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The problem of estimating the missing mass or total probability of unseen elements in a sequence of n random samples is considered under the squared error loss function. The worst-case risk of the popular Good-Turing estimator is shown to be between 0.6080/n and 0.6179/n. The minimax risk is shown to be lower bounded by 0.25/n. This appears to be the first such published result on minimax risk for...
An approach for modeling in-depth destination and mode choice is to deploy discrete choice models. Large scale transportation demand models usually are macroscopic, thus using aggregated demand data. In the new transportation model for the federal state of Upper Austria discrete choice models for the mode and destination choice have been implemented. Considering the special characteristics when applying...
Current procedure in travel demand estimation models is to separately deal with attraction, production and trip distribution, where the latter typically assumes inverse distance proportionality. We show that this procedure leads to errors in the demand estimation, particularly when dealing with very specific zones and heterogeneous travel behavior. We argue that this traditional procedure is rooted...
Discrete choice models are widely used to explain transportation behaviors, including a household's decision to own a car. They show how some distinct choice of human behavior or preference influences a decision. They are also used to project future demand estimates to support policy exploration. This latter use for prediction is indirectly aligned with and conditional to the model's estimation which...
The increasing penetration of Intermittent Generation (IG) is being accompanied by the revision of the needs of traditional regulation reserves, as well as the discussion of new flexibility products for system balancing. In the context of electricity generation models, it is of high relevance to adequately represent, not only the energy, but also the reserve dispatch constraints, by providing the...
This paper uses high-frequency intraday electricity prices from the EPEX market to estimate and forecast realised volatility. Variation is broken down into jump and continuous components using quadratic variation theory. Then several heterogeneous autoregressive models are estimated for the logarithmic and standard deviation transformations. GARCH structures are included in the error terms of the...
We attack the problem of predicting nitrate concentrations in a stream by using a genetic algorithm to minimize the difference between observed and predicted concentrations on hydrologic nitrate concentration model based on a US Geological Survey collected data set. Nitrate plays a significant role in maintaining ecological balance in aquatic ecosystems and any advances in nitrate prediction accuracy...
In this paper, a Model Free Control based Nonlinear Integral Backstepping Control (MFC-NIB) strategy is developed and applied to blood glucose regulation systems, which is a typical biological system with parameter variations, uncertainties and external disturbances. Firstly, an Intelligent Proportional controller (iP), which is based on model-free theory and whose algebraic estimation technique is...
In the paper a new mathematical model of electroencephalographic (EEG) signal has been justified in the form of conditional linear random process (CLRP). The model has been derived from the biophysical nature of brain electrical activity. Random coefficient autoregressive (RCA) model (as a member of the class of discrete-time CLRP) has been applied for EEG signal processing. Using the special statistical...
In Synthetic Biology, the idea of using feedback control for the mitigation of perturbations to gene regulatory networks due to disease and environmental disturbances is gaining popularity. To facilitate the design of such synthetic control circuits, a suitable model that captures the relevant dynamics of the gene regulatory network is essential. Traditionally, Michaelis-Menten models with Hill-type...
In the present study we conduct analysis of methods and models of origin destination matrices reconstruction with regard to urban transport processes, we point out their advantages and disadvantages. As a result of the analysis the relational method of OD matrices reconstruction is chosen and the trilinear model (EVA) is considered as the most applicable for OD matrices reconstruction. To show the...
Driving a process to optimal conditions under various uncertainties is a key issue for meeting objectives of productivity and quality of batch or fed-batch product. To overcome a limitation of two-step approaches unable to cope with nonparametric or large uncertainty, several gradient based iterative optimization methods have been proposed. Among these, latent variable model based approaches have...
Marine robots and unmanned surface vehicles will increasingly be deployed in rivers and riverine environments. The structure produced by flowing waters may be exploited for purposes of estimation, planning, and control. This paper adopts a widely acknowledged model for the geometry of watercourse channels, namely sine-generated curves, as a basis for estimators that predict the shape of the yet unseen...
A mathematical model of expertise with the participation of several independent expert groups is proposed. Methods for estimating the proximity of the expert judgment vectors based on various metrics within the framework of the proposed model are considered.
The technology world for visually impaired people has evolved over the past few years, making their day-to-day life more functional. However, there are still gaps such as in the area of aesthetics and visual image that need to be more explored. Thus, this article describes the first validation in the development of a Web platform in aid of the combination of clothing for blindness people. This project...
Energy management is one of the most important problems in the world today, due among other reasons to the increase in clients electricity demand which generates the Peak Load problem. The rise of Smart Grids as a new paradigm, developed mainly to address those requirements, allows the design and implementation of more complex management and monitoring systems. In this paper, we propose a modelization...
Human body analysis raises special interest because it enables a wide range of interactive applications. In this paper we present a gesture estimator that discriminates body poses in depth images. A novel collaborative method is proposed to learn 3D features of the human body and, later, to estimate specific gestures. The collaborative estimation framework is inspired by decision forests, where each...
In this paper, we introduce new parametric generative driven auto-regressive (DAR) models. DAR models provide a nonlinear and non-stationary spectral estimation of a signal, conditionally to another exogenous signal. We detail how inference can be done efficiently while guaranteeing model stability. We show how model comparison and hyper-parameter selection can be done using likelihood estimates....
This paper deals with a fault estimation algorithm to reconstruct faulty process inputs under actuator fault. The presented estimation approach is based on the parameter identification and a Recursive Least Square (RLS) algorithm for the identification of the model parameters. The proposed approach was essentially built for the estimation of the handwriting process inputs under actuator fault, which...
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