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ADMM-based Distributed algorithm for Economic Dispatch in Power Systems With Both Packet Drops and Communication Delays

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IEEE/CAA Journal of Automatica Sinica 2020年第3期7卷 842-852页

作者： Qing Yang Gang Chen Ting Wang State Key Laboratory of Power Transmission Equipment and System Security and New Technology and the School of AutomationChongqing UniversityChongqing 400044China IEEE

By virtue of alternating direction method of multipliers(ADMM), Newton-Raphson method, ratio consensus approach and running sum method, two distributed iterative strategies are presented in this paper to address the economic dispatch problem(EDP) in power systems. Different from most of the existing distributed ED approaches which neglect the effects of packet drops or/and time delays, this paper takes into account both packet drops and time delays which frequently occur in communication networks. Moreover, directed and possibly unbalanced graphs are considered in our algorithms, over which many distributed approaches fail to converge. Furthermore, the proposed schemes can address the EDP with local constraints of generators and nonquadratic convex cost functions, not just quadratic ones required in some existing ED approaches. Both theoretical analyses and simulation studies are provided to demonstrate the effectiveness of the proposed schemes.

关键词： Alternating direction method of multipliers(ADMM) average consensus directed graph(digraph) distributed algorithm economic dispatch packet drops time delays

Efficient algorithm for big data clustering on single machine

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CAAI Transactions on Intelligence Technology 2020年第1期5卷 9-14页

作者： Rasim M.Alguliyev Ramiz M.Aliguliyev Lyudmila V.Sukhostat Institute of Information Technology Azerbaijan National Academy of Sciences9AB.Vahabzade StreetAZ1141 BakuAzerbaijan

Big data analysis requires the presence of large computing powers, which is not always feasible. And so, itbecame necessary to develop new clustering algorithms capable of such data processing. This study proposes a newparallel clustering algorithm based on the k-means algorithm. It significantly reduces the exponential growth ofcomputations. The proposed algorithm splits a dataset into batches while preserving the characteristics of the initialdataset and increasing the clustering speed. The idea is to define cluster centroids, which are also clustered, for eachbatch. According to the obtained centroids, the data points belong to the cluster with the nearest centroid. Real largedatasets are used to conduct the experiments to evaluate the effectiveness of the proposed approach. The proposedapproach is compared with k-means and its modification. The experiments show that the proposed algorithm is apromising tool for clustering large datasets in comparison with the k-means algorithm.

关键词： algorithm. algorithm batch

A genetic algorithm based improved optimal sizing strategy for solar-wind-battery hybrid system using energy filter algorithm

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Frontiers in Energy 2020年第1期14卷 139-151页

作者： Aeidapu MAHESH Kanwarjit Singh SANDHU Department of Electrical Engineering National Institute of TechnologyKurukshetra136119India

In this paper,the genetic algorithm(GA)is applied to optimize a grid connected solar photovoltaic(PV)-wind-battery hybrid system using a novel energy filter algorithm.The main objective of this paper is to minimize the total cost of the hybrid system,while maintaining its reliability.Along with the reliability constraint,some of the important parameters,such as full utilization of complementary nature of PV and wind systems,fluctuations of power injected into the grid and the battery’s state of charge(SOC),have also been considered for the effective sizing of the hybrid system.A novel energy filter algorithm for smoothing the power injected into the grid has been proposed.To validate the proposed method,a detailed case study has been conducted.The results of the case study for different cases,with and without employing the energy filter algorithm,have been presented to demonstrate the effectiveness of the proposed sizing strategy.

关键词： PV-wind-battery hybrid system size optimization genetic algorithm

Evaluating the robustness of image matting algorithm

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CAAI Transactions on Intelligence Technology 2020年第4期5卷 247-259页

作者： Genji Yuan Jinjiang Li Hui Fan School of Computer Science and Technology Shandong Technology and Business UniversityYantai 264005People’s Republic of China Co-innovation Center of Shandong Colleges and Universities:Future Intelligent Computing Shandong Technology and Business UniversityYantai 264005People’s Republic of China

In this study,the authors propose a method to calculate the consistency of alpha masking to assess the robustness of the matting algorithm.This study evaluates consistent alpha masks based on the Gaussian-Hermite moment in combination with gradient amplitude and gradient direction.The gradient direction describes the appearance and shape of local objects in the image,and the gradient amplitude accurately reflects the contrast and texture changes of small details in the image.They selected Gaussian blur,pretzel noise,and combined noise to destroy the image,and then evaluated the consistency of the original alpha mask and noise alpha mask.To determine the robustness of the matting algorithm,they assessed the degree of consistency of the alpha mask using three different evaluation levels.The experimental results show that noise has a greater impact on the performance of the matting algorithm,which shows a decreasing trend as the noise level in the image deepens.In noisy images,the traditional matting algorithm exhibits better robustness compared to the recently proposed trap matting algorithm.Different matting algorithms present different adaptations to different noises.

关键词： image algorithm. algorithm

Enjoy the most beautiful scene now:a memetic algorithm to solve two-fold time-dependent arc orienteering problem

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Frontiers of Computer Science 2020年第2期14卷 364-377页

作者： Chao CHEN Liping GAO Xuefeng XIE Zhu WANG School of Computer Science Chongqing UniversityChongqing 400044China School of Computer Science Northwestern Polytechnical UniversityXi'an 710072China

Traditional route planners commonly focus on finding the shortest path between two points in terms of travel distance or time over road networks.However,in real cases,especially in the era of smart cities where many kinds of transportation-related data become easily available,recent years have witnessed an increasing demand of route planners that need to optimize for multiple criteria,e.g.,finding the route with the highest accumulated scenic score along(utility)while not exceeding the given travel time budget(cost).Such problem can be viewed as a variant of arc orienteering problem(AOP),which is well-known as an NP-hard problem.In this paper,targeting a more realistic AOP,we allow both scenic score(utility)and travel time(cost)values on each arc of the road network are time-dependent(2TD-AOP),and propose a memetic algorithm to solve it.To be more specific,within the given travel time budget,in the phase of initiation,for each population,we iteratively add suitable arcs with high scenic score and build a path from the origin to the destination via a complicate procedure consisting of search region narrowing,chromosome encoding and decoding.In the phase of the local search,each path is improved via chromosome selection,local-improvement-based mutation and crossover operations.Finally,we evaluate the proposed memetic algorithm in both synthetic and real-life datasets extensively,and the experimental results demonstrate that it outperforms the baselines.

关键词： arc orienteering problem two-fold timedependent scenic score travel time memetic algorithm

Initial alignment of compass based on genetic algorithm-particle swarm optimization

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Defence Technology（防务技术） 2020年第1期16卷 257-262页

作者： Yi-feng Liang Peng-fei Jiang Jiang-ning Xu Wen An Miao Wu College of Electrical Engineering Naval Univ.of EngineeringWuhan 430033China

The rapidity and accuracy of the initial alignment influence the performance of the strapdown inertial navigation system(SINS),compass alignment is one of the most important methods for initial alignment.The selection of the parameters of the compass alignment loop directly affects the result of alignment.Nevertheless,the optimal parameters of the compass loop of different SINS are also different Traditionally,the alignment parameters are determined by experience and trial-and-error,thus it cannot ensure that the parameters are optimal.In this paper,the Genetic algorithm-Particle Swarm Optimization(GA-PSO) algorithm is proposed to optimize the compass alignment parameters so as to improve the performance of the initial alignment of strapdown gyrocompass.The experiment results showed that the GA-PSO algorithm can find out the optimal parameters of the compass alignment circuit quickly and accurately and proved the effectiveness of the proposed method.

关键词： Inertial alignment Genetic algorithm SINS Compass alignment

AntLP: ant-based label propagation algorithm for community detection in social networks

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CAAI Transactions on Intelligence Technology 2020年第1期5卷 34-41页

作者： Razieh Hosseini Alireza Rezvanian Department of Computer Engineering Alzahra UniversityTehranIran Department of Computer Engineering University of Science and CultureTehranIran School of Computer Science Institute for Research in Fundamental Sciences(IPM)TehranIran

In social network analysis,community detection is one of the significant tasks to study the structure and characteristics of the networks.In recent years,several intelligent and meta-heuristic algorithms have been presented for community detection in complex social networks,among them label propagation algorithm(LPA)is one of the fastest algorithms for discovering community structures.However,due to the randomness of the LPA,its performance is not suitable for the general purpose of network analysis.In this study,the authors propose an improved version of the label propagation(called AntLP)algorithm using similarity indices and ant colony optimisation(ACO).The AntLP consists of two steps:in the first step,the algorithm assigns weights for edges of the input network using several similarity indices,and in the second step,the AntLP using ACO tries to propagate labels and optimise modularity measure by grouping similar vertices in each community based on the local similarities among the vertices of the network.In order to study the performance of the AntLP,several experiments are conducted on some well-known social network datasets.Experimental simulations demonstrated that the AntLP is better than some community detection algorithms for social networks in terms of modularity,normalised mutual information and running time.

关键词： network networks algorithm

Neural saliency algorithm guide bi-directional visual perception style transfer

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CAAI Transactions on Intelligence Technology 2020年第1期5卷 1-8页

作者： Chunbiao Zhu Wei Yan Xing Cai Shan Liu Thomas HLi Ge Li Shenzhen Graduate School Peking UniversityShenzhenPeople’s Republic of China Tencent America San FranciscoCAUSA China and Advanced Institute of Information Technology Peking UniversityHangzhouPeople’s Republic of China

The artistic style transfer of images aims to synthesise novel images by combining the content of one image with the style of another,which is a long-standing research topic and already has been widely applied in real world.However,defining the aesthetic perception from the human visual system is a challenging problem.In this study,the authors propose a novel method for automatic visual perception style transfer.First,they render a novel saliency detection algorithm to automatically perceive the visual attention of an image.Then,different from conventional style transfer algorithm in which style transferring is applied uniformly across all image regions,the authors apply a saliency algorithm to guide the style transferring process,enabling different types of style transferring to occur in different regions.Extensive experiments show that the proposed saliency detection algorithm and the style transfer algorithm are superior in performance and efficiency。

关键词： visual algorithm transfer

Multi-robot path planning based on a deep reinforcement learning DQN algorithm

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CAAI Transactions on Intelligence Technology 2020年第3期5卷 177-183页

作者： Yang Yang Li Juntao Peng Lingling School of Management China University of Mining and Technology(Beijing)BeijingPeople’s Republic of China School of Information Beijing Wuzi UniversityBeijingPeople’s Republic of China

The unmanned warehouse dispatching system of the‘goods to people’model uses a structure mainly based on a handling robot,which saves considerable manpower and improves the efficiency of the warehouse picking operation.However,the optimal performance of the scheduling system algorithm has high requirements.This study uses a deep Q-network(DQN)algorithm in a deep reinforcement learning algorithm,which combines the Q-learning algorithm,an empirical playback mechanism,and the volume-based technology of productive neural networks to generate target Q-values to solve the problem of multi-robot path planning.The aim of the Q-learning algorithm in deep reinforcement learning is to address two shortcomings of the robot path-planning problem:slow convergence and excessive randomness.Preceding the start of the algorithmic process,prior knowledge and prior rules are used to improve the DQN algorithm.Simulation results show that the improved DQN algorithm converges faster than the classic deep reinforcement learning algorithm and can more quickly learn the solutions to path-planning problems.This improves the efficiency of multi-robot path planning.

关键词： algorithm robot shortcomings