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This paper is based on data analysis and literatures, land use system, demographic factors, and economic development situation, fiscal and financial policies which have influence on the price of the house are studied. In order to discuss housing price purely on the basis of statistical data, the main factors and their weights are calculated based on the survey of house price and usage of grey theory...
Accurate cardinality estimates are essential for a successful query optimization. This is not only true for relational DBMSs but also for RDF stores. An RDF database consists of a set of triples and, hence, can be seen as a relational database with a single table with three attributes. This makes RDF rather special in that queries typically contain many self joins. We show that relational DBMSs are...
In order to optimize the capacity of farmers' income persistently increasing, in this paper we analyze 8 factors affecting farmers' income increasing using PLS model based on the statistical data of Heilongjiang reclamation from 2000 to 2008. The study shows that wage incomes, agricultural subsidy and tax reducing still account for a great proportion of farmers' net incomes, but the level of agricultural...
Taking advantage of the characteristics of few data and poor information, grey system theory sets up differential equation model for accumulated generation series to forecast, which has been extensively used in many areas. In the forecast process of grey model, data sample size and variable number can affect forecast results. This paper puts forward a new method of optimal forecast variable number...
Real-world design optimization problems are typically computationally-expensive and to address this various model-assisted evolutionary frameworks have been proposed. However, often such problems are also high-dimensional and in such settings models tend to have poor accuracy and thus degrade the optimization search. To address this we propose two complementary dimensionality-reduction frameworks...
This paper presents a study for using Kriging metamodeling in combination with Covariance Matrix Adaptation Evolution Strategies (CMA-ES) to find robust solutions. A general, archive based, framework is proposed for integrating Kriging within CMA-ES, including a method to utilize the covariance matrix of the CMA-ES in a straightforward way to improve the accuracy of the Kriging predictions without...
Parasitic extraction is a critical task for modern nano scale semiconductor circuits which are characterized by high speed, small feature size and dense layout. Among the available extraction methodologies is the macro-modeling, which is based on dividing the circuit into smaller parts, then matching those smaller parts to a pre-defined model library whose parasitics are known. In the macro-modeling...
The classification of network users is very important in user behavior analysis. The algorithm which was based entropy and latent Dirichlet allocation (LDA) was used in this paper. It is important but difficult to select an appropriate number of topics for a specific dataset. Entropy was first used to solve the problem. A concept named difference-entropy was built to determine the number of topics...
The use of PC cluster systems composed of many PCs is largely spread in these days. It is important to improve system usage keeping proper fair-share policy. This optimization problem is difficult to solve. In scheduler software, there are many parameters and the effectiveness of many parameters is related with each other complicatedly. Therefore it is more difficult to find optimal parameter configuration...
In-network aggregation is essential for correlated data gathering in wireless sensor networks which are resource-constraint in terms of energy, computation and storage. In this paper, we consider the problem of building a minimum cost hierarchical architecture for correlated data gathering with in-network aggregation, which is formulated as a min-sum optimization problem. To solve the problem, we...
This paper presents a steepest descent based algorithm for the distributed optimization towards to data regression modeling in wireless sensor networks (WSNs). In doing this, the junction tree based routing structure is employed to organize the nodes in coordination to accomplish the in-network implementation of the distributed iterative scheme. Experimental results are reported to demonstrate the...
Currently considerable attention has been given to the effect of data correlation on statistical process control (SPC). Use of traditional SPC methods when observations are correlated often leads to misleading conclusions as to whether or not the process is under control. The objective of this paper is to develop an algorithm to adjust a Dynamic Linear Model, to calculate the run length distribution...
Intensive research has focused on redundance reduction in wireless sensor networks among sensory data due to the spatial and temporal correlation embedded therein. In this paper, we propose a novel approach termed asynchronous sampling that complements existing study. The key idea of asynchronous sampling is to spread the sampling times of the sensor nodes over the time line instead of performing...
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