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4th International Conference on electrical engineering and computer Science, ICECOS 2024

作者： Sari, Desi Windi Dwijayanti, Suci Suprapto, Bhakti Yudho Universitas Sriwijaya Faculty of Engineering Department of Electrical Engineering Doctoral Program in Engineering Science Palembang Indonesia Universitas Sriwijaya Faculty of Engineering Department of Electrical Engineering Palembang Indonesia

ISBN: (纸本)9798350368253

A crucial aspect of autonomous vehicles is path planning, which involves using various algorithms to determine the most efficient routes. This study investigates the application of the Ant Colony Optimization (ACO) algorithm to create a system for identifying the shortest path. The process includes collecting coordinate data from specific test locations on the Universitas Sriwijaya Indralaya campus to generate a graph-based map for analysis. The study begins with gathering route data through the Google Maps application, which includes 182 routes. In simulation tests, the routes generated by ACO differ from those suggested by Google Maps;sometimes, the ACO route is longer and sometimes shorter, due to the probabilistic nature of the ACO algorithm in finding optimal routes. The average error compared to Google Maps is 11.7 meters. Additional tests introduce obstacles on the road, requiring the system to find alternative routes. For instance, the initial route from the auditorium to the Faculty of Science and Math is 38-39-11-53-110-54-111 with a route length of 670 m. After adding a roadblock in front of the Faculty of engineering, the route changes to 38-39-116-40-70-69-56-111, with a route length of 730 meters. These tests demonstrate that the ACO algorithm effectively identifies the best routes for autonomous vehicles, both under normal conditions and when obstacles are present. © 2024 IEEE.

关键词： Ant colony optimization

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The Design and Development of a Low SWaP Communications System for a IP Cube Satellite

The Design and Development of a Low SWaP Communications Syst...

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2024 IEEE MIT Undergraduate Research Technology Conference, URTC 2024

作者： Harounian, Joseph Blanchard, Patrick Marnell, Trout Basnet, Saurav Wentworth Institute of Technology Program of Electrical and Computer Engineering Boston United States

ISBN: (纸本)9798331531003

The increased use of small satellites in commercial, defense, and research industries reinforces the need for efficient and reliable communication subsystems. Compact, space rated, communication subsystems present their own unique design challenges. This paper presents the design and implementation of a lower power, low form factor communication subsystem for a IP pico-satellite, developed by a team of undergraduate students from Wentworth Institute of Technology. This effort aims to contribute to the growing field of small satellite communications by providing a robust and efficient solution. © 2024 IEEE.

关键词： Small satellites

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A Case Study for Broadband Networks Performance Testing in Leon County

A Case Study for Broadband Networks Performance Testing in L...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Joseph, Nancy Bernadin, Shonda FAMU-FSU College of Engineering Electrical and Computer Engineering Department Tallahassee United States

ISBN: (纸本)9798331504847

The rapid development of mobile broadband technology has increased the need for coverage. This paper investigates the coverage mapping of the actual cell coverage provided by the three major cellular carriers in Leon County. In addition, the network performance of the three carriers is compared using passive measurements, such as signal strength. The results show AT&T has the best signal strength and signal quality at FAMU-FSU College of engineering. © 2025 IEEE.

关键词： Signaling

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Question Selection for Multi-Modal Code Search Synthesis using Probabilistic Version Spaces

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IEEE Transactions on Software engineering 2025年第6期51卷 1724-1744页

作者： Wu, Jiarong Jiang, Yanyan Wei, Lili Xu, Congying Cheung, Shing-Chi Xu, Chang The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong McGill University Department of Electrical and Computer Engineering Montreal Canada Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing China

Searching the occurrences of specific code patterns (code search) is a common task in software engineering, and programming by example (PBE) techniques have been applied to ease customizing code patterns. However, previous PBE tools only synthesize programs meeting the input-output examples, which may not always align with the user intent. To bridge this gap, this paper proposes Excalibur, a multi-modal (example and natural language description) and interactive synthesizer for code search. Excalibur ensures that the generated programs are correct for the provided examples (soundness) and include the user-intended program (bounded completeness). Furthermore, Excalibur helps the user identify the user-intended program through question-answer interaction. To minimize the required interaction efforts, question selection is crucial. To improve question selection for code search, we propose probabilistic version spaces (ProbVS), in which the user-intended program’s probability is high and others are low. ProbVS combines traditional version spaces for compactly representing extensive programs and large language models (on the user-provided natural language description) for adjusting programs’ probabilities to align with users’ intents. Extensive experiments on a benchmark of 44 tasks demonstrated the effectiveness of Excalibur and ProbVS and demystified how ProbVS affects probability distributions and how the configurable parameters affect ProbVS. © 1976-2012 IEEE.

关键词： Normal distribution

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Temperature Detection System Using Oxide TFTs on a Flexible Substrate

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2023年第6期2卷 464-471页

作者： Shrivastava, Suyash Bahubalindruni, Pydi Ganga Choudhary, Vaishali Iiser Bhopal Department of Electrical Engineering and Computer Science Bhopal462066 India

This article presents a compact temperature detection system using amorphous-InGaZnO thin-film transistor (TFT) technology. The proposed system is fabricated on a 30-μ m-thick polymide substrate. This system consists of a temperature readout circuit together with a novel high-performance clocked comparator. Individual blocks and the complete system have been characterized from measurements at a low-power supply voltage of 3 V and a load of 10 M||12 pF. The measured sensitivity of the temperature readout circuit is 16 mV/C. On the other hand, the comparator is capable of discriminating a minimum differential input voltage (Vin min) of 30 mV and showing the input referred noise of 5 mV at an input signal frequency ( fin) of 10 kHz. The circuit is showing a reliable operation up to an fin of 22.5 kHz with a slight degradation in the output voltage swing. This circuit shows a propagation delay of 3 μ s and a power consumption of 48.15 μ W from measurements. The complete system is able to discriminate a temperature variation down to 2 C. This system would find potential application in biomedical wearable devices for temperature detection. © 2022 IEEE.

关键词： Substrates

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Real Time Fault Detection in 3D Printers Using Convolutional Neural Networks and Acoustic Signals

Real Time Fault Detection in 3D Printers Using Convolutional...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Waheed, Muhammad Fasih Bernadin, Shonda Hassan, Ali Francois, Jayson FAMU-FSU College of Engineering Electrical and Computer Engineering Department Tallahassee United States

ISBN: (纸本)9798331504847

The reliability and quality of 3D printing processes are critically dependent on the timely detection of mechanical faults. Traditional monitoring methods often rely on visual inspection and hardware sensors, which can be both costly and limited in scope. This paper explores a scalable and contactless method for the use of real-time audio signal analysis for detecting mechanical faults in 3D printers. By capturing and classifying audio data during the printing process, we aim to identify common faults such as nozzle clogging, filament breakage, pully skipping and various other mechanical faults. Utilizing Convolutional neural networks, we implement algorithms capable of real-time audio classification to detect these faults promptly. Our methodology involves conducting a series of controlled experiments to gather audio data, followed by the application of advanced machine learning models for fault detection. Additionally, we review existing literature on audio-based fault detection in manufacturing and 3D printing to contextualize our research within the broader field. Preliminary results demonstrate that audio signals, when analyzed with machine learning techniques, provide a reliable and cost-effective means of enhancing real-time fault detection. © 2025 IEEE.

关键词： Model predictive control

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Flexible and Wearable Capacitive Pressure Sensor for Monitoring Heart Parameters

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2023年第2期2卷 77-83页

作者： Singh, Lakhvir Bhattacharjee, Mitradip Iiser Bhopal Department of Electrical Engineering and Computer Science Bhopal462066 India

The increase in global average life expectancy over the past century has been largely attributed to medical science developments. In this study, we demonstrate a polymer-based capacitive pressure sensor for measuring heart rate and pulse pressure (PP) signal. The fabricated sensor has been characterized to evaluate its performance and to verify its repeatability and precision. It is observed that the sensor works in the range of 0-8-kPa pressure range and is suitable for measuring the PP. This sensor has a high sensitivity of 7.11 kPa-1 in the low-pressure range of 0-8 kPa. Furthermore, the sensor demonstrated a considerable amount of repeatability with an error of less than 6% in consecutive measurements. Moreover, the sensor demonstrated the use of a wearable, flexible piezo-capacitive pressure sensor for PP monitoring. The wrist pulse was used as the stimulus for the experimental characterization of the sensor, and health markers related to the measured response were also discussed. © 2022 IEEE.

关键词： Pressure sensors

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Rotor Angle Stability Prediction Using Temporal and Topological Embedding Deep Neural Network Based on Grid-informed Adjacency Matrix

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 695-706页

作者： Peiyuan Sun Long Huo Xin Chen Siyuan Liang the School of Electrical Engineering Xi’an Jiaotong UniversityXi’an 710049China the School of Electrical Engineering and the Center of Nanomaterials for Renewable EnergyState Key Laboratory of Electrical Insulation and Power EquipmentXi’an Jiaotong UniversityXi’an 710049China the Department of Computer Science and Engineering The Chinese University of Hong KongShatin999077Hong KongChina

Rotor angle stability(RAS)prediction is critically essential for maintaining normal operation of the interconnected synchronous machines in power *** wide deployment of phasor measurement units(PMUs)promotes the development of data-driven methods for RAS *** paper proposes a temporal and topological embedding deep neural network(TTEDNN)model to accurately and efficiently predict RAS by extracting the temporal and topological features from the PMU *** grid-informed adjacency matrix incorporates the structural and electrical parameter information of the power *** the small-signal RAS with disturbance under initial operating conditions and the transient RAS with short circuits on transmission lines are *** studies of the IEEE 39-bus and IEEE 300-bus power systems are used to test the performance,scalability,and robustness against measurement uncertainties of the TTEDNN *** show that the TTEDNN model performs best among existing deep learning ***,the superior transfer learning ability from small-signal RAS conditions to transient RAS conditions has been proved.

关键词： Rotor angle stability prediction topological embedding deep learning graph convolution network

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Optimal Design of a Variable Reluctance Resolver to Minimize Position Error

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Transactions of the Korean Institute of electrical Engineers 2024年第1期73卷 44-49页

作者： Kang, Jungmoon Choi, Gilsu Dept. of Electrical and Computer Engineering Inha University Korea Republic of

The precision of position estimation in VR resolvers significantly relies on the harmonic components present in the induced voltage across the secondary winding. The degraded accuracy can be compensated by carefully designing the stator windings and core geometry. This paper presents an optimized design process aimed at minimizing the position error of VR resolvers. The alterations in the resolver output signal's position information, resulting from the phase shift of the stator windings, third harmonic injection-based rotor geometry modifications, and changes to the stator core shape, are thoroughly analyzed using finite element analysis (FEA). Subsequently, a genetic algorithm-based optimization technique is applied to construct an optimal design process that effectively minimizes the position error in VR resolvers. Case study results demonstrate an approximately 80% enhancement in position estimation accuracy for the optimal model compared to the initial model. © 2024 Korean Institute of electrical Engineers. All rights reserved.

关键词： Genetic algorithms

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Synergistic Integration of Machine Learning and Mathematical Optimization for Unit Commitment

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IEEE Transactions on Power Systems 2024年第1期39卷 391-401页

作者： Wu, Jianghua Luh, Peter B. Chen, Yonghong Yan, Bing Bragin, Mikhail A. University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States National Taiwan University Department of Electrical Engineering Taipei10617 Taiwan Miso CarmelIN46032 United States Rochester Institute of Technology Department of Electrical and Microelectronic Engineering RochesterNY14623 United States University of California Department of Electrical and Computer Engineering RiversideCA92507 United States

Unit Commitment (UC) is important for power system operations. With increasing challenges, e.g., growing intermittent renewables and intra-hour net load variability, traditional mathematical optimization could be time-consuming. Machine learning (ML) is a promising alternative. However, directly learning good solutions is difficult in view of the combinatorial nature of UC. This paper synergistically integrates ML within our recent decomposition and coordination method of Surrogate Lagrangian Relaxation to learn 'good enough' subproblem solutions of deterministic UC. Compared to original UC, a subproblem is much easier to learn. Nevertheless, predicting good-enough subproblem solutions is still challenging because of the 'jumps' of binary decisions and many types of constraints. To overcome these issues, subproblem dimensionality is reduced via aggregating multipliers. Multiplier distributions are novelly specified based on 'jumps' for effective learning. Loss functions are innovatively designed to improve prediction qualities. Ordinal Optimization and branch-and-cut are used as backups for unfamiliar cases. Furthermore, online self-learning is seamlessly integrated with offline learning to exploit solutions from daily operations. Results on the IEEE 118-bus system and the Polish 2383-bus system demonstrate that continual learning keeps on improving the subproblem-solving process with near-optimality of the overall solutions maintained. Our method opens a new direction to solve complicated UC. © 1969-2012 IEEE.

关键词： Integer programming

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