Fast Estimation of Plant Steady State, with Application to Static RTO
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A non-parametric method of distribution estimation for univariate data is presented. The idea is to adapt the smoothing spline procedure used in regression to the estimation of distributions via a scatterplot smoothing of theempirical distribution function ...
An invasive plate and frame capacitance probe was designed for dielec. const. measurements over a wide frequency range (10-5000 kHz) in a 5-L stirred-tank reactor. Preliminary measurements with polyethylene beads showed a linear variation of the effective ...
Nous passons en revue des techniques de rééchantillonnage utilisées pour l'estimation de variance en sondage. Les techniques de rééchantillonnage considérées sont basées sur la linéarisation, le jackknife, les répétitions équilibrées répétées, et le bootst ...
In this paper we aim to explore what is the most appropriate number of data samples needed when measuring the temporal correspondence between a chosen set of video and audio cues in a given audio-visual sequence. Presently the optimal model that connects s ...
Le problème d'estimation de tables origine-destination (OD) à partir de données de comptages est de première importance pour un grand nombre d'applications impliquant la modélisation d'un système de transport. En effet, ces tables appréhendent statistiquem ...
This paper deals with the problem of probability density estimation with the goal of finding a good probabilistic representation of the data. One of the most popular density estimation methods is the Gaussian mixture model (GMM). A promising alternative to ...
We pose the estimation of the parameters of multiple superimposed exponential signals in additive Gaussian noise problem as a Maximum Likelihood (ML) estimation problem. The ML problem is very non linear and hard to solve. Some previous works focused on fi ...
State estimation is a widely used concept in the control community, and the literature mostly concentrates on the estimation of all states. However, in soft sensor problems, the emphasis is on estimating a few soft outputs as accurately as possible. The co ...
This monograph presents a unified mathematical framework for a wide range of problems in estimation and control. The authors discuss the two most commonly used methodologies: the stochastic H2 approach and the deterministic (worst-case) H approach. Despite ...
Society for Industrial and Applied Mathematics1999
In this paper, we introduce a novel technique for adaptive scalar quantization. Adaptivity is useful in applica- tions, including image compression, where the statistics of the source are either not known a priori or will change over time. Our algorithm us ...