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10th International Conference on Wireless and Telematics, ICWT 2024

作者： Putra, Muhammad Taufik Dwi Adiwilaga, Anugrah Munggaran, Jezzy Putra Adhitama, Muhammad Azka As'ad, Raihan Anwar Alhafidz, Ahmad Ali Nurmalasari, Rin Rin Juhana, Agus Universitas Pendidikan Indonesia Computer Engineering Study Program Bandung Indonesia UIN Sunan Gunung Djati Department of Electrical Engineering Bandung Indonesia Universitas Pendidikan Indonesia Multimedia Education Study Program Bandung Indonesia

ISBN: (纸本)9798350377057

This article explores the development and implementation of futuristic bins with automated waste sortation systems to address improper and mixed waste disposal challenges. Despite the existence of categorized waste bins, individuals often dispose of waste improperly, hindering the sorting and decomposition processes. Integrating advanced technologies, such as Artificial Intelligence (AI), futuristic bins offer innovative solutions. Futuristic bins with automated sorting systems facilitate accurate waste sorting, reduce mixing, and optimize waste disposal. These bins increase efficiency, reduce costs, and optimize collection routes while promoting environmental cleanliness and awareness of waste management. The article describes the research methodology, including preliminary study, specification requirements analysis, prototype development, and testing. The results demonstrate the implemented system's accuracy and real-time object detection capabilities. The study also acknowledges limitations and suggests future research directions, including improving communication between object detection algorithms and microcontrollers. Overall, the development of futuristic bins with automated waste sortation systems represents a significant advancement in waste management, with the potential to revolutionize waste management systems and create sustainable cities and communities. © 2024 IEEE.

关键词： Waste disposal

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An In-depth Investigation on the Structure, Characteristics, and Potential Applications of GaN/SiC Mosfet-Based systems 6

An In-depth Investigation on the Structure, Characteristics,...

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6th International Conference on Information Technologies and electrical engineering, ICITEE 2023

作者： Ren, Yan Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy12180 United States

ISBN: (纸本)9798400708299

In an era of rapid technological innovation, this article provides a comprehensive examination of Gallium Nitride (GaN) Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFETs). The background section highlights the emergence and application of GaN MOSFETs and their significant advantages over silicon-based and Silicon Carbide (SiC) MOSFETs, such as smaller size, faster switching speed, less heat generation, and optimum operation at high temperatures, indicating that GaN MOSFETs are leading us into a more advanced, digitized future. The article delves into the basic theory of GaN MOSFETs, detailing their unique structure and properties that demonstrate massive potential and practicality in real-world applications such as electric vehicles and 5G technology. The conclusion emphasizes that GaN MOSFETs have already shown their importance in a wider range of industrial sectors and are set to revolutionize various industries, thereby driving overall progress. With diversified applications and broad prospects, the impact and value of GaN MOSFETs have gone beyond single-component applications, and their societal significance is undergoing profound changes. © 2023 ACM.

关键词： Gallium nitride

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Optimal design of Fractional order PID controller based Automatic voltage regulator system using gradient-based optimization algorithm

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Journal of King Saud University - engineering Sciences 2024年第1期36卷 32-44页

作者： Altbawi, Saleh Masoud Abdallah Mokhtar, Ahmad Safawi Bin Jumani, Touqeer Ahmed Khan, Ilyas Hamadneh, Nawaf N. Khan, Afrasyab School of Electrical Engineering Universiti Teknologi Malaysia Johor Johor Bahru81310 Malaysia Department of Electrical Engineering Mehran University of Engineering and Technology SZAB Campus Khairpur Mirs 66020 Sindh Pakistan Department of Mathematics College of Science Al-Zulfi Majmaah University Al-Majmaah11952 Saudi Arabia Department of Basic Sciences College of Science and Theoretical Studies Saudi Electronic University Riyadh11673 Saudi Arabia Institute of Engineering and Technology Department of Hydraulics and Hydraulic and Pneumatic Systems South Ural State University Lenin Prospect 76 Chelyabinsk454080 Russia

Considering the superior control characteristics and increased tuning flexibility of the Fractional-Order Proportional Integral Derivative (FOPID) controller than the conventional PID regulator, this article attempts to explore its application in the optimal design of the Automatic Voltage Regulator (AVR). Since FOPID has two additional tuning parameters (µ and ʎ) than its mentioned conventional counterpart, its tuning process is comparatively difficult. To overcome the stated issue, a self-regulated off-line optimal tuning method based on the Gradient-Based Optimization (GBO) algorithm is adopted in the current study. The optimal FOPID gains are obtained by minimizing the selected Fitness Function (FF) that is chosen as Integral Time Absolute Error (ITAE) in the current study. The simulations are performed using MATLAB/SIMULINK 2018a to test and compare the performance of the proposed GBO-based optimal AVR design based on the dynamic response, stability, and robustness evaluation metrics with some of the recently published metaheuristic optimization algorithm based optimal AVR designs in the literature. The results show that the proposed AVR design provides the most optimal dynamic response and enhanced stability among the considered AVR designs, thus proves its efficacy and essence. © 2021

关键词： Optimal control systems

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Hybrid 2D-3D Modeling Method for Electromagnetic Acoustic Phased Array 16

Hybrid 2D-3D Modeling Method for Electromagnetic Acoustic Ph...

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16th IEEE International Conference on Electronic Measurement and Instruments, ICEMI 2023

作者： Zhao, Yuxuan Wang, Shen Wang, Youtian Li, Haihan Huang, Songling Tsinghua University State Key Lab of Power Systems Department of Electrical Engineering Beijing China

ISBN: (纸本)9798350327144

As emerging nondestructive testing technologies, the electromagnetic acoustic and ultrasonic phased array inspection technologies have broad application prospects. By combining these two technologies, the electromagnetic acoustic transducer (EMAT) phased array is of great research significance. When building the model of EMAT phased array, due to the difficulty in solving the three-dimensional multi-physical coupling model, existing literature lacks research on the imaging of two-dimensional EMAT arrays in three-dimensional space. Therefore, this paper established a 3D model of EMAT phased arrays by means of mapping in the multiphysics coupling simulation software COMSOL and carried out simulation solutions. In this paper, firstly, the process of electromagnetic field and elastic dynamic field coupling to generate ultrasonic waves during EMAT operation was derived, as well as the control mode of acoustic beam deflection and focusing in ultrasonic phased arrays. Then, an EMAT transducer array unit was constructed in COMSOL, and a mapping-based simplification was proposed to address the difficulties in constructing a pure 3D model. The calculation results of the electromagnetic field of the 2D axisymmetric model were mapped into the 3D model, thereby achieving the construction of the 3D model under the constraints of computational time and complexity. Finally, based on this, multiple array units were combined to form an EMAT phased array. Models of 1D linear array and 2D planar array were established, and the deflection and focusing of the ultrasonic beam were achieved. The simulation results were compared with the relevant theories to verify the accuracy of the modeling, thus confirming the feasibility of building a 2D-3D Mixture model by mapping. © 2023 IEEE.

关键词： Electromagnetic fields

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PROMISE: preconditioned stochastic optimization methods by incorporating scalable curvature estimates

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The Journal of Machine Learning Research 2024年第1期25卷 16974-17030页

作者： Zachary Frangella Pratik Rathore Shipu Zhao Madeleine Udell Department of Management Science and Engineering Stanford University Department of Electrical Engineering Stanford University Department of Systems Engineering Cornell University

Ill-conditioned problems are ubiquitous in large-scale machine learning: as a data set grows to include more and more features correlated with the labels, the condition number increases. Yet traditional stochastic gradient methods converge slowly on these ill-conditioned problems, even with careful hyperparameter tuning. This paper introduces PROMISE (Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates), a suite of sketching-based preconditioned stochastic gradient algorithms that deliver fast convergence on ill-conditioned large-scale convex optimization problems arising in machine learning. PROMISE includes preconditioned versions of SVRG, SAGA, and Katyusha; each algorithm comes with a strong theoretical analysis and effective default hyperparameter values. Empirically, we verify the superiority of the proposed algorithms by showing that, using default hyperparameter values, they outperform or match popular tuned stochastic gradient optimizers on a test bed of 51 ridge and logistic regression problems assembled from benchmark machine learning repositories. On the theoretical side, this paper introduces the notion of quadratic regularity in order to establish linear convergence of all proposed methods even when the preconditioner is updated infrequently. The speed of linear convergence is determined by the quadratic regularity ratio, which often provides a tighter bound on the convergence rate compared to the condition number, both in theory and in practice, and explains the fast global linear convergence of the proposed methods.

关键词： stochastic optimization preconditioning randomized low-rank approximation lazy Hessians

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Unknown input observer design for T-S fuzzy systems with time-varying bounded delays

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International Journal of systems, Control and Communications 2023年第4期14卷 304-326页

作者： Naami, Ghali Ouahi, Mohamed Karite, Touria Udris, Dainius Tadeo, Fernando Engineering Systems and Applications Laboratory National School of Applied Sciences University Sidi Mohammed Ben Abdellah Fez Morocco Department of Electrical Engineering Faculty of Electronics Vilnius Gediminas Technical University Vilnius Lithuania Industrial Engineering School University of Valladolid Valladolid Spain

The paper discusses the design of functional observers with unknown inputs for nonlinear systems described by Takagi-Sugeno fuzzy models and time-varying bounded delays. The considered system extends previous works by considering a T-S model with time bounded delays and unknown inputs. The main innovative idea behind the design of the functional observer is that the unknown inputs are present in both the state and output equations, and bounds on the delays maybe used to reduce conservatism. The observer design and analysis are based on the Lyapunov-Krasovskii stability theory, and the resulting observer convergence conditions are formulated in terms of linear matrix inequalities (LMIs), which can be numerically and symbolically solved using YALMIP/MATLAB toolbox. In the final section of the paper, some examples are provided to demonstrate the application of this approach. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Linear matrix inequalities

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Experimental Analysis of Methods Used to Solve Linear Regression Models

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Computers, Materials & Continua 2022年第9期72卷 5699-5712页

作者： Mua’ad Abu-Faraj Abeer Al-Hyari Ziad Alqadi Computer Information Systems Department The University of JordanAqaba77110Jordan Electrical Engineering Department Al-Balqa Applied UniversityAs Salt19117Jordan Computers and Networks Engineering Department Amman15008Jordan

Predicting the value of one or more variables using the values of other variables is a very important process in the various engineering experiments that include large data that are difficult to obtain using different measurement *** is one of the most important types of supervised machine learning,in which labeled data is used to build a prediction model,regression can be classified into three different categories:linear,polynomial,and *** this research paper,different methods will be implemented to solve the linear regression problem,where there is a linear relationship between the target and the predicted *** methods for linear regression will be analyzed using the calculated Mean Square Error(MSE)between the target values and the predicted outputs.A huge set of regression samples will be used to construct the training dataset with selected sizes.A detailed comparison will be performed between three methods,including least-square fit;Feed-Forward Artificial Neural Network(FFANN),and Cascade Feed-Forward Artificial Neural Network(CFFANN),and recommendations will be *** proposed method has been tested in this research on random data samples,and the results were compared with the results of the most common method,which is the linear multiple regression *** should be noted here that the procedures for building and testing the neural network will remain constant even if another sample of data is used.

关键词： Linear regression ANN CFFANN FFANN MSE training cycle training set

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Using Combined Knapsack and Shortest Path Problems for Planning Optimal Navigation Paths for Robotic Deliveries 10

Using Combined Knapsack and Shortest Path Problems for Plann...

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10th International Conference on Automation, Robotics, and Applications, ICARA 2024

作者： Voloch, Nadav Zadok, Yair Voloch-Bloch, Noa Hajaj, Maor Meir Ruppin Academic Center Computer Engineering department Emek Hefer Israel Achva Academic College Information Systems department Shikmim Israel Ruppin Academic Center Electrical Engineering department Emek Hefer Israel University of Haifa Information Systems department Haifa Israel

ISBN: (纸本)9798350394245

Today., the use of robotic navigational tools is widely used both in academical research and the technological industry., there is a growing need for planning paths of navigation to autonomous machines., robots and transportation vehicles. In this paper we relate to a specific problem in which the robot has to navigate between several points of delivery., every point contains several items that all have different weights and different values. There are two widely researched problems in computer science algorithms., namely the knapsack problem and the shortest paths on weighted graphs problem. Typically., dynamic programming solutions for the knapsack problem involve using the shortest path problem and creating a knapsack graph. However., this approach only considers the weight and value of each item or vertex. In our case., we introduce a different problem where we consider three properties: item weight., item value., and edge weight (which connects two items but is not dependent on its vertices). Each vertex in this particular graph represents a set of knapsack items. This scenario is applicable to real-life situations where a path has a non-dependent attribute (such as physical distance or travel time) and various types of items need to be selected at different locations along this path. The problem we address here is finding the most efficient path between two vertices (source and target) based on three aspects: minimal edge weight., maximum knapsack value., or a combination of both properties. We present an algorithm for finding these optimal paths., along with detailed explanations of its decision-making process and implementation. © 2024 IEEE.

关键词： Dynamic programming

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Aggregated Model of a Flexible Multi-Energy Distribution System for Coordinated TSO-DSO Fast Frequency Services Provision

Aggregated Model of a Flexible Multi-Energy Distribution Sys...

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2023 International Conference on Smart Energy systems and Technologies, SEST 2023

作者： Barac, Bojana Krpan, Matej Capuder, Tomislav University of Zagreb Faculty of Electrical Engineering and Computing Department of Energy and Power Systems Zagreb Croatia

ISBN: (纸本)9798350397901

The massive penetration of distributed energy resources (DER) encourages the provision of frequency regulation services at the distribution network level following large frequency disturbances occurring at the transmission system side. The availability of reduced-order or aggregated models will facilitate the coordination between transmission and distribution system operators. As there are no models that can capture the dynamics of diverse distributed technologies and their control systems by a unified single device model, this paper presents the single equivalent device based on the virtual synchronous machine (VSM) technology that can subsume the frequency response of different DER and loads. The validation of the proposed model is performed on the adjusted IEEE 9-bus system for different multi-energy systems. Simulated scenarios have demonstrated the accuracy of proposed aggregation with a maximum relative error of 0.1% as well as computational improvement of almost 100%. © 2023 IEEE.

关键词： Electric power distribution

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Bio-Inspired Home Localization Using Event-Based Vision and Spiking Neural Networks in Simulated Environment

Bio-Inspired Home Localization Using Event-Based Vision and ...

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2025 IEEE International Conference on Simulation, Modeling, and programming for Autonomous Robots, SIMPAR 2025

作者： Kausar, Rizwana Zayer, Fakhreddine Sudevan, Vidya Viegas, Jaime Dias, Jorge Khalifa University Department of Electrical Engineering United Arab Emirates Khalifa University Center for Autonomous Robotic Systems United Arab Emirates Khalifa University Department of Computer and Information Engineering United Arab Emirates

ISBN: (纸本)9798331516857

In this work, we present a bio-inspired approach for home localization using event-based visual data and spiking convolutional neural networks (S-CNNs) in a simulated environment within NVIDIA Omniverse. Drawing inspiration from the navigational strategies of insects, which utilize visual cues and homing vectors to return to their nests, our approach leverages event cameras that mimic the asynchronous change-driven visual processing of insect eyes. To process this sparse, event-driven input, we employ spiking neural networks (SNNs), which mirror the spike-based transmission of information in biological neural systems. The proposed system utilizes a quadrotor equipped with an event camera to capture dynamic, asynchronous visual data and processes it using an S-CNN trained to estimate relative home vectors. This vector encoding method is inspired by the unit-circle representation of gaze directions observed in insect homing behavior, as outlined in recent studies. By integrating event-based vision and SNNs, our approach ensures energy-efficient computation, robustness to lighting variations, and adaptability to dynamic scenes. We validate the framework in a 3D environment modeled within ISAAC Sim, where the quadrotor autonomously navigates back to a designated "nest"location. Comparative analysis with conventional frame-based neural networks demonstrates the superiority of the proposed system in terms of efficiency, robustness, and accuracy. This work establishes a novel bio-inspired framework for integrating event-based data and spiking neural networks, paving the way for energy-efficient localization in robotics and smart home environments. Future work will explore the deployment of the system in a multi-quadrotor environment to coordinate collaborative tasks including the integration of multi-modal sensory inputs such as Camera, IMU, Gas sensors and GPS data, and the extension of the framework to real-world settings for further validation and scalability. © 2025 IE

关键词： Convolutional neural networks

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