The ability of newer generation of commercially available radios used on the sensor nodes operating on different channels (called multi-channel) provides an opportunity to alleviate the effects of interference and con...
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ISBN:
(纸本)9781467359900
The ability of newer generation of commercially available radios used on the sensor nodes operating on different channels (called multi-channel) provides an opportunity to alleviate the effects of interference and consequently improve network performance significantly. In this paper, we investigate a multichannel communication algorithm associated with routing process (called UMRC - mUltichannel and Multihop clustering Communication scheme) for Wireless Sensor Networks (WSN) in order to improve the capacity and network performance with the following features: interference and contention-free, multi-path routing, energy efficiency and load balance. We use simulation technique to evaluate and compare the performance of our new algorithm to other similar single channel schemes in terms of energy consumption, traffic load balance, number of a living node and network throughput. The simulation results show that the proposed multi-channel scheme can increase the performance significantly compared to other proposed schemes.
A time sequence clustering algorithm based on edit distance is proposed in the paper, which solves the problem that the existing clustering algorithms for time sequence data is inefficient because of ignorance of diff...
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ISBN:
(纸本)9783037858462
A time sequence clustering algorithm based on edit distance is proposed in the paper, which solves the problem that the existing clustering algorithms for time sequence data is inefficient because of ignorance of different time span of time sequence data. Firstly, the algorithm calculates the distance between time sequences on which a distance matrix is determined. In the second place, for a given time sequence set, a forest with n binary trees is established in terms of the distance matrix and then merge the trees. Finally, a cluster clustering algorithm is called to dynamically adjust the clustering results, and then real-time clustering structure is obtained. Experimental results demonstrated that the algorithm has higher efficiency and clustering quality.
The main purpose of this paper is to investigate the relationship between the entropy, the similarity measure and the distance measure for hesitant fuzzy sets (HFSs) and interval-valued hesitant fuzzy sets (IVHFSs). T...
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The main purpose of this paper is to investigate the relationship between the entropy, the similarity measure and the distance measure for hesitant fuzzy sets (HFSs) and interval-valued hesitant fuzzy sets (IVHFSs). The primary goal of the study is to suggest the systematic transformation of the entropy into the similarity measure for HFSs and vice versa. Achieving this goal is important to the task of introducing new formulas for the entropy and the similarity measure of HFSs. With results having been obtained for HFSs, similar results are also obtainable for IVHFSs. This paper also discusses the need for proposing a new entropy for HFSs and subsequently a new similarity measure for HFSs. Finally, two clustering algorithms are developed under a hesitant fuzzy environment in which indices of similarity measures of HFSs and IVHFSs are applied in data analysis and classification. Moreover, two practical examples are examined to compare the proposed methods with the existing ones. (C) 2013 Elsevier Inc. All rights reserved.
Nowadays the data collected in the process in maintenance systems comprise a big portion of the related databases. Analyzing these maintenance data provides the firms, enterprises and organizations with a tremendous c...
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Nowadays the data collected in the process in maintenance systems comprise a big portion of the related databases. Analyzing these maintenance data provides the firms, enterprises and organizations with a tremendous competitive edge both in manufacturing and service sectors. As maintenance management is a costly and inevitable part of the organization, ensuring that the maintenance activities are performed in an effective manner, is of outmost importance. In other words, organizations can precede with the cost reduction operations, for instance, if and only if the unproductive maintenance activities and processes can be identified. Subsequently, rectifying or removing these kinds of activities or taking other means of modification can help enterprises and organizations to reduce their costs. Data mining is known to be an excellent tool which helps the decision makers to discover the hidden knowledge and patterns when dealing with a large amount of data. Seeing a gap in the related literature reviewed and in order to fill it, this study proposes a data mining based model to identify the unproductive maintenance activities in a maintenance system. By identifying specific inefficient maintenance activities, this model supports the maintenance decision makers to set goals to make amendments in the maintenance systems under their supervisions. Consequently, the organizations can focus on rectifying these fruitless activities and therefore reducing the costs associated with performing them. Finally, the model was used to identify the unproductive activities in a maintenance system comprising of independent components (an urban bus network).
Histone modifications play a crucial role in regulating gene expression and cell lineage determination and maintenance at the epigenetic level. To systematically investigate this phenomenon, this paper presented a sta...
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Histone modifications play a crucial role in regulating gene expression and cell lineage determination and maintenance at the epigenetic level. To systematically investigate this phenomenon, this paper presented a statistical hybrid clustering algorithm to identify common combinatorial histone modification patterns. We applied the algorithm to 39 histone modification marks in human CD4+ T cells and detected 854 common combinatorial histone modification patterns. Our results could cover 211 (76.17%) patterns among 277 patterns identified by the tandem mass spectrometry experiments. Based on the frequency statistical analysis, it was found that the co-occurrence frequencies of 20 backbone modifications are greater than or close to 0.2 in the 854 patterns, we also found that 15 modifications (H2BK120ac, H4K91ac, H2BK20ac, etc.), three histone acetylations (H2AK9ac, H4K16ac, and H4K12ac) and five histone methylations (H3K79me1, H3K79me2, 3K79me3, H4K20me1, and H2BK5me1) were most likely prone to coexist respectively in these patterns. In addition, we found that DNA methylation tends to combine with histone acetylation rather than histone methylation. (c) 2012 Elsevier B.V. All rights reserved.
Run-to-run (R2R) process control has attracted much attention in research and has been widely used in practice. It has been proved effective at compensating for process disturbances by using R2R controllers at a singl...
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Run-to-run (R2R) process control has attracted much attention in research and has been widely used in practice. It has been proved effective at compensating for process disturbances by using R2R controllers at a single stage. However, most manufacturing processes span across multiple stages;variation in earlier stages can be magnified stage by stage if they are not properly eliminated. In addition, products are processed batch by batch in certain manufacturing processes. In such cases, the traditional EWMA controller might not effectively reduce the variation. This paper focuses on developing a process control strategy for batch production in a multistage process. In the newly proposed framework, a batch-allocation operation is introduced to group products into similar clusters before each stage;an R2R controller is then implemented to generate customized recipes for each batch. This framework emphasizes better coordination among the stages in a multistage process. Simulation results show that the proposed strategy is effective for the reduction of variation. (C) 2012 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.
Finding solutions to green manufacturing, green production, and increasing energy efficiency is definitely our responsibility to resist changing the vulnerable environment dramatically. Over the past, several practica...
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Finding solutions to green manufacturing, green production, and increasing energy efficiency is definitely our responsibility to resist changing the vulnerable environment dramatically. Over the past, several practical techniques have been proposed to reduce the greenhouse gas emissions, e.g., increasing energy efficiency, reducing power usage, using sustainable energy, and recycling. This paper first gives a brief review of green computing and then presents a case study for energy efficiency called energy efficient particle swarm optimization (EEPSO). The proposed algorithm integrates particle swarm optimization and triangle inequality for improving energy efficiency of computers, by using the clustering results to adjust the CPU frequency of network management system. Simulation results show that not only can the proposed algorithm significantly reduce the computation time, but it can also be extended to enhance the performance of network traffic control system to further reduce the power they consume.
Large sets of bioinformatical data provide a challenge in time consumption while solving the cluster identification problem, and that is why a parallel algorithm is so needed for identifying dense clusters in a noisy ...
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Large sets of bioinformatical data provide a challenge in time consumption while solving the cluster identification problem, and that is why a parallel algorithm is so needed for identifying dense clusters in a noisy background. Our algorithm works on a graph representation of the data set to be analyzed. It identifies clusters through the identification of densely intraconnected subgraphs. We have employed a minimum spanning tree (MST) representation of the graph and solve the cluster identification problem using this representation. The computational bottleneck of our algorithm is the construction of an MST of a graph, for which a parallel algorithm is employed. Our high-level strategy for the parallel MST construction algorithm is to first partition the graph, then construct MSTs for the partitioned subgraphs and auxiliary bipartite graphs based on the subgraphs, and finally merge these MSTs to derive an MST of the original graph. The computational results indicate that when running on 150 CPUs, our algorithm can solve a cluster identification problem on a data set with 1,000,000 data points almost 100 times faster than on single CPU, indicating that this program is capable of handling very large data clustering problems in an efficient manner. We have implemented the clustering algorithm as the software CLUMP.
Given the increasing demand for blood smear analysis in the Hematology department of Oran (Algeria) Hospital and worldwide, in the literature there are some methods directed to the automation of this important problem...
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ISBN:
(纸本)9781467316583
Given the increasing demand for blood smear analysis in the Hematology department of Oran (Algeria) Hospital and worldwide, in the literature there are some methods directed to the automation of this important problem. This paper presents a state-of-art about the used clustering methods.
Traditionally, the countries classification is performed based on several features, that are related to economic and social factors. However, the classification process is costly due to the difficulty of obtaining tho...
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ISBN:
(纸本)9781479931941
Traditionally, the countries classification is performed based on several features, that are related to economic and social factors. However, the classification process is costly due to the difficulty of obtaining those features and the need for intervention of human expertise. In this paper, we propose an intelligent agent that classifies countries based on financial indices. The artificial agent calculates the probability density function (pdf) of the return series of financial indices. This pdf characterizes the fluctuation of a market. Based on the return series and pdf, the volatility and the B coefficient of the exponential function, that describe the behavior of world markets, are estimated. Then, the intelligent agent classifies the indices from developed and developing countries using a Self-Organizing Map (SOM) neural network. The results show that the proposed intelligent agent is an accurate, fast and cheap alternative to the classifications provided by traditional organizations.
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