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Iranian Conference on Electrical engineering (ICEE)

作者： Arash Iranfar Mohammad Soleymannejad Behzad Moshiri Hamid D. Taghirad School of Electrical and Computer Engineering University College of Engineering University of Tehran Tehran Iran Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Advanced Robotics and Automated Systems (ARAS) Industrial Control Center of Excellence Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran

Artificial intelligence systems are usually implemented either using machine learning or expert systems. Machine learning methods are usually more accurate and applicable to a broader range of applications. Expert systems, on the other hand, require much less data for training and generate more comprehensible results. These characteristics are typically desired in the fields of surgery and medicine because there isn't much data available. In order to give a machine's decisions a deeper level of semantics, it is also advantageous to incorporate a doctor's expertise into it. Furthermore, it is safer to understand the reasoning behind a machine's choices. In this paper, a Dempster-Shafer Theory (DST) based expert system is suggested for the task of surgical training skill assessment. An interval-based probabilistic feature analysis was applied to the data to assign values to the mass functions. Zhang's rule of combination was applied to handle the conflicting evidence in the prediction phase. The performance of the proposed method was compared to another DST classifier, SVM, and XGBoost. Our method outperforms SVM and other DST classifiers, but it is not as precise as XGBoost. By reducing the size of the dataset, the added benefit of using an expert system as opposed to a machine learning method was explored further. The performance of the suggested method is not adversely affected by the size of the dataset, whereas the XGBoost classifier is.

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Sustainability and Predictive Accuracy Evaluation of Gel and Embroidered Electrodes for ECG Monitoring

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SSRN 2024年

作者： Othman, Ghada Ben Kumar, Atal Anil Hassine, Faten Ben Copot, Dana Sidhom, Lilia Kamavuako, Ernest N. Trabelsi, Mohamed Ionescu, Clara Mihaela Chihi, Inès Ghent University Department of Electromechanics Systems and Metal Engineering Research Group on Dynamical Systems and Control Tech Lane Science Park 125 Zwijnaarde9052 Belgium Department Of Engineering Faculty of Science Technology and Medicine University of Luxembourg Kirchberg Luxembourg Faculty of Sciences of Tunis Tunis Tunisia National Engineering School of Bizerte Carthage University Tunis LAPER- Faculty of Sciences of Tunis El Manar University Tunis Tunisia Technical University of Cluj-Napoca Department of Automation Memorandumului no. 28 Cluj-Napoca400114 Romania Department of Informatics King’s College London London United Kingdom Electronics and Communications Engineering Department Kuwait College of Science and Technology Kuwait City Kuwait

Despite standards on electrocardiogram (ECG) monitoring in medical diagnostics, signal acquisition is prone to noisy artefacts and rely greatly on the quality of skin contact and signal transducing interference. Electrodes, serving as indispensable conduits in ECG signal acquisition, act as the crucial interface between the human body and recording instrumentation. The usage of traditional gel electrodes may provoke skin irritation, by contrast, the advent of embroidered electrodes, a contemporary innovation, holds the promise of more comfort monitoring. However, the sustainability quotient and predictive precision of these novel electrodes require more in-depth investigation. This paper endeavors a comprehensive evaluation of both gel and embroidered electrodes concerning ECG signal acquisition. Simultaneously, the study aims to construct a polynomial regression model leveraging advanced machine learning (ML) tools. The inferred model predicts embroidered signals based on experimental data obtained from gel electrodes. The methodology encompasses systematic data collection, preprocessing, insightful analysis, and the application of data-driven techniques. The findings of this study highlight the viability of embroidered electrodes as a compelling alternative, transcending traditional paradigms. Employing ML tools, the developed model achieves predictive accuracy, reflected in robust R2 values extending up to 94.9%. © 2024, The Authors. All rights reserved.

关键词： Electrocardiograms

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Prescribed-Time Tracking control for Robotic systems with Uncertain Dynamics

Prescribed-Time Tracking Control for Robotic Systems with Un...

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Data Driven control and Learning systems (DDCLS)

作者： Hang Yang Yingbo Huang Jing Na Qiang Chen Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming China Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming China Data-Driven Intelligent Systems Laboratory College of Information Engineering Zhejiang University of Technology Hangzhou China

This paper presents a prescribed-time tracking control scheme for robotic systems with unknown dynamics. One salient feature is that the robotic systems can achieve prescribed-time stability with the prescribed transient performance, which is different with the traditional work that achieves uniformly ultimately bounded (UUB) and/or asymptotic stability. To realize this purpose, the prescribed performance dynamics (PPD) instead of the traditional prescribed performance function (PPF) is suggested, by which both the transient and steady-state tracking performance can be guaranteed within a specified region in-priori. To avoid using the function approximators (i.e., neural networks (NNs), fuzzy logic systems (FLSs)), an approximation-free-based prescribed-time controller is established with the inertial matrix of robotic systems and the finite-time function merely, which can not only address the system unknown uncertainties but also regulate the control error to zero in a prescribed-time. Furthermore, the Lyapunov-based theoretical analysis is conducted to prove the prescribed-time stability of the closed-loop system. Finally, comparative numerical simulation results are provided to demonstrate the effectiveness of the proposed method.

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Knowledge Graph Completion with Low-Dimensional Gated Hierarchical Hyperbolic Embedding

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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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Graph-Based Visual-Kinematic Fusion and Monte Carlo Initialization for Fast-Deployable Cable-Driven Robots

Graph-Based Visual-Kinematic Fusion and Monte Carlo Initiali...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： R. Khorrambakht H. Damirchi M.R. Dindarloo A. Saki S.A. Khalilpour Hamid D. Taghirad Stephan Weiss Electrical & Computer Engineering Department Control/Robotics Research Laboratory Tandon School of Engineering New York University Brooklyn NY School of Computer Science University of Adelaide Australia Faculty of Electrical Engineering University of Technology Tehran Iran Department of Smart Systems Technologies University of Klagenfurt Austria

Ease of calibration and high-accuracy task-space state-estimation purely based on onboard sensors is a key requirement for enabling easily deployable cable robots in real-world applications. In this work, we incorporate the onboard camera and kinematic sensors to drive a statistical fusion framework that presents a unified localization and calibration system which requires no initial values for the kinematic parameters. This is achieved by formulating a Monte-Carlo algorithm that initializes a factor-graph representation of the calibration and localization problem. With this, we are able to jointly identify both the kinematic parameters and the visual odometry scale alongside their corresponding uncertainties. We demonstrate the practical applicability of the framework using our state-estimation dataset recorded with the ARAS-CAM suspended cable driven parallel robot, and published as part of this manuscript.

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Robust Reinforcement Learning-based Vehicle control with Object Avoidance

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IFAC-PapersOnLine 2024年第10期58卷 134-139页

作者： Attila Lelkó Balázs Németh András Mihály Olivier Sename Péter Gáspár HUN-REN Institute for Computer Science and Control Kende u. 13-17 H-1111 Budapest Hungary Department of Control for Transportation and Vehicle Systems Faculty of Transportation Engineering and Vehicle Engineering Budapest University of Technology and Economics Műegyetem rkp. 3. H-1111 Budapest Hungary Grenoble Institute of Technology 621 avenue Centrale 38400 Saint-Martin-d’Héres France

This paper presents a control design framework for robust reinforcement learning (RL) based vehicle control enhanced with object avoidance using a model predictive approach. The proposed integration method is applied for motion control of autonomous road vehicles. In the motion control, longitudinal and lateral dynamics are incorporated, and the high-performance motion of the vehicle, through the RL-based control agent is achieved. ’H. co based robust control is used as a baseline parallel to the RL-agent to ensure stable operation. The object avoidance and limited error path following are achieved using a model-based predictive algorithm. Finally, the control signals of the three controllers are combined using a supervisor layer, which solves a constrained optimization task to ensure high performance and safe motion. The effectiveness of the proposed control method using simulation scenarios is illustrated.

关键词： Autonomous Vehicles Robust control Motion control Reinforcement learning control Learning adaptation in autonomous vehicles

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Advancing Africa's Sustainable Energy Development Through Small Modular Reactors (SMRs) as Low-Carbon Power Solution

Advancing Africa's Sustainable Energy Development Through Sm...

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IEEE International Conference on Smart Energy Grid engineering (SEGE)

作者： Mark Amoah Nyasapoh Samuel Gyamfi Seth Kofi Debrah Hossam A. Gabber Nana Sarfo Agyemang Derkyi Department of Renewable Energy Engineering School of Energy & Regional Centre of Energy and Environmental Sustainability (RCEES) - University of Energy and Natural Resources (UENR) Sunyani Ghana Nuclear Power Institute Ghana Atomic Energy Commission Accra Ghana Department of Nuclear Engineering School of Nuclear and Allied Sciences University of Ghana - Legon Accra Ghana Smart Energy Systems Lab (SESL) and Advanced Plasma Engineering Lab (APEL) Faculty of Energy Systems and Nuclear Science and Faculty of Engineering and Applied Science Ontario Tech University Ontario Canada Department of Energy and Petroleum Engineering School of Energy University of Energy and Natural Resources (UENR) Sunyani Ghana

ISBN: (数字)9798350377378

ISBN: (纸本)9798350377385

Access to affordable, reliable, and clean energy is vital for achieving the United Nations' Sustainable Development Goals. In Africa, where about 79% of electricity comes from fossil fuels, transitioning to low-carbon systems is crucial to limit global warming and reduce greenhouse gas emissions. However, the reliance on intermittent renewables, like solar and wind poses challenges due to their variable supply. This paper explores the role of small modular reactors (SMRs) in advancing sustainable energy development in Africa. SMRs are advanced nuclear reactors with capacities of up to 300 MW(e) per unit, offering a promising alternative to fossil fuel gensets, particularly for underserved communities. The paper examines key issues in Africa's energy sector and highlights SMRs as a viable solution. The paper reveals that SMRs, with their modular design, efficiency, flexibility, safety features, and extended fuel cycles, can bridge the energy gap in remote and rural African communities. Thus, integrating SMRs with existing grids or operating off-grid, can provide electricity to those currently lacking basic services. Combining SMRs with solar and wind energy systems could enhance universal energy access, thereby, supporting the ambitious sustainable energy future. Therefore, advancing Africa's sustainable energy development through SMRs is essential for achieving low-carbon power generation.

关键词： Wind energy Electricity Africa Fossil fuels Smart grids Safety Reliability Inductors Sustainable development Power generation

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Flexible control of the Interline DC Power Flow controller Using Virtual Capacitor in VSC-HVDC Grids

Flexible Control of the Interline DC Power Flow Controller U...

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Power Electronic and Drive systems and Technologies Conference (PEDSTC)

作者： Mirhamed Pourmirasghariyan Mirsajed Pourmirasghariyan Seyed Saeid Heidari Yazdi Seyyed Yousef Mousazadeh Mousavi Kumars Rouzbehi G. B. Gharehpetian Departement of Electrical and Computer Engineering Amirkabir University of Technology Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Electrical & Computer Engineering School of Engineering and Digital Science Nazarbayev University Nur-Sultan Kazakhstan Faculty of Engineering and Technology University of Mazandaran Sari Iran Department of Systems and Automatic Control Engineering University of Seville Seville Spain

The performance of Interline DC Power Flow controllers (IDC-PFCs) in Voltage Source Converters (VSC)-based High Voltage Direct Current (HVDC) grids, can be affected due to different issues. The current limitation of HVDC lines, the voltage limitation of HVDC buses, and DC voltage of the IDC-PFC intermediary capacitor prevent effective and efficient operation of IDC-PFCs. In this paper, it is shown that this issue can be overcome by using a virtual capacitor in parallel with the IDC-PFC intermediary capacitor. Also, an energy control-based scheme is proposed for the operation of IDC-PFCs in VSC-HVDC grid. The benefits of using the virtual capacitor are: widening the operational area of the IDC-PFCs for the determined duty cycle and injecting more voltage in series to the interconnected HVDC line to control the related HVDC line current. The proposed solution is successfully evaluated on a CIGRE three-terminal VSC-HVDC grid which is modeled by linearized space-state equations.

关键词： Fluctuations HVDC transmission Capacitors High-voltage techniques Boosting Mathematical models Power conversion

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Exchange Strategies for Multi-Colony Ant Algorithms in Dynamic Environments

Exchange Strategies for Multi-Colony Ant Algorithms in Dynam...

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Congress on Evolutionary Computation

作者： Michalis Mavrovouniotis Changhe Li Danial Yazdani Diofantos Handjimitsis ERATOSTHENES Centre of Excellence Limassol Cyprus Department of Civil Engineering and Geomatics Cyprus University of Technology Limassol Cyprus Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation China University of Geosciences Wuhan China Faculty of Engineering & Information Technology University of Technology Sydney Sydney Australia

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

In dynamic optimization problems where optimal solutions change over time, traditional ant colony optimization (ACO) algorithms face limitations. This study explores the adaptation of multi-colony ACO algorithms, known for their enhanced search capabilities in stationary problems, to tackle optimization problems in dynamic environments. Various strategies for exchanging information between colonies, which is a critical factor influencing algorithm performance, are investigated. Using the dynamic traveling salesman problem as a foundation, we generate test cases to reflect real-world complexities. Our results on a set of problem instances reveal that the choice of communication strategy between colonies significantly impacts the adaptability and efficiency of multi-colony ACO algorithms in tracking moving optimum.

关键词： Schedules Satellites Processor scheduling Heuristic algorithms Evolutionary computation Traveling salesman problems Search problems

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Application of probabilistic models to material strength, structural strength and disaster occurrence

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Zairyo/Journal of the Society of Materials science, Japan 2021年第11期70卷 861-867页

作者： Nakamura, Yuki Ito, Tsutomu Kuroda, Masatoshi Sakaida, Akiyoshi Matsuda, Shinya Matsumura, Takashi Sakai, Tatsuo Department of Mechanical Engineering National Institute of Technology Toyota College Eisei-cho Toyota471-8525 Japan Department of Mechanical Systems Engineering Toyama Prefectural University Kurokawa Imizu 939-0398 Japan Faculty of Advanced Science and Technology Kumamoto University Chuo-ku Kumamoto860-8555 Japan Department of Mechanical Engineering National Institute of Technology Akashi College Uozumi-dho Akashi674-8501 Japan Department of Engineering and Design Kagawa University Hayashi-cho Takamatsu761-0396 Japan Department of Mechanical and Intelligent Systems Engineering University of Electro-Communications Chofugaoka Chofu182-8585 Japan Research Organization of Science and Technology Ritsumeikan University Kusatsu 525-8577 Japan

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