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Self-Organizing Distributed State Estimators
Published in Fei Hu, Qi Hao, Intelligent Sensor Networks, 2012
The design challenge of any embedded system is to realize given functionalities, in this case the ones of the local estimation algorithm, on a given hardware platform while satisfying a set of nonfunctional requirements, such as response times, dependability, power efficiency, etc. Modelbased design has been proven to be a successful methodology for supporting the system design process. Model-based methodologies use multiple models to capture the relevant properties of the design (when the required functionalities are mapped onto a given hardware configuration), for example, a model of the required functionalities, temporal behavior, power consumption, and hardware configuration. These models can then be used for various purposes, such as automatic code generation, architecture design, protocol optimization, system evolution, and so on. Important for the design process are the interactions between the different models, which can be expressed as constraints, dependencies, etc. In this section, a model-based design methodology is followed to assure dependability for state estimation in a sensor network via runtime reconfiguration.
Modelling practices from local to global
Published in Raimund Bleischwitz, Holger Hoff, Catalina Spataru, Ester van der Voet, Stacy D. VanDeveer, Routledge Handbook of the Resource Nexus, 2017
Enrique Kremers, Andreas Koch, Jochen Wendel
Multi-scale modelling refers to a way of modelling in which multiple models at different scales are used simultaneously to describe a system. It involves combining models from different scales already in its early stages of development. The purpose, besides quantitative modelling, should aim at better understanding and exploring a complex system.
Cash flow at risk valuation of mining project using Monte Carlo simulations with stochastic processes calibrated on historical data
Published in The Engineering Economist, 2018
Mathieu Sauvageau, Mustafa Kumral
The MJD and Heston SV models are more complex and are harder to calibrate than the GBM. In a robust valuation workflow, different kinds of models should be tested and benchmarked against the GBM. Considering multiple scenarios and using different models can help the risk management team of mining companies to better assess the exposure to market risk. Each model has its own limitations and it is important, from a management point of view, to use multiple models. For example, the GBM model yields reliable results when markets are stable, whereas the MJD and Heston SV models are better suited when there is volatility. By tracking the potential loss when markets become turbulent to the stable market, it is possible to better design hedging programs. Moreover, it is far less expensive to implement a hedge on commodity prices when volatility is low, because speculators who take the other position of the hedge will demand a lower premium.