Concave clouds will cause miscalculation by the power prediction model based on cloud ieatures for distributed photovoltaic （PV） plant. The algorithm for decomposing concave cloud into convex images is proposed. Ado...Concave clouds will cause miscalculation by the power prediction model based on cloud ieatures for distributed photovoltaic （PV） plant. The algorithm for decomposing concave cloud into convex images is proposed. Adopting minimum polygonal approximation （MPP） to demonstrate the contour of concave cloud, cloud features are described and the subdivision lines of convex decomposition for the concave clouds are determined by the centroid point scattering model and centroid angle func- tion, which realizes the convex decomposition of concave cloud. The result of MATLAB simulation indicates that the proposed algorithm can accurately detect cloud contour comers and recognize the concave points. The proposed decomposition algorithm has advantages of less time complexity and decomposition part numbers compared to traditional algorithms. So the established model can make the convex decomposition of complex concave clouds completely and quickly, which is available for the existing prediction algorithm for the ultra-short-term power output of distributed PV system based on the cloud features.展开更多
Support vector clustering （SVC） is an important boundary-based clustering algorithm in multiple applications for its capability of handling arbitrary cluster shapes.However,SVC＇s popularity is degraded by its highl...Support vector clustering （SVC） is an important boundary-based clustering algorithm in multiple applications for its capability of handling arbitrary cluster shapes.However,SVC＇s popularity is degraded by its highly intensive time complexity and poor label performance.To overcome such problems,we present a novel efficient and robust convex decomposition based cluster labeling （CDCL） method based on the topological property of dataset.The CDCL decomposes the implicit cluster into convex hulls and each one is comprised by a subset of support vectors （SVs）.According to a robust algorithm applied in the nearest neighboring convex hulls,the adjacency matrix of convex hulls is built up for finding the connected components;and the remaining data points would be assigned the label of the nearest convex hull appropriately.The approach＇s validation is guaranteed by geometric proofs.Time complexity analysis and comparative experiments suggest that CDCL improves both the efficiency and clustering quality significantly.展开更多
Mobile anchors are widely used for localization in WSNs.However,special properties over 3D terrains limit the implementation of them.In this paper,a novel 3D localization algorithm is proposed,called 3 DT-PP,which uti...Mobile anchors are widely used for localization in WSNs.However,special properties over 3D terrains limit the implementation of them.In this paper,a novel 3D localization algorithm is proposed,called 3 DT-PP,which utilizes path planning of mobile anchors over complex 3 D terrains,and simulations based upon the model of mountain surface network are conducted.The simulation results show that the algorithm decreases the position error by about 91%,8.7%and lowers calculation overhead by about 75%,1.3%,than the typical state-of-the-art localization algorithm(i.e.,'MDS-MAP','Landscape-3D').Thus,our algorithm is more potential in practical WSNs which are the characteristic of limited energy and 3D deployment.展开更多
Inspired by an interesting idea of Cai and Zhang,we formulate and prove the convex k-sparse decomposition of vectors that is invariant with respect to the l_(1) norm.This result fits well in discussing compressed sens...Inspired by an interesting idea of Cai and Zhang,we formulate and prove the convex k-sparse decomposition of vectors that is invariant with respect to the l_(1) norm.This result fits well in discussing compressed sensing problems under the Restricted Isometry property,but we believe it also has independent interest.As an application,a simple derivation of the RIP recovery conditionδk+θk,k<1 is presented.展开更多
基金Supported by the National High Technology Research and Development Programme of China(No.2013AA050405)Doctoral Fund of Ministry of Education(No.20123317110004)+1 种基金Foundation of Zhejiang Province Key Science and Technology Innovation Team(No.2011R50011)the Natural Science Foundation of Zhejiang Province(No.LY15E070004)
文摘Concave clouds will cause miscalculation by the power prediction model based on cloud ieatures for distributed photovoltaic （PV） plant. The algorithm for decomposing concave cloud into convex images is proposed. Adopting minimum polygonal approximation （MPP） to demonstrate the contour of concave cloud, cloud features are described and the subdivision lines of convex decomposition for the concave clouds are determined by the centroid point scattering model and centroid angle func- tion, which realizes the convex decomposition of concave cloud. The result of MATLAB simulation indicates that the proposed algorithm can accurately detect cloud contour comers and recognize the concave points. The proposed decomposition algorithm has advantages of less time complexity and decomposition part numbers compared to traditional algorithms. So the established model can make the convex decomposition of complex concave clouds completely and quickly, which is available for the existing prediction algorithm for the ultra-short-term power output of distributed PV system based on the cloud features.
基金supported by the National Natural Science Foundation of China under Grant No. 60972077 and partially under Grant No. 70921061the National Science and Technology Major Program under Grant No. 2010ZX03003-003-01+1 种基金the Natural Science Foundation of Beijing under Grant No. 9092009the Fundamental Research Funds for the Central Universities under Grant No.2011RC0212
文摘Support vector clustering （SVC） is an important boundary-based clustering algorithm in multiple applications for its capability of handling arbitrary cluster shapes.However,SVC＇s popularity is degraded by its highly intensive time complexity and poor label performance.To overcome such problems,we present a novel efficient and robust convex decomposition based cluster labeling （CDCL） method based on the topological property of dataset.The CDCL decomposes the implicit cluster into convex hulls and each one is comprised by a subset of support vectors （SVs）.According to a robust algorithm applied in the nearest neighboring convex hulls,the adjacency matrix of convex hulls is built up for finding the connected components;and the remaining data points would be assigned the label of the nearest convex hull appropriately.The approach＇s validation is guaranteed by geometric proofs.Time complexity analysis and comparative experiments suggest that CDCL improves both the efficiency and clustering quality significantly.
基金Supported by the Important National Science and Technology Specific Project of China(No.20112X03002-002-03)the National NatureScience Foundation of China(No.61133016,61163066)
文摘Mobile anchors are widely used for localization in WSNs.However,special properties over 3D terrains limit the implementation of them.In this paper,a novel 3D localization algorithm is proposed,called 3 DT-PP,which utilizes path planning of mobile anchors over complex 3 D terrains,and simulations based upon the model of mountain surface network are conducted.The simulation results show that the algorithm decreases the position error by about 91%,8.7%and lowers calculation overhead by about 75%,1.3%,than the typical state-of-the-art localization algorithm(i.e.,'MDS-MAP','Landscape-3D').Thus,our algorithm is more potential in practical WSNs which are the characteristic of limited energy and 3D deployment.
基金This research was partially supported by the National 973 Project of China(No.2013CB834205)Zhiqiang Xu was supported by National Natural Science foundation of China(Nos.11171336,11331012,11021101)National Basic Research Program of China(973 Program No.2010CB832702).
文摘Inspired by an interesting idea of Cai and Zhang,we formulate and prove the convex k-sparse decomposition of vectors that is invariant with respect to the l_(1) norm.This result fits well in discussing compressed sensing problems under the Restricted Isometry property,but we believe it also has independent interest.As an application,a simple derivation of the RIP recovery conditionδk+θk,k<1 is presented.
