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Guaranteed Cost Gain-Scheduled Control of Typical Aeroelastic Section Model

JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2025年第3期48卷 714-722页

作者： Pereira, Renan L. Gonzalez, Pedro J. Silvestre, Flavio J. Aeronaut Inst Technol Elect Engn Div BR-12228900 Sao Jose Dos Campos Brazil Tech Univ Berlin Flight Control & Aeroelast FMRA D-10587 Berlin Germany Tech Univ Berlin Flight Control & Aeroelast FMRA Marchstr 12 D-10587 Berlin Germany

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Research on the Operation optimization of Wind Storage Combined System Based on Compressed Air Energy Storage Technology

Research on the Operation Optimization of Wind Storage Combi...

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2025 International Conference on Electrical Automation and Artificial Intelligence, ICEAAI 2025

作者： Lang, Yitao Zhou, Zuxu Liu, Quanliang PowerChina Renewable Energy Company Ltd. Beijing China Institute of Renewable Energy and Energy Storage Technologies PowerChina Renewable Energy Company Ltd. Beijing China POWERCHINA HeBei Electric Power Engineering Co. Ltd. HeBei China Hebei Electric Power Design & Survey Technology Innovation Center HeBei China

ISBN: (纸本)9798331506797

With the expansion of the scale of renewable energy units connected to the power system, the problems of volatility and instability brought about by them are becoming more and more prominent. Compressed air energy storage technology has the advantages of high storage capacity, long life and small special investment, which is expected to become an effective means to promote the consumption of renewable energy and maintain the stability of the power grid. However, the current relationship between CAES and renewable energy capacity is not clear, which leads to its low economic efficiency and affects its promotion. In this paper, CAES technology is combined with wind power generation to construct a new joint system configuration scheme, constructs a physical structure model, and considers the equipment operation constraints, establishes a two-layer optimization model, with the upper layer taking the ROI as the objective, and the lower layer taking the volume of the storage tank as the objective, and solves the problem using the NSGA-II algorithm. The example results show that the two-layer optimization model is economical and can provide a reference for the operation decision of the wind storage system. © 2025 IEEE.

关键词： combined wind storage system compressed air energy storage operation optimization optimization algorithm

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Study on soil salinity inversion of different crop types based on multi-time series

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PLANT AND SOIL 2025年 1-17页

作者： Chen, Xi Yang, Shuqing Wen, Xiaoyu Wang, Yuxuan Wang, Wei Inner Mongolia Agr Univ Coll Water Conservancy & Civil Engn Hohhot 010018 Peoples R China Baotou Inst Agr & Anim Husb Sci & Technol Baotou 014020 Peoples R China Inner Mongolia Hengyuan Water Conservancy Engn Co Hohhot 010010 Inner Mongolia Peoples R China

Background and AimsSoil salinization is a major cause of land degradation and ecological damage. Traditional soil salinity monitoring techniques are limited in coverage and scalability, while remote sensing offers broader applicability and efficiency. This study addresses spatiotemporal variations in soil salt content (SSC) inversion across crop types in Tongliao City, Inner Mongolia, China, using an innovative integration of multi-temporal data and crop cover types, improving remote sensing monitoring *** sampling data and Sentinel-2 images from June to September in 2021 and 2022 were utilized. The deep learning U-net model classified key crops, including sunflower (33%), beet (12%), and maize (55%), and analyzed the effects of crop coverage on SSC across multiple time series. Six spectral variables were selected using the SVR-RFE model (R2 = 0.994, MAE = 0.016). SSC prediction models were developed using three machine learning methods (DBO-RF, PSO-SVM, BO-BP) and a deep learning method (Transformer).ResultsConsidering crop coverage variations improved the sensitivity of spectral variables to SSC response, enhancing predictive accuracy and model stability. Crop classification showed that the salinity index (SIs) correlated more strongly with SSC than the vegetation index (VIs), with SI6 having the highest correlation coefficient of 0.50. The Transformer model, using multi-time series data, outperformed other algorithms, achieving an average R2 of 0.71. The SSC inversion map from the Transformer model closely matched field survey *** research provides a novel approach to soil salinity prediction using satellite remote sensing, offering a scalable solution for monitoring salinization and valuable insights for environmental management and agricultural planning.

关键词： Soil salinity prediction optimization algorithm Deep learning Crop classification

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Virtual Target Selection for a Multiple-Pursuer-Multiple-Evader Scenario

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2025年第5期22卷 391-400页

作者： Weintraub, Isaac E. Von Moll, Alexander Casbeer, David W. Manyam, Satyanarayana G. Air Force Res Lab Control Sci Ctr Wright Patterson AFB OH 45433 USA InfoSciTex Corp 4027 Col Glenn Hwy Dayton OH 45431 USA

This paper considers an N-pursuer-M-evader scenario involving L virtual targets. The virtual targets serve as an intermediary target for the pursuers, allowing the pursuers to delay their final assignment to the evaders. However, upon reaching the virtual target, the pursuers must decide which evader to capture. It is assumed that there are more pursuers than evaders and that the pursuers are faster than the evaders. The objective is two-part: first, assign each pursuer to a virtual target and evader such that the pursuer team's energy is minimized, and, second, choose the virtual targets' locations for this minimization problem. The approach taken is to consider the Apollonius geometry between each pursuer's virtual target location and each evader. Using the constructed Apollonius circles, the pursuer's travel distance and maneuver at a virtual target are obtained. These metrics serve as a gauge for the total energy required to capture a particular evader and are used to solve the joint virtual target selection and pursuer-evader assignment problem. This paper provides a mathematical definition of this problem, the solution approach taken, and an example. Following the example, a Monte Carlo analysis is performed, demonstrating the efficacy of the algorithm and its suitability for real-time applications.

关键词： Mixed Integer Linear Programming Optimal Control Theory optimization algorithm Weapon Target Assignment Monte Carlo Analysis Guidance, Navigation, and Control Systems Missile Defense

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optimization algorithms for Maximizing the Slot Filling Factor of Technically Feasible Slot Geometries and Winding Layouts 6

Optimization Algorithms for Maximizing the Slot Filling Fact...

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6th International Electric Drives Production Conference (EDPC)

作者： Herrmann, Patrick Stenzel, Peer Voegele, Ulrich Endisch, Christian Tech Hsch Ingolstadt Res Grp Electromobil & Learning Syst D-85049 Ingolstadt Germany AUDI AG D-85045 Ingolstadt Germany

ISBN: (纸本)9781509029099

High power density and efficiency are key factors for automotive traction machines. One possible way to reach these goals is to increase the slot filling factor. As yet, most research has either been focused on optimizing the slot geometry with a given magnet wire diameter or on finding the optimal diameter for a given geometry. Oftentimes the submitted results lead either to a magnetically suboptimal stator geometry or the suggested winding pattern and geometries are not producible. The introduction of the needle winding technology, as an alternative to the insertion technology for the manufacturing of stators of automotive traction machines, enabled a defined wire placement in the slot. To use the full benefit of this advantage an optimal and producible winding layout is necessary. Therefore, in this article new optimization algorithms are proposed and compared to algorithms found in literature with regard to reachable slot filling factors and producibility. In a case study, the best performing algorithm was used to obtain an optimal combination of wire diameter and slot geometry to maximize the filling factor. With the proposed algorithm feasible winding patterns and slot geometries with an optimized filling factor can be obtained.

关键词： Slot Filling Factor optimization algorithm Slot Geometry Filling Factor optimization Needle Winding

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The Bedbug Meta-heuristic algorithm to Solve optimization Problems

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Journal of Bionic Engineering 2023年第5期20卷 2465-2485页

作者： Kouroush Rezvani Ali Gaffari Mohammad Reza Ebrahimi Dishabi Department of Computer Engineering Miyaneh BranchIslamic Azad UniversityMiyanehIran Department of Computer Engineering Tabriz BranchIslamic Azad UniversityTabrizIran

Small parasitic Hemipteran insects known as bedbugs(Cimicidae)feed on warm-blooded mammal’s *** most famous member of this family is the Cimex lectularius or common *** current paper proposes a novel swarm intelligence optimization algorithm called the Bedbug Meta-Heuristic algorithm(BMHA).The primary inspiration for the bedbug algorithm comes from the static and dynamic swarming behaviors of bedbugs in *** two main stages of optimization algorithms,exploration,and exploitation,are designed by modeling bedbug social interaction to search for *** proposed algorithm is benchmarked qualitatively and quantitatively using many test functions including *** results of evaluating BMHA prove that this algorithm can improve the initial random population for a given optimization problem to converge towards global optimization and provide highly competitive results compared to other well-known optimization *** results also prove the new algorithm's performance in solving real optimization problems in unknown search *** achieve this,the proposed algorithm has been used to select the features of fake news in a semi-supervised manner,the results of which show the good performance of the proposed algorithm in solving problems.

关键词： Bedbug Meta-Heuristic algorithm optimization algorithm BMHA

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optimization algorithms used in Cognitive Radio Networks: An Overview 2

Optimization Algorithms used in Cognitive Radio Networks: An...

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2nd International Conference on Computer Science and Software Engineering (CSASE)

作者： Salih, Kurdistan Mohsin Shakir, Mohammed Ahmed Univ Duhok Elect & Comp Engn Duhok Kurdistan Regio Iraq

ISBN: (纸本)9781665426329

With the massive demand for spectrum resources due to the massive increase of wireless devices, it was necessary to manage the scarcity of radio spectrum resources. Cognitive Radio is a technology for efficiently using the available spectrum resources in a wireless communication system. However, with the help of using various optimization algorithms, Cognitive Radio can manage and utilize the spectrum of resources more efficiently. This paper gives an overview of the state-of-art research that utilizes many optimization algorithms for different purposes such as sensing, allocating, sharing, and mobilizing the spectrum for better utilization and improving the throughput, convergence speed, delay, and minimization the interference. The main algorithms enclosed in this paper are Genetic algorithm, Particle Swarm optimization, Ant Colony optimization, and Artificial Bee Colony optimization algorithm.

关键词： Cognitive Radio optimization algorithm Genetic algorithm Particle Swarm optimization Ant Colony optimization Bee Colony optimization

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HFSA: hybrid feature selection approach to improve medical diagnostic system

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PEERJ COMPUTER SCIENCE 2025年 11卷 e2764-e2764页

作者： Rabie, Asmaa H. Aldawsari, Mohammed Saleh, Ahmed I. Saraya, M. S. Rashad, Metwally Mansoura Univ Fac Engn Comp & Control Syst Engn Dept Mansoura Egypt Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Alddawasir Comp Engn & Informat Dept Al Kharj Saudi Arabia Benha Univ Fac Comp & Artificial Intelligence Banha Egypt

Thanks to the presence of artificial intelligence methods, the diagnosis of patients can be done quickly and accurately. This article introduces a new diagnostic system (DS) that includes three main layers called the rejection layer (RL), selection layer (SL), and diagnostic layer (DL) to accurately diagnose cases suffering from various diseases. In RL, outliers can be removed using the genetic algorithm (GA). At the same time, the best features can be selected by using a new feature selection method called the hybrid feature selection approach (HFSA) in SL. In the next step, the filtered data is passed to the naive Bayes (NB) classifier in DL to give accurate diagnoses. In this work, the main contribution is represented in introducing HFSA as a new selection approach that is composed of two main stages;fast stage (FS) and accurate stage (AS). In FS, chi-square, as a filtering methodology, is applied to quickly select the best features while Hybrid optimization algorithm (HOA), as a wrapper methodology, is applied in AS to accurately select features. It is concluded that HFSA is better than other selection methods based on experimental results because HFSA can enable three different classifiers called NB, K-nearest neighbors (KNN), and artificial neural network (ANN) to provide the maximum accuracy, precision, and recall values and the minimum error value. Additionally, experimental results proved that DS, including GA as an outlier rejection method, HFSA as feature selection, and NB as diagnostic mode, outperformed other diagnosis models.

关键词： Feature selection Diagnosis NB classifier optimization algorithm Diseases Artificial intelligence Machine learning Filter methods Wrapper methods Healthcare

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optimization algorithms for Local Features

Optimization Algorithms for Local Features

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International Conference on Automation, Communication, Architectonics and Materials

作者： Cao, Jian Chen, Hongqian Mao, Mingyi Beijing Technol & Business Univ Coll Comp & Informat Engn Beijing 100048 Peoples R China

ISBN: (纸本)9783037851036

Feature spaces optimization plays a very important role in object recognition and categorization. After analyzing of several fashionable local features at present, some optimization algorithms based on the information theory are proposed. In this paper, we describe the approaches to recognize generic objects using these features which have been optimized. As baselines for comparison, we also implemented some additional recognition systems using other optimization algorithms. The performance analysis on the obtained experimental results demonstrates that the proposed optimization algorithms are effective and efficient.

关键词： optimization algorithm local features object recognition feature selection clustering

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Compressor airfoil optimization method driven by data-mechanism integration based on evolutionary multi-tasking algorithm

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AEROSPACE SCIENCE AND TECHNOLOGY 2024年 148卷

作者： Cheng, Jinxin Zhang, Yong Chen, Jiang Ma, Hui Liu, Beiying Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221008 Peoples R China Beihang Univ Sch Energy & Power Engn Beijing 100191 Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China

To address the challenge of the "curse of dimensionality" in aerodynamic design optimization of compressors, this study introduces an innovative optimization technique suitable for compressor airfoil design. This technique, rooted in a hybrid mechanism-data-driven approach, seamlessly integrates a hierarchical parameterization method, based on elliptic topological deformation, into a multitasking evolutionary algorithm framework. This integration deviates from the conventional approach of treating parameterization methods and optimization algorithms as distinct elements. The proposed method positions airfoil parameterization as its core, constructing two tasks within the optimization algorithm. It leverages the critical influence of the parameterization method on the aerodynamic performance landscape of the airfoils and the intrinsic qualities of the hierarchical parameterization method in the design space. The multitasking evolutionary optimization framework facilitates effective information exchange between tasks, significantly boosting optimization efficiency. In comparison to standard data-driven multitasking evolutionary algorithms, the proposed method achieves superior optimized solutions with merely 11 x D aerodynamic performance evaluations, where D denotes the number of design variables.

关键词： Hybrid driven optimization Multitasking evolutionary optimization algorithm Curse of dimensionality

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