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In this paper, we propose a throughput-maximum resource provision scheme in the OFDMA-based Wireless Virtual Network (WVN). The proposed scheme takes the dynamics of both traffic arrivals and wireless channels into consideration. Furthermore, we consider a more flexible service contract where average resource provision is guaranteed for each slice. As it is practically impossible to know future traffic...
This paper presents the details of software development and implementation of a simulation model to be used for performance analysis of the control applied for management of a plug-in electric vehicles charging network. The model is designed to provide an evaluation of the tested control algorithms in terms of efficiency and stability operation. Apart from the control algorithm evaluation the model...
Crime distribution forecasting has a positive impact on social stability and has drew much attention in academia. Existing research methods are not applicable for specific research problems or specific data sets very well. So we build the Vector Motion Model and propose a new algorithm named as TPML-WMA (Transition Probability Matrix Learning and Weighted Moving Average algorithm) to predict a future...
User viewing feature can be extracted from TV user's channel viewing data for improving the targeted TV advertising. In this paper, we propose a basic viewing feature extraction algorithm in small sample environment to prove the algorithm logic and check the analysis process quickly. However if the viewing behavior data come from mass TV audience, it requires higher speed feature extraction algorithm...
TBB (Thread Building Blocking) is currently a representative parallel computing platform of multi-core processors. The ant colony algorithm is used to solve combinatorial optimization problem of discrete-time systems. With the expansion of the problem scale, it often results in rapid increase of calculation. Based on TBB a parallel ant colony algorithm was researched and developed to improve the efficiency...
Dimension reduction (DR) algorithms are generally categorized into feature extraction and feature selection algorithms. In the past, few works have been done to contrast and unify the two algorithm categories. In this work, we introduce a matrix trace oriented optimization framework to provide a unifying view for both feature extraction and selection algorithms. We show that the unified view of DR...
It is an extreme challenge to produce a nonlinear SVM classifier on very large scale data. In this paper we describe a novel P-packSVM algorithm that can solve the support vector machine (SVM) optimization problem with an arbitrary kernel. This algorithm embraces the best known stochastic gradient descent method to optimize the primal objective, and has 1/?? dependency in complexity to obtain a solution...
Dimension reduction for large-scale text data is attracting much attention lately due to the rapid growth of World Wide Web. We can consider dimension reduction algorithms in two categories: feature extraction and feature selection. An important problem remains: it has been difficult to integrate these two algorithm categories into a single framework, making it difficult to reap the benefit of both...
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