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Behavior research is limited by the accessibility of the microscopic behavior data. In this study, a PEO (Person-Environment-Object) model is proposed to decompose the diverse microscopic behavior in real world into simple and measurable variables such as position, status, time, and etc., which serves as a foundation for the behavioral data collection. According to the PEO model, then the framework...
Simulations with trace dataset and synthetic movement models are the two main methods for measuring routing protocols and QoS schemes in Opportunistic Networks. But the trace dataset cannot meet the needs of all situations and it is notoriously difficult to be collected. Traditional synthetic movement models are easy to be used, but they do not capture the detailed mobile characteristics of the human...
Batch processes are often characterized by uneven-length durations and multistage characteristics. To reflect the inherent stage nature to improve the performances of process monitoring, simultaneously considering dynamic characteristics within the process variables for some complicated cases, stage-based variable sampling period multi-model dynamic principal component analysis (VSP-MDPCA) modeling...
According to the continuity and monotonicity of industrial real time data, an auto regression compression method (for short ARCM) is proposed. Firstly, the auto regression model of a group of sampled sequence is established. Secondly, the next sampled data can be predicted by the model. If the error between the actual data and the predictive data is in the allowable range, we save the parameters of...
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