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Cost-Oriented Design optimization of Single Building Curtain Wall

BUILDINGS 2023年第3期13卷

作者： Liu, Lei Wang, Lu Guo, Zhijian Luo, Zhiwei Hubei Univ Technol Sch Civil Engn Architecture & Environm Wuhan 430068 Peoples R China China Construction Dongfang Decorat Co Ltd Shanghai 200435 Peoples R China

A building curtain wall is an outer protective structure of a building composed of a panel and a supporting structure system. As the main materials of a building curtain wall, the optimization of the keel and panel is a problem that has attracted much attention from decoration enterprises. In order to reduce the cost of constructing a single building curtain wall and improve the economic benefits of decoration enterprises, in this study, the keel is taken as the key optimization objective, and a comprehensive optimization scheme is proposed for the deepening design stage of a single building curtain wall. This scheme firstly reduces the cost of the keel by optimizing the radius of the keel and then considers the overall degree of fit between the panel and the keel while optimizing the keel. Finally, in order to improve the processing efficiency of the keel, a reverse-cutting method is proposed based on the traditional forward-cutting method and then combined with the forward-cutting method to form a comprehensive cutting method. The feasibility of the optimization of both design and driving cost is verified with practical cases. The results show the following: Firstly, compared with the theoretical scheme, the average rate of cost saving for the keel when using the comprehensive optimization scheme is 9.56%. Secondly, the overall degree of fit between the optimized keel and the panel is evaluated, which effectively reduces the discontinuity of panel installation. Finally, the comprehensive cutting method proposed here is shown to improve the material utilization rate compared with the single forward-cutting method.

关键词： single curtain wall comprehensive optimization scheme optimization algorithm comprehensive cutting deepening design

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A Jaya algorithm based wrapper method for optimal feature selection in supervised classification

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第6期34卷 3851-3863页

作者： Das, Himansu Naik, Bighnaraj Behera, H. S. Veer Surendra Sai Univ Technol Dept Informat Technol Sambalpur 768018 Odisha India Veer Surendra Sai Univ Technol Dept Comp Applicat Sambalpur 768018 Odisha India

In recent years, Jaya optimization algorithm has been successfully applied in several optimization problems. This paper presents a novel feature selection (FS) approach based on Jaya optimization algorithm (FSJaya) along with supervised machine learning techniques to select the optimal features. This approach uses a search technique to find the best suitable features by updating the worst features to reduce the dimensions of the feature space. This improves the performance of supervised machine learning techniques. The effectiveness of the proposed approach is evaluated for ten benchmark datasets and compared with several FS approaches such as FS using genetic algorithm (FSGA), FS using particle swarm optimization algorithm (FSPSO), and FS using differential evolutionary (FSDE). The experimental result has shown that the average classification accuracy of FSJaya on most of the datasets is superior over the existing methods such as FSGA, FSPSO, and FSDE. The proof of statistical significance of the proposed approach has been validated by using Friedman and Holm test. This proposed approach is found efficient in selecting an optimal subset of features as compared to other counterparts. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of King Saud University.

关键词： Feature selection Supervised classification Jaya optimization algorithm Wrapper method

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optimization of Multispacecraft Maneuvers for Mobile Target Tracking from Low Earth Orbit

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JOURNAL OF SPACECRAFT AND ROCKETS 2023年第2期60卷 581-590页

作者： Morgan, Sarah J. McGrath, Ciara N. de Weck, Olivier L. Aerosp Corp Performance Modeling & Anal Dept 14745 Lee Rd Chantilly VA 20151 USA Univ Manchester Dept Mech Aerosp & Civil Engn Aerosp Syst Oxford Rd Manchester M13 9PL England MIT Dept Aeronaut & Astronaut Apollo Program Astronaut & Engn Syst 77 Massachusetts Ave Cambridge MA 02139 USA

This paper presents a method of planning spacecraft maneuvers for mobile target tracking. Agile, maneuverable spacecraft have been proposed as a means of modifying satellite orbits to observe discrete targets on the Earth on demand. This work adapts this concept to propose a method of using maneuverable spacecraft to observe a mobile target as it moves across the Earth. Previous work has shown the potential of such an approach to increase persistence of coverage of a moving target. This work applies a suitable optimizer to select possible maneuvers for a spacecraft, or a constellation of spacecraft, to repeatedly observe a moving target. A biased random key genetic algorithm is used, which adjusts the delta V for each maneuver to minimize the total delta V used and maximize target coverage over the full sequence of maneuvers. The developed method is applied to two case studies concerning monitoring of tropical storms. The results indicate that, using relatively small maneuvers, spacecraft orbits can be adjusted to improve the quantity and quality of views of a moving target. In the Typhoon Megi case study, a single spacecraft using less than 2.5 m/s delta V is shown to double the access time and provide two additional observations of the storm eye compared with a nonmaneuvering spacecraft in an identical initial orbit. Opportunities for observations increase as the number of maneuverable spacecraft are increased, with a three-spacecraft constellation able to provide five complete observations of the storm eye for a total change in velocity of 12 m/s.

关键词： Low Thrust Maneuvers Maneuverable Spacecraft Satellite Constellations Genetic algorithm Spacecraft Guidance optimization algorithm

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Multiscale Quantum Harmonic Oscillator algorithm With Strict Metastability Constraints for Multi-Modal optimization

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IEEE ACCESS 2019年 7卷 17377-17388页

作者： Li, Bo Wang, Peng Jin, Jin Chinese Acad Sci Inst Comp Applicat Chengdu 610041 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Tangshan Univ Dept Comp Sci & Technol Tangshan 063000 Peoples R China Southwest Minzu Univ Coll Comp Sci & Technol Chengdu 610225 Sichuan Peoples R China

Multi-modal optimization is a troublesome problem faced by optimization algorithms. The multiscale quantum harmonic oscillator algorithm (MQHOA) utilizes group statistics strategy to evaluate the state of the population and neglects the individual state. It will lead the particles to be trapped in local optima when addressing multi-modal optimization problems. This paper proposes a modified MQHOA by introducing strict metastability constraints strategy (MQHOA-SMC). The new strategy adopts a joint constraint mechanism to make the particle states mutual constraint with each other. The modified algorithm enhances the ability to find a better quality solution in local areas. To demonstrate the efficiency and effectiveness of the proposed algorithm, simulations are carried out with SPSO2011, ABC, and QPSO on classical benchmark functions and with the newly CEC2013 test suite, respectively. The computational results demonstrate that MQHOA-SMC is a competitive algorithm for multi-modal problems.

关键词： Multiscale quantum harmonic oscillator algorithm metastability multi-modal optimization optimization algorithm

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Smart Tool Development for Customized Charging Services to EV Users

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WORLD ELECTRIC VEHICLE JOURNAL 2022年第8期13卷

作者： Zambrano Galbis, Alberto Anton Garcia, Moises Martinez Garcia, Ana Isabel Karatzas, Stylianos Chassiakos, Athanasios Lazari, Vasiliki Ageli, Olympia ETRA Dept Technol Valencia 46018 Spain Univ Patras Civil Engn Dept Rion 26500 Greece

E-mobility is a key element in the future energy systems. The capabilities of EVs are many and vary since they can provide valuable system flexibility services, including management of congestion in transmission grids. According to the literature, leaving the charging process uncontrolled could hinder some of the present challenges in the power system. The development of a suitable charging management system is required to address different stakeholders' needs in the electro-mobility value chain. This paper focuses on the design of such a system, the TwinEV module, that offers high-value services to electric vehicles (EV) users. This module is based on a Smart Charging Tool (SCT), aiming to deliver a more user-central and cooperative approach to the EV charging processes. The methodology of the SCT tool, as well as the supportive optimization algorithm, are explained thoroughly. The architecture and the web applications of TwinEV module are analyzed. Finally, the deployment and testing results are presented.

关键词： electric vehicles charging stations smart charging optimization algorithm

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Rolling Bearing Fault Diagnosis Based on Parameter-Optimized ICEEMDAN and IPOA-SVM 6

Rolling Bearing Fault Diagnosis Based on Parameter-Optimized...

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6th IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： He, Fei Shen, Xiangyun Liaoning Technical University College of Electrical and Control Engineering Huludao China

ISBN: (纸本)9798350305623

Aiming at the problem of low accuracy of traditional rolling bearing fault diagnosis, a fault diagnosis model of parameter optimization Improved Adaptive Noise Complete Ensemble Empirical Mode Decomposition (ICEEMDAN) and Improved Pelican optimization algorithm (IPOA) optimized Support Vector Machine (SVM) was proposed. We employ the optimized ICEEMDAN method, along with Multiscale Fuzzy Entropy (MFE) and Linear Discriminant Analysis (LDA) for modal decomposition and feature extraction of the original bearing vibration signals. To enhance the accuracy and convergence speed of the algorithm, we introduce Tent chaotic mapping, Levy flights, and differential variation perturbation factors into the basic Pelican optimization algorithm. Furthermore, the improved Pelican optimization algorithm is utilized to optimize the penalty parameter (c) and kernel function (g) of SVM, thus enhancing the fault diagnosis accuracy of the SVM algorithm. Experimental results on a public dataset demonstrate that the proposed method achieves an overall recognition rate of 99% for fault diagnosis of rolling bearings under 10 operating conditions. © 2023 IEEE.

关键词： Fault diagnosis Feature extraction Machine learning optimization algorithm Rolling bearing

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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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A new experimental and theoretical approach for viscosity Iranian heavy crude oils based on tuning friction theory and friction volume theory parameters

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INORGANIC CHEMISTRY COMMUNICATIONS 2022年 139卷

作者： Farajpour, Ehsan Ghotbi, Cysus Behbahani, Taraneh Jafari Sharif Univ Technol Dept Chem Engn Tehran Iran Res Inst Petr Ind Tehran *** Iran

In this research work, Friction Theory and Free Volume Theory are applied to live oil characterized based on SARA TEST for viscosity modeling and make a new model in combination with two equation of state (PR and PCSAFT). Parameters for pseudo-components are obtained by tuning the viscosity at atmospheric pressure and temperatures of 10, 20, and 40 ?. A new fitting approach is suggested where the number of fitting parameters is 17 and 12 for FT and FVT model, respectively. These parameters are tuned using the Genetic algorithm and Particle Swarm optimization and make eight new models. The results show that PC-SAFT provides viscosity predictions for all models with less deviation from experimental viscosity. The FT and FVT models have less error for oils with API > 40 and API < 40, respectively. The PC-SAFT + PSO improves the accuracy in viscosity modeling for both FT and FVT models. PSO can play a significant role even more than PC-SAFT.

关键词： Crude oil characterization Friction Theory Free Volume Theory optimization algorithm

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Identification of tissue-specific tumor biomarker using different optimization algorithms

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GENES & GENOMICS 2019年第4期41卷 431-443页

作者： Bhowmick, Shib Sankar Bhattacharjee, Debotosh Rato, Luis Heritage Inst Technol Dept Elect & Commun Engn Kolkata 700107 India Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India Univ Evora Dept Informat P-7004516 Evora Portugal

BackgroundIdentification of differentially expressed genes, i.e., genes whose transcript abundance level differs across different biological or physiological conditions, was indeed a challenging task. However, the inception of transcriptome sequencing (RNA-seq) technology revolutionized the simultaneous measurement of the transcript abundance levels for thousands of *** this paper, such next-generation sequencing (NGS) data is used to identify biomarker signatures for several of the most common cancer types (bladder, colon, kidney, brain, liver, lung, prostate, skin, and thyroid)MethodsHere, the problem is mapped into the comparison of optimization algorithms for selecting a set of genes that lead to the highest classification accuracy of a two-class classification task between healthy and tumor samples. As the optimization algorithms Artificial Bee Colony (ABC), Ant Colony optimization, Differential Evolution, and Particle Swarm optimization are chosen for this experiment. A standard statistical method called DESeq2 is used to select differentially expressed genes before being feed to the optimization algorithms. Classification of healthy and tumor samples is done by support vector machineResultsCancer-specific validation yields remarkably good results in terms of accuracy. Highest classification accuracy is achieved by the ABC algorithm for Brain lower grade glioma data is 99.10%. This validation is well supported by a statistical test, gene ontology enrichment analysis, and KEGG pathway enrichment analysis for each cancer biomarker signatureConclusionThe current study identified robust genes as biomarker signatures and these identified biomarkers might be helpful to accurately identify tumors of unknown origin

关键词： Biomarker Machine learning tools Messenger RNA optimization algorithm Pathway analysis

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Automated Launch Period Selection

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JOURNAL OF SPACECRAFT AND ROCKETS 2022年第5期59卷 1795-1799页

作者： Jesick, Mark CALTECH Jet Prop Lab Inner Planet Mission Anal Grp 4800 Oak Grove Dr Pasadena CA 91109 USA

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