Routing strategies and security issues are the greatest challenges in Wireless Sensor Network(WSN).Cluster-based routing Low Energy adaptive Clustering Hierarchy(LEACH)decreases power consumption and increases net-wor...Routing strategies and security issues are the greatest challenges in Wireless Sensor Network(WSN).Cluster-based routing Low Energy adaptive Clustering Hierarchy(LEACH)decreases power consumption and increases net-work lifetime considerably.Securing WSN is a challenging issue faced by researchers.Trust systems are very helpful in detecting interfering nodes in WSN.Researchers have successfully applied Nature-inspired Metaheuristics Optimization Algorithms as a decision-making factor to derive an improved and effective solution for a real-time optimization problem.The metaheuristic Elephant Herding Optimizations(EHO)algorithm is formulated based on ele-phant herding in their clans.EHO considers two herding behaviors to solve and enhance optimization problem.Based on Elephant Herd Optimization,a trust-based security method is built in this work.The proposed routing selects routes to destination based on the trust values,thus,finding optimal secure routes for transmitting data.Experimental results have demonstrated the effectiveness of the proposed EHO based routing.The Average Packet Loss Rate of the proposed Trust Elephant Herd Optimization performs better by 35.42%,by 1.45%,and by 31.94%than LEACH,Elephant Herd Optimization,and Trust LEACH,respec-tively at Number of Nodes 3000.As the proposed routing is efficient in selecting secure routes,the average packet loss rate is significantly reduced,improving the network’s performance.It is also observed that the lifetime of the network is enhanced with the proposed Trust Elephant Herd Optimization.展开更多
为提高含电缆-架空混合线路配电网故障定位精度,提出一种基于改进象群优化算法的配电网混合线路故障定位方法。首先,根据故障行波的传播特性以及配电网的结构特点,分析了多端行波信息差异矩阵的“唯一性”,并以此为基础构建了故障定位模...为提高含电缆-架空混合线路配电网故障定位精度,提出一种基于改进象群优化算法的配电网混合线路故障定位方法。首先,根据故障行波的传播特性以及配电网的结构特点,分析了多端行波信息差异矩阵的“唯一性”,并以此为基础构建了故障定位模型,将定位问题转化为寻优求解问题。然后,利用改进象群优化算法和OPTICS(ordering points to identify the clustering structure)聚类算法对第1次寻优结果中多端行波信息差异矩阵的各元素进行聚类分析,找出存在时间同步误差的坏数据。最后,在考虑正常数据的情况下进行第2次寻优,实现故障的精确定位。仿真结果表明,该方法在不需预设行波波速的情况下,能够实现较准确的故障定位,且具有较强时间误差鲁棒性。展开更多
The time series data is chaotic,non seasonal,non stationary and random in nature.It becomes quite challenging to discover the hidden patterns of time series data.In this paper the time series data is predicted with th...The time series data is chaotic,non seasonal,non stationary and random in nature.It becomes quite challenging to discover the hidden patterns of time series data.In this paper the time series data is predicted with the help of a machine learning algorithm i.e.Elephant Herd Optimization(EHO).Three different types of time series data are used to testify the superiority of the proposed method namely stock market data,currency exchange data and absenteeism at work.The data are first subjected to feature selection methods namely ANOVA and Friedman test.The feature selection methods provide relevant set of features which is fed to the neural network trained with the method.The proposed method is also compared with other methods such as local linear radial basis functional neural network and particle swarm optimization.The results prove supremacy of EHO over other methods.展开更多
In the field of fault tolerance estimation,the increasing attention in electrical motors is the fault detection and diagnosis.The tasks performed by these machines are progressively complex and the enhancements are li...In the field of fault tolerance estimation,the increasing attention in electrical motors is the fault detection and diagnosis.The tasks performed by these machines are progressively complex and the enhancements are likewise looked for in the field of fault diagnosis.It has now turned out to be essential to diagnose faults at their very inception;as unscheduled machine downtime can upset deadlines and cause heavy financial burden.In this paper,fault diagnosis and speed control of permanent magnet synchronous motor(PMSM)is proposed.Elman Neural Network(ENN)is used to diagnose the fault of permanent magnet synchronous motor.Both the fault location and fault severity are considered.In this,eccentricity fault may occur in the motor.To control the speed of the permanent magnet synchronous motor,Dolphin Swarm Optimization(DSO)algorithm is used.The proposed work is simulated by using MATLAB in terms of amplitude,speed and torque.The comparison graph of speed vs.torque obtained by the proposed method gives better result compared to the other existing techniques.The proposed work is also compared with Particle Swarm Optimization(PSO)and Elephant Herding Optimization(EHO)algorithm.The proposed usage of Elman Neural Network to detect the fault and the usage of Dolphin Swarm Optimization algorithm to control the speed of the permanent magnet synchronous motor gives better outcome.展开更多
Big Data applications face different types of complexities in classifications.Cleaning and purifying data by eliminating irrelevant or redundant data for big data applications becomes a complex operation while attempt...Big Data applications face different types of complexities in classifications.Cleaning and purifying data by eliminating irrelevant or redundant data for big data applications becomes a complex operation while attempting to maintain discriminative features in processed data.The existing scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.Recently ensemble methods have made a mark in classification tasks as combine multiple results into a single representation.When comparing to a single model,this technique offers for improved prediction.Ensemble based feature selections parallel multiple expert’s judgments on a single topic.The major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple algorithms.The major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple algorithms.Further,individual outputs produced by methods producing subsets of features or rankings or voting are also combined in this work.KNN(K-Nearest Neighbor)classifier is used to classify the big dataset obtained from the ensemble learning approach.The results found of the study have been good,proving the proposed model’s efficiency in classifications in terms of the performance metrics like precision,recall,F-measure and accuracy used.展开更多
Purpose-The principal intention behind the activity is to regulate the speed,current and commutation of the brushless DC(BLDC)motor.Thereby,the authors can control the torque.Design/methodology/approach-In order to re...Purpose-The principal intention behind the activity is to regulate the speed,current and commutation of the brushless DC(BLDC)motor.Thereby,the authors can control the torque.Design/methodology/approach-In order to regulate the current and speed of the motor,the Multiresolution PID(MRPID)controller is proposed.The altered Landsman converter is utilized in this proposed suppression circuit,and the obligation cycle is acclimated to acquire the ideal DC-bus voltage dependent on the speed of the BLDC motor.The adaptive neuro-fuzzy inference system-elephant herding optimization(ANFISEHO)calculation mirrors the conduct of the procreant framework in families.Findings-Brushless DC motor’s dynamic properties are created,noticed and examined by MATLAB/Simulink model.The performance will be compared with existing genetic algorithms.Originality/value-The presented approach and performance will be compared with existing genetic algorithms and optimization of different structure of BLDC motor.展开更多
文摘Routing strategies and security issues are the greatest challenges in Wireless Sensor Network(WSN).Cluster-based routing Low Energy adaptive Clustering Hierarchy(LEACH)decreases power consumption and increases net-work lifetime considerably.Securing WSN is a challenging issue faced by researchers.Trust systems are very helpful in detecting interfering nodes in WSN.Researchers have successfully applied Nature-inspired Metaheuristics Optimization Algorithms as a decision-making factor to derive an improved and effective solution for a real-time optimization problem.The metaheuristic Elephant Herding Optimizations(EHO)algorithm is formulated based on ele-phant herding in their clans.EHO considers two herding behaviors to solve and enhance optimization problem.Based on Elephant Herd Optimization,a trust-based security method is built in this work.The proposed routing selects routes to destination based on the trust values,thus,finding optimal secure routes for transmitting data.Experimental results have demonstrated the effectiveness of the proposed EHO based routing.The Average Packet Loss Rate of the proposed Trust Elephant Herd Optimization performs better by 35.42%,by 1.45%,and by 31.94%than LEACH,Elephant Herd Optimization,and Trust LEACH,respec-tively at Number of Nodes 3000.As the proposed routing is efficient in selecting secure routes,the average packet loss rate is significantly reduced,improving the network’s performance.It is also observed that the lifetime of the network is enhanced with the proposed Trust Elephant Herd Optimization.
文摘为提高含电缆-架空混合线路配电网故障定位精度,提出一种基于改进象群优化算法的配电网混合线路故障定位方法。首先,根据故障行波的传播特性以及配电网的结构特点,分析了多端行波信息差异矩阵的“唯一性”,并以此为基础构建了故障定位模型,将定位问题转化为寻优求解问题。然后,利用改进象群优化算法和OPTICS(ordering points to identify the clustering structure)聚类算法对第1次寻优结果中多端行波信息差异矩阵的各元素进行聚类分析,找出存在时间同步误差的坏数据。最后,在考虑正常数据的情况下进行第2次寻优,实现故障的精确定位。仿真结果表明,该方法在不需预设行波波速的情况下,能够实现较准确的故障定位,且具有较强时间误差鲁棒性。
文摘The time series data is chaotic,non seasonal,non stationary and random in nature.It becomes quite challenging to discover the hidden patterns of time series data.In this paper the time series data is predicted with the help of a machine learning algorithm i.e.Elephant Herd Optimization(EHO).Three different types of time series data are used to testify the superiority of the proposed method namely stock market data,currency exchange data and absenteeism at work.The data are first subjected to feature selection methods namely ANOVA and Friedman test.The feature selection methods provide relevant set of features which is fed to the neural network trained with the method.The proposed method is also compared with other methods such as local linear radial basis functional neural network and particle swarm optimization.The results prove supremacy of EHO over other methods.
文摘In the field of fault tolerance estimation,the increasing attention in electrical motors is the fault detection and diagnosis.The tasks performed by these machines are progressively complex and the enhancements are likewise looked for in the field of fault diagnosis.It has now turned out to be essential to diagnose faults at their very inception;as unscheduled machine downtime can upset deadlines and cause heavy financial burden.In this paper,fault diagnosis and speed control of permanent magnet synchronous motor(PMSM)is proposed.Elman Neural Network(ENN)is used to diagnose the fault of permanent magnet synchronous motor.Both the fault location and fault severity are considered.In this,eccentricity fault may occur in the motor.To control the speed of the permanent magnet synchronous motor,Dolphin Swarm Optimization(DSO)algorithm is used.The proposed work is simulated by using MATLAB in terms of amplitude,speed and torque.The comparison graph of speed vs.torque obtained by the proposed method gives better result compared to the other existing techniques.The proposed work is also compared with Particle Swarm Optimization(PSO)and Elephant Herding Optimization(EHO)algorithm.The proposed usage of Elman Neural Network to detect the fault and the usage of Dolphin Swarm Optimization algorithm to control the speed of the permanent magnet synchronous motor gives better outcome.
文摘Big Data applications face different types of complexities in classifications.Cleaning and purifying data by eliminating irrelevant or redundant data for big data applications becomes a complex operation while attempting to maintain discriminative features in processed data.The existing scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.Recently ensemble methods have made a mark in classification tasks as combine multiple results into a single representation.When comparing to a single model,this technique offers for improved prediction.Ensemble based feature selections parallel multiple expert’s judgments on a single topic.The major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple algorithms.The major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple algorithms.Further,individual outputs produced by methods producing subsets of features or rankings or voting are also combined in this work.KNN(K-Nearest Neighbor)classifier is used to classify the big dataset obtained from the ensemble learning approach.The results found of the study have been good,proving the proposed model’s efficiency in classifications in terms of the performance metrics like precision,recall,F-measure and accuracy used.
文摘Purpose-The principal intention behind the activity is to regulate the speed,current and commutation of the brushless DC(BLDC)motor.Thereby,the authors can control the torque.Design/methodology/approach-In order to regulate the current and speed of the motor,the Multiresolution PID(MRPID)controller is proposed.The altered Landsman converter is utilized in this proposed suppression circuit,and the obligation cycle is acclimated to acquire the ideal DC-bus voltage dependent on the speed of the BLDC motor.The adaptive neuro-fuzzy inference system-elephant herding optimization(ANFISEHO)calculation mirrors the conduct of the procreant framework in families.Findings-Brushless DC motor’s dynamic properties are created,noticed and examined by MATLAB/Simulink model.The performance will be compared with existing genetic algorithms.Originality/value-The presented approach and performance will be compared with existing genetic algorithms and optimization of different structure of BLDC motor.
