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A TRUST-REGION METHOD FOR SOLVING TRUNCATED complex SINGULAR VALUE DECOMPOSITION

Journal of Computational Mathematics 2024年第4期42卷 999-1031页

作者： Jiaofen Li Lingchang Kong Xuefeng Duan Xuelin Zhou Qilun Luo School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and ComputationGuangxi Key Laboratory of Automatic Detecting Technology and InstrumentsGuilin University of Electronic TechnologyCenter for Applied Mathematics of Guangxi(GUET)Guilin 541004China School of Mathematical Sciences South China Normal UniversityGuangzhou 510000China School of Mathematics and Computing Science Guilin University of Electronic TechnologyGuilin 541004China School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and ComputationGuilin University of Electronic TechnologyCenter for Applied Mathematics of Guangxi(GUET)Guilin 541004China School of Mathematics and Statistics Yunan UniversityKunming 650000China

The truncated singular value decomposition has been widely used in many areas of science including engineering,and statistics,*** this paper,the original truncated complex singular value decomposition problem is formulated as a Riemannian optimiza-tion problem on a product of two complex Stiefel manifolds,a practical algorithm based on the generic Riemannian trust-region method of Absil et *** presented to solve the underlying problem,which enjoys the global convergence and local superlinear conver-gence *** experiments are provided to illustrate the efficiency of the proposed *** with some classical Riemannian gradient-type methods,the existing Riemannian version of limited-memory BFGS algorithms in the MATLAB toolbox Manopt and the Riemannian manifold optimization library ROPTLIB,and some latest infeasible methods for solving manifold optimization problems,are also provided to show the merits of the proposed approach.

关键词： Truncated singular value decomposition Riemannian optimization Trust-region method

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A data-driven rolling optimization method for trajectory tracking error prediction of CNC machine tools

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Science China(Technological Sciences) 2025年第1期68卷 270-285页

作者： Yinxin GUAN Jixiang YANG Shizhong TAN Han DING School of Mechanical Science and Engineering State Key Laboratory of Intelligent Manufacturing Equipment and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

The dynamic performance of the feed-drive system in CNC machine tools directly influences the accuracy of machined *** enhance the motion control performance of CNC machine tools,a high-precision model of the feed-drive system is ***,current modeling methods for feed-drive systems seldom consider time-varying factors such as loads,wear,and *** a result,the model accuracy degrades when the system characteristics are affected by these time-varying *** this paper,a rolling optimization method with partial weights frozen is developed to realize quick iterative learning of a data-driven model for a feed drive system with time-varying characteristics using a small amount of ***,the long short-term memory fully connected(LSTM-FC)network is built and divided into feature extraction and output fitting parts based on their ***,a weight freezing-based rolling optimization method is *** weights in the feature extraction part are frozen,which preserves the learned common knowledge and patterns by solidifying the way that high-dimensional features are extracted from the *** adjusting the weights in the output fitting part,the extracted highdimensional features are remapped to the new data distribution changed by time-varying ***,the performance of the developed rolling optimization method is confirmed by *** results show that the proposed rolling optimization method reduces the maximum prediction errors by 49.5%and the total training time by 96.3%compared with existing methods,which demonstrates that the proposed method can restore model accuracy when the system characteristics change due to timevarying factors,and significantly accelerate the optimization process by rolling optimization.

关键词： rolling optimization feed-drive system data-driven time-varying machine tool

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Accurate Parameter Determination for Photovoltaic systems Using Meta-Heuristic methods: A Comparative Study with Particle Swarm optimization and Multiple Objective Functions 6

Accurate Parameter Determination for Photovoltaic Systems Us...

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6th International Symposium on Advanced Electrical and Communication Technologies, ISAECT 2024

作者： Abazine, Ismail Elyaqouti, Mustapha Arjdal, El Hanafi Saadaoui, Driss Hmamou, Dris Ben Elhammoudy, Abdelfattah Lidaighbi, Souad Choulli, Imade Ibn Zohr University Laboratory of Materials Signals Systems and Physical Modelling Faculty of Science Agadir Morocco

ISBN: (纸本)9798331529987

The effective implementation of photovoltaic (PV) systems heavily relies on accurate electrical modeling, where The Single Diode Model (SDM) stands out for its ability to accurately capture the complex behavior of photovoltaic (PV) modules. The SDM involves five unknown parameters, presenting a challenge for precise determination. This study investigates and compares of meta-heuristic methods in addressing this parameter determination challenge. Specifically, Particle Swarm optimization (PSO) is employed using two distinct objective functions: and Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The validation of these methods is conducted on a PV module provided by RTC FRANCE under standard test conditions (STC). The outcomes of PSO indicate a realistic and accurate determination of the unknown parameters and precise prediction of the I-V curve for the PV module. This research contributes valuable insights into the application of meta-heuristic methods for enhancing the precision and reliability of electrical modeling in PV systems. © 2024 IEEE.

关键词： Mean square error

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A hybrid Feature Selection Based on Fisher Score and SVM-RFE for Microarray Data

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第1期48卷 57-68页

作者： Hamla, Hind Ghanem, Khadoudja Laboratory of Modelling and Implementation of Complex System Department of Computer Science University of Abdelhamid Mehri Constantine 2 Constantine Algeria

Microarray data analysis has played a significant role in disease diagnosis and tumor type identification over the last two decades. However, due to the curse of dimensionality issues, microarray data classification remains a challenging task. This issue arises from a situation where the number of features is large, but the number of samples is small. As a result, dimension reduction techniques, specifically feature selection methods, are critical for removing non-informative features and improving cancer classification. This paper presents a Filter-embedded hybrid feature selection method to address the gene selection challenge in microarray data analysis. First, it selects the features with the highest Fisher score to create a candidate subset for the next embedded stage. Second, the proposed method employs support vector machine-recursive feature elimination (SVM-RFE) on the candidate subset to identify the optimal set of features to enhance cancer classification. Extensive experiments were conducted with ten high-dimensional microarray datasets to assess the efficacy of the proposed approach. The results show that the proposed method improves classifier performance significantly regarding classification accuracy, number of selected features, and computational efficiency. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Computational efficiency

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Bed inventory balance and stability of dual circulating fluidized bed systems

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Particuology 2024年第1期84卷 98-106页

作者： Yangjun Wei Leming Cheng David Pallares Bo Leckner Institute for Thermal Power Engineering State Key Laboratory of Clean Energy UtilizationZhejiang UniversityHangzhou310027China Department of Space Earth and EnvironmentChalmers University of TechnologyS-41296GöteborgSweden

Experiments and modelling are conducted for general understanding on the imbalance phenomenon and for discussion on the strategies to improve the bed inventory balance and stability of a dual circulating fluidized bed(DCFB)*** experiments are carried out in a 15.5 m high pilot-scale DCFB cold test system.A fluid-dynamic DCFB model is developed based on a 1.5-dimensional semi-empirical model of an industrial CFB boiler,validated by experimental *** effects of control methods and operation conditions on the bed inventory balance and on the stability of the pilot-and large-scale DCFB systems are discussed.A"stable-unbalanced"state,where the system reaches a steady state,consisting of un-balanced bed inventories,is observed in both experiments and model *** maintain a balanced state and similar bed inventory in the DCFB systems with similar cross-sectional areas,it is recom-mended to keep similar total pressure drops or gas velocities in both reactors.

关键词： Bed inventory balance Stability Dual circulating fluidized bed(DCFB) Experiment modelling

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A novel method for predicting cutting force considering tool wear by power consumption in milling process

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Journal of Manufacturing Processes 2025年 149卷 632-646页

作者： Shi, Kaining Shi, Xudong Li, Jiale Qiang, Biyao Ren, Junxue Shi, Yaoyao Key Laboratory of High Performance Manufacturing for Aero Engine Ministry of Industry and Information Technology Northwestern Polytechnical University Shaanxi Xi'an710072 China Engineering Research Center of Advanced Manufacturing Technology for Aero Engine Ministry of Education Northwestern Polytechnical University Shaanxi Xi'an710072 China State Key Laboratory of Cemented Carbide Northwestern Polytechnical University Shaanxi Xi'an710072 China

Accurate prediction of cutting forces is crucial for machining process monitoring, parameter optimization, and tool wear management. Traditional dynamometer-based methods are limited by cost and applicability constraints, especially under complex cutting conditions. This study proposes an innovative indirect method for predicting cutting forces by integrating tool wear considerations into a power consumption model. A mechanistic model incorporating shearing and edge forces affected by flank wear is developed, with power consumption further characterized by cutting parameters and tool wear conditions. A simulated annealing (SA) optimization algorithm is employed to identify key model parameters, enabling the accurate prediction of instantaneous cutting forces. Experimental validation, conducted under varying spindle speeds, feed rates, and cutting depths, demonstrates the effectiveness of the proposed method. The mean tool wear and cutting force prediction errors are maintained below 11 μm and 20 N, respectively. Comparative analysis of search strategies highlights the superior performance of exponential cooling strategy in SA optimization, ensuring convergence efficiency and prediction accuracy. Furthermore, the model's robustness is validated across novel cutting conditions and independent datasets. This methodology offers a cost-effective alternative to conventional monitoring systems by leveraging machine power signals, enhancing process adaptability while ensuring reliability. The proposed method significantly contributes to machining precision, proactive maintenance, and real-time process optimization. Future work will expand its applicability to multi-axis milling and complex surfaces, addressing challenges in instantaneous force prediction under diverse cutting scenarios. © 2025 The Society of Manufacturing Engineers

关键词： Milling (machining)

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Advanced Machine Learning methods for Prediction of Blast-Induced Flyrock Using hybrid SVR methods

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1595-1617页

作者： Ji Zhou Yijun Lu Qiong Tian Haichuan Liu Mahdi Hasanipanah Jiandong Huang College of Civil and Environmental Engineering Hunan University of Science and EngineeringYongzhou425006China Hunan Provincial Key Laboratory of Intelligent Protection and Utilization Technology in Masonry Artifacts Hunan University of Science and EngineeringYongzhou425006China School of Civil Engineering Guangzhou UniversityGuangzhou510006China Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam School of Engineering&Technology Duy Tan UniversityDa Nang550000Vietnam

Blasting in surface mines aims to fragment rock masses to a proper ***,flyrock is an undesirable effect of blasting that can result in human *** this study,support vector regression(SVR)is combined with four algorithms:gravitational search algorithm(GSA),biogeography-based optimization(BBO),ant colony optimization(ACO),and whale optimization algorithm(WOA)for predicting flyrock in two surface mines in ***,three other methods,including artificial neural network(ANN),kernel extreme learning machine(KELM),and general regression neural network(GRNN),are employed,and their performances are compared to those of four hybrid SVR *** modeling,the measured and predicted flyrock values are validated with some performance indices,such as root mean squared error(RMSE).The results revealed that the SVR-WOA model has the most optimal accuracy,with an RMSE of 7.218,while the RMSEs of the KELM,GRNN,SVR-GSA,ANN,SVR-BBO,and SVR-ACO models are 10.668,10.867,15.305,15.661,16.239,and 18.228,***,combining WOA and SVR can be a valuable tool for accurately predicting flyrock distance in surface mines.

关键词： Flyrock induced by blasting optimization algorithms SVR GRNN

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A call to action for building energy system modelling in the age of decarbonization

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Journal of Building Performance Simulation 2024年第3期17卷 383-393页

作者： Wetter, Michael Sulzer, Matthias Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

As urban energy systems become decarbonized and digitalized, buildings are increasingly interconnected with one another and with the industrial and transportation sector. Transformation strategies to cost-effectively integrate distributed energy sources, and to increase load flexibility and efficiency, generally increase complexity. This complexity causes challenges that the industry is unprepared to deal with. Today's simulation programs, and the processes in which they are used, have not been developed to meet the challenges of decarbonization. Nor have they been designed for, or do they keep pace with, the energy system digitalization. Modeling, simulation and optimization tools, and the processes in which they are used, need to undergo an innovation jump. We show a path to more holistic tools and workflows that address the new requirements brought forward by the increased complexity. Without concerted actions, the building simulation community will fall short of supporting the 2050 decarbonization targets declared by many governments. ©, This work was authored as part of the Contributor's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.

关键词： Decarbonized energy systems optimization modelling complexity risk building energy model

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Review on the Research Progress and Application of IPMC Sensors

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Journal of Bionic Engineering 2024年第6期21卷 2687-2716页

作者： Gengying Wang Yi Sun Aihong Ji GuoXiao Yin Hengzao Ge Xuefei Liu Xiaojie Tong Min Yu Jiangsu Key Laboratory of Bionic Materials and Equipment College of Mechanical and Electrical EngineeringNanjing University of Aeronautics and AstronauticsNanjing210016China

Ionic Polymer Metal Composites(IPMCs)are considered important electroactive polymers that have recently attracted the attention of the scientific community owing to their simple structure,adaptable form,high degree of flexibility,and biocompatibility during their utilization as sensing *** these lines,in this work,the recent developments in performance optimization,model construction,and applications of IPMC sensors were *** methods were introduced to enhance the sensitivity,preparation efficiency,and stability of the IPMC sensors including optimising the electrode and substrate membrane preparation,as well as implementing structural and shape modifications,*** IPMC sensing model,which serves as the theoretical foundation for the IPMC sensor,was summarized herein to offer directions for future application research *** applications of these sensors in a wide range of areas were also reviewed,such as wearable electronic devices,flow sensors,humidity sensors,energy harvesting devices,etc.

关键词： Ionic polymer metal composites Sensors Performance optimisation modelling simulation IPMC sensing applications

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Optimal Sliding Mode Control Based on Particle Swarm optimization for Wind Turbine 6

Optimal Sliding Mode Control Based on Particle Swarm Optimiz...

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6th International Conference on Intelligent Computing in Data Sciences, ICDS 2024

作者： Laina, Hajar Bougdour, Soukaina Sefriti, Bouchra Sefriti, Selma Boumhidi, Ismail Ben Abdellah University LISAC Laboratory Faculty of Sciences Sidi Mohammed Dept. of Physics Fes Morocco

ISBN: (纸本)9798350351200

The following research provides a unique technique that optimizes wind turbine control through the merging of Sliding Mode Control (SMC) and Particle Swarm optimization (PSO). Wind turbines are complex systems subject to non-linearities and external disturbances, necessitating advanced control strategies for efficient operation. Sliding Mode Control is robust against such uncertainties, providing stability and high performance. However, determining the optimal gain for SMC remains a challenge. This study employs PSO, a powerful heuristic optimization technique, to fine-Tune the SMC gain of robustness. The PSO algorithm iteratively searches for the optimal solution by simulating a social behavior pattern, which is effective in navigating the high-dimensional parameter space of the wind turbine model. The simulation findings demonstrate that the suggested SMC-PSO hybrid strategy significantly enhances the turbine's efficiency and stability under varying wind conditions, outperforming traditional methods. This optimization framework provides a robust and effective solution for maximizing the energy output of wind turbines, ensuring sustainable and efficient renewable energy generation. © 2024 IEEE.

关键词： Sliding mode control

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