Truncated Models for Probabilistic Weighted Retrieval

Existing probabilistic retrieval models do not restrict the domain of the random variables that they deal with. In this article, we show that the upper bound of the normalized term frequency ( tf ) from the relevant documents is much smaller than the upper bound of the normalized tf from the whole collection. As a result, the existing models suffer from two major problems: (i) the domain mismatch causes data modeling error, (ii) since the outliers have very large magnitude and the retrieval models follow tf hypothesis, the combination of these two factors tends to overestimate the relevance score. In an attempt to address these problems, we propose novel weighted probabilistic models based on truncated distributions. We evaluate our models on a set of large document collections. Significant performance improvement over six existing probabilistic models is demonstrated.

1D Stochastic Inversion of Airborne Time-Domain Electromagnetic Data with Realistic Prior and Accounting for the Forward Modeling Error

Airborne electromagnetic surveys may consist of hundreds of thousands of soundings. In most cases, this makes 3D inversions unfeasible even when the subsurface is characterized by a high level of heterogeneity. Instead, approaches based on 1D forwards are routinely used because of their computational efficiency. However, it is relatively easy to fit 3D responses with 1D forward modelling and retrieve apparently well-resolved conductivity models. However, those detailed features may simply be caused by fitting the modelling error connected to the approximate forward. In addition, it is, in practice, difficult to identify this kind of artifacts as the modeling error is correlated. The present study demonstrates how to assess the modelling error introduced by the 1D approximation and how to include this additional piece of information into a probabilistic inversion. Not surprisingly, it turns out that this simple modification provides not only much better reconstructions of the targets but, maybe, more importantly, guarantees a correct estimation of the corresponding reliability.

A nonparametric probabilistic method to enhance PGD solutions with data-driven approach, application to the automated tape placement process

A nonparametric method assessing the error and variability margins in solutions depicted in a separated form using experimental results is illustrated in this work. The method assess the total variability of the solution including the modeling error and the truncation error when experimental results are available. The illustrated method is based on the use of the PGD separated form solutions, enriched by transforming a part of the PGD basis vectors into probabilistic one. The constructed probabilistic vectors are restricted to the physical solution’s Stiefel manifold. The result is a real-time parametric PGD solution enhanced with the solution variability and the confidence intervals.

Design of Computational Models for Hydroturbine Units Based on a Nonparametric Regression Approach with Adaptation by Evolutionary Algorithms

This article deals with the problem of designing regression models for evaluating the parameters of the operation of complex technological equipment—hydroturbine units. A promising approach to the construction of regression models based on nonparametric Nadaraya–Watson kernel estimates is considered. A known problem in applying this approach is to determine the effective values of kernel-smoothing coefficients. Kernel-smoothing factors significantly impact the accuracy of the regression model, especially under conditions of variability of noise and parameters of samples in the input space of models. This fully corresponds to the characteristics of the problem of estimating the parameters of hydraulic turbines. We propose to use the evolutionary genetic algorithm with an addition in the form of a local-search stage to adjust the smoothing coefficients. This ensures the local convergence of the tuning procedure, which is important given the high sensitivity of the quality criterion of the nonparametric model. On a set of test problems, the results were obtained showing a reduction in the modeling error by 20% and 28% for the methods of adjusting the coefficients by the standard and hybrid genetic algorithms, respectively, in comparison with the case of an arbitrary choice of the values of such coefficients. For the task of estimating the parameters of the operation of a hydroturbine unit, a number of promising approaches to constructing regression models based on artificial neural networks, multidimensional adaptive splines, and an evolutionary method of genetic programming were included in the research. The proposed nonparametric approach with a hybrid smoothing coefficient tuning scheme was found to be most effective with a reduction in modeling error of about 5% compared with the best of the alternative approaches considered in the study, which, according to the results of numerical experiments, was the method of multivariate adaptive regression splines.

Multilevel Assimilation of Inverted Seismic Data With Correction for Multilevel Modeling Error

With large amounts of simultaneous data, like inverted seismic data in reservoir modeling, negative effects of Monte Carlo errors in straightforward ensemble-based data assimilation (DA) are enhanced, typically resulting in underestimation of parameter uncertainties. Utilization of lower fidelity reservoir simulations reduces the computational cost per ensemble member, thereby rendering the possibility of increasing the ensemble size without increasing the total computational cost. Increasing the ensemble size will reduce Monte Carlo errors and therefore benefit DA results. The use of lower fidelity reservoir models will however introduce modeling errors in addition to those already present in conventional fidelity simulation results. Multilevel simulations utilize a selection of models for the same entity that constitute hierarchies both in fidelities and computational costs. In this work, we estimate and approximately account for the multilevel modeling error (MLME), that is, the part of the total modeling error that is caused by using a multilevel model hierarchy, instead of a single conventional model to calculate model forecasts. To this end, four computationally inexpensive approximate MLME correction schemes are considered, and their abilities to correct the multilevel model forecasts for reservoir models with different types of MLME are assessed. The numerical results show a consistent ranking of the MLME correction schemes. Additionally, we assess the performances of the different MLME-corrected model forecasts in assimilation of inverted seismic data. The posterior parameter estimates from multilevel DA with and without MLME correction are compared to results obtained from conventional single-level DA with localization. It is found that multilevel DA (MLDA) with and without MLME correction outperforms conventional DA with localization. The use of all four MLME correction schemes results in posterior parameter estimates with similar quality. Results obtained with MLDA without any MLME correction were also of similar quality, indicating some robustness of MLDA toward MLME.

Suppression of Residual Vibration in Nonlinear Systems with Temporal Variation and Uncertainty in Parameters by Elimination of the Natural Frequency Component

A systematic approach is developed for determining a control input for the point-to-point control of an overhead crane that exhibits temporal variation of rope length in addition to damping and nonlinearity, without inducing residual vibration. Complete suppression of the residual vibration is achieved by eliminating the natural frequency component of the cargo from the apparent external force, which is defined to include the effects of damping, nonlinearity, and parameter variation. Furthermore, an effective technique previously proposed by the authors for improving robustness to the modeling error of the natural frequency is extended. Numerical simulation results show that, even when cargo is hoisted up or down during operation, the proposed method realizes accurate positioning of the cargo without inducing residual vibration and sufficiently improves robustness. To the best of the authors' knowledge, this is the first frequency-domain robust open-loop control strategy that ensures a theoretical zero amplitude for residual vibration in the absence of modeling error in nonlinear crane hoisting operation. The developed method is not only a contribution to the realization of low-cost and efficient crane hoisting operation, but is also applicable to the control of other nonlinear damped systems that include time-varying parameters.

Behavioral Modeling of a Multimode Power Amplifier Using Perceptrons with AM-AM and AM-PM Complex-Valued Activation Function

Three-layer perceptron (TLP) is one of the approaches to the behavioral modeling of radio frequency (RF) power amplifiers (PAs) for wireless communication systems. A low-pass equivalent PA behavioral modeling should be able to represent the AM-AM and AM-PM characteristic curves, besides generating only useful contributions. This work shows that adapting a complex-valued activation function toencompass the strict characteristics from the AM-AM and AM-PM curves at the same time fulfilling the bandpass constraint gives better results in terms of network complexity and accuracy. Besides that, a novel TLP-based model for a multimode PA is proposed. This type of PA has characteristics that add difficulty to its inverse modeling. A modification in the ascendant method is also proposed for application with TLP-based PA models. This method simplifies the models in terms of operations needed for implementation. In a scenario of same number of network parameters, a significant reduction in modeling error is achieved when a complex-valued TLP that accounts for both AM-AM and AM-PM is used instead of a complex-valued TLP that accounts only for AM-AM. Such reduction is quantified by up to 5.73 dB (-35.53 dB to -41.26 dB) improvements in normalized mean square error (NMSE) metric. Moreover, when applying the ascendant method a 67% reduction in active parameters is achieved (36 to 12 active parameters), deteriorating the NMSE by less than 0.5 dB.