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Nighttime traffic flow congestion estimation is a challenging problem, the use of a model for describing the traffic parameters is a good solutions but it is necessary to have an accurate information about the traffic's state before constructing the model. In this paper we propose a new method for developing a road traffic flow model in this challenging situation using accurately measured traffic...
The purpose of this study is to analyze the reliability growth of Open Source Software (OSS) using Software Reliability Growth Models (SRGM). This study uses defects data of twenty five different releases of five OSS projects. For each release of the selected projects two types of datasets have been created; datasets developed with respect to defect creation date (created date DS) and datasets developed...
Forecasting time series accurately is critical to ensure the safety and reliability of complex system. So, time series prediction has been a popular subject. Normally, the information used in time series prediction is always mined from multi-variable time series and small simple data. Thus, based on grey prediction theory, an adaptive prediction model with multi-variable small simple time series data...
The construction of a neural network simulation metamodel requires the generation of training data; design points (inputs) and the estimate of the corresponding output generated by the simulation model. A common methodology is to focus some simulation effort in obtaining accurate estimates of the expected output values by executing several simulation replications at each point and taking the average...
This paper addresses an interesting problem, which is to find top-k L-Regions of a given object in a fact table with multiple dimensions and each dimension has one hierarchy with multiple levels. L-Region is a variant of combination of members from each dimension. To tackle the problem of the large solution space, we propose a materialized strategy called LR-Cube, which balances storage and time overhead...
In this paper, the Adaptive Frequency Sampling(AFS) technique for Model Order Reduction is modified to macromodel the RF components. The conventional AFS can just express the frequency response as a rational function, but ends up with inefficiency in macromodelling So the proposed scheme here enables the AFS to macromodel the problem by finding the equivalent circuit of the frequency response, since...
Taking the investigated data from 40 samples plots of natural secondary oak stand in Baotianman natural reserve for the research object, BP-ANN model was created by using relative diameter of tree as the input variable, and accumulated frequency of tree number as output variable. Through training and optimal seeking by the software of MATLAB, the idea network model was created. In the performance...
According to the existing problems that gravity data, digital elevation model are very difficult to obtain in China. The method of refining the geoid with the new "remove-restore" technique based on model geoid height calculated by EGM2008 and model geoid height, model height anomaly calculated by EGM96 are studied respectively. Verify the feasibility by using model geoid height of EGM2008...
The question which how to select the most superior fitting mode of variogram is one of basic questions which in current geostatistics not yet completely solves. In view of this question, this article proposed take lag as the weight coefficient to establish objective function and establish the variogram fitting method based on the evolutionary programming. This method dynamic estimates the parameter...
Based on the data of household income of Shanghai low-rent housing families, a GM(1,1) forecast model and a Back-Propagation Artificial Neural Network (BPANN) forecast model are established respectively to predict the average household income of low-rent housing families. The comparison between the GM(1,1) and the BPANN model showed that the BPANN model is better than the GM(1,1) model at the aspects...
The study of growth model is a basic research in forest growth and yield modeling. Most of growth models were developed using ordinary regression method. It is assumed that the observations were independent and obey Gauss distribution. Those models reflect the average growth across different plots, but neglect the correlation and variance between individuals and plots. However, mixed-effects models...
This paper presents an on-the-fly model-driven validation of data points for random sample consensus methods (RANSAC). The novelty resides in the idea that an analysis of the outcomes of previous random model samplings can benefit subsequent samplings. Given a sequence of successful model samplings, information from the inlier sets and the model errors is used to provide a validness of a data point...
The paper deals with the numerical validation, performance evaluation and robustness assessment of a procedure for the direct identification of passive transfer matrices based on convex programming. Validation is pursued by producing data sets from lumped multiport systems with random parameters (passive, non passive, and possibly affected by random noise), then evaluating the identification ability...
The non-equal-interval direct Verhulst new information GM(1,1) model with two times fitting was built which extended equal interval to non-equal-interval and suited for general data modeling and estimating parameters of direct Verhulst GM(1,1). The new model chooses the nth component of X(0) as the starting conditions of the grey differential model. The new model need not pre-process the primitive...
The guidelines for the construction of the data transformation have been proposed in the paper based on the mechanism, and a new type of transformation was constructed. A number of properties of the new transformation also have been discussed. A new GM (1, 1) model based on the new transformation is established. It is proved that the prediction accuracy of GM (1, 1) can be improved by the new transformation...
Test data processing is an important role in test process. The principle, under which the Grey systems theory is applied in data processing, is that the test system can be considered as a Grey system. In such a system, unknown system's information can be determined by using known information. The non-equal-interval direct Verhulst GM(1,1) model through two times fitting was built which extended equal...
In terrestrial laser scanning, sphere target is an important accessory. With it, multiple point clouds data obtained form different viewpoints can be transformed into a common coordinate system. In order to complete the task mentioned above, we must determine centre of sphere target. Least square method is commonly used to locating sphere center coordinates, but it has no ability to resist outliers,...
The non-equal-interval direct optimum Verhulst GM(1,1) model was built which extended equal interval to non-equal-interval and suited for general data modeling and estimating parameters of direct Verhulst GM(1,1) by optimizing the background value. The new model need not pre-process the primitive data, accumulated generating operation (AGO) and inverse accumulated generating operation (IAGO). It was...
According to specificity of the complex system process with stochastic uncertainty and correlation, a kind of predictive method based on gray system theories is presented. A nonlinear and multi-variable predictive model approach is established by the Taylor series expansion extended the single deformation prediction model in order to estimate more. The effectiveness of the model is confirmed through...
The soft-error vulnerability of flip-flops has become an important factor in IC reliability in sub-100-nm CMOS technologies. In the present work the soft-error rate (SER) of a 65-nm flip-flop has been investigated with the use of alpha-accelerated testing. Simulations have been applied to study the flip-flop SER sensitivity in detail. Furthermore, an easy-to-use approach is presented to make an accurate...
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