Probability density of displacement and overturning length scales under diverse stratification
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This work describes the development of a software framework allowing to simulate range data acquired with different kind of sensors such as stereo camera and ultrasonic probe. The sensors characteristic error are modeled with probability density functions. ...
A continuous time process of alternating stochastic growths and deterministic decays is proposed as a simple model for the interannual dynamics of snow water equivalent (SWE) storage and melting. The related stationary properties are studied, and an integr ...
In this paper, probabilistic observers are considered for a class of continuous biological processes described by mass-balance-based models. It is assumed that the probability density functions (PDFs) of the uncertain parameters and inputs of the model, as ...
This paper compares the performance of seven disaggregation models, based on various approaches and/or concepts, for the generation of 10mn time step rainfall series from hourly rainfall series. The so-called constant disaggregation model and a linear mode ...
We present the optical tomography of the probability density of quasiparticles, the microcavity polaritons, confined in three dimensions by cylindrical traps. Collecting the photoluminescence emitted by the quasimodes under continuous nonresonant laser exc ...
We introduce the notion of permissiveness in transactional memories (TM). Intuitively, a TM is permissive if it never aborts a transaction when it need not. More specifically, a TM is permissive with respect to a safety property p if the TM accepts every ...
We introduce the notion of permissiveness in transactional memories (TM). Intuitively, a TM is permissive if it never aborts a transaction when it need not. More specifically, a TM is permissive with respect to a safety property p if the TM accepts every h ...
In this paper, the design of probabilistic observers for mass-balance based bio- process models is investigated. It is assumed that the probability density function of every uncertain parameter, input and/or initial state is known a priori. Then, the proba ...
In this paper, the design of probabilistic observers for mass-balance based bioprocess models is investigated. It is assumed that the probability density of every uncertain parameter, input and/or initial state is known a priori. Then, the probability dens ...
[1] A numerical model was developed for the prediction of the density stratification of lakes and reservoirs. It combines a buoyancy-extended k-epsilon model with a seiche excitation and damping model to predict the diffusivity below the surface mixed laye ...