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This paper proposes an improved correlation-based just-in-time modeling method, referring to as the ICoJIT, for improving the prediction accuracy and real-time performance of the conventional correlation-based just-in-time (CoJIT) modeling method. To achieve this objective, a novel adaptive local domain partition method has been developed based on the moving window technique and the fitting precision,...
The prediction of horizontal displacement is significant important to the safety-control in dam. A number of regression analysis-based methods have been proposed. However, most of the methods have unsatisfied performance on prediction accuracy in case of the sample size is unknown. In this paper, a method based on changeable sliding window is proposed to predict horizontal displacement of dam foundation...
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...
On-demand resource provisioning is with great challenge in cloud systems. The key problem is how to learn about the future workload in advance to help determine resource allocation. There are various prediction models developed to predict the future workload. The major problem of previous researches is that they assume that application workload has static pattern. In practice, so many application...
Thermal issues have become critical roadblocks for the development of advanced chip-multiprocessors (CMPs). In this paper, we introduce a new angle to view transient thermal analysis - based on predicting thermal profile, instead of calculating it. We develop a systematic framework that can learn different thermal profiles of a CMP by using an autoregressive (AR) model. The proposed AR model can serve...
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