To smooth the correlation process from bio-virus diffusion to emergency relief response,the Gaussian plume model is used to describe the diffusion of dangerous sources,where the bio-virus concentration at any given po...To smooth the correlation process from bio-virus diffusion to emergency relief response,the Gaussian plume model is used to describe the diffusion of dangerous sources,where the bio-virus concentration at any given point in affected areas can be calculated.And the toxic load rule is introduced to define the borderline of the dangerous area at different levels.Combined with this,different emergency levels of different demand points in dangerous areas are confirmed using fuzzy clustering,which allows demand points at the same emergency level to cluster in a group.Some effective emergency relief centers are chosen from the candidate hospitals which are located in different emergency level affected areas by set covering.Bioterrorism experiments which were conducted in Nanjing,Jiangsu province are simulated,and the results indicate that the novel method can be used efficiently by decision makers during an actual anti-bioterrorism relief.展开更多
In this paper, the idea of interest coverage is provided to form clusters in sensor network, which mean that the distance among data trends gathered by neighbor sensors is so small that, in some period, those sensors ...In this paper, the idea of interest coverage is provided to form clusters in sensor network, which mean that the distance among data trends gathered by neighbor sensors is so small that, in some period, those sensors can be clustered, and certain sensor can be used to replace the cluster to form the virtual sensor network topology. In detail, the Jensen-Shannon Divergence (JSD) is used to characterize the distance among different distributions which represent the data trend of sensors. Then, based on JSD, a hierarchical clustering algorithm is provided to form the virtual sensor network topology. Simulation shows that the proposed approach gains more than 50% energy saving than Sta- tistical Aggregation Methods (SAM) which transmitted data gathered by sensor only when the differ- ence among data exceed certain threshold.展开更多
This paper presents SFES: a scalable, fault-tolerant, efficient search scheme in a peer-to-peer network. The idea is based on the fact that data distribution in an information society has structured features. We desig...This paper presents SFES: a scalable, fault-tolerant, efficient search scheme in a peer-to-peer network. The idea is based on the fact that data distribution in an information society has structured features. We designed an algorithm to cluster peers that have similar interests. When receiving a query request, a peer will preferentially forward it to another peer which belongs to the same cluster and shares more similar interests. By this method, search efficiency will be remarkably improved and at the same time good resistance against peer failure (the ability to withstand peer failure) is reserved. Keyword partial-match is supported, too.展开更多
Ambient Assisted Living(AAL) is becoming an important research field. Many technologies have emerged related with pervasive computing vision, which can give support for AAL. One of the most reliable approaches is base...Ambient Assisted Living(AAL) is becoming an important research field. Many technologies have emerged related with pervasive computing vision, which can give support for AAL. One of the most reliable approaches is based on wireless sensor networks(WSNs). In this paper, we propose a coverage-aware unequal clustering protocol with load separation(CUCPLS) for data gathering of AAL applications based on WSNs. Firstly, the coverage overlap factor for nodes is introduced that accounts for the degree of target nodes covered. In addition, to balance the intra-cluster and inter-cluster energy consumptions, different competition radiuses of CHs are computed theoretically in different rings, and smaller clusters are formed near the sink. Moreover, two CHs are selected in each cluster for load separation to alleviate the substantial energy consumption difference between a single CH and its member nodes. Furthermore, a backoff waiting time is adopted during the selection of the two CHs to reduce the number of control messages employed. Simulation results demonstrate that the CUCPLS not only can achieve better coverage performance, but also balance the energy consumption of a network and prolong network lifetime.展开更多
Remote sensing based land cover mapping at large scale is time consuming when using either supervised or unsupervised clas- sification approaches. This article used a fast clustering method---Clustering by Eigen Space...Remote sensing based land cover mapping at large scale is time consuming when using either supervised or unsupervised clas- sification approaches. This article used a fast clustering method---Clustering by Eigen Space Transformation （CBEST） to pro- duce a land cover map for China. Firstly, 508 Landsat TM scenes were collected and processed. Then, TM images were clus- tered by combining CBEST and K-means in each pre-defined ecological zone （50 in total for China）. Finally, the obtained clusters were visually interpreted as land cover types to complete a land cover map. Accuracy evaluation using 2159 test sam- pies indicates an overall accuracy of 71.7% and a Kappa coefficient of 0.64. Comparisons with two global land cover products （i.e., Finer Resolution Observation and Monitoring of Global Land Cover （FROM-GLC） and GlobCover 2009） also indicate that our land cover result using CBEST is superior in both land cover area estimation and visual effect for different land cover types.展开更多
There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling ...There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data,which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects.After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals,an interval-valued fuzzy c-means(FCM)clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study.Such a process can significantly improve the clustering effect;specifically,the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results.Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region,China,and the TM sensor for Yushu,Qinghai,China,were conducted,as well as experiments involving the conventional FCM algorithm,the results of which were used for comparative analysis.Next,a supervised classification method was used to validate the clustering results.The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery.展开更多
基金The National Natural Science Foundation of China(No.70671021)the National Key Technology R&D Program of China during the 11th Five-Year Plan Period(No.2006BAH02A06)
文摘To smooth the correlation process from bio-virus diffusion to emergency relief response,the Gaussian plume model is used to describe the diffusion of dangerous sources,where the bio-virus concentration at any given point in affected areas can be calculated.And the toxic load rule is introduced to define the borderline of the dangerous area at different levels.Combined with this,different emergency levels of different demand points in dangerous areas are confirmed using fuzzy clustering,which allows demand points at the same emergency level to cluster in a group.Some effective emergency relief centers are chosen from the candidate hospitals which are located in different emergency level affected areas by set covering.Bioterrorism experiments which were conducted in Nanjing,Jiangsu province are simulated,and the results indicate that the novel method can be used efficiently by decision makers during an actual anti-bioterrorism relief.
基金the National Natural Science Foundation of China (No.60472067)Jiangsu Education Bureau (5KJB510091)State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications (BUPT).
文摘In this paper, the idea of interest coverage is provided to form clusters in sensor network, which mean that the distance among data trends gathered by neighbor sensors is so small that, in some period, those sensors can be clustered, and certain sensor can be used to replace the cluster to form the virtual sensor network topology. In detail, the Jensen-Shannon Divergence (JSD) is used to characterize the distance among different distributions which represent the data trend of sensors. Then, based on JSD, a hierarchical clustering algorithm is provided to form the virtual sensor network topology. Simulation shows that the proposed approach gains more than 50% energy saving than Sta- tistical Aggregation Methods (SAM) which transmitted data gathered by sensor only when the differ- ence among data exceed certain threshold.
文摘This paper presents SFES: a scalable, fault-tolerant, efficient search scheme in a peer-to-peer network. The idea is based on the fact that data distribution in an information society has structured features. We designed an algorithm to cluster peers that have similar interests. When receiving a query request, a peer will preferentially forward it to another peer which belongs to the same cluster and shares more similar interests. By this method, search efficiency will be remarkably improved and at the same time good resistance against peer failure (the ability to withstand peer failure) is reserved. Keyword partial-match is supported, too.
基金supported by the National Nature Science Foundation of China (61170169, 61170168)
文摘Ambient Assisted Living(AAL) is becoming an important research field. Many technologies have emerged related with pervasive computing vision, which can give support for AAL. One of the most reliable approaches is based on wireless sensor networks(WSNs). In this paper, we propose a coverage-aware unequal clustering protocol with load separation(CUCPLS) for data gathering of AAL applications based on WSNs. Firstly, the coverage overlap factor for nodes is introduced that accounts for the degree of target nodes covered. In addition, to balance the intra-cluster and inter-cluster energy consumptions, different competition radiuses of CHs are computed theoretically in different rings, and smaller clusters are formed near the sink. Moreover, two CHs are selected in each cluster for load separation to alleviate the substantial energy consumption difference between a single CH and its member nodes. Furthermore, a backoff waiting time is adopted during the selection of the two CHs to reduce the number of control messages employed. Simulation results demonstrate that the CUCPLS not only can achieve better coverage performance, but also balance the energy consumption of a network and prolong network lifetime.
基金partially supported by the National High-tech R&D Program of China(Grant No.2009AA12200101)a research grant from Tsinghua University(Grant No.2012Z02287)
文摘Remote sensing based land cover mapping at large scale is time consuming when using either supervised or unsupervised clas- sification approaches. This article used a fast clustering method---Clustering by Eigen Space Transformation （CBEST） to pro- duce a land cover map for China. Firstly, 508 Landsat TM scenes were collected and processed. Then, TM images were clus- tered by combining CBEST and K-means in each pre-defined ecological zone （50 in total for China）. Finally, the obtained clusters were visually interpreted as land cover types to complete a land cover map. Accuracy evaluation using 2159 test sam- pies indicates an overall accuracy of 71.7% and a Kappa coefficient of 0.64. Comparisons with two global land cover products （i.e., Finer Resolution Observation and Monitoring of Global Land Cover （FROM-GLC） and GlobCover 2009） also indicate that our land cover result using CBEST is superior in both land cover area estimation and visual effect for different land cover types.
基金supported by the National Natural Science Foundation of China(Grant Nos.41272359&11001019)the Specialized Research Fund for the Doctoral Program of Higher Education(SRFDP)the Fundamental Research Funds for the Central Universities
文摘There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data,which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects.After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals,an interval-valued fuzzy c-means(FCM)clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study.Such a process can significantly improve the clustering effect;specifically,the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results.Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region,China,and the TM sensor for Yushu,Qinghai,China,were conducted,as well as experiments involving the conventional FCM algorithm,the results of which were used for comparative analysis.Next,a supervised classification method was used to validate the clustering results.The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery.
