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International Conference on Big Data, Information and Computer Network (BDICN)

作者： Cheng, Pengfei Pan, Siche Xian Univ Sci & Technol Xian 710699 Peoples R China Dalian Univ Sci & Technol Dalian 116052 Peoples R China

ISBN: (纸本)9781665484763

Occluded face recognition is a key problem to be solved in a reality-oriented face recognition system. From the point of view of subspace regression, this paper reviews the general methods of mainstream linear regression classifiers to deal with occlusion problems: collaborative representation, dictionary representation of occlusion, and learning and compression techniques of occlusion dictionaries. The existing iterative weight error coding methods are reviewed from two aspects of noise suppression and occlusion detection. In this paper, two ideas to solve the occlusion problem in a modern face recognition system are proposed: the restoration of the occlusion region and the location of occlusion based on attention. The application systems for these two ideas are given respectively. This paper emphasizes the importance of feature extraction in solving the problem of occluded face recognition. Finally, it points out the challenges that occluded face recognition brings to computer vision;the main problems existing in existing methods in optimization algorithms and feature extraction, and the key problems to be considered in using convolution neural networks to deal with occlusion problems in the future. It is introduced that solving the problem of face recognition under occlusion brings many benefits to life.

关键词： occluded face recognition feature extraction dictionary representation occlusion detection optimization algorithms

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Coverage Path Planning Techniques for Inspection of Disjoint Regions With Precedence Provision

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IEEE ACCESS 2021年 9卷 5412-5427页

作者： Khanam, Zeba Saha, Sangeet Ehsan, Shoaib Stolkin, Rustam Mcdonald-Maier, Klaus Univ Essex Sch Comp Sci & Elect Engn CSEE Colchester CO4 3SQ Essex England Univ Birmingham Sch Met & Mat Birmingham B15 2TT W Midlands England

Recent times are witnessing an emergence of sites that are hazardous for human access. This has created a global demand to equip agents with the ability to autonomously inspect such environments by computing a coverage path effectively and efficiently. However, inspection of such sites requires agents to consider the correlation of work, providing precedence provision in visiting regions. The current approaches to compute coverage path in the hazardous sites, however, do not consider precedence provision. To this end, coverage path planning strategies are proposed, which provide precedence provision. To meet the challenges, the problem is divided into two phases: inter-region and intra-region path planning. In the 'inter-region' path planning of the approach, the site comprising of multiple disjoint regions is modelled as connectivity graph. Two novel approaches, Mixed Integer Linear Programming (MILP) solution and heuristic based techniques, are proposed to generate the ordered sequence of regions to be traversed. In the 'intra-region' path planning of the approach, each region is decomposed into a grid and Boustrophedon Motion is planned over each region. The ability of combined approach to provide complete coverage is proved under minor assumption. An investigative study has been conducted to elucidate the efficiency of the proposed approach in different scenarios using simulation experiments. The proposed approach is evaluated against baseline approaches. The results manifest a significant reduction in cost and execution time, which caters to inspection of target sites comprising of multiple disjoint regions with precedence provision.

关键词： Inspection Path planning Sensors Robots Degradation Radiation monitoring Oils Autonomous systems coverage path planning precedence provision inspection optimization algorithms

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SmartControl: Interactive PID controller design powered by LLM agents and control system expertise

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SOFTWAREX 2025年 31卷

作者： Tohma, Kadir Okur, Halil Ibrahim Gursoy-Demir, Handan Aydin, Merve Nilay Yeroglu, Celaleddin Iskenderun Tech Univ Comp Engn Hatay Turkiye Iskenderun Tech Univ Management Informat Syst Hatay Turkiye

SmartControl is an interactive PID controller design tool powered by the novel integration of Large Language Model (LLM) agents and control system expertise. Users articulate system performance requirements in natural language, which the software converts into numerical targets. Optimal PID gains are determined using Particle Swarm optimization and Differential Evolution algorithms, and the system's step response is simulated and presented graphically. The result evaluation module analyses performance data via an LLM agent, providing interactive feedback and educational support. Furthermore, the tool generates MATLAB code and PDF reports, thereby focusing its utility on efficient control system design workflows in academic research and simulation-driven design studies.

关键词： LLM PID control Interactive simulation Control system design optimization algorithms

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Incremental Affine Abstraction of Nonlinear Systems

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IEEE CONTROL SYSTEMS LETTERS 2021年第2期5卷 653-658页

作者： Hassaan, Syed M. Khajenejad, Mohammad Jensen, Spencer Shen, Qiang Yong, Sze Zheng Arizona State Univ Sch Engn Matter Transport & Energy Tempe AZ 85281 USA Shanghai Jiao Tong Univ Sch Aeronaut & Astronaut Shanghai 200240 Peoples R China

In this letter, we propose an incremental abstraction method for dynamically over-approximating nonlinear systems in a bounded domain by solving a sequence of linear programs, resulting in a sequence of affine upper and lower hyperplanes with expanding operating regions. Although the affine abstraction problem can be solved using a single linear program, existing approaches suffer from a computation space complexity that grows exponentially with the state dimension. Thus, the motivation for incremental abstraction is to reduce the space complexity of abstraction algorithms for high-dimensional systems or systems with limited on-board resources. Specifically, we start with an operating region that is a subregion of the state space and compute a pair of affine hyperplanes that bracket the nonlinear function locally. Then, by incrementally expanding the operating region, we dynamically update the two affine hyperplanes such that we eventually yield hyperplanes that are guaranteed to over-approximate the nonlinear system over the entire domain. Finally, the effectiveness of the proposed approach is demonstrated using several numerical examples.

关键词： Computational methods model controller reduction optimization algorithms large-scale systems

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Distributed Continuous-Time optimization with Uncertain Time-Varying Quadratic Cost Functions

arXiv

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arXiv 2023年

作者： Jiang, Liangze Wu, Zheng-Guang Wang, Lei College of Control Science and Engineering Zhejiang University Hangzhou310027 China

This paper studies distributed continuous-time optimization for time-varying quadratic cost functions with uncertain parameters. We first propose a centralized adaptive optimization algorithm using partial information of the cost function. It can be seen that even if there are uncertain parameters in the cost function, exact optimization can still be achieved. To solve this problem in a distributed manner when different local cost functions have identical Hessians, we propose a novel distributed algorithm that cascades the fixed-time average estimator and the distributed optimizer. We remove the requirement for the upper bounds of certain complex functions by integrating state-based gains in the proposed design. We further extend this result to address the distributed optimization where the time-varying cost functions have nonidentical Hessians. We prove the convergence of all the proposed algorithms in the global sense. Numerical examples verify the proposed algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： optimization algorithms

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AC&DC optimal power flow incorporating centralized/decentralized multi-region grid control employing the whale algorithm

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AIN SHAMS ENGINEERING JOURNAL 2021年第2期12卷 1907-1922页

作者： Swief, R. A. Hassan, Nada Mamdouh Hasanien, Hany M. Abdelaziz, Almoataz Youssef Kamh, M. Z. Ain Shams Univ Fac Engn Cairo Egypt Future Univ Egypt Fac Engn & Technol Cairo Egypt

In this paper, different optimization algorithms are introduced to solve the security-constrained optimal power flow between two connected regions, applying two control modes, namely the centralized and decentralized modes. Traditionally, the decentralized control, i.e., each power system controls its variables regardless of the boundary buses, is the most commonly used approach. However, the centralized control has been recently utilized to enhance the system security, because the active power of the generation units and/or the voltage buses on the boundaries are the two variables, which can mitigate the changes, when compared to the decentralized control solution. The optimization techniques applied propose using the Genetic Algorithm (GA) as a tool to validate the solution reached through the Whale optimization Algorithm. The proposed methodology is investigated on the IEEE 48-bus electric power system, which consists of two regions connected to each other through three transmission lines. The problem can be solved by applying both the AC and DC Optimal Power Flow respectively, using three approaches, namely: 1- Changing the active power of generation unit;2- Controlling the voltage magnitude and 3- Combining the changes in the real power of generation units and the voltage variations. All the proposed approaches are implemented taking into consideration the technical constraints. All of these different techniques are constructed and modeled by utilizing the Matlab code. The results of each case study are presented;and a final comparison between all the methods utilized is drawn to demonstrate the advantages of each method. The novelty of this paper is represented in investigating the effectiveness of applying the AC Optimal Power Flow and the DC Power Flow, utilizing meta-heuristic techniques. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University.

关键词： AC optimal power flow Centralized technique DC optimal power flow Decentralized technique Genetic algorithm Optimal power flow optimization algorithms Whale optimization algorithm

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Developed Approach Based on Equilibrium Optimizer for Optimal Design of Hybrid PV/Wind/Diesel/Battery Microgrid in Dakhla, Morocco

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IEEE ACCESS 2021年 9卷 13655-13670页

作者： Kharrich, Mohammed Kamel, Salah Abdeen, Mohamed Mohammed, Omar Hazem Akherraz, Mohammed Khurshaid, Tahir Rhee, Sang-Bong Mohammed V Univ Mohammadia Sch Engn Dept Elect Engn Rabat 10090 Morocco Aswan Univ Dept Elect Engn Fac Engn Aswan 81542 Egypt Al Azhar Univ Dept Elect Engn Fac Engn Cairo 11651 Egypt Northern Tech Univ Dept Tech Power Engn Tech Coll Mosul 41002 Iraq Yeungnam Univ Dept Elect Engn Gyongsan 38541 South Korea

In this paper, a new application of Equilibrium Optimizer (EO) is proposed for design hybrid microgrid to feed the electricity to Dakhla, Morocco, as an isolated area. EO is selected to design the microgrid system due to its high effectiveness in determining the optimal solution in very short time. EO is presented for selecting the optimal system design which can minimize the cost, improve the system stability, and cover the load at different climate conditions. Microgrid system consists of photovoltaic (PV), wind turbine (WT), battery, and diesel generator. The objective function treated in this paper is to minimize the net present cost (NPC), respecting several constraints such as the reliability, availability, and renewable fraction. The sensitivity analysis is conducted in two stages: Firstly, the impact of wind speed, solar radiation, interest rate, and diesel fuel on the NPC, and levelized cost of energy (LCOE) is analyzed. Secondly, the influence of size variation on loss of power supply probability (LPSP) is investigated. The results obtained by EO are compared with those obtained by recent metaheuristics optimization algorithms, namely, Harris Hawks Optimizer (HHO), Artificial Electric Field Algorithm (AEFA), Grey Wolf Optimizer (GWO), and Sooty Tern optimization Algorithm (STOA). The results show that the optimal system design is achieved by the proposed EO, where renewable energy sources (PV and WT) represent 97% of the annual contribution and fast convergence characteristics are obtained by EO. The best NPC, LCOE, and LPSP are obtained via EO achieving 74327 $, 0.0917 $/kWh, and 0.0489, respectively.

关键词： optimization Microgrids Generators Renewable energy sources Batteries Wind speed Wind energy Economic energy optimal system design optimization algorithms net present cost hybrid renewable energy system microgrid solar energy PV panels wind energy energy storage

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Optimality Conditions for Interval-Valued optimization Problems on Riemannian Manifolds Under a Total Order Relation

arXiv

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arXiv 2023年

作者： Bhat, Hilal Ahmad Iqbal, Akhlad Aftab, Mahwash Department of Mathematics Aligarh Muslim University Uttar Pradesh Aligarh202002 India

This article explores fundamental properties of convex interval-valued functions defined on Riemannian manifolds. The study employs generalized Hukuhara directional differentiability to derive KKT-type optimality conditions for an interval-valued optimization problem on Riemannian manifolds. Based on type of functions involved in optimization problems, we consider the following cases: • objective function as well as constraints are real-valued;• objective function is interval-valued and constraints are real-valued;• objective function as well as contraints are interval-valued. The whole theory is justified with the help of examples. The order relation that we use throughout the paper is a total order relation defined on the collection of all closed and bounded intervals in R. Copyright © 2023, The Authors. All rights reserved.

关键词： optimization algorithms

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Hyperspectral image segmentation using 3D regularized subspace clustering model

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JOURNAL OF APPLIED REMOTE SENSING 2021年第1期15卷

作者： Hinojosa, Carlos Rojas, Fernando Castillo, Sergio Arguello, Henry Univ Ind Santander Dept Comp Sci Bucaramanga Colombia

The accurate segmentation of remotely sensed hyperspectral images has widespread attention in the Earth observation and remote sensing communities. In the past decade, most of the efforts focus on the development of different supervised methods for hyperspectral image classification. Recently, the computer vision community is developing unsupervised methods that can adapt to new conditions without leveraging expensive supervision. In general, among unsupervised classification methods, sparse subspace clustering (SSC) is a popular tool that achieves good clustering results on experiments with real data. However, for the specific case of hyperspectral clustering, the SSC model does not take into account the spatial information of such images, which limits its discrimination capability and hampering the spatial homogeneity of the clustering results. As a solution, we propose to incorporate a regularization term to the SSC model, which takes into account the neighboring spatial information of spectral pixels in the scene. Specifically, the proposed method uses a three-dimensionall (3D) Gaussian filter to perform a 3D convolution on the sparse coefficients, obtaining a piecewise-smooth representation matrix that enforces an averaging constraint in the SSC optimization program. Extensive simulations demonstrate the effectiveness of the proposed method, achieving an overall accuracy of up to 99% in the selected hyperspectral remote sensing datasets. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： hyperspectral image segmentation subspace clustering sparse subspace clustering optimization algorithms

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The "Black-Box" optimization Problem: Zero-Order Accelerated Stochastic Method via Kernel Approximation

arXiv

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arXiv 2023年

作者： Lobanov, Aleksandr Bashirov, Nail Gasnikov, Alexander Moscow Institute of Physics and Technology 9 Institutskiy per. Dolgoprudny141701 Russia Skolkovo Institute of Science and Technology 30 Bolshoy Boulevard bld. 1 Moscow121205 Russia ISP RAS Research Center for Trusted Artificial Intelligence 25 A. Solzhenitsyn st. Moscow125047 Russia Institute for Information Transmission Problems 19 B. Karetny per. Moscow127051 Russia Innopolis University 1 Universitetskaya Str. Innopolis420500 Russia

In this paper, we study the standard formulation of an optimization problem when the computation of gradient is not available. Such a problem can be classified as a "black box" optimization problem, since the oracle returns only the value of the objective function at the requested point, possibly with some stochastic noise. Assuming convex, and higher-order of smoothness of the objective function, this paper provides a zero-order accelerated stochastic gradient descent (ZO-AccSGD) method for solving this problem, which exploits the higher-order of smoothness information via kernel approximation. As theoretical results, we show that the ZO-AccSGD algorithm proposed in this paper improves the convergence results of state-of-the-art (SOTA) algorithms, namely the estimate of iteration complexity. In addition, our theoretical analysis provides an estimate of the maximum allowable noise level at which the desired accuracy can be achieved. Validation of our theoretical results is demonstrated both on the model function and on functions of interest in the field of machine learning. We also provide a discussion in which we explain the results obtained and the superiority of the proposed algorithm over SOTA algorithms for solving the original problem. Copyright © 2023, The Authors. All rights reserved.

关键词： optimization algorithms

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