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Study on Cluster-based Dynamic Routing algorithm in Power Line Communication Network

Study on Cluster-based Dynamic Routing Algorithm in Power Li...

引用

IEEE International Conference on Automation and Logistics (ICAL)

作者： Huang Chenchen Ning Yonghai Henan Univ Sci & Technol Elect & Informat Engn Coll Luoyang 471003 Henan Province Peoples R China

ISBN: (纸本)9781467303644

Power Line Communication (PLC) networks have some characters, such as unpredictable physical topology and time-varying channel. In order to enhance the reliability of PLC networks, cluster-based dynamic routing algorithm is proposed and thoroughly discussed in this paper. This algorithm uses the ideology of minimum identification (ID) clustering algorithm to establish routing and maintain effective routing on-demand. It can reduce the routing overhead to ensure the reliability and stability of the communication routing, and thus enhance the overall performance and efficiency of power line communication network. Simulation result shows that the algorithm has certain validity.

关键词： power line communication clustering algorithm dynamic route self-adaptive

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Study on surface crack detection of low voltage current transformer 29

Study on surface crack detection of low voltage current tran...

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第29届中国控制与决策会议

作者： Gao Xiaozheng Lei Huan Zhou Guangbin Yang Wenjie Guangdong University of Technology School of Automation

ISBN: (纸本)9781509046584

Objective For low-voltage current transformer surface crack detection,traditional methods can not effectively distinguish cracks and scratches problem,crack detection method is proposed based on geometrical features and Moment *** Extraction algorithm by osmosis from the gray image of the target area,according to the crack and scratches different texture features,the use of geometric features and invariant moments,determine the characteristic parameters threshold,and finally using clustering algorithm to determine the threshold determination cracks and scratches to be *** After tests proved that the method can effectively distinguish cracks and scratches,and to solve the noise problem on low-voltage current transformer crack *** Compared with the traditional object of cracks and scratches detection methods,the method proposed in this paper has the mathematical property of invariant to rotation,translation and size of image,and it is also used to detect the crack image in the moving state.

关键词： Penetration algorithms Feature extraction Crack Scratches clustering algorithm

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Application Workload Pattern and Its Implications for Resource Provisioning in Cloud: An Early Experience

Application Workload Pattern and Its Implications for Resour...

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IEEE Region 10 Conference (TENCON)

作者： Hubballi, Neminath Suryanarayanan, Vinoth Indian Inst Technol Indore Discipline Comp Sci & Engn Indore Madhya Pradesh India Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England

ISBN: (纸本)9781479940752

Cloud computing is a flexible computing model where resources are allocated and deal located dynamically. The dynamic nature of allocating resources provide scope for optimizing the resources utilization. However the key challenge is to optimize the resource utilization without impacting the applications. In this context we argue that knowing the usage (behavior) of applications is important and using this information we can predict the future requirements of resources and plan the capacity accordingly. In this paper we describe our experiences with production level cloud applications and their usage pattern discovered through a clustering algorithm. Our technique is a completely automated technique for usage pattern discovery. We also share preliminary results on how these usage patterns can be related to resource utilization and help in planning resource allocation.

关键词： cloud computing pattern clustering resource allocation application workload pattern clustering algorithm resource provisioning resource utilization Capacity planning clustering algorithms Data mining Equations Resource management Vectors Virtual machining Resource utilization clustering algorithms Cloud Computing management of resources virtual machines Capacity planning Cluster Analysis data mining virtual machining

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Evaluation Method of Module Granularity Partition for Intelligent Service Robot Based on D-S Evidence Theory

Evaluation Method of Module Granularity Partition for Intell...

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IEEE International Conference on Information and Automation (ICIA)

作者： Jia, Songmin Zhang, Guoliang Li, Boyang Ding, Mingchao Beijing Univ Technol Coll Elect & Control Engn Beijing 100124 Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Minist Educ Engn Res Ctr Digital Commun Beijing 100124 Peoples R China

ISBN: (纸本)9781509041022

The granularity partition for functional modules is a fundamental research topic in robot distributed control technology. How to evaluate the module partition scheme with different granularity, and then obtain the optimum scheme is the urgent problem. In this paper, we proposed a novel evaluation strategy for the granularity partition of functional modules in robotic system using RTM as control platform based on D-S evidence theory. The fuzzy clustering algorithm is primarily used to get the collection of granularity partition schemes for RT Components encapsulated by the platform of OpenRTM. As the two source of evidence, the indices of cohesion and coupling for the robotic system are achieved to measure the degree of module independence by analyzing the correlation matrix of RT Components. Then the Dempster's combination rule and the priority method for utility intervals are applied to obtain the optimal partition granularity. In the end, the effectiveness and progressiveness of the novel evaluation strategy are verified by applying it to the robotic 3D mapping system.

关键词： module division robot technology middleware D-S evidence theory clustering algorithm decentralized control

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Analytical and Simulation Modeling to Analyze Reliability State of Wireless Sensor Networks

Analytical and Simulation Modeling to Analyze Reliability St...

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World Conference on Information Systems and Technologies (WorldCIST)

作者： Pal, Vipin Yogita Singh, Girdhari Yadav, R. P. Vivekananda Global Univ Jaipur Rajasthan India Panjab Univ Univ Inst Engn & Technol Chandigarh India Malaviya Natl Inst Technol Jaipur Jaipur Rajasthan India

ISBN: (纸本)9783319164861;9783319164854

Wireless sensor networks have gained abundant interest due to their potential wide range of applications. Reliability of deployed wireless sensor network is defined by covered area of alive nodes and redundancy of data. Redundancy in data occurs because of overlapped sensed area. An initial reliable wireless sensor network switches to unreliable state because nodes perish in the field randomly. Consequently, the quality of data starts diminishing. It is imperative to know when the network will switch to unreliable state from reliable state so that proper action can be conducted in the field. Work of this paper analyzes and compares analytical and simulation modeling for reliability state of wireless sensor network. Multi-objective genetic algorithm based method is operated for analytical modeling which determines minimum number of nodes (randomly) that covers almost complete area while having required minimum overlapped area. clustering algorithm, LEACH, is implemented in NS-2 for simulation modeling. Comparative results of analytical and simulation modeling are different because of their different nature but both highlights that reliability of wireless sensor network is salient.

关键词： Reliability Redundancy Genetic algorithm clustering algorithm

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Wind Power and Photovoltaic Scene Generation and Reduction Method Based on Improved Gaussian Mixture Model of Cuckoo Search 13

Wind Power and Photovoltaic Scene Generation and Reduction M...

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13th International Conference of Information and Communication Technology, ICTech 2024

作者： Han, Bo Ni, Xueyan Tang, Rui Yu, Chengxin Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China State Grid Liaoning Ultra High Voltage Company Shenyang China

ISBN: (纸本)9798350376258

A method for generating and reducing distributed power generation output scenarios based on improved clustering analysis is proposed to address the issues of low accuracy and susceptibility to local optima in typical Gaussian mixture model clustering. Using the K-means clustering results as the initial value, the cuckoo search algorithm is used to solve for the optimal solution of GMM parameters, forming the CS-GMM improved clustering algorithm. Based on Latin hypercube median sampling, generate a large number of distributed power generation output scenarios, use CS-GMM algorithm to complete scene reduction, and obtain the reduced scenario results and the proportion of each scenario. This article verifies through experiments that the CS-GMM algorithm outperforms other comparison algorithms and has good application prospects by comparing the silhouette coefficient, Davies-Bouldin index and Calinski Harabasz score after clustering. ©2024 IEEE.

关键词： clustering algorithm cuckoo search distributed generation Gaussian mixture model scene generation and reduction

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Personalized Recommendation algorithm for Electronic Commerce Based on Artificial Intelligence Technology

Personalized Recommendation Algorithm for Electronic Commerc...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Zhang, Yiling Li, Caicai Shaanxi Fashion Engineering University Economics and Management College Shaanxi Xianyang China

ISBN: (纸本)9798350315455

In the era of e-commerce, a large amount of data and complex business models make traditional manual recommendations difficult to adapt to constantly changing personalized needs. Finding a more efficient product recommendation algorithm has become an important topic in the field of e-commerce. The scale of online shopping is constantly expanding, and it is necessary to match higher technological investments to explore the value of data. By comparing the advantages, disadvantages, and application scenarios of collaborative filtering algorithms and artificial intelligence technology, this article constructs a personalized recommendation system for e-commerce according to data mining. The results show that the recall rate of AI based recommendation algorithms is about 0.1 higher than that of collaborative filtering-based recommendation algorithms, and after using artificial intelligence algorithms for personalized recommendations, eBay's recommendation accuracy has improved by about 8.26%. The recommendation system designed in this article can provide recommendations for products or services that meet users' needs according to their interests as well as preferences, thereby improving their shopping experience. © 2023 IEEE.

关键词： Artificial Intelligence clustering algorithm Electronic Commerce Personalized Recommendation

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3D Segmentation and quantification of Magnetic Resonance data: Application to the Osteonecrosis of the femoral head

3D Segmentation and quantification of Magnetic Resonance dat...

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Medical Imaging 1999 Conference - Image Processing

作者： Klifa, C Lynch, J Zaim, S Genant, H Univ Calif San Francisco Osteoporosis & Arthrit Res Grp San Francisco CA 94143 USA

ISBN: (纸本)0819431338

The general objective of our study is the development of a clinically robust three-dimensional segmentation and quantification technique of Magnetic Resonance (MR) data, for the objective and quantitative evaluation of the osteonecrosis (ON) of the femoral head. This method will help evaluate the effects of joint preserving treatments for femoral head osteonecrosis from MR data. The disease is characterized by tissue changes (death of bone and marrow cells) within the weight-bearing portion of the femoral head. Due to the fuzzy appearance of lesion tissues and their different intensity patterns in various MR sequences, we proposed a semi-automatic multispectral segmentation of MR data introducing data constraints (anatomical and geometrical) and using a classical K-means unsupervised clustering algorithm. The method was applied on ON patient data. Results of volumetric measurements and configuration of various tissues obtained with the semiautomatic method were compared with quantitative results delineated by a trained radiologist.

关键词： segmentation MR quantitative analysis features extraction anatomical constraints clustering algorithm multispectral analysis

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Solution of Fire Coordination Scheme of Equipment System Based on Fuzzy clustering-auction Mechanism

Solution of Fire Coordination Scheme of Equipment System Bas...

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第三十九届中国控制会议

作者： Ke-hu Xu Guo-sheng Wang Department of Weapon and Control Army Academy of Armored Force

Aiming at the problem of the large-scale weapon target distribution model, the conventional algorithms are low efficient to obtain the solution and can not obtain the fire optimization scheme untimely. Thus a new two-level task optimization distribution model is constructed, which can obtain a distribution scheme in a shorter time by combining the fuzzy clustering algorithm with the auction algorithm. The numerical example shows that the established collaborative task allocation model and the corresponding solution method can get the task allocation scheme in time and effectively, and solve the problem of the fire power optimal allocation of the large-scale weapon-target.

关键词： Collaborative task allocation clustering algorithm auction algorithm

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Residential Load Pattern clustering Based on Smart Meter Data Using Weighted Self-Organizing Map

Residential Load Pattern Clustering Based on Smart Meter Dat...

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第33届中国控制与决策会议

作者： Qing Peng Ming Chi Mingxi Zhu Yunfan Yu DiANDian Wan Zhi-Wei Liu School of Articial Intelligence and Automation Huazhong University of Science and Technology

With arrival of big data of smart meters,a large number of residential power consumption data are collected according to different sampling frequency,namely Residential Load Profiles(RLPs).In this paper,RLPs of smart meter customers are analyzed by clustering,which is of great significance to load management of smart grid.A twostage Weighted Self-Organizing Map(WSOM) clustering algorithm and a clustering performance evaluation method,SSE-DBI,combining Sum of Squares Error(SSE) and Davies-Bouldin(DBI) are *** first stage,Principal Component Analysis(PCA) is used to reduce the dimension of the *** dimension reduced data is fed into SOM network for clustering,update of weights of SOM is weighted according to PCA,and these clustering centers,namely Typical Residential Load Profiles(TRLPs) of each customer are obtained after some iterations of *** second stage,above processing is repeated for TRLPs of each customer,TRLPs of all customer are *** to SSE-DBI,final optimal cluster number and clustering performance score of the model are *** with several benchmark methods,the proposed method obtains optimal performance.

关键词： smart meters RLPs WSOM clustering algorithm SSE-DBI PCA TRLPs

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