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Multi-Objective Optimization of Pi Controller for Bldc Motor Speed Control and Energy Saving in Electric Vehicles: A Constrained Swarm-Based Approach

SSRN

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SSRN 2023年

作者： Naqvi, Syed Shehryar Ali Jamil, Harun Iqbal, Naeem Khan, Salabat Lee, Dong-In Park, Youn Cheol Kim, Do Hyeun Department of Electronics Engineering Jeju National University Jeju-do Jeju63243 Korea Republic of School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Queensgate BelfastBT7 1NN United Kingdom Big Data Research Center Jeju National University Jeju-do Jeju63243 Korea Republic of Department of Information Communication Engineering Yeungnam University Daehak-ro Gyeongsangbuk-do 280 Korea Republic of Department of Mechanical System Engineering Jeju National University Jeju-do Jeju63243 Korea Republic of Department of Computer Engineering Jeju National University Jeju-do Jeju63243 Korea Republic of

Proportional Integral Derivative (PID) controllers are widely employed in industrial applications due to their effectiveness, simplicity, and versatility. Within the automotive sector, one notable application of the Proportional Integral (PI) controller is in the realm of electric motors, particularly the Brushless Direct Current (BLDC) motors powering Electric Vehicles (EVs). The precision of speed control over BLDC motors, renowned for their efficiency and reliability, is paramount for optimizing vehicle performance and ensuring energy efficiency. PID’s need to be tuned each time they are used in an application to optimize its performance. A well-known empirical hit and trial tuning strategy is the Ziegler-Nichols method, which, however, results in suboptimal performance in the presence of local optimal solutions of large-dimensional search spaces. Existing Artificial Intelligence methods tailored for PID controller tuning often focus on singular optimization aspects, thereby neglecting potential performance gains achievable through multi-objective considerations. Furthermore, prevalent swarm based PID optimization methods typically initialize the population randomly without imposing any bounds on input parameters, thus rendering them susceptible to inconsistent, unreliable sub-optimal solutions in unconstrained search environments. In our research endeavors, we mathematically model the multi-objective optimization of BLDC motor speed control, energy usage and efficiency using constrained Differential Evolution and Particle Swarm Optimized PID controllers. The Ziegler- Nichols tuning method is used to ascertain the initial PI parameters and define bounds within the input space. The results underscore the superior performance of the proposed approach for excellent motor speed control and at the same time minimizing energy consumption for enhancing overall energy efficiency in electric vehicles in comparison to the benchmark methods. © 2023, The Authors. All rights

关键词： Three term control systems

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Investigation of traffic radar coverage efficiency under different placement strategies

Investigation of traffic radar coverage efficiency under dif...

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International Conference on Internet of Things 2024 (ICIoT 2024)

作者： Shun Yao Jin Zhou David Chieng Chiew Foong Kwong Boon Giin Lee Jianqiang Li Yusen Chen Next-Generation Internet of Everything Laboratory Department of Electrical and Electronic Engineering University of Nottingham Ningbo People's Republic of China School of Computer Science University of Nottingham Ningbo People's Republic of China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen People's Republic of China Zhejiang Communication Investment Group Co. Ltd. Zhejiang People's Republic of China

ISBN: (数字)9781837242177

This paper presents a comprehensive investigation of traffic radar coverage efficiency under different placement strategies in collaboration with Zhejiang communications Investment Group Company Limited (CICO). The overall goal is to maximise coverage while minimising blind spots and the number of radars, given the constraints of the road. The NSGA-II multi-objective optimisation algorithm is found to be capable of generating a set of optimal solutions for the proposed radar placement configurations. When aiming for 100% coverage, the results indicate that a multi-antenna radar configuration requires the lowest radar density. Overall, this study provides valuable insights to transportation infrastructure owners in determining the best radar placement strategies for road traffic monitoring.

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Adaptive Information Bottleneck Guided Joint Source and Channel Coding for Image Transmission

arXiv

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arXiv 2022年

作者： Sun, Lunan Yang, Yang Chen, Mingzhe Guo, Caili Saad, Walid Poor, H. Vincent The Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Beijing Laboratory of Advanced Information Net works School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Wireless@VT Group Bradley Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA United States The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Joint source and channel coding (JSCC) for image transmission has attracted increasing attention due to its robustness and high efficiency. However, the existing deep JSCC research mainly focuses on minimizing the distortion between the transmitted and received information under a fixed number of available channels. Therefore, the transmitted rate may be far more than its required minimum value. In this paper, an adaptive information bottleneck (IB) guided joint source and channel coding (AIB-JSCC) method is proposed for image transmission. The goal of AIB-JSCC is to reduce the transmission rate while improving the image reconstruction quality. In particular, a new IB objective for image transmission is proposed so as to minimize the distortion and the transmission rate. A mathematically tractable lower bound on the proposed objective is derived, and then, adopted as the loss function of AIB-JSCC. To trade off compression and reconstruction quality, an adaptive algorithm is proposed to adjust the hyperparameter of the proposed loss function dynamically according to the distortion during the training. Experimental results show that AIB-JSCC can significantly reduce the required amount of transmitted data and improve the reconstruction quality and downstream task accuracy. Copyright © 2022, The Authors. All rights reserved.

关键词： Economic and social effects

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Dynamic Resource Management in Integrated NOMA Terrestrial-Satellite Networks using Multi-Agent Reinforcement Learning

arXiv

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arXiv 2023年

作者： Nauman, Ali Alshahrani, Haya Mesfer Nemri, Nadhem Othman, Kamal M. Aljehane, Nojood O. Maashi, Mashael Dutta, Ashit Kumar Assiri, Mohammed Khan, Wali Ullah Department of Information and Communication Engineering Yeungnam University Korea Republic of Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems College of Science & Art at Mahayil King Khalid University Saudi Arabia Department of Electrical Engineering College of Engineering Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Department of Software Engineering College of Computer and Information Sciences King Saud University Po Box 103786 Riyadh11543 Saudi Arabia Department of Computer Science and Information System College of Applied Sciences AlMaarefa University Riyadh11597 Saudi Arabia Department of Computer Science College of Sciences and Humanities- Aflaj Prince Sattam bin Abdulaziz University Aflaj16273 Saudi Arabia University of Luxembourg Luxembourg City1855 Luxembourg

The integration of terrestrial and satellite wireless communication networks offers a practical solution to enhance network coverage, connectivity, and cost-effectiveness. Moreover, in today’s interconnected world, connectivity’s reliable and widespread availability is increasingly important across various domains. This is especially more crucial for applications like the Internet of Things (IoT), remote sensing, disaster management, and bridging the digital divide. However, allocating the limited network resources efficiently and ensuring seamless handover between satellite and terrestrial networks present significant challenges. Therefore, this study introduces a resource allocation framework for integrated satellite-terrestrial networks to address these challenges. The framework leverages local cache pool deployments and non-orthogonal multiple access (NOMA) to reduce time delays and improve energy efficiency. Our proposed approach utilizes a multi-agent enabled deep deterministic policy gradient algorithm (MADDPG) to optimize user association, cache design, and transmission power control, resulting in enhanced energy efficiency. The approach comprises two phases: User Association and Power Control, where users are treated as agents, and Cache Optimization, where the satellite (Bs) is considered the agent. Through extensive simulations, we demonstrate that our approach surpasses conventional single-agent deep reinforcement learning algorithms in addressing cache design and resource allocation challenges in integrated terrestrial-satellite networks. Specifically, our proposed approach achieves significantly higher energy efficiency and reduced time delays compared to existing methods. This research highlights the importance and addresses the need for efficient resource allocation and cache design in integrated terrestrial-satellite networks, paving the way for enhanced connectivity and improved network performance in various applications. Copyright © 2023, The Aut

关键词： Resource allocation

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An Investigation and Development into the Use of AI-Based Analytical Methods for Forecasting the Stock Market

An Investigation and Development into the Use of AI-Based An...

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Smart Electronics and communication (ICOSEC), International Conference on

作者： M. Sheeba D. Ranjith Kumar S. Kirubakaran Ila Dixit D. Leela Rani Ajay Singh Yadav Department of B. Com Information System Management Guru Nanak College (Autonomous) Chennai Tamil Nadu India Department of B. Com Corporate Secretaryship Guru Nanak College (Autonomous) Chennai Tamil Nadu India Department of Computer Science and Engineering CMR College of Engineering and Technology Hyderabad Telangana India Department of Management Maharaja Agarsen International College Raipur Chhattisgarh India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Tirupati Andhra Pradesh India Department of Mathematics SRM Institute of Science and Technology Ghaziabad Utter Pradesh India

Forecasting the stock market is difficult due to the fact that a number numerous factors, including the current situation of economy, current politics, and market sentiment. Traditional analytical methods have limitations in accurately predicting stock prices, leading to increased curiosity about using algorithms based on artificial intelligence (AI) and machine learning (ML) stock market forecasting. This paper investigates the use of AI-based analytical methods for forecasting the stock market and presents a detailed analysis of various algorithms used in AI-based stock market forecasting. The use of AI-based analytical methods for forecasting the stock market has several benefits. Firstly, AI-based models can analyse vast amounts of financial data much faster than traditional methods, allowing for more accurate and timely predictions. Secondly, AI-based models can handle complex relationships between different factors affecting stock prices, such as market trends, news events, and economic indicators, which can be challenging to capture using traditional methods. This study also discusses about the challenges and limitations of using AI-based models for stock market forecasting research in the future research. Here, this study uses a novel theory of recurrent neural networks and a random forest model to enable prediction with more accuracy. One of the commonly used algorithms in AI-based stock market forecasting is the recurrent neural network (RNN), which model that uses deep learning. RNNs are very helpful for modelling time-series information, like stock prices, just because they may ensnare the temporal data dependencies. In addition, RNNs can be used to generate forecasts by predicting the next value in a set of timestamps based on previous values. Another popular algorithm for stock market forecasting is the Random Forest (RF) ensemble model. RF models are a type of decision tree-based algorithm that can generate accurate predictions by combining the output

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Efficient Compression Sensing Mechanism Based WBAN system Using Blockchain

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Security and communication Networks 2023年第1期2023卷

作者： Pathak, Vinay Singh, Karan Chandan, Radha Raman Gupta, Sachin Kumar Kumar, Manoj Bhushan, Shashi Jayaprakash, Sujith School of Computer and System Sciences Jawaharlal Nehru University New Delhi110067 India UP Varanasi221011 India School of Electronics and Communication Engineering Shri Mata Vaishno Devi University Jammu and Kashmir Katra India Faculty of Engineering and Information Sciences University of Wollongong Dubai United Arab Emirates MEU Research Unit Middle East University Amman11831 Jordan Department of Computer Science & Engineering Amity University Punjab Punjab Mohali India BlueCrest University College Accra Ghana

The hybrid wireless sensor network is made up of Wireless Body Area Network (WBAN). Generally, many hospitals use cellular networks to support telemedicine. To provide the treatment to the patient on time, for this, an early diagnosis is required, for treatment. With the help of WBANs, collections and transmissions of essential biomedical data to monitor human health becomes easy. Compressor Sensing (CS) is an emerging signal compression/acquisition methodology that offers a protruding alternative to traditional signal acquisition. The proposed mechanism reduces message exchange overhead and enhances trust value estimation via response time and computational resources. It reduces cost and makes the system affordable to the patient. According to the results, the proposed scheme in terms of Compression Ratio (CR) is 18.18% to 88.11% better as compared to existing schemes. Also in terms of Percentage Root-Mean-Squared Difference (PRD) value, the proposed scheme is 18.18% to 34.21% better than with respect to existing schemes. The consensus for any new block is achieved in 24% less time than the Proof-of-Work (PoW) approach. The shallow CPU usage is required for the leader election mechanism. CPU utilization while the experiment lies in the range of 0.9% and 14%. While simulating a one-hour duration, the peak CPU utilization is 21%. © 2023 Vinay Pathak et al.

关键词： Patient treatment

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Positioning Using Visible Light communications: A Perspective Arcs Approach

arXiv

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arXiv 2022年

作者： Zhu, Zhiyu Guo, Caili Bao, Rongzhen Chen, Mingzhe Saad, Walid Yang, Yang The Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical Engineering Princeton University Princeton NJ08544 United States Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Wireless@VT Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24060 United States

Visible light positioning (VLP) is an accurate indoor positioning technology that uses luminaires as transmitters. In particular, circular luminaires are a common source type for VLP, that are typically treated only as point sources for positioning, while ignoring their geometry characteristics. In this paper, the arc feature of the circular luminaire and the coordinate information obtained via visible light communication (VLC) are jointly used for positioning, and a novel perspective arcs approach is proposed for VLC-enabled indoor positioning. The proposed approach does not rely on any inertial measurement unit and has no tilted angle limitaion at the user. First, a VLC assisted perspective circle and arc algorithm (V-PCA) is proposed for a scenario in which a complete luminaire and an incomplete one can be captured by the user. Based on plane and solid geometry theory, the relationship between the luminaire and the user is exploited to estimate the orientation and the coordinate of the luminaire in the camera coordinate system. Then, the pose and location of the user in the world coordinate system are obtained by single-view geometry theory. Considering the cases in which parts of VLC links are blocked, an anti-occlusion VLC assisted perspective arcs algorithm (OA-V-PA) is proposed. In OA-V-PA, an approximation method is developed to estimate the projection of the luminaire's center on the image and, then, to calculate the pose and location of the user. Simulation results show that the proposed indoor positioning algorithm can achieve a 90th percentile positioning accuracy of around 10 cm. Moreover, an experimental prototype is implemented to verify the feasibility. In the established prototype, a fused image processing method is proposed to simultaneously obtain the VLC information and the geometric information. Experimental results in the established prototype show that the average positioning accuracy is less than 5 cm for different tilted angles of the user.

关键词： Visible light communication

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Resource Allocation for Multi-Modal Semantic communication in UAV Collaborative Networks

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IEEE Transactions on communications 2025年

作者： Hu, Han Zhu, Xingwu Zhou, Fuhui Wu, Wei Hu, Rose Qingyang Zhu, Hongbo Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Wireless Communications Nanjing210003 China Nanjing University of Posts and Telecommunications Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing210003 China Southeast University School of Cyber Science and Engineering Nanjing211189 China Nanjing University of Aeronautics and Astronautics College of Artificial Intelligence Nanjing210000 China Xidian University State Key Laboratory of Integrated Services Networks Xian710071 China Nanjing University of Posts and Telecommunications College of Communication and Information Engineering China Utah State University Department of Electrical and Computer Engineering LoganUT210003 United States

Semantic communication is envisioned as a potential communication paradigm enabled by artificial intelligence and is promising to break the Shannon limit for future 6G networks. This paradigm benefits unmanned aerial vehicles (UAVs) to conserve communication resources and minimize latency by only transmitting task-relevant semantic information. However, resource allocation in the multiple collaborative UAV scenarios remains unexplored, particularly regarding multi-modal semantic communication. To tackle this challenge, this paper investigates a semantic-aware intelligent resource allocation method for multi-UAV-assisted semantic communication networks in the UAV image-sensing task-oriented scenario. A multi-modal semantic communication framework with multi-UAV relay collaboration is developed. At the semantic level, a novel quality of experience (QoE) and the transmission cost model are introduced, based on which a semantic-aware resource allocation problem is formulated, aiming to maximize QoE while minimizing the transmission cost by jointly optimizing the UAV trajectory, the spectrum bandwidth, the transmit power and the number of the transmitted semantic symbols. To deal with optimization challenges involving hybrid variables and coordination among UAVs, a multi-UAV hybrid decision-controlled deep reinforcement learning (DRL) scheme is proposed. Simulation results demonstrate the effectiveness of the proposed scheme compared with the benchmark schemes in achieving a good balance between the QoE and the transmission cost. © 1972-2012 IEEE.

关键词： Deep reinforcement learning

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On the Information Freshness of A Two-Sensor Status Update system

On the Information Freshness of A Two-Sensor Status Update S...

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IEEE Conference on Vehicular Technology (VTC)

作者： Tianqing Yang Zhengchuan Chen Howard H. Yang Nikolaos Pappas Min Wang Yunjian Jia Tony Q. S. Quek School of Microelectronics and Communication Engineering Chongqing University Chongqing China Zhejiang University/University of Illinois at Urbana-Champaign Institute Zhejiang University Zhejiang China Department of Computer and Information Science Linköping University Linköping Sweden School of Optoelectronics Engineering Chongqing University of Posts and Telecommunications Chongqing China Information System Technology and Design Pillar Singapore University of Technology and Design Singapore

This work studies the average Age of Information (AoI) of a remote monitoring system in which two sensors observe the same physical process and update the status to a common monitor using orthogonal channels. While using redundant devices to update the status of a process can improve the information timeliness at the monitor, the out-of-order arrivals of updates impose a challenge to the AoI analysis. We first model the system as two parallel M/M/1/1 queues. By leveraging tools from stochastic hybrid systems, we obtain analytically the average AoI of the system. In particular, when the arrival or service rates are the same for the two sensors, the average AoI is given in closed form. Our analysis reveals that the average AoI of the considered system is reduced by 16.44% compared to the single-sensor system when the arrival and service rates are equal to 1. Numerical results show that the considered system outperforms the M/M/2 system in average AoI at high arrival rates.

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IoT enabled elbow range of motion for rehabilitation therapy

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AIP Conference Proceedings 2024年第1期2937卷

作者： K. Praveen J. S. Leena Jasmine R. Dhanagopal R. Suresh Kumar P. T. Vasanth Raj A. Jaya Rosy S. Subathra Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai India Department of Computer Science and Engineering Velammal Engineering College Chennai India Centre for System Design Chennai Institute of Technology Chennai India UG Scholar Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai India

In order to analyze and improve in physical therapy, professional sports, and the performing arts, it is crucial to monitor human joint angle movement. For both acute and chronic injuries, as well as to assess how well a treatment is functioning, range of motion assessment is essential. Athletes are more susceptible to fractures from acute elbow injuries (e.g. in gymnasts falling on an extended elbow). The industry standard for assessing joint ROM is now manual ROM measurements using a hand-held goniometer because of their accessibility and ease of use. We now present the fibre optic approach to assess elbow range of motion because the precision and repeatability of manual goniometer measurement have been questioned. In terms of size and robustness, optical fibre technology provides an exceptional solution for motion capture sensing. The method under study is based on an approach that has been used in the past to quantify spine movement and respiration rate: angular displacement between two separated fibres using intensity modulated optical fibre. By adding IoT, all data are uploaded to the cloud, where they are processed and conclusions are drawn for use in Rehabilation therapy using Functional Electrical Stimulation Therapy.

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