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  • Open Access

    ARTICLE

    A Method of Identifying Thunderstorm Clouds in Satellite Cloud Image Based on Clustering

    Lili He1,2, Dantong Ouyang1,2, Meng Wang1,2, Hongtao Bai1,2, Qianlong Yang1,2, Yaqing Liu3,4, Yu Jiang1,2,*

    CMC-Computers, Materials & Continua, Vol.57, No.3, pp. 549-570, 2018, DOI:10.32604/cmc.2018.03840

    Abstract In this paper, the clustering analysis is applied to the satellite image segmentation, and a cloud-based thunderstorm cloud recognition method is proposed in combination with the strong cloud computing power. The method firstly adopts the fuzzy C-means clustering (FCM) to obtain the satellite cloud image segmentation. Secondly, in the cloud image, we dispose the ‘high-density connected’ pixels in the same cloud clusters and the ‘low-density connected’ pixels in different cloud clusters. Therefore, we apply the DBSCAN algorithm to the cloud image obtained in the first step to realize cloud cluster knowledge. Finally, using the method of spectral threshold recognition and… More >

  • Open Access

    ARTICLE

    An Evidence Combination Method based on DBSCAN Clustering

    Kehua Yang1,2,*, Tian Tan1, Wei Zhang1

    CMC-Computers, Materials & Continua, Vol.57, No.2, pp. 269-281, 2018, DOI:10.32604/cmc.2018.03696

    Abstract Dempster-Shafer (D-S) evidence theory is a key technology for integrating uncertain information from multiple sources. However, the combination rules can be paradoxical when the evidence seriously conflict with each other. In the paper, we propose a novel combination algorithm based on unsupervised Density-Based Spatial Clustering of Applications with Noise (DBSCAN) density clustering. In the proposed mechanism, firstly, the original evidence sets are preprocessed by DBSCAN density clustering, and a successfully focal element similarity criteria is used to mine the potential information between the evidence, and make a correct measure of the conflict evidence. Then, two different discount factors are adopted… More >

  • Open Access

    ARTICLE

    Method of Time Series Similarity Measurement Based on Dynamic Time Warping

    Lianggui Liu1,*, Wei Li1, Huiling Jia1

    CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 97-106, 2018, DOI:10.32604/cmc.2018.03511

    Abstract With the rapid development of mobile communication all over the world, the similarity of mobile phone communication data has received widely attention due to its advantage for the construction of smart cities. Mobile phone communication data can be regarded as a type of time series and dynamic time warping (DTW) and derivative dynamic time warping (DDTW) are usually used to analyze the similarity of these data. However, many traditional methods only calculate the distance between time series while neglecting the shape characteristics of time series. In this paper, a novel hybrid method based on the combination of dynamic time warping… More >

  • Open Access

    ARTICLE

    A Spark Scheduling Strategy for Heterogeneous Cluster

    Xuewen Zhang1, Zhonghao Li1, Gongshen Liu1,*, Jiajun Xu1, Tiankai Xie2, Jan Pan Nees1

    CMC-Computers, Materials & Continua, Vol.55, No.3, pp. 405-417, 2018, DOI: 10.3970/cmc.2018.02527

    Abstract As a main distributed computing system, Spark has been used to solve problems with more and more complex tasks. However, the native scheduling strategy of Spark assumes it works on a homogenized cluster, which is not so effective when it comes to heterogeneous cluster. The aim of this study is looking for a more effective strategy to schedule tasks and adding it to the source code of Spark. After investigating Spark scheduling principles and mechanisms, we developed a stratifying algorithm and a node scheduling algorithm is proposed in this paper to optimize the native scheduling strategy of Spark. In this… More >

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