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30th International Conference on systems, Signals and Image Processing, IWSSIP 2023

作者： Bozanis, Dimitrios Evgenidis, Nikos G. Papanikolaou, Vasilis K. Bouzinis, Pavlos S. Tegos, Sotiris A. Dowhuszko, Alexis A. Diamantoulakis, Panagiotis D. Karagiannidis, George K. Department of Electrical and Computer Engineering Thessaloniki54124 Greece Germany University of Macedonia Department of Applied Informatics Thessaloniki54636 Greece Aalto University Department of Communications and Networking Espoo02150 Finland Cyber Security Systems and Applied Ai Research Center Lebanon

ISBN: (纸本)9798350337297

Since radio-frequency signals are affected signifi-cantly by multipath fading, the localization accuracy they offer cannot meet the sixth generations (6G) demanding specifications. On the other hand, visible light positioning (VLP) has emerged as a low cost alternative that takes into account the highly deterministic channel characteristics of visible light communications to provide cm-level accuracy provided that the investigated spaces are adequately covered by LEDs. However, imperfect channel state information and modeling can lead to positioning errors. In this paper, localization accuracy is quantified in terms of channel estimation errors via low complexity lateration algorithm. The channel estimation errors can be caused by a variety of issues and the provided analysis is general so that it can be easily applied to a plethora of applications. Finally, Monte Carlo simulations validate the proposed method and offer valuable insights for the design and performance bounds of VLP systems. © 2023 IEEE.

关键词： Monte Carlo methods

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Fast Debiasing of the LASSO Estimator

arXiv

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arXiv 2025年

作者： Banerjee, Shuvayan Saunderson, James Srivastava, Radhendushka Rajwade, Ajit Department of Mathematics IIT Bombay IIT Bombay Monash Research Academy India Department of Electrical and Computer Systems Engineering Monash University Australia Department of Mathematics IIT Bombay India Department of Computer Science and Engineering IIT Bombay India

In high-dimensional sparse regression, the Lasso estimator offers excellent theoretical guarantees but is well-known to produce biased estimates. To address this, [5] introduced a method to "debias" the Lasso estimates for a random sub-Gaussian sensing matrix A. Their approach relies on computing an "approximate inverse" M of the matrix AＴA/n by solving a convex optimization problem. This matrix M plays a critical role in mitigating bias and allowing for construction of confidence intervals using the debiased Lasso estimates. However the computation of M is expensive in practice as it requires iterative optimization. In the presented work, we re-parameterize the optimization problem to compute a "debiasing matrix" W := AMＴ directly, rather than the approximate inverse M. This reformulation retains the theoretical guarantees of the debiased Lasso estimates, as they depend on the product AMＴ rather than on M alone. Notably, we provide a simple, computationally efficient, closed-form solution for W under similar conditions for the sensing matrix A used in the original debiasing formulation, with an additional condition that the elements of every row of A have uncorrelated entries. Also, the optimization problem based on W guarantees a unique optimal solution, unlike the original formulation based on M. We verify our main result with numerical simulations. © 2025, CC BY.

关键词： Iterative methods

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Trajectory Tracking of Tractor-Trailer Wheeled Mobile Robots via Dynamic Feedback Linearization in Forward and Backward Motion 10

Trajectory Tracking of Tractor-Trailer Wheeled Mobile Robots...

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10th International Conference on Control, Instrumentation and Automation, ICCIA 2024

作者： Olyai, Mohammad Alipour, Khalil Tarvirdizadeh, Bahram Sorouri, Majid Ghamari, Mohammad Laboratory Department of Mechatronics Eng School of Intelligent Systems Engineering College of Interdisciplinary Science and Technology Tehran Iran Maynooth University Maynooth Department of Electronic Engineering Co. Kildare Ireland Islamic Azad University Science and Research Branch Department of Computer and Mechatronics Engineering Tehran Iran California Polytechnic State University Electrical Engineering Department San Luis ObispoCA93407 United States

ISBN: (纸本)9798331509972

One type of wheeled mobile robot widely used in public transportation and for carrying high payloads is the tractor-Trailer wheeled robots (TTWRs). This study considers a differentially-driven tractor under pure rolling conditions, which is subject to nonholonomic constraints. Controlling a TTWR in both backward and forward motion is challenging due to inherent instability. To address this issue and achieve trajectory tracking control for these systems, this paper employs dynamic feedback linearization (DFL) to overcome the limitations of static feedback linearization (SFL). The system's response will be examined under various trajectories and initial conditions for both forward and backward motion. © 2024 IEEE.

关键词： Tractors (truck)

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Dual-Objective Mixed Integer Linear Program and Memetic Algorithm for an Industrial group Scheduling Problem

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IEEE/CAA Journal of Automatica Sinica 2021年第6期8卷 1199-1209页

作者： Ziyan Zhao Shixin Liu MengChu Zhou Abdullah Abusorrah State Key Laboratory of Synthetical Automation for Process Industries and the College of Information Science and Engineering Northeastern UniversityShenyang 110819China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah 21481Saudi Arabia Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia

group scheduling problems have attracted much attention owing to their many practical *** work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time,release time,and due *** is originated from an important industrial process,i.e.,wire rod and bar rolling process in steel production *** objective functions,i.e.,the number of late jobs and total setup time,are minimized.A mixed integer linear program is established to describe the *** obtain its Pareto solutions,we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods,i.e.,an insertion-based local search and an iterated greedy *** computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its *** high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems.

关键词： Insertion-based local search iterated greedy algorithm machine learning memetic algorithm nondominated sorting genetic algorithm II(NSGA-II) production scheduling

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Can a few decide for many? the metric distortion of sortition 24

Can a few decide for many? the metric distortion of sortitio...

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Proceedings of the 41st International Conference on Machine Learning

作者： Ioannis Caragiannis Evi Micha Jannik Peters Department of Computer Science Aarhus University Aarhus Denmark Computer Science Harvard University Cambridge Research Group Efficient Algorithms Faculty IV - Electrical Engineering and Computer Science TU Berlin Berlin Germany

Recent works have studied the design of algorithms for selecting representative sortition panels. However, the most central question remains unaddressed: Do these panels reflect the entire population's opinion? We present a positive answer by adopting the concept of metric distortion from computational social choice, which aims to quantify how much a panel's decision aligns with the ideal decision of the population when preferences and agents lie on a metric space. We show that uniform selection needs only logarithmically many agents in terms of the number of alternatives to achieve almost optimal distortion. We also show that Fair Greedy Capture, a selection algorithm introduced recently by Ebadian & Micha (2024), matches uniform selection's guarantees of almost optimal distortion and also achieves constant ex-post distortion, ensuring a "best of both worlds" performance.

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Generalized Continuous-Time Models for Nesterov’s Accelerated Gradient Methods

arXiv

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arXiv 2024年

作者： Park, Chanwoong Cho, Youngchae Yang, Insoon Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Automation and Systems Research Institute Seoul National University Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Automation and Systems Research Institute Seoul National University Seoul08826 Korea Republic of

Recent research has indicated a substantial rise in interest in understanding Nesterov’s accelerated gradient methods via their continuous-time models. However, most existing studies focus on specific classes of Nesterov’s methods, which hinders the attainment of an in-depth understanding and a unified perspective. To address this deficit, we present generalized continuous-time models that cover a broad range of Nesterov’s methods, including those previously studied under existing continuous-time frameworks. Our key contributions are as follows. First, we identify the convergence rates of the generalized models, eliminating the need to determine the convergence rate for any specific continuous-time model derived from them. Second, we show that six existing continuous-time models are special cases of our generalized models, thereby positioning our framework as a unifying tool for analyzing and understanding these models. Third, we design a restart scheme for Nesterov’s methods based on our generalized models and show that it ensures a monotonic decrease in objective function values. Owing to the broad applicability of our models, this scheme can be used to a broader class of Nesterov’s methods compared to the original restart scheme. Fourth, we uncover a connection between our generalized models and gradient flow in continuous time, showing that the accelerated convergence rates of our generalized models can be attributed to a time reparametrization in gradient flow. Numerical experiment results are provided to support our theoretical analyses and results. © 2024, CC BY-NC-ND.

关键词： Continuous time systems

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A robust multi-objective optimization algorithm for accurate parameter estimation for solar cell models

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Soft Computing 2024年第19期28卷 11265-11277页

作者： Singla, Manish Kumar Gupta, Jyoti Alsharif, Mohammed H. Kim, Mun-Kyeom Aljaidi, Mohammad Safaraliev, Murodbek Department of Interdisciplinary Courses in Engineering Chitkara University Institute of Engineering & ampTechnology Chitkara University Punjab India Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology K. R. Mangalam University Gurgaon Haryana122003 India Department of Electrical Engineering College of Electronics and Information Engineering Sejong University 209 Neungdong-Ro Gwangjin-Gu Seoul05006 Korea Republic of School of Energy System Engineering Chung-Ang University 84 Heukseok-Ro Dongjak-Gu Seoul06974 Korea Republic of Department of Computer Science Zarqa University Zarqa13110 Jordan Department of Automated Electrical Systems Ural Federal University Yekaterinburg620002 Russia

The accuracy of solar cell models is crucial for enhancing the performance of solar photovoltaic (PV) systems. However, existing solar cell models lack precise parameters, and the manufacturer's datasheet does not provide the required information for reliable modeling. Consequently, accurate parameter estimation becomes necessary. This paper presents a simple multi-objective optimization algorithm (Hybrid Particle Swarm Optimization and Rat Search Algorithm (PSORSA)) designed to estimate cell parameters based on this observation. Unlike other optimization algorithms addressing this issue, the proposed algorithm aims to overcome challenges related to local minima and premature convergence, which often lead to suboptimal results. The paper focuses on assessing the reliability of the proposed algorithm by comparing its performance with other well-known optimization algorithms. The proposed optimizing algorithm is tested on the CEC 2019 benchmark function. Experimental results (RMSE), including statistical analysis, validate the algorithm's effectiveness by comparing them with other algorithms. At the end, non-parametric test is performed to justify the outcomes, vouching for the better performance of the proposed algorithm. The findings demonstrate that the proposed algorithms are particularly well-suited for estimating solar PV models. With its simple structure and high accuracy, the proposed algorithm exhibits great potential for various applications in the field of solar energy. Moreover, its computational efficiency and ease of implementation further contribute to its practicality. © The Author(s) 2024.

关键词： Solar cells

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Binary Bleed: Fast Distributed and Parallel Method for Automatic Model Selection

Binary Bleed: Fast Distributed and Parallel Method for Autom...

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2024 IEEE High Performance Extreme Computing Conference, HPEC 2024

作者： Barron, Ryan Eren, Maksim E. Bhattarai, Manish Boureima, Ismael Matuszek, Cynthia Alexandrov, Boian S. Theoretical Division Los Alamos National Laboratory Los Alamos United States University of Maryland Baltimore County Department of Computer Science and Electrical Engineering MD United States Advanced Research in Cyber Systems Los Alamos National Laboratory Los Alamos United States

ISBN: (纸本)9798350387131

In several Machine Learning (ML) clustering and dimensionality reduction approaches, such as nonnegative matrix factorization (NMF), RESCAL, and K-Means clustering, users must select a hyper-parameter k to define the number of clusters or components that yield an ideal separation of samples or clean clusters. This selection, while difficult, is crucial to avoid overfitting or underfitting the data. Several ML applications use scoring methods (e.g., Silhouette and Davies Boulding scores) to evaluate the cluster pattern stability for a specific k. The score is calculated for different trials over a range of k, and the ideal k is heuristically selected as the value before the model starts overfitting, indicated by a drop or increase in the score resembling an elbow curve plot. While the grid-search method can be used to accurately find a good k value, visiting a range of k can become time-consuming and computationally resource-intensive. In this paper, we introduce the Binary Bleed method based on binary search, which significantly reduces the k search space for these grid-search ML algorithms by truncating the target k values from the search space using a heuristic with thresholding over the scores. Binary Bleed is designed to work with single-node serial, single-node multi-processing, and distributed computing resources. In our experiments, we demonstrate the reduced search space gain over a naive sequential search of the ideal k and the accuracy of the Binary Bleed in identifying the correct k for NMFk, K-Means pyDNMFk, and pyDRESCALk with Silhouette and Davies Boulding scores. We make our implementation of Binary Bleed for the NMF algorithm available on GitHub. © 2024 IEEE.

关键词： Dimensionality reduction

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Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization

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Neural Computing and Applications 2024年第18期36卷 10501-10525页

作者： Zitar, Raed Abu Alhadhrami, Esra Abualigah, Laith Barbaresco, Frederic Seghrouchni, Amal ElFallah Thales/SCAI Sorbonne University Abu Dhabi United Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Computer Science Department Al al-Bayt University Mafraq25113 Jordan MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon Research Center University of Tabuk Tabuk71491 Saudi Arabia Thales Land and Air Systems Paris France Mohammed VI Polytechnic University-Ai movement Rabat Morocco Sorbonne University Paris France College of Engineering Yuan Ze University Taoyuan Taiwan School of Engineering and Technology Sunway University Malaysia Petaling Jaya Malaysia Applied science research center Applied science private university Amman Jordan

This paper presents a novel hybrid optimization method to solve the resource allocation problem for multi-target multi-sensor tracking of drones. This hybrid approach, the Improved Prairie Dog Optimization Algorithm (IPDOA) with the Genetic Algorithm (GA), utilizes the strengths of both algorithms to improve the overall optimization performance. The goal is to select a set of sensors based on norms of weighted distances cost function. The norms are the Euclidean distance and the Mahalanobis distance between the drone location and the sensors. The second one depends on the predicted covariance of the tracker. The Extended Kalman Filter (EKF) is used for state estimation with proper clutter and detection models. Since we use Multi-objects to track, the Joint Probability Distribution Function (JPDA) estimates the best measurement values with a preset gating threshold. The goal is to find a sensor or minimum set of sensors that would be enough to generate high-quality tracking based on optimum resource allocation. In the experimentation simulated with Stone Soup, one radar among five radars is selected at every time step of 50-time steps for 200 tracks distributed over 20 different ground truths. The proposed IPDOA provided optimum solutions for this complex problem. The obtained solution is an optimum offline solution that is used to select one or more sensors for any future flights within the vicinity of the 5 radars. Environment and conditions are assumed to be similar in future drone flights within the radars’ defined zone. The IPDOA performance was compared with the other 8 metaheuristic optimization algorithms and the testing showed its superiority over those techniques for solving this complex problem. The proposed simulated model can find the most relevant sensor(s) capable of generating the best quality tracks based on weighted distance criteria (Euclidean and Mahalanobis). That would cut down the cost of operating extra sensors and then it would be possible to

关键词： Clutter (information theory)

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Tuning PID Controller Parameters of Automatic Voltage Regulator (AVR) Using Particle Swarm Optimization : A Comparative Study

Tuning PID Controller Parameters of Automatic Voltage Regula...

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IEEE PES Conference on Innovative Smart Grid Technologies - Middle East (ISGT Middle East)

作者： Sultan Alghamdi Awadh Ba Wazir Hassan Hadi H Awaji Abdullah Ali Alhussainy Hatem Faiz Sindi Muhyaddin Rawa Department of Electrical and Computer Engineering Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia Department of Electrical and Computer Engineering King Abdulaziz University1 Jeddah Saudi Arabia

Proportional integral derivative controller is commonly used to control devices, which is a transfer function consisting of three parameters which are: Proportional Gain (kp), Integral Gain (ki) and Derivative Gain (kd). The problem of the PID controller is that the gains (kp, ki and kd) must be chosen carefully in order to get better performance. In this paper, Particle swarm optimization (PSO) applies to obtain these gains. PID controller applies to Automatic Voltage Regulator in this paper. Furthermore, this paper presents simulation results that are used for comparing the proposed gains of PID controller and base system gains based on performance criteria in the time domain (Transient Analysis), including the overshoot, rise time, settling time, and steadystate error. In addition, this paper presents a simulation for the terminal voltage response to show the efficiency of the PSO method compared with the Genetic Algorithm (GA) method and the simulated Annealing (SA) method.

关键词： Regulators Transfer functions Automatic voltage control Steady-state Smart grids Particle swarm optimization Transient analysis

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