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Spindle Thermal Error Optimization Modeling of a Five-axis Machine Tool

Chinese Journal of Mechanical Engineering 2017年第3期30卷 746-753页

作者： Qianjian GUO Shuo FAN Rufeng XU Xiang CHENG Guoyong ZHAO Jianguo YANG School of Mechanical Engineering Shandong University of Technology School of Mechanical Engineering Shanghai Jiao Tong University

Aiming at the problem of low machining accu- racy and uncontrollable thermal errors of NC machine tools, spindle thermal error measurement, modeling and compensation of a two turntable five-axis machine tool are researched. Measurement experiment of heat sources and thermal errors are carried out, and GRA（grey relational analysis） method is introduced into the selection of tem- perature variables used for thermal error modeling. In order to analyze the influence of different heat sources on spindle thermal errors, an ANN （artificial neural network） model is presented, and ABC（Artificial bee colony） algorithm is introduced to train the link weights of ANN, a new ABC- NN（Artificial bee colony-based neural network） modeling method is proposed and used in the prediction of spindle thermal errors. In order to test the prediction performance of ABC-NN model, an experiment system is developed, the prediction results of LSR （least squares regression）, ANN and ABC-NN are compared with the measurement results of spindle thermal errors. Experiment results show that the prediction accuracy of ABC-NN model is higher than LSR and ANN, and the residual error is smaller than 3 pm, the new modeling method is feasible. The proposed research provides instruction to compensate thermal errors and improve machining accuracy of NC machine tools.

关键词： Five-axis machine tool Artificial bee colony Thermal error modeling artificial neural network

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Optimization and design of an aircraft's morphing wing-tip demonstrator for drag reduction at low speed, Part Ⅰ–Aerodynamic optimization using genetic, bee colony and gradient descent algorithms

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Chinese Journal of Aeronautics 2017年第1期30卷 149-163页

作者： Andreea Koreanschi Oliviu Sugar Gabor Joran Acotto Guillaume Brianchon Gregoire Portier Ruxandra Mihaela Botez Mahmoud Mamou Youssef Mebarki LARCASE Laboratory of Applied Research in Active Control Avionics and Aeroservoelasticity Ecole de Technologie Superieure Montreal H3C1K3 Canada Aerodynamics Laboratory NRC Aerospace National Research Council Canada Ottawa K1A0R6 Canada

In this paper, an ‘in-house＇ genetic algorithm is described and applied to an optimization problem for improving the aerodynamic performances of an aircraft wing tip through upper surface morphing. The algorithm＇s performances were studied from the convergence point of view, in accordance with design conditions. The algorithm was compared to two other optimization methods,namely the Artificial bee colony and a gradient method, for two optimization objectives, and the results of the optimizations with each of the three methods were plotted on response surfaces obtained with the Monte Carlo method, to show that they were situated in the global optimum region. The optimization results for 16 wind tunnel test cases and 2 objective functions were presented. The 16 cases used for the optimizations were included in the experimental test plan for the morphing wing-tip demonstrator, and the results obtained using the displacements given by the optimizations were evaluated.

关键词： Artificial bee colony Airfoil optimization Genetic algorithm Morphing wing Optimization

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Email Filtering Using Hybrid Feature Selection Model

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Computer Modeling in Engineering & Sciences 2022年第8期131卷 435-450页

作者： Adel Hamdan Mohammad Sami Smadi Tariq Alwada’n Computer Science Department The World Islamic Sciences and Education UniversityAmmanJordan Information System and Networks Department The World Islamic Sciences and Education UniversityAmmanJordan Network and Cybersecurity Department Teesside UniversityMiddlesbroughUK

Undoubtedly,spam is a serious problem,and the number of spam emails is increased rapidly.Besides,the massive number of spam emails prompts the need for spam detection techniques.Several methods and algorithms are used for spam filtering.Also,some emergent spam detection techniques use machine learning methods and feature extraction.Some methods and algorithms have been introduced for spam detecting and filtering.This research proposes two models for spam detection and feature selection.The first model is evaluated with the email spam classification dataset,which is based on reducing the number of keywords to its minimum.The results of this model are promising and highly acceptable.The second proposed model is based on creating features for spam detection as a first stage.Then,the number of features is reduced using three well-known metaheuristic algorithms at the second stage.The algorithms used in the second model are Artificial bee colony(ABC),Ant colony Optimization(ACO),and Particle Swarm Optimization(PSO),and these three algorithms are adapted to fit the proposed model.Also,the authors give it the names AABC,AACO,and APSO,respectively.The dataset used for the evaluation of this model is Enron.Finally,well-known criteria are used for the evaluation purposes of this model,such as true positive,false positive,false negative,precision,recall,and F-Measure.The outcomes of the second proposed model are highly significant compared to the first one.

关键词： Feature selection Artificial bee colony ant colony optimization particle swarm optimization spam detection emails filtering

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Optimal Machine Learning Driven Sentiment Analysis on COVID-19 Twitter Data

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Computers, Materials & Continua 2023年第4期75卷 81-97页

作者： Bahjat Fakieh Abdullah S.AL-Malaise AL-Ghamdi Farrukh Saleem Mahmoud Ragab Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department HECI SchoolDar Alhekma UniversityJeddahSaudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Centre for artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia

The outbreak of the pandemic,caused by Coronavirus Disease 2019(COVID-19),has affected the daily activities of people across the globe.During COVID-19 outbreak and the successive lockdowns,Twitter was heavily used and the number of tweets regarding COVID-19 increased tremendously.Several studies used Sentiment Analysis(SA)to analyze the emotions expressed through tweets upon COVID-19.Therefore,in current study,a new Artificial bee colony(ABC)with Machine Learning-driven SA(ABCMLSA)model is developed for conducting Sentiment Analysis of COVID-19 Twitter data.The prime focus of the presented ABCML-SA model is to recognize the sentiments expressed in tweets made uponCOVID-19.It involves data pre-processing at the initial stage followed by n-gram based feature extraction to derive the feature vectors.For identification and classification of the sentiments,the Support Vector Machine(SVM)model is exploited.At last,the ABC algorithm is applied to fine tune the parameters involved in SVM.To demonstrate the improved performance of the proposed ABCML-SA model,a sequence of simulations was conducted.The comparative assessment results confirmed the effectual performance of the proposed ABCML-SA model over other approaches.

关键词： Sentiment analysis twitter data data mining COVID-19 machine learning Artificial bee colony

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Improved PLL Tuning of Shunt Active Power Filter for Grid Connected Photo Voltaic Energy System

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Circuits and Systems 2016年第10期7卷 3063-3080页

作者： Rajmohan Rajalakshmi Vairamani Rajasekaran Department of Electrical and Electronics Engineering Coimbatore Institute of Technology Coimbatore India Department of Electrical and Electronics Engineering PSNA College of Engineering and Technology Dindigul India

This research work brings out the unique predictive current control method for attaining an efficient grid connected Photo Voltaic (PV) system by Shunt Active Power Filter (SAPF) as grid connected converter. The major objective of the research work is to address the presence of Direct Current (DC) component, frequency improvement, quicker theta response, voltage magnitude estimation in the input signal of the Phase Locked Loop (PLL) which is challenging. This work focuses on tuning the PLL block (Kp, Ki, Kv and Ko) through Artificial bee colony (ABC) optimization algorithm. The proposed ABC based modified three-phase PLL method is based on adding a new loop inside the PLL structure. In power converters, ABC algorithm is used to select the optimal switching states. The voltage vector which minimizes a cost optimization function is selected. Simulation is carried out for both balanced and unbalanced system and the results validate that the performance of the proposed approach is better in terms of harmonic compensation as per the IEEE standards within ±5%, power factor improvement of the system, quicker theta tracking and suppression of frequency jump with the interconnection of PV system.

关键词： Shunt Active Filter Phase Locked Loop Artificial bee colony Current Control Method PV System

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bee Spices Transition with Rapid Global Optimization Algorithm

Bee Spices Transition with Rapid Global Optimization Algorit...

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作者： Xu-ming HAN Lin-lin WANG Li ZHENG Yuan-yuan DANG Li-min WANG School of Computer Science and Engineering Changchun University of Technology School of Management Science and Information Engineering Jilin University Finance and Economics Changchun

Swarm intelligence algorithm is a general term for a class of intelligent groups with self-organized behavior. A novel Artificial bee colony algorithm is introduced in this paper, named bee Spices Transition with Rapid Global Optimization Algorithm（BSTRGOA）. The contribution of BSTRGOA algorithm consists of two parts. First, the evolution strategy of bee species balances the global exploration ability and the local search ability. Second, the fast search mechanism of global optimization is proposed. It reinforces global learning of the colony. The BSTRGOA algorithm is experimented with the CEC 2017 benchmark functions. It shows the BSTRGOA algorithm can effectively improve the convergence speed and optimization accuracy. And the BSTRGOA algorithm has a good performance in solving the problem of multi-dimensional function optimization. In addition, BSTRGOA algorithm is also applicable to dynamic economic scheduling analysis, and the conclusion also shows it can significantly improve the time efficiency.

关键词： bee spices transition Function optimization Meta heuristic Artificial bee colony Dynamic economic dispatch

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