The Experts below are selected from a list of 11274 Experts worldwide ranked by ideXlab platform
Xiaofei Chen - One of the best experts on this subject based on the ideXlab platform.
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Research on Load Clustering Based on Singular Value Decomposition and K-means Clustering Algorithm
2020 Asia Energy and Electrical Engineering Symposium (AEEES), 2020Co-Authors: Jiao Wang, Kaiyan Wang, Rong Jia, Xiaofei ChenAbstract:Considering the shortcomings of existing clustering algorithms in clustering quality, this paper proposes a load clustering research based on Singular Value decomposition and K-means clustering algorithm. First, eight characteristic indexes of load are extracted, and the Singular Value is used to decompose the load characteristics of the user side. The solved Singular Value reflects the importance of this type of load characteristic. The load characteristics corresponding to the data with Large Singular Value are taken as the main load characteristics to complete the dimensionality reduction of the data. Then, the clustering evaluation index SSE is used to compare the effects of direct clustering of load characteristics and clustering after Singular Value decomposition. The results show that the proposed method has better clustering effect on load characteristics. Finally, K-means clustering algorithm is used to cluster the load characteristics.
Jiao Wang - One of the best experts on this subject based on the ideXlab platform.
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Research on Load Clustering Based on Singular Value Decomposition and K-means Clustering Algorithm
2020 Asia Energy and Electrical Engineering Symposium (AEEES), 2020Co-Authors: Jiao Wang, Kaiyan Wang, Rong Jia, Xiaofei ChenAbstract:Considering the shortcomings of existing clustering algorithms in clustering quality, this paper proposes a load clustering research based on Singular Value decomposition and K-means clustering algorithm. First, eight characteristic indexes of load are extracted, and the Singular Value is used to decompose the load characteristics of the user side. The solved Singular Value reflects the importance of this type of load characteristic. The load characteristics corresponding to the data with Large Singular Value are taken as the main load characteristics to complete the dimensionality reduction of the data. Then, the clustering evaluation index SSE is used to compare the effects of direct clustering of load characteristics and clustering after Singular Value decomposition. The results show that the proposed method has better clustering effect on load characteristics. Finally, K-means clustering algorithm is used to cluster the load characteristics.
Kaiyan Wang - One of the best experts on this subject based on the ideXlab platform.
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Research on Load Clustering Based on Singular Value Decomposition and K-means Clustering Algorithm
2020 Asia Energy and Electrical Engineering Symposium (AEEES), 2020Co-Authors: Jiao Wang, Kaiyan Wang, Rong Jia, Xiaofei ChenAbstract:Considering the shortcomings of existing clustering algorithms in clustering quality, this paper proposes a load clustering research based on Singular Value decomposition and K-means clustering algorithm. First, eight characteristic indexes of load are extracted, and the Singular Value is used to decompose the load characteristics of the user side. The solved Singular Value reflects the importance of this type of load characteristic. The load characteristics corresponding to the data with Large Singular Value are taken as the main load characteristics to complete the dimensionality reduction of the data. Then, the clustering evaluation index SSE is used to compare the effects of direct clustering of load characteristics and clustering after Singular Value decomposition. The results show that the proposed method has better clustering effect on load characteristics. Finally, K-means clustering algorithm is used to cluster the load characteristics.
Rong Jia - One of the best experts on this subject based on the ideXlab platform.
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Research on Load Clustering Based on Singular Value Decomposition and K-means Clustering Algorithm
2020 Asia Energy and Electrical Engineering Symposium (AEEES), 2020Co-Authors: Jiao Wang, Kaiyan Wang, Rong Jia, Xiaofei ChenAbstract:Considering the shortcomings of existing clustering algorithms in clustering quality, this paper proposes a load clustering research based on Singular Value decomposition and K-means clustering algorithm. First, eight characteristic indexes of load are extracted, and the Singular Value is used to decompose the load characteristics of the user side. The solved Singular Value reflects the importance of this type of load characteristic. The load characteristics corresponding to the data with Large Singular Value are taken as the main load characteristics to complete the dimensionality reduction of the data. Then, the clustering evaluation index SSE is used to compare the effects of direct clustering of load characteristics and clustering after Singular Value decomposition. The results show that the proposed method has better clustering effect on load characteristics. Finally, K-means clustering algorithm is used to cluster the load characteristics.
Zhiao Shi - One of the best experts on this subject based on the ideXlab platform.
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Simplified grid computing through spreadsheets and NetSolve
Proceedings. Seventh International Conference on High Performance Computing and Grid in Asia Pacific Region 2004., 2004Co-Authors: D. Abramso, Jack Dongarra, E. Meek, P Roe, Zhiao ShiAbstract:Grid computing has great potential but to enter the mainstream it must be simplified. Tools and libraries must make it easier to solve problems by being simpler and at the same time more sophisticated. We describe how grid computing can be achieved through spreadsheets. No parallel programming or complex tools need to be used. So long as dependencies allow it, formulae in a spreadsheet can be evaluated concurrently on the grid. Thus, grid computing becomes accessible to all those who can use a spreadsheet. The story is completed with a sophisticated backend system, NetSolve, which can solve complex linear algebra systems with minimal intervention from the user. We present the architecture of the system for performing such simple yet sophisticated grid computing and a case study which performs a Large Singular Value decomposition.
