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Electroencephalogram based communication system for locked in state person using mentally spelled tasks with optimized network model

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2020年第0期102卷 101766-000页

作者： Xu Xiaoxiao Bin, Luo Ramkumar, S. Saravanan, S. Balaji, M. Sundar Prakash Dhanasekaran, S. Thimmiaraja, J. Wuhan Univ Technol Sch Entrepreneurship Wuhan 430070 Hubei Peoples R China Wuhan Business Univ Sch Foreign Languages Wuhan 430056 Peoples R China Kalasalingam Acad Res & Educ Sch Comp Krishnankoil India CMR Inst Technol Dept Informat Sci & Engn Bangalore Karnataka India Dept ECE RVS Tech Campus Coimbatore Tamil Nadu India

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.

关键词： Locked in state Continuous Wavelet Transform Spinal Cord Injury Information Transfer Rate Brain Computer Interface whale optimization algorithm Electroencephalogram

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whale inspired algorithm based MPPT controllers for grid-connected solar photovoltaic system 6

Whale inspired algorithm based MPPT controllers for grid-con...

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6th International Conference on Emerging and Renewable Energy - Generation and Automation (ICEREGA)

作者： Ebrahim, M. A. Osama, Adham Kotb, Khaled Mohamed Bendary, Fahmy Benha Univ Fac Engn Shoubra Dept Elect Engn Cairo Egypt Natl Water Res Ctr Elqanater Elkhayrya 13411 Egypt

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

关键词： Maximum Power Point Tracking Incremental conductance Perturb and Observe whale optimization algorithm Proportional-Integral controller Photovoltaic

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Estimation of Parameters in Fractional order Financial Chaotic system with Nature Inspired algorithms

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Procedia Computer Science 2020年 173卷 18-27页

作者： Sangeeta Gupta Gaurava Gautam Deepak Divyanshu Vats Pragya Varshney Smriti Srivastava Department of Instrumentation and Control Engineering Netaji Subhas Institute of Technology Dwarka Sec-3 New Delhi -110078 India

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.

关键词： optimization Techniques Fractional Calculus Fractional Order Fractional Order Financial Chaotic system whale optimization algorithm Ant colony optimization Grey wolf optimization Artificial bee colony optimization

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whale optimization approaches for wrapper feature selection

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APPLIED SOFT COMPUTING 2018年 62卷 441-453页

作者： Mafarja, Majdi Mirjalili, Seyedali Birzeit Univ Dept Comp Sci POB 14 West Bank Palestine Griffith Univ Inst Integrated & Intelligent Syst Nathan Campus Brisbane Qld 4111 Australia

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.

关键词： Feature selection optimization whale optimization algorithm Classification WOA Crossover Mutation Selection Evolutionary operators

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A Lion-whale optimization-based migration of virtual machines for data centers in cloud computing

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2018年第8期31卷 e3539-e3539页

作者： Krishna, J. Venkata Naidu, G. Apparao Upadhayaya, Niraj Sree Vahini Inst Sci & Technol Dept CSE Tiruvuru AP India JB Inst Engn & Technol Hyderabad Telangana India

The scalability, reliability, and flexibility in the cloud computing services are the obligations in the growing demand of computation power. To sustain the scalability, a proper virtual machine migration (VMM) approach is needed with apt balance on quality of service and service-level agreement violation. In this paper, a novel VMM algorithm based on Lion-whale optimization is developed by integrating the Lion optimization algorithm and the whale optimization algorithm. The optimal virtual machine (VM) migration is performed by the Lion-whale VMM based on a new fitness function in the regulation of the resource use, migration cost, and energy consumption of VM placement. The experimentation of the proposed VM migration strategy is performed over 4 cloud setups with a different configuration which are simulated using CloudSim toolkit. The performance of the proposed method is validated over existing optimization-based VMM algorithms, such as particle swarm optimization and genetic algorithm, using the performance measures, such as energy consumption, migration cost, and resource use. Simulation results reveal the fact that the proposed Lion-whale VMM effectively outperforms other existing approaches in optimal VM placement for cloud computing environment with reduced migration cost of 0.01, maximal resource use of 0.36, and minimal energy consumption of 0.09.

关键词： cloud computing Lion optimization algorithm virtual machine migration whale optimization algorithm

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Combined Regularized Discriminant Analysis and Swarm Intelligence Techniques for Gait Recognition

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SENSORS 2020年第23期20卷 6794-6794页

作者： Krzeszowski, Tomasz Wiktorowicz, Krzysztof Rzeszow Univ Technol Fac Elect & Comp Engn Al Powstancow Warszawy 12 PL-35959 Rzeszow Poland

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.

关键词： gait recognition biometrics regularized discriminant analysis particle swarm optimization grey wolf optimization whale optimization algorithm

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Modeling and daily operation optimization of a distributed energy system considering economic and energy aspects

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2018年第11期42卷 3477-3495页

作者： Tan, Yuhua Wang, Xin Zheng, Yihui Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Elect & Elect Expt Teaching Ctr Shanghai 200240 Peoples R China

This paper presents a distributed energy system (DES) for a local district and formulates a constrained nonlinear multiobjective optimization model for the daily operation of the system. The main objective of the study is to increase the efficiency by minimizing energy cost, energy consumption, and energy losses. It is implemented through the integration and complementation of renewable energies and fossil fuels as well as the recycling utilization of waste heat in the DES. The consideration of network topology and energy losses of water heating network could also contribute to the improvement of energy efficiency. To solve the optimization problem, a novel whale optimization algorithm is employed. Furthermore, the economic and energy performance of the DES are evaluated and compared with that of conventional centralized energy systems, ie, the EG and MG energy-supply modes. After simulation studies, the hourly optimal energy (both natural gas and electricity) purchasing schedule as well as the hourly optimal set points of mass water flow rates and supply/return water temperatures could be determined. The results show that the DES saves more than 50% of energy costs/energy consumption than the MG mode and over 22% than the EG mode for a whole day, verifying the competitive advantage and great potential of both energy saving and cost reduction of the DES.

关键词： distributed energy system daily operation optimization energy losses energy consumption energy cost whale optimization algorithm

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Improvement optimal power flow solution under loading margin stability using new partitioning whale algorithm

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INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT 2019年第1期14卷 64-77页

作者： Mahdad, Belkacem Univ Biskra Dept Elect Engn Biskra Algeria

This paper focuses on the solution of the optimal power flow (OPF) problem considering load growth using a novel metaheuristic optimization algorithm called whale optimization algorithm (WOA). An interactive partitioning procedure is proposed to achieve a better solution and to improve search space diversity. In this study the new variant named partitioning WOA is applied to improve the security optimal power flow (SOPF) by minimization the total production cost with different complexities and constraints such as valve point effect and prohibited zones, total power loss and total voltage deviation considering critical load growth. The efficiency of the proposed algorithm has been validated on the IEEE 30-Bus, the Algerian 59-bus power system and to the large test system IEEE 118-Bus for various standard objective functions. For the test system IEEE-30 Bus, a competitive optimized quadratic cost (798.9225 $) is achieved compared to many recent techniques;also significant amounts of power loss and voltage deviation have been reduced for all test systems without affecting security constraints in particular for the IEEE 118-Bus, the total loss being reduced from 132.86 MW at base case (without optimization) to a competitive value 8.6099 MW. The proposed strategy proves its remarkable ability in solving the practical security constrained OPF for modern large power systems.

关键词： Power system planning OPF whale optimization algorithm partitioning strategy boundary handling loading margin stability SVC

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Swarm Intelligence optimization Techniques for Obstacle-Avoidance Mobility-Assisted Localization in Wireless Sensor Networks

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IEEE ACCESS 2018年 6卷 22368-22385页

作者： Alomari, Abdullah Phillips, William Aslam, Nauman Comeau, Frank Dalhousie Univ Dept Engn Math & Internetworking Halifax NS B3H 4R2 Canada Albaha Univ Fac Comp Sci & Informat Technol Albaha 65527 Saudi Arabia Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England St Francis Xavier Univ Engn Dept Antigonish NS B2G 2W5 Canada

In many applications of wireless sensor networks (WSNs), node location is required to locate the monitored event once occurs. Mobility-assisted localization has emerged as an efficient technique for node localization. It works on optimizing a path planning of a location-aware mobile node, called mobile anchor (MA). The task of the MA is to traverse the area of interest (network) in a way that minimizes the localization error while maximizing the number of successful localized nodes. For simplicity, many path planning models assume that the MA has a sufficient source of energy and time, and the network area is obstacle-free. However, in many real-life applications such assumptions are rare. When the network area includes many obstacles, which need to be avoided, and the MA itself has a limited movement distance that cannot be exceeded, a dynamic movement approach is needed. In this paper, we propose two novel dynamic movement techniques that offer obstacle-avoidance path planning for mobility-assisted localization in WSNs. The movement planning is designed in a real-time using two swarm intelligence based algorithms, namely grey wolf optimizer and whale optimization algorithm. Both of our proposed models, grey wolf optimizer-based path planning and whale optimization algorithm-based path planning, provide superior outcomes in comparison to other existing works in several metrics including both localization ratio and localization error rate.

关键词： Wireless sensor networks path planning mobility models localization models optimization grey wolf optimizer whale optimization algorithm obstacle-avoidance path planning

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A Wireless Sensor Network Location algorithm Based on whale algorithm 10

A Wireless Sensor Network Location Algorithm Based on Whale ...

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10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Lang, Fenghao Su, Jun Ye, ZhiWei Shi, XiaoXiao Chen, Feng Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9781728140698

In the research of wireless sensor network, how to improve the positioning accuracy of nodes is the key problem. At this stage, some scholars combine swarm intelligence optimization algorithm with wireless sensor network, and have achieved good results. However, in wireless sensor network node localization, the combination of some optimization algorithms and node localization still has some problems, such as slow convergence speed and low localization accuracy. In this paper, we use whale optimization algorithm to optimize the formula of the relationship between received signal strength and signal transmission distance, and train the parameters A and n, so as to optimize the relationship between signal strength and signal transmission distance. Through simulation experiments, the whale optimization algorithm is compared with the particle swarm optimization algorithm. The whale optimization algorithm has certain competitiveness and practicability.

关键词： Wireless Sensor Network whale optimization algorithm Trilateration

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