Scalability has been an important issue in mobile ad-hoe network (MANET). clustering is one of the mechanisms that are used in handling the scalability issue. Many cluster formation and cluster maintenance algorithms ...
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ISBN:
(纸本)0780393996
Scalability has been an important issue in mobile ad-hoe network (MANET). clustering is one of the mechanisms that are used in handling the scalability issue. Many cluster formation and cluster maintenance algorithms have been proposed for MANET This paper presents the hardware implementation and study of some of the proposed cluster formation algorithms such as lowest ID, highest degree, K-clustering, and weighted clustering algorithm (WCA). The FPGA implementation shows that for all four clustering algorithms implemented, the CLB slices used is between 123 slices and 559 slices with operating frequency between 137.95 MHz and 325.5 MHz. For the same algorithms implemented, the total power consumption is between 667.5 m W and 795.4 mW while the total current consumption is between 329.2 mA and 401.3 mA.
The paper introduced the attempt to apply the Association Rule Mining (ARM) method to discovering heuristic rules for Power System Restoration (PSR) to guide a fast restoration process. In order to employ the popular ...
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ISBN:
(纸本)9810557027
The paper introduced the attempt to apply the Association Rule Mining (ARM) method to discovering heuristic rules for Power System Restoration (PSR) to guide a fast restoration process. In order to employ the popular algorithms of ARM, the process of PSR is represented as a series of actions out of a finite action set. The interesting attributes of each action are mapped as items and the actions are mapped as transactions. Fuzzy set and clustering method are adopted to evaluate the performance of individual action. A sample test is given to demonstrate the validity of the proposed method.
Assembly operations arise in many practical situations, including assembly lines in production plants, mixing operations in chemical industries and data flow through computer systems. Assembly-like queueing systems ar...
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Assembly operations arise in many practical situations, including assembly lines in production plants, mixing operations in chemical industries and data flow through computer systems. Assembly-like queueing systems are used for modelling such operations. Our interest here stems from the need to solve the resource allocation problems in assembly-like queueing systems. However, there is not an effective technique to solve the optimal allocation problems of queueing networks. In this study, based on the results of computer simulations, we investigate various resource allocations and examine the performance of the systems in terms of throughputs, and present some heuristic policies for effective resource allocation in such systems. Further, we propose an algorithm to maximize the throughput in systems. Numerical tests show the algorithm yields satisfactory results.
In this letter, a real-time C-V (Characteristic-Vector) clustering algorithm is put forth to treat with vast action data which are dynamically collected from web site. The algorithm cites the concept of C-V to denote ...
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In this letter, a real-time C-V (Characteristic-Vector) clustering algorithm is put forth to treat with vast action data which are dynamically collected from web site. The algorithm cites the concept of C-V to denote characteristic, synchronously it adopts two-value [0,1]input and self-definition vigilance parameter to design clustering-architecture. Vector Degree of Matching (VDM) plays a key role in the clustering algorithm, which determines the magnitude of typical characteristic. Making use of stability analysis, the classifications are confirmed to have reliably hierarchical structure when vigilance parameter shifts from 0.1 to 0.99. This non-linear relation between vigilance parameter and classification upper limit helps mining out representative classifications from net-users according to the actual web resource, then administering system can map them to web resource space to implement the intelligent configuration effectually and rapidly.
Research has shown that nearly 80% of the costs and problems associated with product design are created during product development, and cost and quality are essentially designed into products during the conceptual des...
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Research has shown that nearly 80% of the costs and problems associated with product design are created during product development, and cost and quality are essentially designed into products during the conceptual design stage. Failure identification procedures (such as failure modes and effects analysis (FMEA), failure modes, effects and criticality analysis (FMECA) and fault tree analysis (FTA)) and design of experiments are currently being used for both quality control and for the detection of potential failure modes during the design stage or post-product launch. Although all of these methods have their own advantages, they do not provide the designer with an indication of the predominant failures that should receive considerable attention while the product is being designed. The work presented here proposes a statistical clustering procedure to identify potential failures in the conceptual design stage. A functional approach, which hypothesizes that similarities exist between different failure modes based on the functionality of the product/component, is employed to identify failure modes. The various steps of the methodology are illustrated using a hypothetical design example. Copyright (C) 2004 John Wiley Sons, Ltd.
Research has shown that nearly 80% of the costs and problems associated with product design are created during product development, and cost and quality are essentially designed into products during the conceptual des...
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Research has shown that nearly 80% of the costs and problems associated with product design are created during product development, and cost and quality are essentially designed into products during the conceptual design stage. Failure identification procedures (such as failure modes and effects analysis (FMEA), failure modes, effects and criticality analysis (FMECA) and fault tree analysis (FTA)) and design of experiments are currently being used for both quality control and for the detection of potential failure modes during the design stage or post-product launch. Although all of these methods have their own advantages, they do not provide the designer with an indication of the predominant failures that should receive considerable attention while the product is being designed. The work presented here proposes a statistical clustering procedure to identify potential failures in the conceptual design stage. A functional approach, which hypothesizes that similarities exist between different failure modes based on the functionality of the product/component, is employed to identify failure modes. The various steps of the methodology are illustrated using a hypothetical design example. Copyright (C) 2004 John Wiley Sons, Ltd.
Because the functional repertiore of RNA molecules, like proteins, is closely linked to the diversity of their shapes, uncovering RNA's structural repertoire is vital for identifying novel RNA;3, especially in gen...
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Because the functional repertiore of RNA molecules, like proteins, is closely linked to the diversity of their shapes, uncovering RNA's structural repertoire is vital for identifying novel RNA;3, especially in genomic sequences. To help expand the limited number of known RNA families, we use graphical representation and clustering analysis of RNA secondary structures to predict novel RNA topologies and their abundance as a function of size. Representing the essential topological properties of RNA, secondary structures as graphs enables enumeration, generation, and prediction of novel RNA motifs. We apply a probabilistic graph-growing, method to construct the RNA structure space encompassing the topologies of existing and hypothetical RNAs and cluster all RNA topologies into two groups using topological descriptors and a standard clustering algorithm. Significantly, we find that nearly all existing RNAs fall into one group, which we refer to as "RNA-like";we consider the other group "non-RNAlike". Our method predicts many candidates for novel RNA secondary topologies, some of which are remarkably similar to existing structures;interestingly, the centroid of the RNA-like group is the tmRNA fold, a pseudoknot having both tRNA-like and mRNA.-like functions. Additionally, our approach allows estimation of the relative abundance of pseudoknot and other (e.g. tree) motifs using the "edge-cut" property of RNA graphs. This analysis suggests that pseudoknots dominate the RNA structure universe, representing more than 90% when the sequence length exceeds 120 nt;the predicted trend for < 100 nt agrees with data for existing RNAs. Together with our predictions for novel "RNA-like" topologies, our analysis can help direct the design of functional RNAs and identification of novel RNA folds in genomes through an efficient topology-directed search, which grows much more slowly in complexity with RNA size compared to the traditional sequence-based search. 2004 Elsevier Ltd. All right
Many validity measures have been proposed for evaluating clustering results. Most of these popular validity measures do not work well for clusters with different densities and/or sizes. They usually have a tendency of...
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Many validity measures have been proposed for evaluating clustering results. Most of these popular validity measures do not work well for clusters with different densities and/or sizes. They usually have a tendency of ignoring clusters with low densities. In this paper, we propose a new validity measure that can deal with this situation. In addition, we also propose a modified K-means algorithm that can assign more cluster centres to areas with low densities of data than the conventional K-means algorithm does. First, several artificial data sets are used to test the performance of the proposed measure. Then the proposed measure and the modified K-means algorithm are applied to reduce the edge degradation in vector quantisation of image compression.
When optimizing operations of large wireless ad hoc networks, neither global nor local information-based approaches fit well: they require either information about the entire network structure. which is in most cases ...
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ISBN:
(纸本)0780383443
When optimizing operations of large wireless ad hoc networks, neither global nor local information-based approaches fit well: they require either information about the entire network structure. which is in most cases not possible to get, or are not capable of optimizing beyond a very narrow horizon. We propose a novel optimization scheme based on regional information, to compute network-wide optimizations taking the peculiarities of large ad hoc networks into account, obtaining an "emergent algorithm" out of a global optimization algorithm. Our solution uses a clustering algorithm to define regions but needs neither cluster maintenance nor inter-cluster communication protocols, thus is expected to be very robust. The problem of distributed frequency assignment is used as a case study to demonstrate the performance of our method, compared to algorithms based on local- or global information.
Constructing a batch of differentiable entropy functions touniformly approximate an objective function by means of the maximum-entropy principle, a new clustering algorithm, called maximum-entropy clustering algorithm...
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Constructing a batch of differentiable entropy functions touniformly approximate an objective function by means of the maximum-entropy principle, a new clustering algorithm, called maximum-entropy clustering algorithm, is proposed based on optimization theory. This algorithm is a soft generalization of the hard C-means algorithm and possesses global convergence. Its relations with other clustering algorithms are discussed.
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