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Currently, dynamic causal modeling (DCM) is one of the most widely used models for an effective brain connectivity network, but it also has some disadvantages (e.g., researchers' selection of cerebral regions of interest [ROIs] is subjective, a substantial time is required for computation, etc.). Statistical Parametric Mapping (SPM) is the most popular statistical data analysis software for brain...
With the advent of the Web of Linked Data, new challenges to federated query processing are emerging. Different from traditional federated database systems which do static data integration, this Web of Data is open and ever-changing. In this paper, we present a agent-based architecture providing a flexible and decoupled solution for the federated queries over Linked Data. Based on the presented architecture,...
Air pollution has been a huge problem for a long time, more and more scientists focus on this hot topic, In this paper we presented a series data analysis methods for Los Angeles Long Beach datasets by Seasonal ARIMA(autoregressive integrated moving average) model and MCMC(Markov chain Monte Carlo) method. The MCMC methods are studied with LA long beach air pollution PM 2.5 traffic from 1997 to 2008...
As more and more people enter into the virtual world based on the internet, reputation has become the key to punish the bad behaviors of people and encourage the good behaviors of them. How to evaluate effectively the reputation of the participators is the urgent problem which should be solved. The paper provides a combined reputation model TRUST for distributed environment. This model, according...
The generalized autoregressive conditional heteroskedasticity (GARCH) model has become the most popular choice in the analysis of time series datas. In this paper, an autoregressive moving average (ARMA)-GARCH model was built, and it also provided parameter estimation, diagnostic checking procedures to model, and predict Dow and S&P 500 indices data from 1988 to 2008, which extracted from yahoo...
An autoregressive integrated moving average (ARIMA) model was one of the most popular linear models in financial time series forecasting in the past. In this context, a time series analysis of the NASDAQ composite indices is provided study its movement in 1998-2008. This paper proposed a general expression of seasonal ARIMA models with periodicity and provide parameter estimation, diagnostic checking...
A new fusion model is proposed, which is the combination of BP neural networks and rough set algorithm, to solve the problems of low precision rate in aircraft engine fault diagnosis by traditional methods. The method realizes feature level fusion of all subjective data and expert experiments on different parts of engine, and the predominance compensation of different models. In simulation experiment,...
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