizing maintenance actions. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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A Unified Formulation of Geometry-aware Discrete Dynamic Movement Primitives

arXiv

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arXiv 2022年

作者： Abu-Dakka, Fares J. Saveriano, Matteo Kyrki, Ville Electronic and Computer Science Department Faculty of Engineering Mondragon Unibertsitatea Arrasate20500 Spain Automatic Control Lab Department of Industrial Engineering University of Trento Trento Italy Intelligent Robotics Group Department of Electrical Engineering and Automation Aalto University Aalto Finland

Learning from demonstration (LfD) is considered as an efficient way to transfer skills from humans to robots. Traditionally, LfD has been used to transfer Cartesian and joint positions and forces from human demonstrations. The traditional approach works well for some robotic tasks, but for many tasks of interest, it is necessary to learn skills such as orientation, impedance, and/or manipulability that have specific geometric characteristics. An effective encoding of such skills can be only achieved if the underlying geometric structure of the skill manifold is considered and the constrains arising from this structure are fulfilled during both learning and execution. However, typical learned skill models such as dynamic movement primitives (DMPs) are limited to Euclidean data and fail in correctly embedding quantities with geometric constraints. In this paper, we propose a novel and mathematically principled framework that uses concepts from Riemannian geometry to allow DMPs to properly embed geometric constrains. The resulting DMP formulation can deal with data sampled from any Riemannian manifold including, but not limited to, unit quaternions and symmetric and positive definite matrices. The proposed approach has been extensively evaluated both on simulated data and real robot experiments. The performed evaluation demonstrates that beneficial properties of DMPs, such as convergence to a given goal and the possibility to change the goal during operation, apply also to the proposed formulation. © 2022, CC BY-NC-ND.

关键词： Demonstrations

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2D-DOA Estimation for Polarized Massive Antenna Array: Geometric and Algorithmic Guidelines

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IEEE Transactions on Vehicular Technology 2025年

作者： Wen, Fangqing Wang, Han Gui, Guan Yuen, Chau Sari, Hikmet Adachi, Fumiyuki China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Yichang443002 China Hubei University of Automotive Technology Institute of Vehicle Information Control and Network Technology Shiyan442002 China Yichun University College of Physical Sciences and Engineering Technology Yichun336000 China City University of Macau Faculty of Data Science China Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing210003 China Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore Sendai980-8572 Japan

The utilization of an Electromagnetic Vector Sensor (EMVS) array presents valuable insights in wireless communications and radars by incorporating the advantages of spatial, temporal, and polarization diversities. This study focuses on addressing the problem of estimating the Two-Dimensional (2D) Direction-of-Arrival (DOA) for an arbitrarily spaced EMVS array. Firstly, the Cramer-Rao Bounds (CRB) for estimating the 2D-DOA and polarized parameters are derived, which provides design guideline for array geometry. As suggested by the CRB, a sparse planar EMVS array is a more appealing choice compared to the popular coprime EMVS array. Thereafter, a Parallel Factor (PARAFAC)-based weighted phase fitting algorithm is presented, designed to fully explore the aperture of the conformal array as well as capitalize on the tensorial structure inherent in array data. The proposed PARAFAC algorithm not only provides a closed-form solution for conformal arrays but also exhibits superior accuracy in 2D-DOA estimation compared to existing approaches. Numerical results corroborate the theoretical advantages of the proposed array geometry and PARAFAC algorithm. © 1967-2012 IEEE.

关键词： Cramer-Rao bounds

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Distributed formation control of networked mechanical systems

arXiv

引用

arXiv 2022年

作者： Javanmardi, Najmeh Borja, P. Yazdanpanah, M.J. Scherpen, J.M.A. Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Cognitive Robotics Delft University of Technology Delft Netherlands Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen Netherlands

This paper investigates a distributed formation tracking control law for large-scale networks of mechanical systems. In particular, the formation network is represented by a directed communication graph with leaders and followers, where each agent is described as a port-Hamiltonian system with a constant mass matrix. Moreover, we adopt a distributed parameter approach to prove the scalable asymptotic stability of the network formation, i.e., the scalability with respect to the network size and the specific formation preservation. A simulation case illustrates the effectiveness of the proposed control approach. © 2022, CC BY.

关键词： Networked control systems

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Spirometer and sEMG respiratory patterns for clinical decision support system

Spirometer and sEMG respiratory patterns for clinical decisi...

引用

IEEE Instrumentation and Measurement Technology Conference

作者： Amit Krishan Kumar Mansour H. Assaf Voicu Z. Groza Emil M. Petriu Faculty of Electrical-Electronic Engineering Duy Tan University Da Nang Vietnam Key Laboratory of Complex System Intelligent Control and Decision School of Automation Beijing Institute of Technology Beijing China School of Information Technology Engineering Mathematics and Physics The University of the South Pacific Suva Republic of Fiji School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

To improve support system for subjects with respiratory ailments, this work analyses the respiratory patterns from spirometer and surface electromyography (sEMG). The patterns are integrated with risk and biochemical systems model to optimise the clinical decision support system (CDSS). The work considers terminal events for the spirometer and sEMG. The events and their associated patterns are correlated and projected on a hyperspace prior implementing it in decision support system. The proposed method shows that the different characteristics of breathing indicate some essential hidden information which when used as 4D scattered projection, optimises the decision making in CDSS. The analysis shows the effectiveness of bimodal respiratory patterns for different breathing capacities, which is promising for the modelled conditions.

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Recent Development, Trends and Challenges in IoT Security 7th

Recent Development, Trends and Challenges in IoT Security

引用

7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Talezari, Morteza Tu, Shanshan Rehman, Sadaqat ur Chuangbai, Xiao Ahmad, Basharat Waqas, Muhammad Rehman, Obaid ur Research Center of Intelligent Perception and Autonomous Control Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Namal Institute Mianwali42250 Pakistan Department of Electronic Engineering Tsinghua University Beijing China Institute of Engineering Sciences and Technology Topi23460 Pakistan Department of EE Sarhad University of Science and IT Peshawar Pakistan

ISBN: (纸本)9783030786113

A large amount of data that IoT applications deal, is users’ Private data, and their privacy is a significant issue. However, if users are not sure about the security of their data, they will not desire to use such applications. The goal of this study is to improve the security and privacy of users in the Internet of Things (IoTs) setting. Since, the application of IoT technology is growing day by day. Also, the importance of users’ data security is always top of the list and difficult to achieve. In this study, we studied latest development in the security of Internet of Things (IoTs) so that we can use it to investigate its challenges and advantages. © 2021, Springer Nature Switzerland AG.

关键词： Internet of things

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Nearest-Better Network for Fitness Landscape Analysis of Continuous Optimization Problems

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IEEE Transactions on Evolutionary Computation 2024年

作者： Diao, Yiya Li, Changhe Zeng, Sanyou Yang, Shengxiang Coello, Carlos A. Coello China University of Geosciences School of Automation Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Anhui University of Sciences and Technology School of Artificial Intelligence Hefei China China University of Geosciences School of Mechanical Engineering and Electronic Information Wuhan China De Montfort University School of Computer Science and Informatics LeicesterLE1 9BH United Kingdom Department of Computer Science CINVESTAV-IPN 07360 Mexico Tecnologico de Monterrey Faculty of Excellence The School of Engineering and Sciences N.L. Monterrey Mexico

Fitness landscape analysis (FLA) is quite important in evolutionary computation. In this paper, we propose a novel FLA method, the nearest-better network (NBN), which uses the nearest-better relationship to simplify the original fitness landscape of continuous optimization problems. We introduce an efficient algorithm to calculate NBN for continuous problems. We also propose four numerical measurements and a 3D visualization method based on NBN. Experiments show that compared to the other main FLA methods, the four numerical measurements proposed here can effectively measure the four intended features: neutrality, ruggedness, modality, and basin of attraction, respectively, and common features of the fitness landscape can be maintained in 3D NBN visualization, regardless of the scale of the problem. NBN also provides a view of how algorithms search in high-dimensional problems with the help of the 3D NBN visualization. © 1997-2012 IEEE.

关键词： Optimization algorithms

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Automated Door to Prevent COVID-19 using Fuzzy Logic

Automated Door to Prevent COVID-19 using Fuzzy Logic

引用

International Conference on Electrical, Communication, and Computer engineering (ICECCE)

作者： Salah-Ud-Din Khokhar Rana Sohaib Abdul Muneeb Irfana Bibi Muhammad Yasir Noor Muhammad Imran School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan P.R.China Department of Computer Science G.C. University Lahore Lahore Pakistan Department of Computer Science Faculty of Computing and Information Technology University of Punjab Lahore Pakistan Department of Electronics G.C. University Lahore Lahore Pakistan

Due to the vagueness and uncertainties due to the coronavirus, it is crucial to implement the standard operating procedures (SOPs) issued by the World Health Organization and the authorities. Therefore an effective control system is required that minimizes the vagueness and uncertainties of people who are on the front line during the period of this pandemic checking the entering persons in an indoor area where it is necessary to maintain a distance of six feet from one another to avoid the spread of coronavirus. In this research work, a robust and efficient system has been proposed that takes protective mask and feverishness values as input and applies them using the fuzzy logic algorithm. If these input values are according to the rules defined in the system, then the door will open as an output. Otherwise, the door will not open. It is, finally, analyzing the output values. This fuzzy model had been implemented using a microcontroller. The genuine difference in the duty cycle of the microcontroller’s output pulse was measured using a 100 MHz digital oscilloscope. Our proposed system was compared with different systems reported in the literature. This work reduces the human effort as well as the negligence of people and builds up the management system without the labor in a mint.

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A DDPG Hybrid with Graph Scheme for Resource Management in Digital Twin-assisted Biomedical Cyber-Physical Systems

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IEEE Transactions on Network and Service Management 2025年

作者： Xu, Yi-Han Ding, Rong-Xing Xu, Xiao-Ren Zhou, Ding Zhou, Wen Qinghai University School of Computer Technology and Application Xining810016 China Nanjing Forestry University College of Information Science and Technology and Artificial Intelligence Nanjing210037 China The University of New South Wales School of Electrical Engineering and Telecommunications Sydney2052 Australia Universiti Malaya Faculty of Engineering Kuala Lumpur50603 Malaysia Guangzhou Maritime University College of Low Altitude Equipment and Intelligent Control Guangzhou510725 China

In this paper, we focus on the resource optimization issue in Wireless Body Area Networks (WBANs) section of a novel Biomedical Cyber Physical System (BM-CPS) in which the Digital Twin (DT) technique is adopted concurrently. We propose a scenario where multiple physiological sensor nodes continuously monitor the electrophysiology signals from patients and connect to a Cyber Managing Center (Cyb-MC) in a wireless way to transmit the physiological indices to the paramedic reliably. Specifically, to optimize the energy efficiency of physiological sensors while ensuring the reliability of emergency-critical electrophysiology signals transmissions and modeling the uncertain stochastic environments, the optimization issue is transformed into a Markov Decision Process (MDP) in terms of joint transmission mode, relay assignment, transmission power and time slot scheduling consideration. Subsequently, we propose a Random Graph-enabled Deep Deterministic Policy Gradient (RG-DDPG) scheme to tackle the challenge while releasing computing complexity. In addition, as an innovative paradigm for reshaping cyberspace applications, the DT of WBAN is created to capture the time-varying resource status, where virtualized and intelligent resource management can be performed uniformly. Finally, extensive simulation studies verified the advancement of the proposed scheme and demonstrated that the DT can mirror the topology, predict the behavior, and administrate the resources of the WBANs. © 2004-2012 IEEE.

关键词： Resource allocation

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