Due to the problem of energy-constraint in WSN,how to make use of the energy and extend the lifetime of the network is the urgent problem needed to be solved by *** algorithm is an important algorithm in Hierarchical ...
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Due to the problem of energy-constraint in WSN,how to make use of the energy and extend the lifetime of the network is the urgent problem needed to be solved by *** algorithm is an important algorithm in Hierarchical *** paper analyzes the formation of cluster header and put forward a conclusion and expectation on the basis of some typical ways of cluster algorithm at present.
Application layer DDoS attack challenges web applications seriously. It launches attack by sending a large number of HTTP Get requests to a web server. The anomaly based method is a promising method, which detects the...
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ISBN:
(纸本)9781538665657
Application layer DDoS attack challenges web applications seriously. It launches attack by sending a large number of HTTP Get requests to a web server. The anomaly based method is a promising method, which detects the DDoS attack by comparing the individual surfing behavior with a reference surfing-behavior profile. Yet due to the exist of noisy web logs caused by web-crawling, it is difficult to build robust reference profile for detection. This paper proposes a novel anomaly-based application DDoS detection scheme base on clustering method Our method could tolerate the web crawling traces in building reference surfing profile, and can detect different Application layer DDoS attack (e.g., repetitively getting several webpages, randomly getting webpages following hyper-lmks etc.). The simulation results show that our method can detect application layer DDoS attacks accurately.
We present the GPU calculation with the common unified device architecture (CUDA) for the Wolff single-cluster algorithm of the Ising model. Proposing an algorithm for a quasi-block synchronization, we realize the Wol...
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We present the GPU calculation with the common unified device architecture (CUDA) for the Wolff single-cluster algorithm of the Ising model. Proposing an algorithm for a quasi-block synchronization, we realize the Wolff single-cluster Monte Carlo simulation with CUDA. We perform parallel computations for the newly added spins in the growing cluster. As a result, the GPU calculation speed for the two-dimensional Ising model at the critical temperature with the linear size L = 4096 is 5.60 times as fast as the calculation speed on a current GPU core. For the three-dimensional Ising model with the linear size L = 256, the GPU calculation speed is 7.90 times as fast as the GPU calculation speed. The idea of quasi-block synchronization can be used not only in the cluster algorithm but also in many fields where the synchronization of all threads is required. (C) 2011 Elsevier Inc. All rights reserved.
We present the GPU calculation with the common unified device architecture (CUDA) for the Swendsen-Wang multi-cluster algorithm of two-dimensional classical spin systems. We adjust the two connected component labeling...
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We present the GPU calculation with the common unified device architecture (CUDA) for the Swendsen-Wang multi-cluster algorithm of two-dimensional classical spin systems. We adjust the two connected component labeling algorithms recently proposed with CUDA for the assignment of the cluster in the Swendsen-Wang algorithm. Starting with the q-state Potts model, we extend our implementation to the system of vector spins, the q-state clock model, with the idea of embedded cluster. We test the performance, and the calculation time on GTX580 is obtained as 2.51 nsec per a spin flip for the q = 2 Potts model (Ising model) and 2.42 nsec per a spin flip for the q = 6 clock model with the linear size L = 4096 at the critical temperature, respectively. The computational speed for the q = 2 Potts model on GTX580 is 12.4 times as fast as the calculation speed on a current CPU core. That for the q = 6 clock model on GTX580 is 35.6 times as fast as the calculation speed on a current CPU core. (C) 2012 Elsevier B.V. All rights reserved.
The hierarchical clustering algorithm has been proved useful in portfolio investment, which is one of the hottest issues in finance. In our new portfolio strategy, central, peripheral and dispersed portfolios construc...
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The hierarchical clustering algorithm has been proved useful in portfolio investment, which is one of the hottest issues in finance. In our new portfolio strategy, central, peripheral and dispersed portfolios constructed from clusters detected using unweighted and weighted modularity are compared according to their past performances, and the optimal portfolio is used in the investment period only if the market index return predicted by the LR, WMA or BP models is positive to avoid losses when the market drops. Our strategy is tested using the daily data of Chinese A-share market from January 4, 2008 and December 31, 2016, and the average investment return during different moving investment periods and 200 repeated runs is calculated. We find that although incorporating dispersed portfolio into our strategy has no significant effect in raising the investment return, it shows a similar performance as the peripheral portfolio, and the strategy constructed using unweighted modularity generally outperforms its counterpart by using weighted modularity. In addition, the market trend prediction can refine the investment return of our strategy. In brief, the strategy constructed using the BP model and unweighted modularity has the best investment return, which also outperforms the Markowitz portfolio. (C) 2018 Elsevier Ltd. All rights reserved.
Based on planar maximally filtered graph (PMFG) and threshold method, we introduced a correlation-based network named PMFG-based threshold network (PTN). We studied the community structure of PTN and applied ISOMAP al...
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Based on planar maximally filtered graph (PMFG) and threshold method, we introduced a correlation-based network named PMFG-based threshold network (PTN). We studied the community structure of PTN and applied ISOMAP algorithm to represent PTN in low dimensional Euclidean space. The results show that the community corresponds well to the cluster in the Euclidean space. Further, we studied the dynamics of the community structure and constructed the normalized mutual information (NMI) matrix. Based on the real data in the market, we found that the volatility of the market can lead to dramatic changes in the community structure, and the structure is more stable during the financial crisis. (C) 2017 Elsevier B.V. All rights reserved.
cluster analysis has been widely used in many fields since its appearance, and the validation method of clustering has received much less attention. In fact, the quality evaluation of clustering results is also a cruc...
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ISBN:
(纸本)9781538653739
cluster analysis has been widely used in many fields since its appearance, and the validation method of clustering has received much less attention. In fact, the quality evaluation of clustering results is also a crucial link in the whole clustering process. clustering validation are generally divided into external clustering validation, internal clustering validation and relative cluster validation. This paper studies the external clustering validation method. Firstly, the article selects several different categories of clustering verification methods. At the same time, the article introduces the Maclaurin symmetric mean (MSM) operator and multiple attribute decision making (MADM) method. Finally, an optimal clustering algorithm selection model for a certain data set can be obtained. The experimental results show that the proposed method can select the corresponding optimal algorithm for inhomogeneity data sets.
Wireless sensor networks are prone to partitioning because of limited energy in sensor nodes. In this paper, a new cluster algorithm will be proposed in order to use the energy more effectively and prolong the lifetim...
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ISBN:
(纸本)9781457720727
Wireless sensor networks are prone to partitioning because of limited energy in sensor nodes. In this paper, a new cluster algorithm will be proposed in order to use the energy more effectively and prolong the lifetime of wireless sensor networks. Firstly, to reduce the energy consumption during the cluster building, the parameters such as node energy, distances between nodes and the numbers of neighbor nodes are used in the stages of cluster heads selection and clusters building to balance the load distribution. Also the conditions are set to induce the frequency of cluster rebuilding. And then the data transmission path between intra-cluster and inter-cluster are set. At last, to solve the problem of the delay bringing by multiple hops transmission, TDMA schedule is also reset. Simulation results show that the proposed algorithm can not only effectively reduce the frequency of network reconstruction, but also can balance energy consumption and prolong the lifetime of the networks
In this study, an advanced K-Medoids clustering algorithm has been developed by using an auto-stopped bisecting scheme and replacing the traditional Euclidean distance by cloud similarity. The proposed algorithm is de...
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In this study, an advanced K-Medoids clustering algorithm has been developed by using an auto-stopped bisecting scheme and replacing the traditional Euclidean distance by cloud similarity. The proposed algorithm is dedicated to cluster the users of Connected TV according to their behavior involved with connected TVs. According to the experimental results, the solutions obtained by the proposed algorithm are quite encouraging and the clustering results are much more stable than Euclidean-distance-based clustering method. The result shows that the proposed method has achieved good results in terms of cluster accuracy and time. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
The noise data results from two aspects, technically, different mobile phones traveling on different roads may generate the same handover sequence;operationally, signal error in mobile communication network possibly c...
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ISBN:
(纸本)9783037853474
The noise data results from two aspects, technically, different mobile phones traveling on different roads may generate the same handover sequence;operationally, signal error in mobile communication network possibly causes wrong handover information. Second, four types of noise data are summarized, that is wrong sample data from signal error, passenger cell phone sample noise data beside roads, vehicle cell phone sample ones between parallel and overlapping roads. Third, hierarchy and K means cluster algorithms are applied with SPSS software for the collected sample data cleaning based on real handover data by try and trial to produce link travel speed, which is compared with practical traffic situation. Finally, historic loop data is used as the baseline to evaluate the accuracy compared with cleaned results.
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