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International Conference on Smart Systems and Technologies (SST)

作者： Kristian Knol Alexander Ulbing Denis Pelin Department of Electromechanical Engineering Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Stress Test Methology KAI Kompetenzzentrum Fuer Industrie- und Automobilelektronik Villach Austria

ISBN: (数字)9798350386394

ISBN: (纸本)9798350386400

LLC topology is proposed for powering low-voltage DC loads from a high-voltage battery. The simulation model of the HV/LV DC-DC converter is obtained in MATLAB/Simulink and parametric analysis of the output voltage during switch ON/OFF loads were carried out. The importance of connecting a low voltage battery on the load side in terms of drop/jump of the output voltage depending on the switching is observed by comparing the voltage indicator according to the standards for system testing. Different load power steps, power change trend (loading and unloading), as well as charging state of the battery were used as parameters of analysis. The responses of the output voltage in transient state were analyzed in terms of identifying the duration of the transient state as well as the maximum change in the instant values of the output voltage. The importance of the gain factor in the feedback loop of the control circuit at the duration of the transient state is indicated. The measurement of the AC component of the output voltage was carried out on the Infineon EVAL-2ED2101-HB-LLC 200W board in order to evaluate the simulation results.

关键词： Low voltage Feedback loop Shape Simulation Loading Switches Batteries State of charge Transient analysis Voltage control

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Network Access Selection for URLLC and eMBB Applications in Sub-6GHz-mmWave-THz Networks: Game Theory Versus Multi-Agent Reinforcement Learning

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IEEE Transactions on Communications 2024年

作者： Thanh Van, Nguyen Thi Le Tuan, Nguyen Luong, Nguyen Cong Nguyen, Tien Hoa Feng, Shaohan Gong, Shimin Niyato, Dusit In Kim, Dong Faculty of Electrical and Electronic Engineering PHENIKAA University Vietnam Hanoi12116 Viet Nam Faculty of Computer Science PHENIKAA University Hanoi12116 Viet Nam School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi100000 Viet Nam Zhejiang Gongshang University Hangzhou310018 China School of Intelligent Systems Engineering Sun Yat-sen University China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

We investigate a heterogeneous network (HetNet) including sub-6GHz base stations (BSs), mmWave BSs, and THz BSs to support enhanced mobile broadband (eMBB) users and ultra-reliable low-latency communication (URLLC) users. We particularly investigate a user-centric network in which the users locally and dynamically select and switch among BSs over time to achieve their highest utility. Two types of users have different Quality of Service (QoS) requirements. Thus, we design two types of utility functions specifically for the eMBB users and URLLC users. Then, to model the dynamic selection behavior of the users, we propose to use a fractional game with the power-law memory. The fractional game allows the eMBB users and the URLLC users to incorporate their past strategies into their current selection, thus improving their utility. Furthermore, we consider the case that the BSs communicate the system state with each other, and we model the network selection of the users as a multi-agent problem. Then, we propose to use a multi-agent deep reinforcement learning (MADRL) algorithm that enables the URLLC users and eMBB users to make their network selection decision online to achieve their long-term utility. Various simulation results are provided to demonstrate the scalability and effectiveness of the proposed approaches. Particularly, compared with the classical game, the fractional game is able to achieve a higher utility but incurs a higher network adaptation cost. Moreover, the different types of URLLC users (in terms of latency and reliability requirements) and the number of URLLC users in the network significantly affect the total utility and the network selection strategies of the eMBB users. Importantly, given the full observations, the MADRL outperforms both classical and fractional games in terms of total network utility. © 2024 IEEE.

关键词： Deep reinforcement learning

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Advanced Stock Market Prediction Using Unsupervised Federated Learning Techniques

Advanced Stock Market Prediction Using Unsupervised Federate...

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International CSI computer Conference, CSICC

作者： Mohsen Tajgardan Atena Shiranzaei Mahtab Jamali Reza Khoshkangini Mahdi Rabbani Faculty of Electrical and Computer Engineering Qom University of Technology Qom Iran Department of Computer Engineering Faculty of Industry and Mining (Khash) University of Sistan and Baluchestan Sistan and Baluchestan Iran Department of Computer Science and Media Technology Sustainable Digitalisation Research Centre Malmö University Malmö Sweden Canadian Institute for Cybersecurity University of New Brunswick NB Canada

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

In the realm of stock market prediction, traditional supervised learning approaches often struggle with the vast and diverse nature of financial data, coupled with privacy concerns. This paper explores a novel methodology that combines unsupervised learning techniques with federated learning system to enhance stock market prediction models. We present a comprehensive system where local models, trained using unsupervised methods, contribute to a global model through federated aggregation. By leveraging federated learning, our approach allows multiple financial institutions to collaboratively train models on their decentralized data while preserving data privacy. This approach addresses the challenges of data heterogeneity and communication efficiency, providing a robust and scalable solution for advanced stock market forecasting. Our experiments demonstrate that integrating unsupervised learning with federated learning not only improves predictive accuracy but also enhances the model's ability to identify emerging market trends and anomalies. Finally, we compare our distributed data model with other machine learning models that use local data.

关键词： Data privacy Federated learning Supervised learning Predictive models Parallel processing Data models Security Stock markets Forecasting Unsupervised learning

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Virtually coupled train set control subject to space-time separation:A distributed economic MPC approach with emergency braking configuration

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High-Speed Railway 2024年第3期2卷 143-152页

作者： Xiaolin Luo Tao Tang Le Wang Hongjie Liu State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong UniversityBeijing 100044China Department of Electrical and Computer Engineering University of CaliforniaRiversideCA 92521USA Beijing Zongheng Electro-Mechanical Technology Development Co. Ltd.Beijing 100081China School of Automation and Intelligence Beijing Jiaotong UniversityBeijing 100044China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong UniversityBeijing 100044China

The emerging virtual coupling technology aims to operate multiple train units in a Virtually Coupled Train Set(VCTS)at a minimal but safe *** guarantee collision avoidance,the safety distance should be calculated using the state-of-the-art space-time separation principle that separates the Emergency Braking(EB)trajectories of two successive units during the whole EB *** this case,the minimal safety distance is usually numerically calculated without an analytic ***,the constrained VCTS control problem is hard to address with space-time separation,which is still a gap in the existing *** solve this problem,we propose a Distributed Economic Model Predictive Control(DEMPC)approach with computation efficiency and theoretical ***,to alleviate the computation burden,we transform implicit safety constraints into explicitly linear ones,such that the optimal control problem in DEMPC is a quadratic programming problem that can be solved *** theoretical analysis,sufficient conditions are derived to guarantee the recursive feasibility and stability of DEMPC,employing compatibility constraints,tube techniques and terminal ingredient ***,we extend our approach with globally optimal and distributed online EB configuration methods to shorten the minimal distance among ***,experimental results demonstrate the performance and advantages of the proposed approaches.

关键词： Virtually coupled train set Space-time separation Economic model predictive control Distributed model predictive control Emergency braking configuration

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D-SVM over Networked Systems with Non-Ideal Linking Conditions

arXiv

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arXiv 2023年

作者： Doostmohammadian, Mohammadreza Aghasi, Alireza Zarrabi, Houman Faculty of Mechanical Engineering Semnan University Semnan Iran Electrical Engineering and Computer Science Department Oregon State University United States Tehran Iran

This paper considers distributed optimization algorithms, with application in binary classification via distributed support-vector-machines (D-SVM) over multi-agent networks subject to some link nonlinearities. The agents solve a consensus-constraint distributed optimization cooperatively via continuous-time dynamics, while the links are subject to strongly sign-preserving odd nonlinear conditions. Logarithmic quantization and clipping (saturation) are two examples of such nonlinearities. In contrast to existing literature that mostly considers ideal links and perfect information exchange over linear channels, we show how general sector-bounded models affect the convergence to the optimizer (i.e., the SVM classifier) over dynamic balanced directed networks. In general, any odd sector-bounded nonlinear mapping can be applied to our dynamics. The main challenge is to show that the proposed system dynamics always have one zero eigenvalue (associated with the consensus) and the other eigenvalues all have negative real parts. This is done by recalling arguments from matrix perturbation theory. Then, the solution is shown to converge to the agreement state under certain conditions. For example, the gradient tracking (GT) step size is tighter than the linear case by factors related to the upper/lower sector bounds. To the best of our knowledge, no existing work in distributed optimization and learning literature considers non-ideal link conditions. © 2023, CC BY.

关键词： Support vector machines

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PAS-SLAM: A Visual SLAM System for Planar Ambiguous Scenes

arXiv

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arXiv 2024年

作者： Hu, Xinggang Wu, Yanmin Zhao, Mingyuan Yang, Linghao Zhang, Xiangkui Ji, Xiangyang School of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China School of Electronic and Computer Engineering Peking University Shenzhen China Department of Automation Tsinghua University Beijing China College of Information Science and Engineering Northeastern University Shenyang China

Visual SLAM (Simultaneous Localization and Mapping) based on planar features has found widespread applications in fields such as environmental structure perception and augmented reality. However, current research faces challenges in accurately localizing and mapping in planar ambiguous scenes, primarily due to the poor accuracy of the employed planar features and data association methods. In this paper, we propose a visual SLAM system based on planar features designed for planar ambiguous scenes, encompassing planar processing, data association, and multi-constraint factor graph optimization. We introduce a planar processing strategy that integrates semantic information with planar features, extracting the edges and vertices of planes to be utilized in tasks such as plane selection, data association, and pose optimization. Next, we present an integrated data association strategy that combines plane parameters, semantic information, projection IoU (Intersection over Union), and non-parametric tests, achieving accurate and robust plane data association in planar ambiguous scenes. Finally, we design a set of multi-constraint factor graphs for camera pose optimization. Qualitative and quantitative experiments conducted on publicly available datasets demonstrate that our proposed system competes effectively in both accuracy and robustness in terms of map construction and camera localization compared to state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Augmented reality

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Feature Selection for Hand Gesture Recognition in Human-Robot Interaction

Feature Selection for Hand Gesture Recognition in Human-Robo...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Matthew McCarver Jing Qin Biyun Xie Department of Mathematics University of Kentucky Lexington KY USA Faculty of Mathematics University of Kentucky Lexington KY USA Faculty of Department of Electrical and Computer Engineering University of Kentucky Lexington KY USA

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Hand gesture recognition has been playing an important role in robotic applications, which allows robots to communicate with humans in an effective way. However, it typically desires to process high-dimensional data, such as images or sensor measurements. To address the computational challenges due to the data growth, it is desirable to select most relevant features during recognition by reducing the redundancy of the data. In this paper, we propose a novel feature selection approach based on the separable nonnegative matrix factorization (NMF) framework for hand gesture recognition. In particular, we adopt a nonconvex regularization term, i.e., the ratio of matrix nuclear norm and Frobenius norm. The proposed method reduces the data dimension by utilizing the data low-rankness in an adaptive way. To address the nonconvexity of the proposed model, we reformulate it by introducing an auxiliary variable and then apply the alternating direction method of multipliers (ADMM). Furthermore, a variety of numerical experiments on binary and grayscale hand gesture images demonstrate the efficiency of the proposed feature selection approach in improving the quality of factorization and its potential impact on robotic applications.

关键词： Adaptation models Redundancy Human-robot interaction Gesture recognition Gray-scale Robot sensing systems Feature extraction Convex functions Numerical models

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Groundbreaking Application of the Corona-Virus Search Optimizer Algorithm for Resilient Speed Control and Stability Assessment of Direct Current Motors Under Uncertain Conditions

Groundbreaking Application of the Corona-Virus Search Optimi...

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electrical Machines and Drives (ICEMD), International Conference on

作者： Peyman Zare Amir Mohammadian SeyedJalal SeyedShenava Babak Mohamadi Department of Electrical and Computer Engineering Faculty of Engineering University of Mohaghegh Ardabili Ardabil Iran Province Electricity Distribution Company (APED Co.) University of Mohaghegh Ardabili and Ardabil Ardabil Iran

ISBN: (数字)9798331507510

ISBN: (纸本)9798331507527

In contemporary industrial systems where precision is paramount, the Direct Current (DC) motor plays a critical role, necessitating a highly efficient method for speed control that ensures both accuracy and responsiveness. Effective motor speed regulation is essential for the flawless execution of mechanical operations. This paper presents an innovative control strategy specifically designed for DC motor velocity regulation. The proposed methodology employs a Fractional Order (FO) controller, utilizing both Proportional-Integral-Derivative (PID) and Fractional Order PID (FOPID) controllers. These controllers are fine-tuned through the application of the Corona-Virus Search Optimizer (CVSO) algorithm, a novel metaheuristic technique inspired by the propagation dynamics of the coronavirus. This marks the first instance of the robust CVSO algorithm being applied in the domain of DC motor speed control. For performance evaluation, the Integral of Squared Time multiplied by Absolute Error (ISTAE) criterion has been adopted as the objective function. Simulations conducted in the MATLAB/Simulink environment have validated the efficacy of the proposed control approach. The controllers were rigorously tested against step torque profiles, with considerations for parametric uncertainties such as variations in electrical resistance and motor torque constants. The results reveal significant improvements in key performance metrics. Specifically, the FOPID controller outperforms the conventional PID controller with an 83.86% improvement in overshoot, a 40.17% improvement in settling time, and a 0.93% improvement in rise time under certain conditions. These findings demonstrate that the proposed FOPID/CVSO controller offers superior speed regulation and enhanced system stability compared to the traditional PID controller, making it a more effective solution for DC motor speed control.

关键词： Resistance Torque Uncertainty Heuristic algorithms Velocity control Stability criteria Metaheuristics DC motors Regulation PD control

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Data Fusion Architecture Empowered with Deep Learning for Breast Cancer Classification

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computers, Materials & Continua 2023年第12期77卷 2813-2831页

作者： Sahar Arooj Muhammad Farhan Khan Tariq Shahzad Muhammad Adnan Khan Muhammad Umar Nasir Muhammad Zubair Atta-ur-Rahman Khmaies Ouahada Faculty of Computing Riphah International UniversityIslamabad45000Pakistan Department of Forensic Sciences University of Health SciencesLahore54000Pakistan Department of Electrical and Electronic Engineering Science University of JohannesburgP.O.Box 524Johannesburg2006South Africa School of Computing Skyline University CollegeUniversity City SharjahSharjah1797United Arab Emirates Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Faculty of Artificial Intelligence&SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science Bahria UniversityLahore CampusLahore54000Pakistan Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia

Breast cancer(BC)is the most widespread tumor in females worldwide and is a severe public health *** is the leading reason of death affecting females between the ages of 20 to 59 around the *** detection and therapy can help women receive effective treatment and,as a result,decrease the rate of breast cancer *** cancer tumor develops when cells grow improperly and attack the healthy tissue in the human *** are classified as benign or malignant,and the absence of cancer in the breast is considered *** learning,machine learning,and transfer learning models are applied to detect and identify cancerous tissue like *** research assists in the identification and classification of *** implemented the pre-trained model AlexNet and proposed model Breast cancer identification and classification(BCIC),which are machine learning-based models,by evaluating them in the form of comparative *** used 3 datasets,A,B,and *** fuzzed these datasets and got 2 datasets,A2C and *** A2C is the fusion of A,B,and C with 2 classes categorized as benign and *** B3C is the fusion of datasets A,B,and C with 3 classes classified as benign,malignant,and *** used customized AlexNet according to our datasets and BCIC in our proposed *** achieved an accuracy of 86.5%on Dataset B3C and 76.8%on Dataset A2C by using AlexNet,and we achieved the optimum accuracy of 94.5%on Dataset B3C and 94.9%on Dataset A2C by using proposed model BCIC at 40 epochs with 0.00008 learning *** proposed fuzzed dataset model using transfer *** fuzzed three datasets to get more accurate results and the proposed model achieved the highest prediction accuracy using fuzzed dataset transfer learning technique.

关键词： Breast cancer classification deep learning machine learning transfer learning learning rate

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IoT Based Optical Sensor Network For Precision Agriculture

IoT Based Optical Sensor Network For Precision Agriculture

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： R S Vishnu Durai R. Vijayakumar S. Lakshmisridevi Shaik Thasleem Bhanu U. Arunkumar Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Tamilnadu India Department of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Rajalakshmi Engineering College Chennai Tamilnadu India Department of Electrical and Electronics Engineering Faculty of Engineering Karpagam Academy of Higher Education Deemed to be University Coimbatore Tamil Nadu India

Precision agriculture is a cutting-edge farming strategy that maximizes harvests by using cutting-edge technology and data-driven decision-making. Optical sensors and other Internet of Things (IoT) devices have great promise to revolutionize farming operations in this setting. Sensor networks and Machine Learning (ML) based tracking devices are in great demand because of the precise data extraction and analysis they give. This research was undertaken with the goal of reducing agricultural hazards and promoting smart farming practices. Diseases caused by insects and other diseases may reduce crop yields if not addressed quickly. Thus, in this study, we provide a unique artificial swarm fish optimized naive bayes (ASFONB) method for keeping an eye on the health of the soil and preventing diseases from manifesting in cotton plants' leaves. In this research, numerous important indicators of crop growth and health were monitored using Internet of Things (IoT) devices equipped with optical sensors. The environmental factors like as temperature, humidity, light intensity, and chlorophyll content are recorded by these sensors. The proposed method involves sending the collected data to a central server for processing and analysis via wireless transmission. Once the disease has been detected, the information will be sent to the farmers via Android app. The Android app can show the chemical concentration in a container with soil factors like humidity, temperature, and wetness. Using an Android app, you may control the relay and hence the power supply and chemical sprinkler system. The experimental findings demonstrate that the proposed solution outperforms the status quo in disease identification.

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