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4th International Conference on Robotics, electrical and Signal Processing Techniques, ICREST 2025

作者： Khan, Irfan Hasan, Mehedi Islam, Shamsul Reza, Suman Reza, Tuhin Efaz, Erteza Tawsif Faridpur Engineering College Department of Computer Science & Engineering Faridpur Bangladesh Louisiana State University Division of Electrical & Computer Engineering Baton RougeLA United States

ISBN: (纸本)9798331530938

In Bangladesh, most four-legged intersections use a static-timed or manually-controlled traffic signaling approach, which creates irrefutable congestion and requires human involvement. This paper proposes an adaptive traffic control system utilizing the YOLO object detection framework within CNNs to address this issue. The system captures and frames images using cameras facing every road at a roundabout to detect and count vehicles in real-time for providing lights based on the traffic conditions, prioritizing emergency vehicles such as ambulances, firetrucks, and police-car while also maintaining queues. It sets the signal duration considering road condition. Using a dataset of 10,462 images across 12 classifications of automobiles and a transfer learning approach in YOLOv8, the model achieved a mAP50 score of 0.90. This automated method reasonably enhances traffic flow management by dynamically adjusting signal lights without human intervention. © 2025 IEEE.

关键词： Adaptive control systems

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Design of PID Droop Control for Fast Power Sharing in Inverter-Based Microgrid

Design of PID Droop Control for Fast Power Sharing in Invert...

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2024 Energy Conversion Congress and Expo Europe, ECCE Europe 2024

作者： Nazeri, Ahmad Ali Friebe, Jens Zacharias, Peter Faculty of Electrical Engineering Computer Science University of Kassel Department of Power Electronics Kassel Germany

ISBN: (纸本)9798350364446

Microgrids (MGs) serve as central interfaces for distributed generation, predominantly employing voltage source inverters (VSIs). These MGs operate in either autonomous or grid-connected modes, facilitating local power generation and storage. However, autonomous MGs, lacking grid support, are susceptible to instability, particularly with multiple generators sharing power. This paper addresses instability challenges in VSI-based MGs through comprehensive system modeling, analysis, and design. A novel approach is proposed, employing a modified virtual inertia PID droop control with virtual impedance, facilitates swift response to frequency and voltage fluctuations, ensuring precise power allocation among paralleled VSIs. Moreover, this paper presents the small signal analysis of the PID droop control and outlines the secondary control design, encompassing DDSRF-PLL, frequency, and voltage restoration loops. System validation is conducted through MATLAB/Simulink modeling and experimental verification on a hardware-in-the-loop (HiL) testbench, confirming the efficacy of the proposed methodologies in low inertia MGs. © 2024 IEEE.

关键词： MATLAB

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A BOND GRAPH BASED OPTIMAL CAUSALITY ASSIGNMENT METHOD FOR MATSAT- A MATLAB STRUCTURAL ANALYSIS TOOLBOX 15

A BOND GRAPH BASED OPTIMAL CAUSALITY ASSIGNMENT METHOD FOR M...

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15th International Conference on Bond Graph Modeling and Simulation, ICBGM 2024

作者： Fernandes, Arnold A. Kimball, Jonathan W. Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO United States

This paper extends the usage of the MATlab Structural Analysis Toolbox (MATSAT) by including an Optimal Causality Assignment (OCA) method as an alternative to the traditional Sequential Causality Assignment Procedure (SCAP). This method will be particularly beneficial for causality assignment on bond graphs with loops/meshes. First the causality assignment problem is converted to a mixed integer linear programming (MILP) problem considering fixed, preferred, and indifferent causalities, along with causality constraints at junctions and transducers. Then, techniques from the MILP literature are applied to solve this problem and results are shown for BG models with and without loops. © 2024 Society for Modeling & Simulation International (SCS)

关键词： MATLAB

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Data Poisoning Against Federated Learning: Comparative Analysis Under Label-Flipping Attacks and GAN-Generated EEG Data 2

Data Poisoning Against Federated Learning: Comparative Analy...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Alsereidi, Maryam Awadallah, Abeer Alkaabi, Alreem Yoon, Sangyoung Yeun, Chan Yeob Khalifa University Electrical Engineering And Computer Science Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350394962

Federated Learning (FL) has emerged as a privacy-preserving machine learning approach, enabling collaborative model training across devices while maintaining the decentralization of raw data. This paper investigates the application of FL in insider threat detection, a critical aspect of organizational security that addresses potential risks posed by individuals with access to sensitive data. We focus on using Electroencephalogram (EEG) data to identify malicious intentions, which consists of highly sensitive brain signals that we aim to safeguard by employing FL. Despite its advantages, FL still encounters a rising threat from data poisoning attacks. This study investigates the resilience of our FL model against label-flipping attacks, utilizing three classifiers: Multiplayer Perceptron (MLP), Convolutional Neural Network (CNN), and an ensemble learning classifier, the Voting Classifier (VC). Due to insufficient EEG data, a Generative Adversarial Network (GAN) model is utilized to augment and increase the size of the data. Our findings reveal that VC demonstrates the highest performance with an accuracy of 95% for the original dataset. In contrast, CNN is the sole classifier that outperformed others with the GAN-generated dataset, achieving an accuracy of 93.5%. Furthermore, we examine various cases and scenarios of label-flipping, demonstrating that compromising one client (device) in an FL framework has the least overall performance degradation on the model, emphasizing the efficacy of FL in fostering collaborative learning. © 2024 IEEE.

关键词： Generative adversarial networks

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Green Collaborative Inference in RIS-Assisted MEC Networks under Computing Backlog Constraints

Green Collaborative Inference in RIS-Assisted MEC Networks u...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yang, Yang Gursoy, M. Cenk Syracuse University Department of Electrical Engineering and Computer Science SyracuseNY13244 United States

ISBN: (纸本)9798350351255

In this paper, we analyze collaborative inference in a mobile edge computing (MEC) network aided by a reconfigurable intelligent surface (RIS). In particular, we consider multiple user equipments (UEs) with collaborative inference tasks. Our objective is to minimize the long-term average energy consumption subject to a long-term average computing queue backlog constraint. We first transform the considered problem into a Lyapunov optimization problem and then propose a deep reinforcement learning (DRL)-based algorithm to solve it. An optimization subroutine is embedded in the proposed algorithm to directly obtain the optimal RIS coefficients, while the UEs' deep neural network (DNN) partition decisions and computational resource allocations at the MEC server are obtained from the DRL-based algorithm. Numerical results show that the proposed algorithm solves the problem efficiently, and the introduced RIS improves the long-term average energy consumption significantly. Furthermore, the impact of bandwidth is analyzed and the effectiveness of our proposed algorithm is validated. © 2024 IEEE.

关键词： Deep reinforcement learning

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A Partial-Givens-Rotation-Based Symbol Detector for GSM MIMO Systems: Algorithm and VLSI Implementation

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IEEE Systems Journal 2023年第4期17卷 6322-6333页

作者： Yen, Mao-Hsu Lu, Hoang-Yang Lin, Shao-Yueh Lu, Ken-Hua Chan, Chia-Chen National Taiwan Ocean University Department of Computer Science Engineering Keelung202 Taiwan National Taiwan Ocean University Department of Electrical Engineering Keelung202 Taiwan

Recently, generalized spatial modulation (GSM) multiple-input multiple-output (MIMO) systems have attracted intensive research interest due to their advantages in balancing spectral efficiency and interchannel interference. Aiming at satisfactory detection performance based on a feasible hardware architecture, in this paper, a partial Givens rotation (PGR)-based symbol detector with a very-large-scale integration (VLSI) hardware architecture is proposed for GSM MIMO systems. The proposed detector contains three main types of modules: PGR blocks, symbol estimation (SE), and minimization. In particular, compared to conventional Givens rotations (GRs), the proposed PGR mechanism can further reduce computational complexity by at least 36 %. In addition, to ensure numerical stability, only adders, shifters, and comparators are used to implement the SE architecture, avoiding the use of dividers. Furthermore, instead of the 2-norm distance measure, the 1-norm distance measure is used in the proposed detector to reduce the number of multipliers, thereby accelerating the detection speed. Finally, computer simulations showthat the proposed algorithm performs achieves near-optimal performance while incurring a lower computational complexity. Additionally, hardware implementation results achieved in TSMC 90-nm CMOS technology at an operating frequency of 704.2 MHz, with a configuration of four transmit antennas, two active transmit antennas, four receive antennas, and 16-ary quadrature amplitude modulation (16-QAM), show that the proposed hardware architecture needs 395.2 k gates and provides a detection throughput of 2347 Mbps and a hardware efficiency of 5.94 Mbps/kGEs for fast fading channels. In comparison to existing works, the proposed detector provides attractive detection performance as well as a feasible hardware architecture. © 2007-2012 IEEE.

关键词： Spectrum efficiency

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Blockchain-based crowdsourcing for human intelligence tasks with dual fairness

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Blockchain(Research and Applications) 2024年第4期5卷 1-13页

作者： Yihuai Liang Yan Li Byeong-Seok Shin School of Information Science and Technology Southwest Jiaotong UniversityChengdu 611756China Department of Electrical and Computer Engineering Inha UniversityIncheon 22212Republic of Korea

Human intelligence tasks(HITs),such as labeling images for machine learning,are widely utilized for crowdsourcing human *** crowdsourcing platforms face challenges of a single point of failure and a lack of service *** blockchain-based crowdsourcing approaches overlook the low scalability problem of permissionless blockchains or inconveniently rely on existing ground-truth data as the root of trust in evaluating the quality of workers’*** propose a blockchain-based crowdsourcing scheme for ensuring dual fairness(i.e.,preventing false reporting and free riding)and improving on-chain efficiency concerning on-chain storage and smart contract *** proposed scheme does not rely on trusted authorities but rather depends on a public blockchain to guarantee dual *** efficient and publicly verifiable truth discovery scheme is designed based on majority voting and cryptographic *** truth discovery scheme aims at inferring ground truth from workers’*** ground truth is further utilized to estimate the quality of workers’***,a novel blockchain-based protocol is designed to further reduce on-chain costs while ensuring *** scheme has O(n)complexity for both on-chain storage and smart contract computation,regardless of the number of questions,where𝑛denotes the number of *** security analysis is provided,and extensive experiments are conducted to evaluate its effectiveness and performance.

关键词： Crowdsourcing Dual fairness Blockchain Human intelligence task Truth discovery

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Multi-label Classification using Self-Supervised Learning: Addressing Class Inter-Dependency and Data Imbalance 23

Multi-label Classification using Self-Supervised Learning: A...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Mirzaee, Ghazaleh Doretto, Gianfranco Adjeroh, Donald West Virginia University Lane Department of Computer Science & Electrical Engineering WV United States

ISBN: (纸本)9798350374889

Developing multi-label classification models under significant class imbalance, and when annotating data requires expert-level knowledge remains a major challenge. Additionally, interdependency and correlation among labels are common in multi-label problems. In this work, we introduce a novel framework to address these challenges. Our approach extracts robust discriminative features from unlabeled data through self-supervised contrastive learning and uses an adaptive data augmentation mechanism (ACBA) to balance the dataset. Independent binary classifiers are trained for each class, using a new custom Focal Weighted Cross-Entropy (FWCE) loss function to focus on hard-to-classify examples. A correlation learning module then refines predictions by integrating statistical and domain-specific knowledge. Finally, a meta-learner, employing a Gated Recurrent Unit (GRU) and multi-head attention, identifies complex relationships between classes, even for those that rarely occur together. We used the detection of thoracic diseases using chest X-rays, a domain with a major class imbalance and highly associated labels, to validate our approach. Our findings demonstrate the potential of our method to apply to other medical and non-medical imaging scenarios with similar multi-label classification problems. © 2024 IEEE.

关键词： Self-supervised learning

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A Remedial Action Scheme Against False Data Injection Cyberattacks Targeting ULTC Transformers in Smart Distribution Systems

A Remedial Action Scheme Against False Data Injection Cybera...

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2024 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2024

作者： Naderi, Ehsan Asrari, Arash Fajri, Poria College of Engineering and Computer Science Arkansas State University Department of Electrical Engineering JonesboroAR United States Purdue University Northwest Department of Electrical and Computer Engineering HammondIN United States University of Nevada Electrical and Biomedical Engineering Department RenoNV United States

ISBN: (纸本)9798350352290

This paper proposes an on-line remedial action scheme (OLRAS) in order to mitigate the voltage violations caused by false data injection attacks (FDIAs) targeting under load tap changing (ULTC) transformers in smart distribution systems. The FDIA framework contains two different phases. In the attack phase, distribution system operator (DSO), being in attacker's shoe, considers cyberattack scenarios through compromising the results of volt-var optimization problem in a radial distribution grid modified with distributed energy resources (DERs) such as photovoltaic (PV) units and wind turbines (WTs). The outcome of the attack phase will be the compromised voltage profile of the distribution grid showing different rates of voltage violations. In the reaction phase, the DSO rapidly identifies a customized distribution feeder reconfiguration (CDFR) in order to update the flows of active and reactive power throughout the targeted distribution system and recover the voltage profile. The objective functions of the proposed CDFR are defined to minimize the impacts of such cyberattacks targeting ULTCs within distribution grids. This will empower DSOs to react to severe cyberattacks, bypassing the detection stage, and address the voltage violations in a timely manner. The effectiveness of the proposed OLRAS is validated on an IEEE test system. © 2024 IEEE.

关键词： Distribution transformers

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Predictive Analysis of Sarajevo's AQI using Machine Learning Models for Varied Data Granularity and Prediction Windows 47

Predictive Analysis of Sarajevo's AQI using Machine Learning...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Zolota, Emina Hasić, Vahidin Mević, Amina Delić, Amra Krivić, Senka University Of Sarajevo Faculty Of Electrical Engineering Department For Computer Science And Informatics Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9798350382495

This study scrutinizes five years of Sarajevo's Air Quality Index (AQI) data using diverse machine learning models - Fourier autoregressive integrated moving average (Fourier ARIMA), Prophet, and Long short-term memory (LSTM) - to forecast AQI levels. Focusing on various prediction frames, we evaluate model performances and identify optimal strategies for different temporal granularities. Our research unveils subtle insights into each model's efficacy, shedding light on their strengths and limitations in predicting AQI across varied timeframes. This research presents a robust framework for automatic optimization of AQI predictions, emphasizing the influence of temporal granularity on prediction accuracy, automatically selecting the most efficient models and parameters. These insights hold significant implications for data-driven decision-making in urban air quality control, paving the way for proactive and targeted interventions to improve air quality in Sarajevo and similar urban environments. © 2024 IEEE.

关键词： Air quality

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