Basic oxygen furnace (BOF) steelmaking plays an important role in steelmaking process. Therefore, research on BOF steelmaking modeling is very necessary. In this paper, a novel combination prediction model has been pr...
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Basic oxygen furnace (BOF) steelmaking plays an important role in steelmaking process. Therefore, research on BOF steelmaking modeling is very necessary. In this paper, a novel combination prediction model has been proposed, which consists of a time series prediction model and a compensation prediction model. Both models are established by k-nearest neighbor-based weighted twin support vector regression (KNNWTSVR) algorithm. By introducing Levy flight algorithm and inertia weight, an improved algorithm of whale optimization algorithm (WOA) called Levy flight WOA has been initially proposed to solve the optimization problem in the objective function of KNNWTSVR. The simulation results show that the proposed models are effective and feasible. Within different error bounds (0.005% for carbon content model and 10 degrees C for temperature model), the strike rates of carbon content and temperature both achieve 93%, and a double strike rate of 86% is obtained, which can provide a significant reference for real BOF applications, and the proposed method is also appropriate for the prediction models of other metallurgical applications.
Accurate estimation of pan evaporation (E-p) is of great significance to the development of agricultural irrigation systems and agricultural water resources management. The purpose of this study was to explore the app...
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Accurate estimation of pan evaporation (E-p) is of great significance to the development of agricultural irrigation systems and agricultural water resources management. The purpose of this study was to explore the applicability of coupling extreme learning machine (ELM) model with two new meta-heuristic algorithms, i.e. whale optimization algorithm (WOA) and flower pollination algorithm (FPA) for monthly E-p prediction. To achieve this goal, two hybrid models of WOAELM and FPAELM were developed for predicting monthly E-p in the Poyang Lake Basin of Southern China as a case study. Their performances were further compared with the differential evolution algorithm-optimized ELM (DEELM), improved M5 model tree (M5P) and artificial neural networks (ANN) models. Monthly climatic parameters, including maximum and minimum temperature (T-max and T-min), sunshine duration (n), relative humidity (RH), wind speed (U) and E-p from four weather stations in the basin from 2001 to 2015 were collected, those of which during 2001-2010 were used for model training and those during 2011-2015 for model testing. The obtained results showed that the hybrid FPAELM model exhibited the highest prediction accuracy at all the four stations, followed by the hybrid WOAELM model, both of which were superior to the other traditional models. Heuristic algorithms, especially FPA, are highly recommended for improving performance of standalone machine learning models. Compared with the combination of multi-meteorological elements, the combination of Tmax Tmin and extraterrestrial solar radiation achieved higher but still satisfactory prediction accuracy, with the absolute error less than 0.1 mm d(-1) averaged over the four stations. Tmax Tmin and extraterrestrial solar radiation were thus suggested to be used for monthly E-p estimation in this area considering the convenience of data acquisition.
In this paper, we study the issue of computation offloading in non-orthogonal multiple access (NOMA)-based multi-access edge computing (MEC) systems. A joint optimization problem of offloading decision, subchannel ass...
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In this paper, we study the issue of computation offloading in non-orthogonal multiple access (NOMA)-based multi-access edge computing (MEC) systems. A joint optimization problem of offloading decision, subchannel assignment, transmit power, and computing resource allocation is investigated to improve system performance in terms of both completion time and energy consumption. The formulated problem is a mixed-integer non-linear programming one, it is therefore hard to solve. To make the problem tractable, we first decompose the problem into subproblems of computing resource allocation (CRA), transmit power control (TPC), and subchannel assignment (SA). Then, we address the CRA subproblem by a convex optimization technique. For the remaining two subproblems TPC and SA, we propose to use a gradient-free swarm intelligence approach, namely whale optimization algorithm, to provide a very general but efficient solution. Computer simulations are performed to show the convergence of the proposed algorithm, also its better performance in comparison with conventional schemes.
Support vector machine (SVM) is a widely used pattern classification method that its classification accuracy is greatly influenced by both kernel parameter setting and feature selection. Therefore, in this study, to p...
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Support vector machine (SVM) is a widely used pattern classification method that its classification accuracy is greatly influenced by both kernel parameter setting and feature selection. Therefore, in this study, to perform parameter optimization and feature selection simultaneously for SVM, we propose an improved whale optimization algorithm (CMWOA), which combines chaotic and multi-swarm strategies. Using several well-known medical diagnosis problems of breast cancer, diabetes, and erythemato-squamous, the proposed SVM model, termed CMWOAFS-SVM, was compared with multiple competitive SVM models based on other optimizationalgorithms including the original algorithm, particle swarm optimization, bacterial foraging optimization, and genetic algorithms. The experimental results demonstrate that CMWOAFS-SVM significantly outperformed all the other competitors in terms of classification performance and feature subset size. (C) 2019 Elsevier B.V. All rights reserved.
Due to growth in population, Individual persons with disabilities are increasing daily. To overcome the disability especially in Locked in State (LIS) due to Spinal Cord Injury (SCI), we planned to design four states ...
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Due to growth in population, Individual persons with disabilities are increasing daily. To overcome the disability especially in Locked in State (LIS) due to Spinal Cord Injury (SCI), we planned to design four states moving robot from four imagery tasks signals acquired from three electrode systems by placing the electrodes in three positions namely T1, T3 and FP1. At the time of the study we extract the features from Continuous Wavelet Transform (CWT) and trained with Optimized Neural Network model to analyze the features. The proposed network model showed the highest performances with an accuracy of 93.86 % then that of conventional network model. To confirm the performances we conduct offline test. The offline test also proved that new network model recognizing accuracy was higher than the conventional network model with recognizing accuracy of 97.50 %. To verify our result we conducted Information Transfer Rate (ITR), from this analysis we concluded that optimized network model outperforms the other network models like conventional ordinary Feed Forward Neural Network, Time Delay Neural Network and Elman Neural Networks with an accuracy of 21.67 bits per sec. By analyzing classification performances, recognizing accuracy and Information Transformation Rate (ITR), we concluded that CWT features with optimized neural network model performances were comparably greater than that of normal or conventional neural network model and also the study proved that performances of male subjects was appreciated compared to female subjects.
The advances in the manufacturing industry make it possible to install wind turbines (WTs) with large capacities in offshore wind farms (OWFs) in deep water areas far away from the coast where there are the best wind ...
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The advances in the manufacturing industry make it possible to install wind turbines (WTs) with large capacities in offshore wind farms (OWFs) in deep water areas far away from the coast where there are the best wind resources. This paper proposes a novel method for OWF optimal planning in deep water areas with a circular boundary. A three-dimensional model of the planning area's seabed is established in a cylindrical coordinate. Two kinds of WTs with capacities of 4 and 8 MW respectively are supposed to be mixed-installed in that area. Baseline cases are analyzed and compared to verify the superiority of a circular layout pattern and the necessity of a non-uniform installation. Based on the establishment of the optimization model and a realistic wind condition, a novel heuristic algorithm, i.e., the whale optimization algorithm (WOA), is applied to solve the problem to obtain the type selection and coordinates of WTs simultaneously. Finally, the feasibility and advantages of the proposed scheme are identified and discussed according to the simulation results.
Over the past decades, meta-heuristic optimization techniques have become surprisingly very popular due to their flexibility and local optima avoidance capability. This paper uses the whale optimization algorithm (WOA...
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Over the past decades, meta-heuristic optimization techniques have become surprisingly very popular due to their flexibility and local optima avoidance capability. This paper uses the whale optimization algorithm (WOA), a swarm-based technique to tune the Proportional-Integral (PI) based Maximum Power Point Tracking (MPPT) controllers of a grid-connected solar Photovoltaic (PV) system. The results of the PI-based Incremental Conductance (IC) MPPT technique are compared with both the conventional incremental conductance and the Perturb & Observe (P&O) MPPT techniques. Various modes of the PI controller are used. I, PI and Fractional order PI (FOPI) gain parameters are determined using WOA. Performance indices are applied to estimate the best parameters of the PI controller. This paper aims to show the effect of using PI-based MPPT controllers on enhancing the performance of a 400-kW grid-connected PV system. Simulation results show the capability of PI-based MPPT controllers on improving the performance of the PV system. It demonstrates the superiority of FOPI controllers over the other modes in enhancing system performance. The proposed work is simulated using MATLAB SIMULINK. (C) 2019 The Authors. Published by Elsevier Ltd
Chaotic system parameter modeling is one of the significant topics in the field of non-linear science and therefore has attracted the attention of many researchers in this field. This paper discusses parameter recogni...
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Chaotic system parameter modeling is one of the significant topics in the field of non-linear science and therefore has attracted the attention of many researchers in this field. This paper discusses parameter recognition of Fractional Order Financial Chaotic System by employing different metaheuristic algorithms. The implemented algorithms include Artificial bee colony, Grey wolf optimizer, whale optimization algorithm and Ant colony optimizer. Mean Square Error is employed as objective function to estimate parameters of system under consideration. Results of optimized parameters using the above-mentioned optimization are compared with each other and discussed in the paper. The overall outcome shows that whale optimization algorithm gives more accurate and robust results with higher convergence rate as compared with other algorithms.
In the gait recognition problem, most studies are devoted to developing gait descriptors rather than introducing new classification methods. This paper proposes hybrid methods that combine regularized discriminant ana...
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In the gait recognition problem, most studies are devoted to developing gait descriptors rather than introducing new classification methods. This paper proposes hybrid methods that combine regularized discriminant analysis (RDA) and swarm intelligence techniques for gait recognition. The purpose of this study is to develop strategies that will achieve better gait recognition results than those achieved by classical classification methods. In our approach, particle swarm optimization (PSO), grey wolf optimization (GWO), and whale optimization algorithm (WOA) are used. These techniques tune the observation weights and hyperparameters of the RDA method to minimize the objective function. The experiments conducted on the GPJATK dataset proved the validity of the proposed concept.
Classification accuracy highly dependents on the nature of the features in a dataset which may contain irrelevant or redundant data. The main aim of feature selection is to eliminate these types of features to enhance...
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Classification accuracy highly dependents on the nature of the features in a dataset which may contain irrelevant or redundant data. The main aim of feature selection is to eliminate these types of features to enhance the classification accuracy. The wrapper feature selection model works on the feature set to reduce the number of features and improve the classification accuracy simultaneously. In this work, a new wrapper feature selection approach is proposed based on whale optimization algorithm (WOA). WOA is a newly proposed algorithm that has not been systematically applied to feature selection problems yet. Two binary variants of the WOA algorithm are proposed to search the optimal feature subsets for classification purposes. In the first one, we aim to study the influence of using the Tournament and Roulette Wheel selection mechanisms instead of using a random operator in the searching process. In the second approach, crossover and mutation operators are used to enhance the exploitation of the WOA algorithm. The proposed methods are tested on standard benchmark datasets and then compared to three algorithms such as Particle Swarm optimization (PSO), Genetic algorithm (GA), the Ant Lion Optimizer (ALO), and five standard filter feature selection methods. The paper also considers an extensive study of the parameter setting for the proposed technique. The results show the efficiency of the proposed approaches in searching for the optimal feature subsets. (C) 2017 Elsevier B.V. All rights reserved.
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