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distribution algorithm FOR TORQUE CONTROL OF NEW ENERGY FOUR-WHEEL HUB MOTOR

RUSSIAN PHYSICS JOURNAL 2022年第9期64卷 1613-1628页

作者： Leng, Shuai Jin, Liqiang Jilin Univ Coll Automot Engn Changchun Peoples R China

Due to the nonlinear, strong coupling and uncertain parameters of the new energy four-wheel hub motor, it is more difficult to control the torque of the motor. In order to solve this problem, a torque control method based on the distribution algorithm is proposed for the new energy four-wheel hub motor. The dynamic model of the new energy four-wheel hub motor is suggested, and the unmeasurable flux, electric power and other state variables in the motor model are derived according to the degree of freedom of the body. The paper studies the whole four-wheel hub motor with the purpose to improve the efficiency of the drive system and proposes the distribution algorithm to control the electromagnetic torque of the motor. According to the simulation results, the torque control for the new energy four-wheel hub motor leads to a longer driving range of the vehicle, small change in the amplitude of stator flux and the stator current, better stability of the motor speed, and better torque control effect.

关键词： distribution algorithm new energy four-wheel hub motor torque control dynamic model

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Preparation of Papers for IFAC Conferences & Symposia: Autonomous distribution algorithm for Formation Satellites Under Emergent Imaging Requests 21

Preparation of Papers for IFAC Conferences & Symposia: Auton...

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21st International-Federation-of-Automatic-Control (IFAC) Symposium on Automatic Control in Aerospace (ACA)

作者： Miao Yue Wang Feng Chen Xueqin Yu Yanjun Li Chaoyong Harbin Inst Technol Sch Astronaut Harbin 150080 Heilongjiang Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou 310027 Zhejiang Peoples R China

In order to quick response to emergencies such as air crash and natural disaster, satellites on orbit are desirable to fast achieve imaging sequence for real-time tasks. To select targets in hot area for formation flying satellites, two types of "select-by-grade" algorithms are presented to obtain an appropriate imaging sequence for each satellite. First, ground targets are divided into different groups with different grades in terms of their significances. By making analysis on constraints of target locations and satellite performances, the autonomous task planning is treated as a stepwise optimization problem where the objective function is to maximize the overall imaging gain for all satellites. Second, to solve the optimization problem, the aforementioned different grades ground targets are assigned to different satellites by pre-defined distribution schemes. Finally, a simulation example is provided to illustrate the effectiveness of the proposed algorithms. Copyright (C) 2019. The Authors. Published by Elsevier Ltd. All rights reserved.

关键词： autonomous task planning formation imaging satellite distribution algorithm optimization imaging sequence

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A novel task resource distribution algorithm based on MDLS and GA

A novel task resource distribution algorithm based on MDLS a...

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6th International Conference on Machine Learning and Cybernetics

作者： Wang, Jiang-Feng Bao, Wei-Dong Zhang, Mao-Jun Natl Univ Def Technol Coll Informat Syst & Management Changsha 410073 Peoples R China

In this paper, we proposed a novel task resource distribution algorithm based on Multidimensional Dynamic List Scheduling and Genetic algorithm. The mathematical formulation and implementation of the inner and outer m... 详细信息

ISBN: (纸本)9781424409723

关键词： distribution algorithm group technology MDLS algorithm genetic algorithm

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A new estimation of distribution algorithm based on learning automata

A new estimation of distribution algorithm based on learning...

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IEEE Congress on Evolutionary Computation

作者： Rastegar, R Meybodi, MR Amirkabir Univ Technol Comp Engn & IT Dept Tehran Iran

ISBN: (纸本)0780393635

In this paper we introduce an estimation of distribution algorithm based on a team of learning automata. The proposed algorithm is a model based search optimization method that uses a team of learning automata as a probabilistic model of high quality solutions seen in the search process. Simulation results show that the proposed algorithm is a good candidate for solving optimization problems.

关键词： learning automata optimisation search problems distribution algorithm model-based search optimization

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Controller node driven hop count based data distribution algorithm in ring connected binary tree network-on-chip for parallel processing

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International Journal of Information Technology (Singapore) 2024年第4期16卷 2535-2547页

作者： Uma, R. Sarojadevi, H. Sanju, V. Department of CSE Nitte Meenakshi Institute of Technology Visvesvaraya Technological University Karnataka Belagavi India School of CSE REVA University Karnataka Bangalore India

An effective distribution of packets is particularly important for the performance of a network-on-chip system. Ring connected binary tree structures do not have a defined distribution method and this paper elaborates the node architecture and a new distribution algorithm for ring connected binary tree structures. The distribution method described in this paper shows how optimized distribution of packets takes place for complex computations in ring connected binary tree. The controller node acts as a centralized node which creates the sub processing tasks and distributes to the computation nodes. The distribution of sub processing tasks is performed after figuring out the proximity of the computation nodes to the controller node in terms of hop count. In this work, the number of clock cycles for parallel processing is reduced by improving the synchronization between the transmit and receive operations thereby reducing the power consumption of the system. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Controller node distribution algorithm Network-on-chip Parallel activities Ring Connected Binary Tree Topology

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Preparation of Papers for IFAC Conferences & Symposia: Autonomous distribution algorithm for Formation Satellites Under Emergent Imaging Requests

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IFAC-PapersOnLine 2019年第12期52卷 460-465页

作者： A. Miao Yue B. Wang Feng C. Chen Xueqin D. Yu Yanjun E. Li Chaoyong

In order to quick response to emergencies such as air crash and natural disaster, satellites on orbit are desirable to fast achieve imaging sequence for real-time tasks. To select targets in hot area for formation flying satellites, two types of “select-by-grade” algorithms are presented to obtain an appropriate imaging sequence for each satellite. First, ground targets are divided into different groups with different grades in terms of their significances. By making analysis on constraints of target locations and satellite performances, the autonomous task planning is treated as a stepwise optimization problem where the objective function is to maximize the overall imaging gain for all satellites. Second, to solve the optimization problem, the aforementioned different grades ground targets are assigned to different satellites by pre-defined distribution schemes. Finally, a simulation example is provided to illustrate the effectiveness of the proposed algorithms.

关键词： autonomous task planning formation imaging satellite distribution algorithm optimization imaging sequence

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A continuous estimation of distribution algorithm by evolving graph structures using reinforcement learning

A continuous estimation of distribution algorithm by evolvin...

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Congress on Evolutionary Computation

作者： Xianneng Li Bing Li Shingo Mabu Kotaro Hirasawa Graduate School of Information Production and Systems Waseda University Japan

ISBN: (纸本)9781467315104

A novel graph-based Estimation of distribution algorithm (EDA) named Probabilistic Model Building Genetic Network Programming (PMBGNP) has been proposed. Inspired by classical EDAs, PMBGNP memorizes the current best individuals and uses them to estimate a distribution for the generation of the new population. However, PMBGNP can evolve compact programs by representing its solutions as graph structures. Therefore, it can solve a range of problems different from conventional ones in EDA literature, such as data mining and Reinforcement Learning (RL) problems. This paper extends PMBGNP from discrete to continuous search space, which is named PMBGNP-AC. Besides evolving the node connections to determine the optimal graph structures using conventional PMBGNP, Gaussian distribution is used for the distribution of continuous variables of nodes. The mean value μ and standard deviation σ are constructed like those of classical continuous Population-based incremental learning (PBILc). However, a RL technique, i.e., Actor-Critic (AC), is designed to update the parameters (μ and σ). AC allows us to calculate the Temporal-Difference (TD) error to evaluate whether the selection of the continuous value is better or worse than expected. This scalar reinforcement signal can decide whether the tendency to select this continuous value should be strengthened or weakened, allowing us to determine the shape of the probability density functions of the Gaussian distribution. The proposed algorithm is applied to a RL problem, i.e., autonomous robot control, where the robot's wheel speeds and sensor values are continuous. The experimental results show the superiority of PMBGNP-AC comparing with the conventional algorithms.

关键词： Continuous Estimation distribution algorithm Evolving Graph Structures

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A NOVEL TASK RESOURCE distribution algorithm BASED ON MDLS AND GA

A NOVEL TASK RESOURCE DISTRIBUTION ALGORITHM BASED ON MDLS A...

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2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： JIANG-FENG WANG WEI-DONG BAO MAO-JUN ZHANG College of Information Systems & Management National University of Defense Technology Changsha 410073 China

In this paper, we proposed a novel task resource distribution algorithm based on Multidimensional Dynamic List Scheduling and Genetic *** mathematical formulation and implementation of the inner and outer module of th... 详细信息

关键词： distribution algorithm Group technology MDLS algorithm Genetic algorithm

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Clustering Molecular Dynamics Trajectories with a Univariate Estimation of distribution algorithm

Clustering Molecular Dynamics Trajectories with a Univariate...

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IEEE Congress on Evolutionary Computation

作者： Rodrigo C. Barros Christian V. Quevedo Renata De Paris Márcio P. Basgalupp Pontifícia Universidade Católica do Rio Grande do Sul Faculdade de Informática Porto Alegre RS Brazil Universidade Federal de S?o Paulo Instituto de Ciência e Tecnologia S?o José dos Campos SP Brazil

ISBN: (纸本)9781479974931

Molecular Dynamics simulations of protein receptors are an emergent tool in rational drug discovery. Nevertheless, employing Molecular Dynamics trajectories in virtual screening of large repositories is a very costly procedure, which ultimately may become unfeasible. Data clustering have been applied in this context with the goal of reducing the overall computational cost in order to make this task feasible. In this paper, we develop a novel estimation of distribution algorithm called Clus-EDA for clustering entire trajectories using structural features from the substrate-binding cavity of the protein receptor. This novel approach is capable of reducing the original trajectory to about 4% of its original size whilst keeping all relevant information for the analysis of receptor-ligand binding. The resulting partition generated by the estimation of distribution algorithm is further validated by analyzing the interactions between 20 ligands and a Fully-Flexible Receptor model containing a 20 ns Molecular Dynamics simulation trajectory. Results show that Clus-EDA is capable of outperforming traditional clustering algorithms such as k-means and hierarchical clustering by providing the smallest variance of the free energy of binding within the conformations in each cluster.

关键词： Clustering Molecular Dynamics Trajectories Univariate Estimation distribution algorithm

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A Fitness Function Model for Detecting Ellipses with Estimation of distribution algorithms

A Fitness Function Model for Detecting Ellipses with Estimat...

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2010 IEEE World Congress on Computational Intelligence

作者： Ochoa, Alberto Tejera, Manuel Soto, Marta Inst Cybernet Math & Phys Havana Cuba Assembled New Technol SL Valencia Valencia Spain Inst Cybernet Math & Phys Dept Interdisciplinary Math Havana Cuba

ISBN: (纸本)9781424481262

This paper introduces a novel method for ellipse detection that is based on Estimation of distribution algorithms. The main contribution is the construction of a new fitness function model that in contrast to existing methods can assign positive evaluations to ellipses that do not exist in the image. This approach produces much smoother fitness landscapes increasing the efficiency of the search. A preliminary study of the role of mutation in dealing with the natural multimodality of the ellipse detection problem is also presented.

关键词： computer vision distribution algorithm ellipse detection evolutionary computation feature extraction fitness function model search efficiency search problems

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