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This paper has proposed a design for networked control systems with random time delays and packet dropout in the forward communication channel by using fuzzy theories. First, a simple predictive model was built by means of fuzzy cluster modeling technology and neural network approximation. Then, a prediction oriented fuzzy sliding mode controller was presented to obtain the future control actions'...
In this paper, a dual-field elliptic curve cryptographic processor is proposed to support arbitrary curves within 576-bit in dual field. Besides, two heterogeneous function units are coupled with the processor for the parallel operations in finite field based on the analysis of the characteristics of elliptic curve cryptographic algorithms. To simplify the hardware complexity, the clustering technology...
Although many different community detection algorithms have been proposed to detect community structures in complex networks, how to effectively detect community structures is still a great challenge. Seed-centric methods is one of the most effective solutions for community detection. To more, in this paper, we propose a novel density-based seed expansion algorithm, namely, DenSeC, which can easily...
In this paper, we propose a novel approach to cluster incomplete images leveraging sparse subspace structure and total variation regularization. Sparse subspace clustering obtains a sparse representation coefficient matrix for input data points by solving an l1 minimization problem, and then uses the coefficient matrix to construct a sparse similarity graph over which spectral clustering is performed...
With the wide application of these technologies in wireless networks such as Multipath transmission, opportunistic routing, Cooperative communication and so on, channel interference and resource contention become more intense, which greatly decrease the network throughput, seriously affect network efficiency[1]. Further, the ripple effect which caused by inappropriate channel switching will result...
For parallel applications running on high-performance clusters, performance is usually satisfied without paying much attention to energy consumption. In this paper, we develop a new scheduling algorithm called Energy efficient Scheduling Algorithm based on DVS and Dynamic threshold (ESADD), which combines dynamic threshold-based task duplication strategy with dynamic voltage scaling (DVS) technique...
High performance heterogeneous clusters have been widely used to commercial and scientific areas. However, huge energy consumption has prevented the further application of large-scale heterogeneous clusters. It is highly desirable to design energy-aware scheduling algorithm for parallel applications running on heterogeneous clusters. In this regard, we propose a novel scheduling algorithm called Efficient-Energy...
Clusters provide powerful computing performance is at cost of huge energy consumption. Scheduling a parallel application with a set of precedence-constrained tasks on cluster is challenging because of high communication cost. Although task duplication based scheduling algorithm is applied to minimize communication overhead, most of them only consider scheduling lengths, however completely ignoring...
In wireless sensor networks (WSNs), Energy consumption will be reduced by grouping all the sensor nodes into clusters and letting the data be aggregated at cluster heads(CHs). In this paper, we address the issue of how many tiers in a given clustering scheme are optimal in saving energy. We present a method to calculate the optimal tier number with consideration to the energy consumption both in data...
In personalized education theory, each learner has different behavioral characteristics. From this perspective, the biggest shortcoming of the Chinese learning website is that it couldn't reflect the individuality. Although some websites have classification of Chinese learners, most of them are based on the initial registration information. This paper proposes a grouping algorithm for Chinese learners...
A flexible and scalable architecture that implements real-time synthetic aperture sonar (SAS) imaging on high performance clusters (HPCs) is introduced in this paper. Combing HPCs with application of a hybrid MPI and OpenMP parallel programming model, the lookup table algorithm for SAS image reconstruction based on the time-domain correlation algorithm is realized in parallel. Considering the huge...
A new method for constructing phylogenetic trees from a given set of objects (proteins, species, etc.) is presented. The method bisects the set of gene sequences so that the sequences within one subset have most similarity, and the gene sequences between different subsets have most difference. Recursively repeating such bisecting until procedure until all the subsets contains only one gene sequence...
In recent years, the mass spectrometry technologies emerge as useful tools for biomarker discovery through studying protein profiles in various biological specimens. In mining mass spectrometry datasets, peak alignment is a critical issue among the preprocessing steps that affect the quality of analysis results. In this paper, we proposed a novel algorithm named Two-Phases Clustering for peak Alignment...
In this paper, we present methodology to Content Based Image Retrieval (CBIR), focusing on developing an efficient image retrieval methodology. This scheme include: a new indexing method based on fuzzy logic to incorporate color, texture, and shape information into a region based approach to improving the retrieval effectiveness and robustness, a novel hierarchical indexing structure and the corresponding...
In this paper, a novel learning method based on kernelized fuzzy clustering and least squares support vector machines (LSSVM) is presented to improve the generalization ability of a Takagi-Sugeno-Kang (TSK) fuzzy modeling. Firstly, the fuzzy partition of the product space of input and output is obtained by kernelized fuzzy clustering. Then, a computationally efficient numerical method is proposed...
Event extraction is a major task of Automatic Content Extraction (ACE) program. This paper focuses on the sub-task of event extraction, event argument identification, and proposes a novel method for Chinese event argument identification. The method involves two steps: (1) weighting features by the ReliefF algorithm for considering the particular contributions of different features on clustering analysis,...
In order to overcome the dimension problem of the traditional fuzzy clustering, we use kernel-based fuzzy c-means clustering (KFCM) to construct first-order TSK fuzzy models. The proposed algorithm is composed of two phases. In the first phase, the antecedent fuzzy sets are obtained by KFCM. We present the expression of the cluster prototypes of KFCM with different kernel functions in original input...
Biclustering the gene expressing data is an important task in bioinformatics. A parallel biclustering algorithm for gene expressing data is presented. The algorithm starts from the data sets containing pair of rows and columns of the data matrix, and gets the biclusters by gradually adding columns and rows on the data sets. A pruning technique is also proposed to reduce computing time. Experimental...
In this paper, we present a text-clustering algorithm of frequent term set-based clustering (FTSC), which uses frequent term sets for texts clustering. This algorithm can reduce the dimensionality of the text data efficiently, thus it can improve accurate rate and running speed of the clustering algorithm. The results of clustering texts by the FTSC algorithm cannot reflect the overlap of texts' classes...
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