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2023 international conference on Mechatronics, IoT and Industrial Informatics, ICMIII 2023

作者： Zhuo, Bihua Officers College of PAP Department of Information and Communication Sichuan Chengdu610213 China

ISBN: (纸本)9798350301397

Massive process measurement message brings great computational complexity and modeling complexity to the traditional breakdown diagnosis algorithm, and the traditional diagnosis algorithm has the disadvantage that it is difficult to make online estimation by using high-order quantities. Modern industrial systems have been developing towards largescale and complexity, which makes the breakdown diagnosis means for industrial systems encounter a series of technical questions. In view of the powerful data representation learning and analysis ability of deep learning technique, breakdown diagnosis based on deep learning has attracted wide attention from industrial and academic circles, and has made intelligent process control more automatic and resultful. The means adopted in this paper is deep learning, which is carried out alternately by convolution and sub-sampling. At last, the algorithm close to the output layer adopts the common multilayer neural network. As a breakthrough in the domain of latter-day artificial intelligence, deep learning can voluntarily learn valuable traits from the original trait set or even the original data. The research in this paper shows that the algorithm in this paper is resultful in breakdown prediction, and it is suitable to be widely applied in practical applications. © 2023 IEEE.

关键词： Multilayer neural networks

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Microlearning-Inspired Approach for Teaching Business modeling to computer Engineering Students 24

Microlearning-Inspired Approach for Teaching Business Modeli...

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25th international conference on computer systems and Technologies, CompSysTech 2024

作者： Popgeorgiev, Angel Ibryamova, Elitsa Ivanova, Galina University of Ruse Angel Kanchev Ruse Bulgaria

ISBN: (纸本)9798400716843

The current generation of students expects education to be an interactive and engaging process that uses contemporary technologies. Teaching financial concepts for business modeling to graduate students with a computer engineering background is challenging. With the complex and technical nature of the subject matter, it is essential to find innovative and effective ways to engage these students to ensure their success. The course exemplifies complex financial concepts using authored software-implemented programming models. This approach involves dividing the theoretical part of learning into smaller segments of project-based learning. When combined with programming models, it seems promising to enhance the understanding and engagement of students in their education. This process can improve students' learning outcomes and make learning more satisfying and effective. A study was conducted to evaluate the effectiveness of a proposed teaching method on two groups of students: those who received traditional classroom-based training and those who received training through a distance learning approach. The study involved a survey of the students to inquire about their opinions on various aspects of the course delivery, including the teaching methodology, the program models used for demonstrations, and the learners' experience implementing the program models. Additionally, learning outcomes and final evaluations are analyzed to conclude the effectiveness of the proposed teaching approach. © 2024 ACM.

关键词： Students

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WAMS challenges and limitations in load modeling, voltage stability improvement, and controlled island protection-A review

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ENERGY REPORTS 2022年 8卷 699-709页

作者： Chintakindi, Raju Mitra, Arghya Visvesvaraya Natl Inst Technol Nagpur 440010 Maharashtra India

Global energy demand pushes transmission lines to their limits, which causes voltage instability on the power grids. Solar and wind farms are being integrated with increased size and capacity to meet increased load demands, and this increased volumes of renewables, combined with their lower inertia and more sensitive voltage profiles, are complicating system operators' roles. The execution of phasor measurement unit (PMU) with novel detection and protection schemes has been a focal point for power engineers. The real-time wide-area monitoring system (WAMS) helps grid operators to improve real-time voltage stability, situational awareness, fault detection, and grid planning. WAMS shows the path for developing substation predictions, improving system accuracy, and identifying control strategies to avoid power blackouts. It conducts post-event analysis and reduces the processing time of the power restoration. The novelty of this article is to describe load modeling, voltage stability, and islanding protection in real-time, as well as the technical challenges, limitations, opportunities, and solutions related to the same. Further, the paper explores and presents vital software and hardware resources, including HIL systems, for future use and research with WAMS technology. (C) 2021 The Author(s). Published by Elsevier Ltd.

关键词： Wide-area monitoring Phasor measurement unit Voltage stability Load modeling Control-islanding Renewable energy integration

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computer System for modeling Fluid Flow Around Bodies and Its Potential in Industry 1

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7th international Russian Automation conference, RusAutoCon 2024

作者： Tsareva, P.E. St. Petersburg State Marine Technical University 3 Lotsmanskaya Str Saint-Petersburg190121 Russia

ISBN: (数字)9783031824944

ISBN: (纸本)9783031824937

Mathematical modeling plays an important role in industry when a physical model is unprofitable, cannot be implemented due to the complexity of the problem, or more accurate values need to be obtained. In particular, the pressing issue of physically plausible fluid modeling is not a new challenge for industry. Despite a lot of research in recent decades, this area remains relatively unexplored and many phenomena lack a general description. As a result, in the industry the main difficulty in fluid modeling lies in the lack of a universal approach and the low predictability of emerging phenomena such as turbulence. For most systems of differential equations, which become the basis of models, it is impossible to find solutions in a general form, so it is necessary to consider individual cases. The most common approaches use the Navier-Stokes equation, however, for three dimensions, solutions can only be found for special cases. Nevertheless, it is the most complete way to describe the behavior of a fluid, which takes into account viscosity and, if necessary, can be supplemented with equations to describe the phenomena of electro- and magnetohydrodynamics, as well as dynamic meteorology. Although high-precision models allow saving physical resources, they consume a large amount of computing resources, so it is advisable to carry out modeling on a high-performance computer or super computer. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Navier Stokes equations

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EEG Multi-Objective Feature Selection using a Genetic Procedure with Hybrid Mutation Operator 24

EEG Multi-Objective Feature Selection using a Genetic Proced...

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24th international conference on Control systems and computer Science, CSCS 2023

作者： Cimpanu, Corina Gheorge Asachi Technical University of Iai Faculty of Automatic Control and Computer Science Computer Science Department Iai Romania

ISBN: (纸本)9798350313390

computer science developments in medical data processing increase modeling accuracy, speed up data analysis, and obtain automated diagnostics. As a result, the role of machine learning algorithms becomes significant, as well as the need to implement faster and more accurate systems. For example, classification is a common component in computer-aided diagnostics, and choosing the proper set of predictors is critical for training a classifier model. Usually, medical datasets are highly dimensional, and their characteristical data correlations require reducing the set of features used for classification. Besides supervised grouping algorithms, genetic optimization procedures are well known for their robust verification of competing attributes by directly estimating their usefulness on a specific classifier. This paper discusses the assessment of Electroencephalogram data samples acquired during working memory load n-back scenarios using a new embedded feature selection procedure based on Multi-Objective Optimization via Genetic Algorithms enhanced with a hybrid mutation operator. Experimental results focus on both the hybrid mutation operator performances, and obtaining overall higher Support Vector Machine classification accuracy while reducing the number of selected features. © 2023 IEEE.

关键词： Genetic algorithms

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Tube-based Distributed Model Predictive Control for Heterogeneous Vehicle Platoons via Convex Optimization 25

Tube-based Distributed Model Predictive Control for Heteroge...

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IEEE 25th international conference on Intelligent Transportation systems (ITSC)

作者： Sun, Hao Dai, Li Chen, Boli UCL Dept Elect & Elect Engn London WC1E 6BT England Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Connected and autonomous driving technology is a current research hotspot, which can effectively ease transportation congestion and enhance road safety. This paper addresses the control problem of heterogeneous vehicle platoons subject to various disturbances, including but not limited to modeling and measurement disturbances. The problem is approached by a tube-based distributed model predictive control (MPC) algorithm that relies on a unidirectional predecessorfollowing communication topology. To improve the computational efficiency of the algorithm, the inherently nonlinear MPC problem is formulated as a convex program in the spatial domain by coordination changes and suitably relaxing the nonconvex constraints. Numerical simulation examples are carried out to show the effectiveness and robustness of the proposed control algorithm.

关键词： Current measurement Computational modeling Transportation Prediction algorithms Numerical simulation Robustness Road safety

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Reinforcement Learning Based Energy Management System to Maximize Efficiency for the Hybrid Marine Propulsion System

Reinforcement Learning Based Energy Management System to Max...

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2024 IEEE international conference on Electrical systems for Aircraft, Railway, Ship Propulsion and Road Vehicles and international Transportation Electrification conference, ESARS-ITEC 2024

作者： Albakri, Saeed Mehran, Kamyar Gadoue, Shady Queen Mary University of London Department of Elcetronics and Computer Science London United Kingdom

ISBN: (纸本)9798350373905

Hybrid maritime power typically employs energy management technologies such as rule-based or reactive control. The general purpose of this project is to investigate energy management and control approaches for hybrid marine boats using Reinforcement Learning (RL). This project aims to explore the potential of using online adaptive approaches to replace traditional energy management tactics. The initial study focused on a straightforward hybrid maritime power system. A Markov Decision Framework Q-Learning - RL algorithm was used to design a decision-making process. Integrated power systems, combining fuel cells and batteries, present a promising solution for marine propulsion systems, offering high energy density and power while reducing CO 2 emissions, aligning with the international Maritime Organization's goal of achieving net zero emissions by 2050. This study focuses on the implementation of fuel cell and battery systems, a dc-to-dc converter, typically a boost converter, to manage power flow between the fuel cell and battery, enhancing peak power output at propeller where induction machine utilized as a primary mover in a hybrid powertrain system. Using modeling and analysis, this research investigates power distribution scenarios based on varying power demands within onboard marine propulsion systems. Reinforcement Learning is employed to assess the State of Charge (SOC) of the battery and fuel cell density, optimizing power flow under different operational states to maximize total rewards and overall system efficiency. This study contributes to the advancement of marine propulsion systems by integrating Reinforcement learning control mechanisms to optimize power distribution. © 2024 IEEE.

关键词： Electric load flow

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Performance Analysis and Predictive modeling of MPI Collective Algorithms in Multi-Core Clusters: A Comparative Study 17

Performance Analysis and Predictive Modeling of MPI Collecti...

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17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Reddy, Manideep Raya V.S.R.S. Raju, Sanjay Ramapogu Girish, K.K. Bhowmik, Biswajit National Institute of Technology Karnataka Maharshi Sushrut CAS Lab BRICS Lab Department of Computer Science and Engineering Surathkal Mangalore India

ISBN: (纸本)9798331531195

Efficient communication is the foundation of parallel computing systems, enabling seamless coordination across multiple processors for optimal performance. At the core of this communication lies the Message Passing Interface, a crucial framework designed to facilitate data exchange between processors through collective operations. However, these MPI operations often face challenges, including fluctuating process counts, varying message sizes, and increased communication overhead. These issues can significantly impact execution times and scalability, leading to potential bottlenecks in large-scale systems. To address these concerns, this paper provides an in-depth evaluation of key MPI collective algorithms - Flat Tree, Chain, and Binary Tree - by examining their performance under varying configurations. By analyzing execution times and communication overhead, the study reveals the trade-offs inherent in each algorithm, offering insights into strategies for reducing communication costs. Through this analysis, we aim to provide valuable guidance to improve the efficiency and scalability of parallel computing, particularly in high-performance systems where communication efficiency is critical. © 2025 IEEE.

关键词： Scalability

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Mean Field Analysis of Serverless Edge Computing for Smart Vehicle Applications 4

Mean Field Analysis of Serverless Edge Computing for Smart V...

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4th international conference on Electronic Information Engineering and computer Communication, EIECC 2024

作者： Yao, Yuan He, Rong Wuhan Institute of Technology School of Computer Science and Engineering Wuhan China

ISBN: (纸本)9798331534622

Smart cities and vehicles generate numerous time sensitive tasks, making edge computing a key solution for reducing latency. However, using serverless models at the edge faces challenges like dynamic city traffic and resource constraints. This study analyzes serverless edge computing for smart vehicles through modeling and experiments. we develop a mean field model captures steady-state system behavior under stochastic job arrivals, enabling efficient analysis of metrics like mean sojourn time and service degradation probability. Through experiments and simulations, we demonstrate the feasibility of serverless edge servers for low-latency offloading, providing insights for optimizing these systems in urban environments. © 2024 IEEE.

关键词： Edge Computing Mean field Model Performance Analysis Serverless

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Automated Control System of a Modular Robot Manipulator for Sorting Objects Based on Neural Networks 5

Automated Control System of a Modular Robot Manipulator for ...

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5th international conference on Control systems, Mathematical modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Zhitenko, Maxim Polyakov, Maxim Volgograd State University Depart. of Information Systems and Computer Modeling Volgograd Russia

ISBN: (纸本)9798350309126

This article is devoted to the development of an automated robot manipulator control system to sort objects based on the use of neural networks. This article discusses the processes associated with the creation of an automated control system and the development of its components. Specifically, a system is described that is designed to detect, classify, and determine the position of objects, as well as a system that calculates the rotation angles of the robot manipulator modules. The principles of operation of the control system and its components, as well as the architecture of neural networks used in their development are given. The results of the experiments demonstrate the effectiveness of using neural networks to solve the tasks of automating the control of robot manipulators. The accuracy of object recognition on the scene was achieved equal to 82.7 percent for a test sample of data. In addition, the selected neural network architecture ensures the positioning accuracy of the system equal to 99.7 percent with an acceptable error of 1 degree. © 2023 IEEE.

关键词： Intelligent systems

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