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Автор: Grafiati
Опубліковано: 14 березня 2023

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Статті в журналах з теми "Data weighting function"

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Dombi, József, and Tamás Jónás. "Towards a general class of parametric probability weighting functions." Soft Computing 24, no. 21 (September 24, 2020): 15967–77. http://dx.doi.org/10.1007/s00500-020-05335-3.

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Abstract In this study, we present a novel methodology that can be used to generate parametric probability weighting functions, which play an important role in behavioral economics, by making use of the Dombi modifier operator of continuous-valued logic. Namely, we will show that the modifier operator satisfies the requirements for a probability weighting function. Next, we will demonstrate that the application of the modifier operator can be treated as a general approach to create parametric probability weighting functions including the most important ones such as the Prelec and the Ostaszewski, Green and Myerson (Lattimore, Baker and Witte) probability weighting function families. Also, we will show that the asymptotic probability weighting function induced by the inverse of the so-called epsilon function is none other than the Prelec probability weighting function. Furthermore, we will prove that, by using the modifier operator, other probability weighting functions can be generated from the dual generator functions and from transformed generator functions. Finally, we will show how the modifier operator can be used to generate strictly convex (or concave) probability weighting functions and introduce a method for fitting a generated probability weighting function to empirical data.
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2

Ying Han, Pang, Andrew Teoh Beng Jin, and Lim Heng Siong. "Eigenvector Weighting Function in Face Recognition." Discrete Dynamics in Nature and Society 2011 (2011): 1–15. http://dx.doi.org/10.1155/2011/521935.

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Graph-based subspace learning is a class of dimensionality reduction technique in face recognition. The technique reveals the local manifold structure of face data that hidden in the image space via a linear projection. However, the real world face data may be too complex to measure due to both external imaging noises and the intra-class variations of the face images. Hence, features which are extracted by the graph-based technique could be noisy. An appropriate weight should be imposed to the data features for better data discrimination. In this paper, a piecewise weighting function, known as Eigenvector Weighting Function (EWF), is proposed and implemented in two graph based subspace learning techniques, namely Locality Preserving Projection and Neighbourhood Preserving Embedding. Specifically, the computed projection subspace of the learning approach is decomposed into three partitions: a subspace due to intra-class variations, an intrinsic face subspace, and a subspace which is attributed to imaging noises. Projected data features are weighted differently in these subspaces to emphasize the intrinsic face subspace while penalizing the other two subspaces. Experiments on FERET and FRGC databases are conducted to show the promising performance of the proposed technique.
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3

Lu, Guangyin, Dongxing Zhang, Shujin Cao, Yihuai Deng, Gang Xu, Yihu Liu, Ziqiang Zhu, and Peng Chen. "Spherical Planting Inversion of GRAIL Data." Applied Sciences 13, no. 5 (March 6, 2023): 3332. http://dx.doi.org/10.3390/app13053332.

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In large-scale potential field data inversion, constructing the kernel matrix is a time-consuming problem with large memory requirements. Therefore, a spherical planting inversion of Gravity Recovery and Interior Laboratory (GRAIL) data is proposed using the L1-norm in conjunction with tesseroids. Spherical planting inversion, however, is strongly dependent on the correct seeds’ density contrast, location, and number; otherwise, it can cause mutual intrusion of anomalous sources produced by different seeds. Hence, a weighting function was introduced to limit the influence area of the seeds for yielding robust solutions; moreover, it is challenging to set customized parameters for each seed, especially for the large number of seeds used or complex gravity anomalies data. Hence, we employed the “shape-of-anomaly” data-misfit function in conjunction with a new seed weighting function to improve the spherical planting inversion. The proposed seed weighting function is constructed based on the covariance matrix for given gravity data and can avoid manually setting customized parameters for each seed. The results of synthetic tests and field data show that spherical planting inversion requires less computer memory than traditional inversion. Furthermore, the proposed seed weighting function can effectively limit the seed influence area. The result of spherical planting inversion indicates that the crustal thickness of Mare Crisium is about 0 km because the Crisium impact may have removed all crust from parts of the basin.
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4

Bedini, L., S. Fossi, and R. Reggiannini. "Generalised crosscorrelator with data-estimated weighting function: a simulation analysis." IEE Proceedings F Communications, Radar and Signal Processing 133, no. 2 (1986): 195. http://dx.doi.org/10.1049/ip-f-1.1986.0030.

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5

KELLER, ANNETTE, and FRANK KLAWONN. "FUZZY CLUSTERING WITH WEIGHTING OF DATA VARIABLES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 08, no. 06 (December 2000): 735–46. http://dx.doi.org/10.1142/s0218488500000538.

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We introduce an objective function-based fuzzy clustering technique that assigns one influence parameter to each single data variable for each cluster. Our method is not only suited to detect structures or groups of data that are not uniformly distributed over the structure's single domains, but gives also information about the influence of individual variables on the detected groups. In addition, our approach can be seen as a generalization of the well-known fuzzy c-means clustering algorithm.
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6

Bedini, L., S. Fossi, and R. Reggiannini. "Erratum: Generalised crosscorrelator with data-estimated weighting function: a simulation analysis." IEE Proceedings F Communications, Radar and Signal Processing 133, no. 3 (1986): 231. http://dx.doi.org/10.1049/ip-f-1.1986.0039.

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7

Jiang, Ray, Shangtong Zhang, Veronica Chelu, Adam White, and Hado van Hasselt. "Learning Expected Emphatic Traces for Deep RL." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 7015–23. http://dx.doi.org/10.1609/aaai.v36i6.20660.

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Off-policy sampling and experience replay are key for improving sample efficiency and scaling model-free temporal difference learning methods. When combined with function approximation, such as neural networks, this combination is known as the deadly triad and is potentially unstable. Recently, it has been shown that stability and good performance at scale can be achieved by combining emphatic weightings and multi-step updates. This approach, however, is generally limited to sampling complete trajectories in order, to compute the required emphatic weighting. In this paper we investigate how to combine emphatic weightings with non-sequential, off-line data sampled from a replay buffer. We develop a multi-step emphatic weighting that can be combined with replay, and a time-reversed n-step TD learning algorithm to learn the required emphatic weighting. We show that these state weightings reduce variance compared with prior approaches, while providing convergence guarantees. We tested the approach at scale on Atari 2600 video games, and observed that the new X-ETD(n) agent improved over baseline agents, highlighting both the scalability and broad applicability of our approach.
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8

Blahak, Ulrich. "An Approximation to the Effective Beam Weighting Function for Scanning Meteorological Radars with an Axisymmetric Antenna Pattern." Journal of Atmospheric and Oceanic Technology 25, no. 7 (July 1, 2008): 1182–96. http://dx.doi.org/10.1175/2007jtecha1010.1.

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Abstract To obtain statistically stable reflectivity measurements by meteorological radars, it is common practice to average over several consecutive pulses during which the antenna rotates at a certain angular velocity. Taking into account the antenna’s continuous motion, the measured reflectivity is determined by an effective beam weighting function, which is different from a single-pulse weighting function—a fact that is widely ignored in applications involving beam weighting. In this paper, the effective beam weighting function is investigated in detail. The theoretical derivation shows that the effective weighting function is essentially a simple moving sum of single-beam weighting functions. Assuming a Gaussian shape of a single pulse, a simple and easy-to-use parameterization of the effective beam weighting function is arrived at, which depends only on the single beamwidth and the ratio of the single beamwidth to the rotational angular averaging interval. The derived relation is formulated in the “radar system” (i.e., the spherical coordinate system consisting of azimuth and elevation angles) that is often applied in practice. Formulas for the “beam system” (two orthogonal angles relative to the beam axis) are also presented. The final parameterization should be applicable to almost all meteorological radars and might be used (i) in specialized radar data analyses (with ground-based or satellite radars) and (ii) for radar forward operators to calculate simulated radar parameters from the results of NWP models.
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9

Nie, Lichao, Zhao Ma, Bin Liu, Zhenhao Xu, Wei Zhou, Chengkun Wang, Junyang Shao, and Xin Yin. "A Weighting Function-Based Method for Resistivity Inversion in Subsurface Investigations." Journal of Environmental and Engineering Geophysics 25, no. 1 (March 2020): 129–38. http://dx.doi.org/10.2113/jeeg19-029.

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There is a high demand for high detection accuracy and resolution with respect to anomalous bodies due to the increased development of underground spaces. This study focused on the weighted inversion of observed data from individual array type electrical resistivity tomography (ERT), and developed an improved method of applying a data weighing function to the geoelectrical inversion procedure. In this method, the weighting factor as an observed data weighting term was introduced into the objective function. For individual arrays, the sensitivity decreases with increasing electrode interval. Therefore, the Jacobian matrices were computed for the observed data of individual arrays to determine the value of the weighting factor, and the weighting factor was calculated automatically during inversion. In this work, 2D combined inversion of ERT data from four-electrode Alfa-type arrays is examined. The effectiveness of the weighted inversion method was demonstrated using various synthetic and real data examples. The results indicated that the inversion method based on observed data weighted function could improve the contribution of observed data with depth information to the objective function. It has been proven that the combined weighted inversion method could be a feasible tool for improving the accuracies of positioning and resolution while imaging deep anomalous bodies in the subsurface.
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10

Vitale, Andrea, and Maurizio Fedi. "Self-constrained inversion of potential fields through a 3D depth weighting." GEOPHYSICS 85, no. 6 (November 1, 2020): G143—G156. http://dx.doi.org/10.1190/geo2019-0812.1.

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A new method for inversion of potential fields is developed using a depth-weighting function specifically designed for fields related to complex source distributions. Such a weighting function is determined from an analysis of the field that precedes the inversion itself. The algorithm is self-consistent, meaning that the weighting used in the inversion is directly deduced from the scaling properties of the field. Hence, the algorithm is based on two steps: (1) estimation of the locally homogeneous degree of the field in a 3D domain of the harmonic region and (2) inversion of the data using a specific weighting function with a 3D variable exponent. A multiscale data set is first formed by upward continuation of the original data. Local homogeneity and a multihomogeneous model are then assumed, and a system built on the scaling function is solved at each point of the multiscale data set, yielding a multiscale set of local-homogeneity degrees of the field. Then, the estimated homogeneity degree is associated to the model weighting function in the source volume. Tests on synthetic data show that the generalization of the depth weighting to a 3D function and the proposed two-step algorithm has great potential to improve the quality of the solution. The gravity field of a polyhedron is inverted yielding a realistic reconstruction of the whole body, including the bottom surface. The inversion of the aeromagnetic real data set, from the Mt. Vulture area, also yields a good and geologically consistent reconstruction of the complex source distribution.
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Дисертації з теми "Data weighting function"

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Sarmah, Dipsikha. "Evaluation of Spatial Interpolation Techniques Built in the Geostatistical Analyst Using Indoor Radon Data for Ohio,USA." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1350048688.

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2

DI, CORSO EVELINA. "Text miner's little helper: scalable self-tuning methodologies for knowledge exploration." Doctoral thesis, Politecnico di Torino, 2019. http://hdl.handle.net/11583/2738395.

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Johnson, Gregory K. "The Optimal Weighting of Pre-Election Polling Data." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2378.pdf.

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4

Moreno, Betancur Margarita. "Regression modeling with missing outcomes : competing risks and longitudinal data." Thesis, Paris 11, 2013. http://www.theses.fr/2013PA11T076/document.

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Les données manquantes sont fréquentes dans les études médicales. Dans les modèles de régression, les réponses manquantes limitent notre capacité à faire des inférences sur les effets des covariables décrivant la distribution de la totalité des réponses prévues sur laquelle porte l'intérêt médical. Outre la perte de précision, toute inférence statistique requière qu'une hypothèse sur le mécanisme de manquement soit vérifiée. Rubin (1976, Biometrika, 63:581-592) a appelé le mécanisme de manquement MAR (pour les sigles en anglais de « manquant au hasard ») si la probabilité qu'une réponse soit manquante ne dépend pas des réponses manquantes conditionnellement aux données observées, et MNAR (pour les sigles en anglais de « manquant non au hasard ») autrement. Cette distinction a des implications importantes pour la modélisation, mais en général il n'est pas possible de déterminer si le mécanisme de manquement est MAR ou MNAR à partir des données disponibles. Par conséquent, il est indispensable d'effectuer des analyses de sensibilité pour évaluer la robustesse des inférences aux hypothèses de manquement.Pour les données multivariées incomplètes, c'est-à-dire, lorsque l'intérêt porte sur un vecteur de réponses dont certaines composantes peuvent être manquantes, plusieurs méthodes de modélisation sous l'hypothèse MAR et, dans une moindre mesure, sous l'hypothèse MNAR ont été proposées. En revanche, le développement de méthodes pour effectuer des analyses de sensibilité est un domaine actif de recherche. Le premier objectif de cette thèse était de développer une méthode d'analyse de sensibilité pour les données longitudinales continues avec des sorties d'étude, c'est-à-dire, pour les réponses continues, ordonnées dans le temps, qui sont complètement observées pour chaque individu jusqu'à la fin de l'étude ou jusqu'à ce qu'il sorte définitivement de l'étude. Dans l'approche proposée, on évalue les inférences obtenues à partir d'une famille de modèles MNAR dits « de mélange de profils », indexés par un paramètre qui quantifie le départ par rapport à l'hypothèse MAR. La méthode a été motivée par un essai clinique étudiant un traitement pour le trouble du maintien du sommeil, durant lequel 22% des individus sont sortis de l'étude avant la fin.Le second objectif était de développer des méthodes pour la modélisation de risques concurrents avec des causes d'évènement manquantes en s'appuyant sur la théorie existante pour les données multivariées incomplètes. Les risques concurrents apparaissent comme une extension du modèle standard de l'analyse de survie où l'on distingue le type d'évènement ou la cause l'ayant entrainé. Les méthodes pour modéliser le risque cause-spécifique et la fonction d'incidence cumulée supposent en général que la cause d'évènement est connue pour tous les individus, ce qui n'est pas toujours le cas. Certains auteurs ont proposé des méthodes de régression gérant les causes manquantes sous l'hypothèse MAR, notamment pour la modélisation semi-paramétrique du risque. Mais d'autres modèles n'ont pas été considérés, de même que la modélisation sous MNAR et les analyses de sensibilité. Nous proposons des estimateurs pondérés et une approche par imputation multiple pour la modélisation semi-paramétrique de l'incidence cumulée sous l'hypothèse MAR. En outre, nous étudions une approche par maximum de vraisemblance pour la modélisation paramétrique du risque et de l'incidence sous MAR. Enfin, nous considérons des modèles de mélange de profils dans le contexte des analyses de sensibilité. Un essai clinique étudiant un traitement pour le cancer du sein de stade II avec 23% des causes de décès manquantes sert à illustrer les méthodes proposées
Missing data are a common occurrence in medical studies. In regression modeling, missing outcomes limit our capability to draw inferences about the covariate effects of medical interest, which are those describing the distribution of the entire set of planned outcomes. In addition to losing precision, the validity of any method used to draw inferences from the observed data will require that some assumption about the mechanism leading to missing outcomes holds. Rubin (1976, Biometrika, 63:581-592) called the missingness mechanism MAR (for “missing at random”) if the probability of an outcome being missing does not depend on missing outcomes when conditioning on the observed data, and MNAR (for “missing not at random”) otherwise. This distinction has important implications regarding the modeling requirements to draw valid inferences from the available data, but generally it is not possible to assess from these data whether the missingness mechanism is MAR or MNAR. Hence, sensitivity analyses should be routinely performed to assess the robustness of inferences to assumptions about the missingness mechanism. In the field of incomplete multivariate data, in which the outcomes are gathered in a vector for which some components may be missing, MAR methods are widely available and increasingly used, and several MNAR modeling strategies have also been proposed. On the other hand, although some sensitivity analysis methodology has been developed, this is still an active area of research. The first aim of this dissertation was to develop a sensitivity analysis approach for continuous longitudinal data with drop-outs, that is, continuous outcomes that are ordered in time and completely observed for each individual up to a certain time-point, at which the individual drops-out so that all the subsequent outcomes are missing. The proposed approach consists in assessing the inferences obtained across a family of MNAR pattern-mixture models indexed by a so-called sensitivity parameter that quantifies the departure from MAR. The approach was prompted by a randomized clinical trial investigating the benefits of a treatment for sleep-maintenance insomnia, from which 22% of the individuals had dropped-out before the study end. The second aim was to build on the existing theory for incomplete multivariate data to develop methods for competing risks data with missing causes of failure. The competing risks model is an extension of the standard survival analysis model in which failures from different causes are distinguished. Strategies for modeling competing risks functionals, such as the cause-specific hazards (CSH) and the cumulative incidence function (CIF), generally assume that the cause of failure is known for all patients, but this is not always the case. Some methods for regression with missing causes under the MAR assumption have already been proposed, especially for semi-parametric modeling of the CSH. But other useful models have received little attention, and MNAR modeling and sensitivity analysis approaches have never been considered in this setting. We propose a general framework for semi-parametric regression modeling of the CIF under MAR using inverse probability weighting and multiple imputation ideas. Also under MAR, we propose a direct likelihood approach for parametric regression modeling of the CSH and the CIF. Furthermore, we consider MNAR pattern-mixture models in the context of sensitivity analyses. In the competing risks literature, a starting point for methodological developments for handling missing causes was a stage II breast cancer randomized clinical trial in which 23% of the deceased women had missing cause of death. We use these data to illustrate the practical value of the proposed approaches
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5

Yoshinaga, Kenji. "Comparison of phase synchronization measures for identifying stimulus－ induced functional connectivity in human magnetoencephalographic and simulated data." Kyoto University, 2020. http://hdl.handle.net/2433/259724.

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Книги з теми "Data weighting function"

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Lerch, F. J. Optimum data weighting and error calibration for estimation of gravitational parameters. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 1989.

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2

Chance, Kelly, and Randall V. Martin. Data Fitting. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199662104.003.0011.

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This chapter explores several of the most common and useful approaches to atmospheric data fitting as well as the process of using air mass factors to produce vertical atmospheric column abundances from line-of-sight slant columns determined by data fitting. An atmospheric spectrum or other type of atmospheric sounding is usually fitted to a parameterized physical model by minimizing a cost function, usually chi-squared. Linear fitting, when the model of the measurements is linear in the model parameters is described, followed by the more common nonlinear fitting case. For nonlinear fitting, the standard Levenberg-Marquardt method is described, followed by the use of optimal estimation, one of several retrieval methods that make use of a priori information to providing regularization for the solution. In the context of optimal estimation, weighting functions, contribution functions, and averaging kernels are described. The Twomey-Tikhonov regularization procedure is presented. Correlated parameters, with the important example of Earth’s atmospheric ozone, are discussed.
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Частини книг з теми "Data weighting function"

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Yamanaka, Masao. "Effective Delayed Neutron Fraction." In Accelerator-Driven System at Kyoto University Critical Assembly, 83–123. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0344-0_4.

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AbstractIn kinetic analyses on ADS, although adjoint flux distribution is defined under the existence of an external neutron source, an issue of the proper determination of the weighting function still remains in the definition to obtain the kinetics parameters in the fixed-source calculations. Here, an alternative methodology is proposed with the combined use of the k-ratio method and the reaction rates obtained by the fixed-source calculations, when the subcriticality level or the spectrum of the external neutron source is varied. In ADS experiments, the measurement of βeff is expected to provide complementary verification of the calculation and reliability of nuclear data. Then, the formulation of the Rossi-α method in the pulsed-neutron source has been already available for application to the subcriticality measurement in the pulsed-neutron source (PNS) experiments. Accordingly, the methodology is applied uniquely to deduce the βeff value with the pulsed-neutron source (spallation neutrons), with the combined use of the results of experiments and calculations. Using parameters α and ρ$, the values of βeff/Λ are deduced at near-critical configurations through experimental analyses. To estimate the numerical precision of Λ, the value of βeff/Λ is used as an index of Λ evaluation that is defined by a ratio of Λ values in the super-critical and subcritical states.
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2

Blackledge, J. M., M. A. Fiddy, and W. A. Ward. "Resolution Enhancement of Processed Seismic Data Using Prior Weighting Functions." In Acoustical Imaging, 207–19. Boston, MA: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4613-2523-9_20.

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3

Paquet, Hugo. "Bayesian strategies: probabilistic programs as generalised graphical models." In Programming Languages and Systems, 519–47. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72019-3_19.

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AbstractWe introduceBayesian strategies, a new interpretation of probabilistic programs in game semantics. This interpretation can be seen as a refinement of Bayesian networks.Bayesian strategies are based on a new form ofevent structure, with two causal dependency relations respectively modelling control flow and data flow. This gives a graphical representation for probabilistic programs which resembles the concrete representations used in modern implementations of probabilistic programming.From a theoretical viewpoint, Bayesian strategies provide a rich setting for denotational semantics. To demonstrate this we give a model for a general higher-order programming language with recursion, conditional statements, and primitives for sampling from continuous distributions and trace re-weighting. This is significant because Bayesian networks do not easily support higher-order functions or conditionals.
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4

Shoji, Isao. "Nonparametric Estimation of Nonlinear Dynamics by Local Linear Approximation." In Chaos and Complexity Theory for Management, 368–79. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2509-9.ch019.

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This chapter discusses nonparametric estimation of nonlinear dynamical system models by a method of metric-based local linear approximation. By specifying a metric such as the standard metric or the square metric on the Euclidean space and a weighting function based on such as the exponential function or the cut-off function, it is possible to estimate values of an unknown vector field from experimental data. It can be shown the local linear fitting with the Gaussian kernel, or the local polynomial modeling of degree one, is included in the class of the proposed method. In addition, conducting simulation studies for estimating random oscillations, the chapter shows the method numerically works well.
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5

Kennedy, Georgina, Mark Dras, and Blanca Gallego. "Augmentation of Electronic Medical Record Data for Deep Learning." In MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation. IOS Press, 2022. http://dx.doi.org/10.3233/shti220144.

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Data imbalance is a well-known challenge in the development of machine learning models. This is particularly relevant when the minority class is the class of interest, which is frequently the case in models that predict mortality, specific diagnoses or other important clinical end-points. Typical methods of dealing with this include over- or under-sampling training data, or weighting the loss function in order to boost the signal from the minority class. Data augmentation is another frequently employed method — particularly for models that use images as input data. For discrete time-series data, however, there is no consensus method of data augmentation. We propose a simple data augmentation strategy that can be applied to discrete time-series data from the EMR. This strategy is then demonstrated using a publicly available data-set, in order to provide proof of concept for the work undertaken in [1], where data is unable to be made open.
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Song, Dongran, Ziqun Li, Jian Yang, Mi Dong, Xiaojiao Chen, and Liansheng Huang. "Nonlinear Intelligent Predictive Control for the Yaw System of Large-Scale Wind Turbines." In Nonlinear Systems - Recent Developments and Advances [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.105484.

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This chapter presents a nonlinear intelligent predictive control using multi-step prediction model for the electrical motor-based yaw system of an industrial wind turbine. The proposed method introduces a finite control set under constraints for the demanded yaw rate, predicts the multi-step yaw error using the control set element and the prediction wind directions, and employs an exhaustive search method to search the control output candidate giving the minimal value of the objective function. As the objective function is designed for a joint power and actuator usage optimization, the weighting factor in the objective function is optimally determined by the fuzzy regulator that is optimized by an intelligent algorithm. Finally, the proposed method is demonstrated by simulation tests using real wind direction data.
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7

Di Cera, Enrico. "[4] Use of weighting functions in data fitting." In Methods in Enzymology, 68–87. Elsevier, 1992. http://dx.doi.org/10.1016/0076-6879(92)10006-y.

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Carnero, María Carmen, and Javier Cárcel-Carrasco. "Effects of Recession on Asset Management Performance in Small Businesses in Spain." In Cases on Optimizing the Asset Management Process, 325–53. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-7943-5.ch013.

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The number of studies that assess the level of maintenance in a country is still very small, despite the contribution of this area to national competitiveness. The literature analyses asset management based on key performance indicators, but not via a multicriteria model. This chapter describes a multicriteria model, constructed by means of the fuzzy analytic hierarchy process (FAHP). The weightings are converted into utility functions, allowing the final utility of an alternative to be calculated via a multi-attribute utility function. Data on the state of asset management in Spain, in 2005 and 2010, are used to produce discrete probability distributions. Finally, a Monte Carlo simulation is applied to estimate the uncertainty of a complex function. In this way, the level of excellence of asset management in small businesses in Spain, before and after the recession, could be determined. The results show that the economic crisis experienced in Spain since 2008 has had a negative effect on the level of asset management in most sectors.
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Büyük, Ersin. "Pareto-Based Multiobjective Particle Swarm Optimization: Examples in Geophysical Modeling." In Swarm Intelligence [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.97067.

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It has been recently revealed that particle swarm optimization (PSO) is a modern global optimization method and it has been used in many real world engineering problems to estimate model parameters. PSO has also led as tremendous alternative method to conventional geophysical modeling techniques which suffer from dependence to initial model, linearization problems and being trapped at a local minimum. An area neglected in using PSO is joint modeling of geophysical data sets having different sensivities, whereas this kind of modeling with multiobjective optimization techniques has become an important issue to increase the uniqueness of the model parameters. However, using of subjective and unpredictable weighting to objective functions may cause a misleading solution in multiobjective optimization. Multiobjective PSO (MOPSO) with Pareto approach allows obtaining set of solutions including a joint optimal solution without weighting requirements. This chapter begins with an overview of PSO and Pareto-based MOPSO presented their mathematical formulation, algorithms and alternate approaches used in these methods. The chapter goes on to present a series synthetic modeled of seismological data that is one kind of geophysical data by using of Pareto-based multiobjective PSO. According to results matched perfectly, we believe that multiobjective PSO is an innovative approach to joint modeling of such data.
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Тези доповідей конференцій з теми "Data weighting function"

1

Galbraith, Mike, Zhengsheng Yao, and Randy Kolesar. "Seismic data interpolation with f‐p domain spectra weighting function." In SEG Technical Program Expanded Abstracts 2011. Society of Exploration Geophysicists, 2011. http://dx.doi.org/10.1190/1.3627838.

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2

Ma, Rui, and John B. Ferris. "Terrain Gridding Using a Stochastic Weighting Function." In ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control. ASMEDC, 2011. http://dx.doi.org/10.1115/dscc2011-6085.

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The development of new stochastic terrain gridding methods are necessitated by new tire and vehicle modeling applications. Currently, grid node locations in the horizontal plane are assumed to be known and only the uncertainty in the vertical height estimates is modeled. This work modifies the current practice of weighting the importance of a particular measured data point (the terrain height at some horizontal location) by the inverse distance between the grid node and that point. A new weighting function is developed to account for the error in the horizontal position of the grid nodes. The geometry of the problem is described and the probability distribution is developed in steps. Although the solution cannot be determined in closed form, an estimate of the median distance is developed within 1% error. This more complete stochastic definition of the terrain can then be used for advanced tire modeling and vehicle simulation.
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3

Cella, F., and M. Fedi. "Inversion of Potential Field Data Using the Structural Index as Weighting Function Rate Decay." In 70th EAGE Conference and Exhibition - Workshops and Fieldtrips. European Association of Geoscientists & Engineers, 2008. http://dx.doi.org/10.3997/2214-4609.20147784.

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4

Liu, Zexin, Heather T. Ma, and Fei Chen. "A new data-driven band-weighting function for predicting the intelligibility of noise-suppressed speech." In 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2017. http://dx.doi.org/10.1109/apsipa.2017.8282082.

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5

Fernandez, Charles, Arun Kr Dev, Rose Norman, Wai Lok Woo, and Shashi Bhushan Kumar. "Dynamic Positioning System: Systematic Weight Assignment for DP Sub-Systems Using Multi-Criteria Evaluation Technique Analytic Hierarchy Process and Validation Using DP-RI Tool With Deep Learning Algorithm." In ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/omae2019-95485.

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Abstract The Dynamic Positioning (DP) System of a vessel involves complex interactions between a large number of sub-systems. Each sub-system plays a unique role in the continuous overall DP function for safe and reliable operation of the vessel. Rating the significance or assigning weightings to the DP sub-systems in different operating conditions is a complex task that requires input from many stakeholders. The weighting assignment is a critical step in determining the reliability of the DP system during complex marine and offshore operations. Thus, an accurate weighting assignment is crucial as it, in turn, influences the decision-making of the operator concerning the DP system functionality execution. Often DP operators prefer to rely on intuition in assigning the weightings. However, it introduces an inherent uncertainty and level of inconsistency in the decision making. The systematic assignment of weightings requires a clear definition of criteria and objectives and data collection with the DP system operating continuously in different environmental conditions. The sub-systems of the overall DP system are characterized by multi-attributes resulting in a high number of comparisons thereby making weighting distribution complicated. If the weighting distribution was performed by simplifying the attributes, making the decision by excluding part of them or compromising the cognitive efforts, then this could lead to inaccurate decision making. Multi-Criteria Decision Making (MCDM) methods have evolved over several decades and have been used in various applications within the Maritime and Oil and Gas industries. DP, being a complex system, naturally lends itself to the implementation of MCDM techniques to assign weight distribution among its sub-systems. In this paper, the Analytic Hierarchy Process (AHP) methodology is used for weight assignment among the DP sub-systems. An AHP model is effective in obtaining the domain knowledge from numerous experts and representing knowledge-guided indexing. The approach involved examination of several criteria in terms of both quantitative and qualitative variables. A state-of-the-art advisory decision-making tool, Dynamic Positioning Reliability Index (DP-RI), is used to validate the results from AHP. The weighting assignments from AHP are close to the reality and verified using the tool through real-life scenarios.
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Kurzawski, Andrew, and Ofodike A. Ezekoye. "Inversion for Fire Heat Release Rate Using Transient Heat Flux Data." In ASME 2017 Heat Transfer Summer Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/ht2017-5107.

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The heat-release rate (HRR) of a burning item is key to understanding the thermal effects of a fire on its surroundings. It is, perhaps, the most important variable used to characterize a burning fuel packet and is defined as the rate of energy released by the fire. HRR is typically determined using a gas measurement calorimetry method. In this study, an inversion algorithm is presented for conducting calorimeter on fires with unknown HRRs located in a compartment. The algorithm compares predictions of a forward model with observed heat fluxes from synthetically generated data sets to determine the HRR that minimizes a cost function. The effects of tuning a weighting parameter in the cost function and the issues associated with two different forward models of a compartment fire are examined.
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Israr, Ali, Hong Z. Tan, James Mynderse, and George T. C. Chiu. "A Psychophysical Model of Motorcycle Handlebar Vibrations." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-41504.

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In this study, we developed a perception-based quantitative model to relate broadband vibrations transmitted through a motorcycle handlebar to a rider’s hands. The test apparatus consisted of the handlebar of a motorcycle rig assembly driven by a computer-controlled actuator. Participants were instructed to hold the handlebar and maintain a sitting posture as they would while riding a motorcycle. In Exp. 1, psychophysical detection thresholds for 10 participants were estimated at ten test frequencies between 20–300 Hz using a two-interval one-up two-down adaptive procedure. The interpolated threshold vs. frequency function specified the minimum acceleration required before a user could perceive the vibration at a particular frequency. In Exp. 2, participants were asked to rate 15 representative handlebar vibrations using a magnitude estimation procedure. The vibration patterns were measured on an actual motorcycle handlebar while the motorcycle traveled at speeds ranging from 25 to 75 mph. Several weighting functions, including the ISO-5349 standards, were applied to the broadband vibration signal in the frequency domain to estimate the total vibration energy by summing up all weighted components. The best weighting function, in the sense that the estimated total energy correlated linearly with the subjective magnitude ratings obtained in Exp. 2, were based on the detection threshold data obtained in Exp. 1. Specifically, the strength of each vibration component was calculated relative to the human detection threshold at the same frequency, thereby taking into account human sensitivity to vibration signals at different frequencies. The resulting weighting function can be applied to other recorded vibration signals to predict user rating of perceived vibration intensities.
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Ramanujan, Devarajan, William Z. Bernstein, Fu Zhao, and Karthik Ramani. "Addressing Uncertainties Within Product Redesign for Sustainability: A Function Based Framework." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47137.

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The Function Impact Method (FIM) is a semi-quantitative eco-design methodology that is targeted specifically towards the early stages of the design process. The FIM allows a designer to predict the environmental impacts associated with a new functional embodiment by extrapolating knowledge from Life cycle assessment (LCA) of similar existing designs. LCA however, is associated with substantial sources of uncertainty. Furthermore, the FIM uses a subjective weighting scheme for representing function-structure affinities. In the authors’ previous work, a Monte-Carlo variation analysis was used to estimate sensitivity of the input data and select the preferred redesign strategy. This paper proposes a method to formalize the input uncertainties in the FIM by modeling the uncertainties present in the results of the LCA’s and the involved function-structure affinities using Info-gap decision theory. The desirability of redesigning a particular function based on the magnitude of its function-connectivity and eco-impact is estimated, and a decision making methodology based on robust satisficing is discussed. This method is applied for making robust redesign decisions with regards to re-designing a pneumatic impact wrench for sustainability.
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Cavalcante, Everton, Thais Batista, Marcel Oliveira, Jorge Pereira, Victor Ribeiro, and Matthieu Oliveira. "A Multidimensional Approach for Logistics Routing in the Smart Territory." In Simpósio Brasileiro de Sistemas de Informação. Sociedade Brasileira de Computação (SBC), 2022. http://dx.doi.org/10.5753/sbsi_estendido.2022.222988.

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This work proposes a multidimensional approach for analyzing the routing problem to determine the best routes considering data related to different domains of a city. The proposed strategy defines (i) a quality function for each considered dimension to evaluate the route quality and (ii) a utility function that simultaneously considers the different dimensions by weighting each of them at the decision maker's choice. The approach was implemented on a georeferenced smart city platform that integrates data from several city domains. As proof of concept, the platform is used to combine routing and public safety data and indicates the best routes according to these criteria.
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Kim, Hong-Min, and Kwang-Yong Kim. "Optimization of Three-Dimensional Angled Ribs With RANS Analysis of Turbulent Heat Transfer." In ASME Turbo Expo 2004: Power for Land, Sea, and Air. ASMEDC, 2004. http://dx.doi.org/10.1115/gt2004-53346.

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A numerical optimization procedure for the shape of three-dimensional channel with angled ribs mounted on one of the walls to enhance turbulent heat transfer is presented. The response surface based global optimization with Reynolds-averaged Navier-Stokes analysis of fluid flow and heat transfer is used. Shear stress transport (SST) turbulence model is used as a turbulence closure. Computational results for local heat transfer rate show a reasonable agreement with the experimental data. The pitch-to-height ratio of the rib and rib height-to-channel height ratio are set to be 9.0 and 0.1, respectively, and width-to-rib height ratio and attack angle of the rib are chosen as design variables. The objective function is defined as a linear combination of heat-transfer and friction-loss related terms with the weighting factor. Full-factorial experimental design method is used to determine the data points. Optimum shapes of the channel have been obtained in the range from 0.0 to 0.1 of the weighting factor.
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