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A Dual Bisection Approach to Economic Dispatch of Generators With Prohibited Operating Zones

IEEE CONTROL SYSTEMS LETTERS 2024年 8卷 1547-1552页

作者： Manieri, Lucrezia Falsone, Alessandro Prandini, Maria Politecn Milan Dipartimento Elettron Informaz & Bioingn I-20133 Milan Italy

We address economic dispatch of power generators with prohibited operating zones. The problem can be formulated as an optimization program with a quadratic cost, non-convex local operating constraints, and a scalar quadratic coupling constraint accounting for load demand and power losses. A duality-based resolution approach integrating a bisection iterative scheme is proposed to reduce computational complexity while guaranteeing finite time feasibility of the primal iterates and a cost improvement throughout iterations. Extensive simulations show that the approach outperforms state-of-the-art competitors and consistently computes feasible primal solutions with a close-to-zero optimality gap at a low computational cost.

关键词： Energy systems large-scale systems optimization algorithms

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Prediction of evaporation from dam reservoirs under climate change using soft computing techniques

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第10期30卷 27912-27935页

作者： Kayhomayoon, Zahra Naghizadeh, Fariba Malekpoor, Mohammadreza Azar, Naser Arya Ball, James Milan, Sami Ghordoyee Payame Noor Univ Dept Geol Tehran Iran Univ Guilan Coll Agr Sci Dept Water Engn Rasht Iran Islamic Azad Univ Dept Civil Engn Azarshahr Branch Azarshahr Iran Univ Tabriz Fac Agr Dept Water Engn Tabriz Iran Univ Technol Sydney Sch Civil & Environm Engn Sydney NSW Australia Univ Tehran Dept Irrigat & Drainage Engn Aburaihan Campus Tehran Iran

This study aimed to predict evaporation from dam reservoirs using artificial intelligence considering climate change. Mahabad Dam, near Lake Urmia, in northwestern Iran, is used to investigate the proposed approach. There are three parts to the study presented herein. In the first part, two machine learning models, namely group method of data handling (GMDH) and least squares support vector regression (LS-SVR), were used to model the inflow to the dam reservoir. Temperature, precipitation, and inflow during the previous month from 1990 to 2017 were used as input data. In the second part, the evaporation from the dam reservoir was modeled using the adaptive neuro-fuzzy inference system (ANFIS) and optimized ANFIS using Harris hawks optimization (HHO) and the arithmetic optimization algorithm (AOA) optimization algorithms. The input parameters in this part were temperature, precipitation, inflow to the dam reservoir, along with evaporation from the dam reservoir in the previous month. In the third part, precipitation and temperature were predicted using the fifth report of IPCC based on RCP2.6, RCP4.5, and RCP8.5 scenarios for the period 2020-2040. Out of 28 models presented in the fifth report, EC-ERATH and FIO-ESM had the greatest similarity with observational data of temperature and precipitation, respectively. The results of scatter plots and Taylor's diagram showed the higher performance of LS-SVR (root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash-Sutcliffe efficiency (NSE) of 8.65, 4.69, and 0.96) compared to GMDH (RMSE, MAPE, and NSE of 11.65, 7.81, and 0.93) in modeling the inflow. Moreover, both hybrid modes (AOA-ANFIS and HHO-ANFIS) improved the performance of ANFIS in modeling the evaporation from the dam reservoir. The RMSE, MAPE, and NSE values for ANFIS were 0.56, 0.52, and 0.89, respectively, while these values for the AOA-ANFIS (RMSE, MAPE, and NSE of 0.31, 0.24, and 0.93) and HHO-ANFIS (RMSE, MAPE, and NSE of 0.20, 0.30, a

关键词： Climate change Machine learning optimization algorithms Reservoir evaporation Time-series data

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The Hierarchical Newton's Method for Numerically Stable Prioritized Dynamic Control

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2023年第4期31卷 1622-1635页

作者： Pfeiffer, Kai Escande, Adrien Gergondet, Pierre Kheddar, Abderrahmane Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore 639798 Singapore Ctr Natl Rech Sci Natl Inst Adv Ind Sci & Technol Joint Robot Lab JRL UMI3218 RL Tsukuba 3058560 Japan AIST CNRS UMI3218 RL Joint Robot Lab JRL Tsukuba 3058560 Japan Univ Montpellier CNRS UMR 5506 LIRMMInteract Digital Human F-34095 Montpellier France

This work links optimization approaches from hierarchical least-squares programming to instantaneous prioritized whole-body robot control. Concretely, we formulate the hierarchical Newton's method which solves prioritized nonlinear least-squares problems in a numerically stable fashion even in the presence of kinematic and algorithmic singularities of the approximated kinematic constraints. These results are then transferred to control problems which exhibit the additional variability of time. This is necessary to formulate acceleration-based controllers and to incorporate the second-order dynamics. However, we show that the Newton's method without complicated adaptations is not appropriate in the acceleration domain. We therefore formulate a velocity-based controller which exhibits second-order proportional derivative (PD) convergence characteristics. Our developments are verified in toy robot control scenarios as well as in complex robot experiments which stress the importance of prioritized control and its singularity resolution.

关键词： Task analysis Robots Kinematics optimization Jacobian matrices Newton method Dynamics Constrained control multibody dynamics optimization algorithms robot control

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Harmonized Integration of GWO and J-SLnO for Optimized Asset Management and Predictive Maintenance in Industry 4.0

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SENSORS 2025年第9期25卷 2896-2896页

作者： Arularasan, A. N. Ganeshkumar, P. Alkhatib, Mohammad Albalawi, Tahani BS Abdur Rahman Crescent Inst Sci & Technol Dept Comp Sci & Engn Chennai 600048 Tamil Nadu India Al Imam Mohammad Ibn Saud Islamic Univ IMSIU Coll Comp & Informat Sci Dept Comp Sci Riyadh 11432 Saudi Arabia

Highlights What are the main findings? The study shows that, in terms of execution speed, cost-effectiveness, and energy usage, GWO and J-SLnO perform better than conventional scheduling algorithms. Compared to GWO, J-SLnO performs predictive maintenance activities with greater accuracy and stability. What is the implication of the main finding? In Industry 4.0, the suggested optimization strategies improve the effectiveness of predictive maintenance and asset management. J-SLnO is a dependable option for practical industrial applications that demand endurance and excellent prediction *** What are the main findings? The study shows that, in terms of execution speed, cost-effectiveness, and energy usage, GWO and J-SLnO perform better than conventional scheduling algorithms. Compared to GWO, J-SLnO performs predictive maintenance activities with greater accuracy and stability. What is the implication of the main finding? In Industry 4.0, the suggested optimization strategies improve the effectiveness of predictive maintenance and asset management. J-SLnO is a dependable option for practical industrial applications that demand endurance and excellent prediction *** The study encompasses the application of two different advanced optimization algorithms on asset management and predictive maintenance in Industry 4.0-Grey Wolf optimization and Jaya-based Sea Lion optimization (J-SLnO). Using this derivative, the authors showed how these techniques could be combined through resource scheduling techniques to demonstrate drastic improvement in the level of efficiency, cost-effectiveness, and energy consumption, as opposed to the standard MinMin, MaxMin, FCFS, and Round Robin. In this sense, GWO results in an execution time reduction between 13 and 31%, whereas, in J-SLnO, there is an execution time reduction of 16-33%. In terms of cost, GWO shows an advantage of 8.57-9.17% over MaxMin and Round Robin, based on costs, while J-SLnO delivers a bette

关键词： Industry 4.0 optimization algorithms predictive maintenance Grey Wolf optimization (GWO) J-SLnO

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Online Projected Gradient Descent for Stochastic optimization With Decision-Dependent Distributions

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IEEE CONTROL SYSTEMS LETTERS 2022年 6卷 1646-1651页

作者： Wood, Killian Bianchin, Gianluca Dall'Anese, Emiliano Univ Colorado Dept Appl Math Boulder CO 80027 USA Univ Colorado Dept Elect Comp & Energy Engn Boulder CO 80033 USA

This letter investigates the problem of tracking solutions of stochastic optimization problems with time-varying costs that depend on random variables with decision-dependent distributions. In this context, we propose the use of an online stochastic gradient descent method to solve the optimization, and we provide explicit bounds in expectation and in high probability for the distance between the optimizers and the points generated by the algorithm. In particular, we show that when the gradient error due to sampling is modeled as a sub-Weibull random variable, then the tracking error is ultimately bounded in expectation and in high probability. The theoretical findings are validated via numerical simulations in the context of charging optimization of a fleet of electric vehicles.

关键词： optimization Stochastic processes Random variables Time-varying systems Gradient methods Extraterrestrial measurements Costs optimization optimization algorithms

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Distributed optimization over Networked Systems: Time-Varying Costs and Privacy Preservation

Distributed Optimization over Networked Systems: Time-Varyin...

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作者： Esteki, Amir-Salar University of California Irvine

学位级别：Ph.D., Doctor of Philosophy

In the past decade, machine learning problems in large-scale settings and the need for optimal operational decision-making tools for networked systems have triggered a flurry of new research in the design of optimization algorithms, both methodologically and theoretically. In the wide variety of these large-scale applications, the optimization problems are in the form of either constrained or unconstrained optimization of the sum of objective functions associated with local data in individual data centers or in-network local objectives. In many applications involving large-scale optimizations, there is also a need for real-time adjustment of the system’s response/decision to the present situation, which, thanks to many affordable and deployable sensors can be measured real-time. The primary objectives of this thesis work encompass expediting convergence rates, and accommodating varying network structures in distributed convex optimization problems. To meet these objectives, we study fast converging consensus/agreement algorithms and the design of Newton-like second-order distributed optimization algorithms, which use the knowledge about the Hessian of the cost for accelerated convergence. For distributed optimization problems with time-varying elements such as time-varying cost and constraints, we present specialized distributed algorithms that explicitly take into account the variation of the elements of the problem with time to achieve asymptotic tracking of the time-varying optimizer of the system, lowering the computational cost of the algorithms. In our designs, we give particular attention to practical aspects such as low computation/communication/storage cost and data privacy preservation. Collectively, this thesis pushes the boundaries of optimization algorithms in distributed systems, addressing critical privacy, convergence, and network structure challenges while expanding the realm of possibilities in this evolving field.

关键词： optimization algorithms Large-scale optimizations Machine learning problems

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A decision support tool to analyze the properties of wheat, cocoa beans and mangoes from their NIR spectra

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JOURNAL OF FOOD SCIENCE 2024年第9期89卷 5674-5688页

作者： Parrenin, Loic Danjou, Christophe Agard, Bruno Marchesini, Giancarlo Barbosa, Flavio Polytech Montreal Dept Math & Genie Ind Lab Intelligence Donnees LID 2500 Chem Polytech Montreal PQ H3T 1J4 Canada Polytech Montreal Dept Math & Genie Ind Lab Polyind 4-0 Montreal PQ Canada UniSENAI Campus Florianopolis Lab AI3 Artificial Intelligence Ind Innovat Florianopolis SC Brazil SENAI Innovat Inst Embedded Syst Florianopolis SC Brazil

Near infrared spectroscopy (NIRS) is an analytical technique that offers a real advantage over laboratory analysis in the food industry due to its low operating costs, rapid analysis, and non-destructive sampling technique. Numerous studies have shown the relevance of NIR spectra analysis for assessing certain food properties with the right calibration. This makes it useful in quality control and in the continuous monitoring of food processing. However, the NIR calibration process is difficult and time-consuming. Analysis methods and techniques vary according to the configuration of the NIR instrument, the sample to be analyzed and the attribute that is to be predicted. This makes calibration a challenge for many manufacturers. This paper aims to provide a data-driven methodology for developing a decision support tool based on the smart selection of NIRS wavelength to assess various food properties. The decision support tool based on the methodology has been evaluated on samples of cocoa beans, grains of wheat and mangoes. Promising results were obtained for each of the selected models for the moisture and fat content of cocoa beans (R(2)cv: 0.90, R(2)test: 0.93, RMSEP: 0.354%;R(2)cv: 0.73, R(2)test: 0.79, RMSEP: 0.913%), acidity and vitamin C content of mangoes (R(2)cv: 0.93, R(2)test: 0.97, RMSEP: 17.40%;R(2)cv: 0.66, R(2)test: 0.46, RMSEP: 0.848%), and protein content of wheat-DS2 (R(2)cv: 0.90, R(2)test:0.92, RMSEP: 0.490%) respectively. Moreover, the proposed approach allows results to be obtained that are better than benchmarks for the moisture and protein content of wheat-DS1 (R(2)cv: 0.90, R(2)test: 94, RMSEP: 0.337%;R(2)cv: 0.99, R(2)test: 0.99, RMSEP: 0.177%), respectively.

关键词： decision support tools machine learning food quality near-infrared spectroscopy optimization algorithms

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Multi-Frequency Tracking via Group-Sparse Optimal Transport

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IEEE CONTROL SYSTEMS LETTERS 2024年 8卷 1048-1053页

作者： Haasler, Isabel Elvander, Filip Ecole Polytech Fed Lausanne Signal Proc Lab LTS4 CH-1015 Lausanne Switzerland Aalto Univ Dept Informat & Commun Engn Espoo 02150 Finland

In this letter, we introduce an optimal transport framework for inferring power distributions over both spatial location and temporal frequency. Recently, it has been shown that optimal transport is a powerful tool for estimating spatial spectra that change smoothly over time. In this letter, we consider the tracking of the spatio-temporal spectrum corresponding to a small number of moving broad-band signal sources. Typically, such tracking problems are addressed by treating the spatio-temporal power distribution in a frequency-by-frequency manner, allowing to use well-understood models for narrow-band signals. This however leads to decreased target resolution due to inefficient use of the available information. We propose an extension of the optimal transport framework that exploits information from several frequencies simultaneously by estimating a spatio-temporal distribution penalized by a group-sparsity regularizer. This approach finds a spatial spectrum that changes smoothly over time, and at each time instance has a small support that is similar across frequencies. To the best of the authors' knowledge, this is the first formulation combining optimal transport and sparsity for solving inverse problems. As is shown on simulated and real data, our method can successfully track targets in scenarios where information from separate frequency bands alone is insufficient.

关键词： Estimation Frequency estimation Costs Broadband communication Time-frequency analysis Target tracking Filter banks optimization algorithms large-scale systems

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The Mandalay Derivative for Nonsmooth Systems: Applications to Nonsmooth Control Barrier Functions

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IEEE CONTROL SYSTEMS LETTERS 2024年 8卷 976-981页

作者： Cortes, Carmen Jimenez Clark, Grant Coogan, Samuel Thitsa, Makhin Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Mercer Univ Sch Elect & Comp Engn Macon GA 31207 USA

One of the most challenging aspects of nonsmooth analysis is to overcome non-differentiability. A possible approach is to use the generalized notions of the classical gradient and directional derivatives. In this letter we define a generalized directional derivative, the Mandalay derivative, based on set-valued Lie derivatives. For this operator, we derive the analogues to the classical chain rule, superposition rule (for linear combinations of functions), product rule, and quotient rule in the form of inequalities, which facilitate the computation of the Mandalay derivative in the context of nonsmooth system analysis and design. Moreover, we demonstrate the application of the Mandalay derivative for both first and high-order nonsmooth Control Barrier Functions in multiple examples.

关键词： Lyapunov methods optimization Control systems Closed-form solutions Urban areas Trajectory Terminology constrained control optimization algorithms

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Towards optimal tuned machine learning techniques based vehicular traffic prediction for real roads scenarios

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AD HOC NETWORKS 2024年 161卷

作者： Qaddoura, Raneem Younes, Maram Bani Boukerche, Azzedine Al Hussein Tech Univ Sch Comp & Informat Amman Jordan Philadelphia Univ Informat Technol Amman Jordan Univ Ottawa PARADISE Res Lab Ottawa ON Canada

Smart cities have been widely investigated, and several algorithms, techniques, and protocols have recently been developed to serve smart city environments. Intelligent traffic management is one of the main research fields in this area. It aims to control and monitor the traffic over downtown and highway scenarios. The realtime traffic characteristics on road networks highly affect safety conditions and driving behaviors. Detecting hazardous traffic conditions and congested areas early helps drivers make the best decision to avoid dangerous situations. After extensive training, machine learning algorithms are mainly used for prediction in this field. In the intelligent traffic control target, trained machines are beneficial in obtaining instant and accurate predictions regarding traffic congestion levels and their distributions. Several parameters control the efficiency of the selected machines. This includes the dataset quality, the regression algorithm's parameters, and how they have been tuned. In this work, we first create high -quality datasets representing the moving vehicles for different main roads in various countries, including Bank St in Canada, North Rodeo Drive (NRD) in Los Angeles, USA, Queen Rania Al Abdullah (QRA) road in Jordan, and Sheikh Zayed Road (SZR) in Saudi Arabia. Then, we test the most popular regression algorithms and evaluate their performance in terms of temporal prediction of traffic characteristics and traffic congestion in different geographical road scenarios. We optimized these regression algorithms and tuned the used parameters using the grid search approach. After that, more advanced metaheuristic optimization algorithms were used to improve the prediction accuracy of the selected machine. Experimentally, we have proved the enhancements in predicting traffic characteristics for the tested road scenarios. Both approaches show enhanced results compared to using the regression algorithm without tuning the parameters. It shows advanc

关键词： Machine learning Traffic characteristics Traffic prediction SUMO KNN Grid search Evolutionary algorithms Swarm intelligence optimization algorithms Metaheuristic algorithms

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