We study the load balancing aspect of routing algorithms in wireless ad hoc networks. We define a statistical measure called local coefficient of variance (lcv) to study the smoothness of the load distribution in the ...
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ISBN:
(纸本)9781612842547
We study the load balancing aspect of routing algorithms in wireless ad hoc networks. We define a statistical measure called local coefficient of variance (lcv) to study the smoothness of the load distribution in the network. The importance of keeping lcv as low as possible in designing load balanced routing algorithms is demonstrated. We analyze how number of nodes, transmission range, network area and different routing algorithms can affect this metric. We introduce a class of algorithms called elliptic routing that reduce the maximum load of nodes in the network by avoiding the highly loaded network center at the same time as keeping the lcv of the load distribution low. Experimental results show that our algorithms outperform other existing algorithms in reducing the maximum load of the network. We also give a technique to reduce the lcv of the load distribution, and hence decrease the maximum load of the nodes in the network further. This technique can be combined with any location-based routing algorithm. We evaluate the performance gain obtained by this technique via simulations.
In this paper, we are continuing to show mutual intersection of two different areas of research: complex networks and evolutionary computation. We demonstrate that dynamics of evolutionary algorithms, that are based o...
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ISBN:
(纸本)9781467376952
In this paper, we are continuing to show mutual intersection of two different areas of research: complex networks and evolutionary computation. We demonstrate that dynamics of evolutionary algorithms, that are based on Darwin theory of evolution and Mendel theory of genetic heritage, can be also visualized as complex networks. Such network can be then analyzed by means of classical tools of complex networks science. Results presented in our previous papers were currently numerical demonstration rather than theoretical mathematical proofs. We opened question whether evolutionary algorithms really create complex network structures and whether this knowledge can be successfully used like feedback for control of evolutionary dynamics and its improvement in order to increase the performance of evolutionary algorithms. This research paper is focused on the dynamics of complex networks from windows time point of view with proposition of a new windows time algorithm to evaluated evolution dynamics. There are described by temporal centralities and change centrality. These centralities are implemented as Gephi plugin and an own tool. At the end are examples of analysis of some networks using implemented algorithms.
Mining generalized association rules is one of important research circa in data mining. If e rise the traditional methods, it frin meet two basic problems. the first is low efficiency in generating, generalized freque...
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ISBN:
(纸本)9780769534282
Mining generalized association rules is one of important research circa in data mining. If e rise the traditional methods, it frin meet two basic problems. the first is low efficiency in generating, generalized frequent itemsets With the items and levels of taxonomy, increasing and the second is that too much redundant itemsets support are counted. This paper proposes an improved Breadth-First Search method to mine generalized association rules. The experiments oil the real-life data shoe. that our method outperforms the well-known and recent algorithms greatly.
we herein propose a new collaborative framework, called cooperative co-evolutionary multi-agent system(CCEMAS), for solving multi-objective layout optimization problems. Every agent in CCEMAS is encoded as a multi-obj...
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ISBN:
(纸本)9781538616451
we herein propose a new collaborative framework, called cooperative co-evolutionary multi-agent system(CCEMAS), for solving multi-objective layout optimization problems. Every agent in CCEMAS is encoded as a multi-objective cooperative co-evolutionary strategy, including an algorithm and its setting. In the iterative procedure, the strategies will evolve along with the evolution of the agent team, and use different algorithms and settings during different stages of problem solving. A multi-objective optimization of satellite module is solved to validate the method and obtain the Pareto optimal solutions. Finally, the evolution process of multi-objective cooperative co-evolutionary strategies in agents is analyzed for constructing new multi-objective cooperative co-evolutionary algorithms for this problem in the future.
Many image retargeting algorithms, despite aesthetically carving images smaller, pay limited attention to image browsing tasks where tiny thumbnails are presented. When applying traditional retargeting methods for gen...
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ISBN:
(纸本)9781457711022
Many image retargeting algorithms, despite aesthetically carving images smaller, pay limited attention to image browsing tasks where tiny thumbnails are presented. When applying traditional retargeting methods for generating thumbnails, several important issues frequently arise, including thumbnail scales, object completeness and local structure smoothness. To address these issues, we propose a novel image retargeting algorithm, Scale and Object Aware Retargeting (SOAR), which has four components: (1) a scale dependent saliency map to integrate size information of thumbnails, (2) objectness (Alexe et al. 2010) for preserving object completeness, (3) a cyclic seam carving algorithm to guide continuous retarget warping, and (4) a thin-plate-spline (TPS) retarget warping algorithm that champions local structure smoothness. The effectiveness of the proposed algorithm is evaluated both quantitatively and qualitatively. The quantitative evaluation is conducted through an image browsing user study to measure the effectiveness of different thumbnail generating algorithms, followed by the ANOVA analysis. The qualitative study is performed on the RetargetMe benchmark dataset. In both studies, SOAR generates very promising performance, in comparison with state-of-the-art retargeting algorithms.
In the paper the task of concurrent analysis of a Petri net is considered. A Petri net is given, and several processes able to simulate transition firings. The methods of analysis described in this paper are based on ...
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ISBN:
(纸本)0769517307;0769517315
In the paper the task of concurrent analysis of a Petri net is considered. A Petri net is given, and several processes able to simulate transition firings. The methods of analysis described in this paper are based on the original approach to net decomposition and oriented for the so-called operational nets and a class of cyclic Petri nets. The methods analyze the nets by reduced state space constructing;both their sequential and parallel versions are described. Also the algorithm of decomposition oriented to concurrent analysis is described. The suggested methods of analysis can be implemented as a multithread application.
We experimentally show the performance of a Virtual Infinite Capacitor (VIC) in suppressing the voltage ripple. VIC mainly uses a bi-directional DC-DC converter with only two small capacitors inside to mimic the filte...
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ISBN:
(纸本)9788026106425
We experimentally show the performance of a Virtual Infinite Capacitor (VIC) in suppressing the voltage ripple. VIC mainly uses a bi-directional DC-DC converter with only two small capacitors inside to mimic the filtering process of a large capacitor by applying suitable control algorithms. It was firstly proposed in [1], [2] and then improved in [3] in both control algorithms and circuit designs. Based on VIC's control algorithm in [3], we injected the DC voltage together with a 50Hz, 85V (peak-to-peak) sinusoidal ripple to the VIC, and we achieved outstanding filtering performance in both the simulation and experiment. The 85V voltage ripple is eliminated and the DC voltage is extracted with no visible 50Hz ripples.
In this paper we introduce an evolutionary approach for the efficient design of prototype-based classifiers using differential evolution (DE). For this purpose we amalgamate ideas from the Learning Vector Quantization...
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ISBN:
(纸本)9781479944613
In this paper we introduce an evolutionary approach for the efficient design of prototype-based classifiers using differential evolution (DE). For this purpose we amalgamate ideas from the Learning Vector Quantization (LVQ) framework for supervised classification by Kohonen [1], [2], with the DE-based automatic clustering approach by Das et al. [3] in order to evolve supervised classifiers. The proposed approach is able to determine both the optimal number of prototypes per class and the corresponding positions of these prototypes in the data space. By means of comprehensive computer simulations on benchmarking datasets, we show that the resulting classifier, named LVQ-DE, consistently outperforms state-of-the-art prototype-based classifiers.
The use of cloud resources for processing and analysing medical data has the potential to revolutionise the treatment of a number of chronic conditions. For example, it has been shown that it is possible to manage con...
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ISBN:
(纸本)9781509042739
The use of cloud resources for processing and analysing medical data has the potential to revolutionise the treatment of a number of chronic conditions. For example, it has been shown that it is possible to manage conditions such as diabetes, obesity and cardiovascular disease by increasing the right forms of physical activity for the patient. Typically, movement data is collected for a patient over a period of several weeks using a wrist worn accelerometer. This data, however, is large and its analysis can require significant computational resources. Cloud computing offers a convenient solution as it can be paid for as needed and is capable of scaling to store and process large numbers of data sets simultaneously. However, because the charging model for the cloud represents, to some extent, an unknown cost and therefore risk to project managers, it is important to have an estimate of the likely data processing and storage costs that will be required to analyse a set of data. This could take the form of data collected from a patient in clinic or of entire cohorts of data collected from large studies. If, however, an accurate model was available that could predict the compute and storage requirements associated with a piece of analysis code, decisions could be made as to the scale of resources required in order to obtain results within a known timescale. This paper makes use of provenance and performance data collected as part of routine e-Science Central workflow executions to examine the feasibility of automatically generating predictive models for workflow execution times based solely on observed characteristics such as data volumes processed, algorithm settings and execution durations. The utility of this approach will be demonstrated via a set of benchmarking examples before being used to model workflow executions performed as part of two large medical movement analysis studies.
In a radio frequency identification system, The lower identification efficiency problem of system brought by multi tags collision has become the bottleneck problem of the application of RFID technolog
ISBN:
(纸本)9781467389808
In a radio frequency identification system, The lower identification efficiency problem of system brought by multi tags collision has become the bottleneck problem of the application of RFID technolog
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