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This paper presents two methodologies for model-free data interpretation to identify and localize anomalous behaviour in civil engineering structures.
This paper describes the concepts and methodologies for model-based vision and intelligent task scheduling for an autonomous human-type robot arm system.
In the second paper, methodologies for modelling of the dispersion phenomenon are presented.
This study describes our methodologies for modeling those PD syndromes based on Bayesian belief network (BBN) formalism.
Methodologies for modelling risks for urban freight transport are outlined including, stochastic programming, multi-objective optimisation, robust optimisation, multi-agent simulation, and traffic simulation.
The overall methodology for model development is depicted in Fig. 1.
Also, Momma and Bennett (2002) developed a fully automated pattern search (PS) methodology for model selection of SVR.
This paper presents a methodology for model based robust fault diagnosis and a methodology for input design to obtain optimal diagnosis of faults.
A Bayesian probabilistic methodology for model updating is first implemented for the purpose of updating the structural model using dynamic data.
Two examples are used to illustrate the proposed methodology for model uncertainty characterisation, with insights, discussions, and comparison with previous methods.
The paper presents the methodology for model setup and the simulation results for the main water balance components of the catchment: total runoff at several gauging stations, runoff components, evapotranspiration and soil moisture.
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