Account Data Uncertainties Research Articles

NURBS-based surface generation from 3D images: spectral construction and data-driven model selection

Abstract In this paper, we present a set of improved algorithms for recovering computer aided design (CAD-type) surface models from three-dimensional (3D) images. The goal of the proposed framework is to generate B-spline or non-uniform rational B-spline (NURBS) surfaces, which are standard mathematical representations of solid objects in digital engineering. To create a NURBS surface, we first compute a control network (a quadrilateral mesh) from a triangular mesh using the Marching Cubes algorithm and Discrete Morse theory. To create a NURBS surface, we first compute a triangular mesh using the Marching Cubes algorithm, then the control network (a quadrilateral mesh) is determined from the triangular mesh by using Discrete Morse theory. Discrete Morse theory uses the critical points of a specific scalar field defined over the triangulation to generate a quad mesh. Such a scalar field is obtained by solving a graph Laplacian eigenproblem over the triangulation. However, the resulting surface is not optimal. We therefore introduce an optimization algorithm to better approximate the geometry of the object. In addition, we propose a statistical method for selecting the most appropriate eigenfunction of the graph Laplacian to generate a control network that is neither too coarse nor too fine, given the precision of the 3D image. To do this, we set up a regression model and use an information criterion to choose the best surface. Finally, we extend our approach by taking into account both model and data uncertainty using probabilistic regression and sampling the posterior distribution with Hamiltonian Markov Chain Monte Carlo.

Synergy of Using Nadir and Limb Instruments for Tropospheric Ozone Monitoring (SUNLIT)

Abstract. Satellite measurements in nadir and limb viewing geometry provide a complementary view of the atmosphere. An effective combination of the limb and nadir measurements can give new information about atmospheric composition. In this work, we present tropospheric ozone column datasets that have been created using a combination of total ozone columns from OMI (Ozone Monitoring Instrument) and TROPOMI (TROPOspheric Monitoring Instrument) with stratospheric ozone column datasets from several available limb-viewing instruments: MLS (Microwave Limb Sounder), OSIRIS (Optical Spectrograph and InfraRed Imaging System), MIPAS (Michelson Interferometer for Passive Atmospheric Sounding), SCIAMACHY (SCanning Imaging Spectrometer for Atmospheric CHartographY), OMPS-LP (Ozone Mapping and Profiles Suite – Limb Profiler), and GOMOS (Global Ozone Monitoring by Occultation of Stars). We have developed further the methodological aspects of the assessment of tropospheric ozone using the residual method supported by simulations with the chemistry transport model SILAM (System for Integrated modeLling of Atmospheric coMposition). It has been shown that the accurate assessment of ozone in the upper troposphere and the lower stratosphere (UTLS) is of high importance for detecting the ground-level ozone patterns. The stratospheric ozone column is derived from a combination of ozone profiles from several satellite instruments in limb-viewing geometry. We developed a method for the data homogenization, which includes the removal of biases and a posteriori estimation of random uncertainties, thus making the data from different instruments compatible with each other. The high-horizontal- and vertical-resolution dataset of ozone profiles is created via interpolation of the limb profiles from each day to a 1∘×1∘ horizonal grid. A new kriging-type interpolation method, which takes into account data uncertainties and the information about natural ozone variations from the SILAM-adjusted ozone field, has been developed. To mitigate the limited accuracy and coverage of the limb profile data in the UTLS, a smooth transition to the model data is applied below the tropopause. This allows for the estimation of the stratospheric ozone column with full coverage of the UTLS. The derived ozone profiles are in very good agreement with collocated ozonesonde measurements. The residual method was successfully applied to OMI and TROPOMI clear-sky total ozone data in combination with the stratospheric ozone column from the developed high-resolution limb profile dataset. The resulting tropospheric ozone column is in very good agreement with other satellite data. The global distributions of tropospheric ozone exhibit enhancements associated with the regions of high tropospheric ozone production. The main datasets created are (i) a monthly 1∘×1∘ global tropospheric ozone column dataset (from ground to 3 km below the tropopause) using OMI and limb instruments, (ii) a monthly 1∘×1∘ global tropospheric ozone column dataset using TROPOMI and limb instruments, and (iii) a daily 1∘×1∘ interpolated stratospheric ozone column from limb instruments. Other datasets, which are created as an intermediate step of creating the tropospheric ozone column data, are (i) a daily 1∘×1∘ clear-sky and total ozone column from OMI and TROPOMI, (ii) a daily 1∘×1∘ homogenized and interpolated dataset of ozone profiles from limb instruments, and (iii) a daily 1∘×1∘ dataset of ozone profiles from SILAM simulations with adjustment to satellite data. These datasets can be used in various studies related to variability and trends in ozone distributions in both the troposphere and the stratosphere. The datasets are processed from the beginning of OMI and TROPOMI measurements until December 2020 and are planned to be regularly extended in the future.

Interval Cross Efficiency Measurement for General Two-Stage Systems

The data envelopment analysis (DEA), which is based on mathematical programming, is employed to measure the relative efficiency of several homogeneous decision-making units (DMUs) with multiple inputs and outputs. There has been extensive research on developing the classical DEA models for various forms and conditions. For example, ranking models such as cross efficiency have been proposed to address the shortcomings of the initial basic models. The data envelopment analysis for network systems has been developed as the network data envelopment analysis (NDEA), and models such as interval data envelopment analysis (IDEA) have been provided to take into account data uncertainty. But using this technique for issues that have all these conditions requires a single model, in which all the conditions are included. In the case study of this paper, the branches in the banking industry are ranked, and several models are presented according to different approaches to the NDEA in order to calculate the interval cross efficiency of a general two-stage network system. Finally, the results of the models are compared.

Finding Most Popular Indoor Semantic Locations Using Uncertain Mobility Data

Knowing popular indoor locations can benefit many applications like exhibition planning and location-based advertising, among others. In this work, we use uncertain historical indoor mobility data to find the top-$k$k popular indoor semantic locations with the highest flow values. In the data we use, an object positioning report contains a set of samples, each consisting of an indoor location and a corresponding probability. The problem is challenging due to the difficulty in obtaining reliable flow values and the heavy computational workload on probabilistic samples for large numbers of objects. To address the first challenge, we propose an indoor flow definition that takes into account both data uncertainty and indoor topology. To efficiently compute flows for individual indoor semantic locations, we design data structures for facilitating accessing the relevant data, a data reduction method that reduces the intermediate data to process, and an overall flow computing algorithm. Furthermore, we design search algorithms for finding the top-$k$k popular indoor semantic locations. All proposals are evaluated extensively on real and synthetic data. The evaluation results show that our data reduction method significantly reduces the data volume in computing, our search algorithms are efficient and scalable, and the top-$k$k popular semantic locations returned are in good accord with ground truth.

Comparative assessment of the competence of experts in the field of higher education by different methods

The methods and means of assessing the competence of experts in the field of higher education are explored. An assessment of the competence of experts in the field of higher education using the methods for assessing the competence of experts, taking into account data uncertainty and on the basis of the analytical hierarchy process according to established criteria was carried out. The results are processed by specialized software “Competence DU 2.1” and “Competence of AHP 1.0”. Specialized software takes into account data uncertainty and applies the analytical hierarchy method. The ratio of the averages for the criteria used to assess the competence of experts in higher education was estimated. The comparative analysis of the obtained data was carried out. The results of the analysis showed the convergence and correlation of the obtained data, and also confirmed the suitability of methods for assessing the competence of experts in the field of higher education. The obtained results showed a small dispersion of average values for the criteria for assessing the competence of experts in the range from 3.7 to 6.6, which indicates a balance. The comparative analysis showed a high coincidence of the results of quantitative assessment of the competence of experts using specialized software, which is based on different methods of evaluation. The scatter of the values obtained by the applied methods indicates the presence of a correlation. The methods for assessing the competence of experts, taking into account data uncertainty and on the basis of the analytical hierarchy, are useful for comparative evaluation of the competence of experts in the field of higher education. This will allow rejecting insufficiently competent specialists, forming competent groups of experts, obtaining more reliable group expert assessments or ensuring the qualified work of agencies, specialized commissions and councils.

A New Method for Analyzing Three-phase Faults under Data Uncertainties

Fault analysis uses a given set of data regarding generations and line branches. The main objective is to calculate voltages and currents as a specific fault location is considered. As the input data are imprecise, this paper incorporates the interval mathematics into fault analysis problem in order to take into account data uncertainties. Therefore, this paper proposes a new method for modeling an interval three-phase fault analysis. At each iteration, the resulting interval linear system of equations is solved using Krawczyk method. The proposed method is implemented in the Matlab environment using the Intlab toolbox. The 14-bus test system is used. Results are later compared with the Monte Carlo Simulation.

A new method for analyzing unsymmetrical faults under data uncertainties

Fault analysis uses a given set of data regarding generation plants and line branches. The main objective is to calculate voltages and currents considering the type and the location associated with a specific fault. As the input data are imprecise, this paper incorporates the interval mathematics into fault analysis problem in order to take into account data uncertainties. Therefore, this paper proposes a new method for modeling an interval analysis for unsymmetrical faults. Single line-to-ground and line-to-line faults are investigated. The corresponding fault analysis equations are written in terms of voltage expressed in rectangular coordinates, and the iterative solving process for the resulting linear system of interval equations uses Krawczyk method. The proposed method is implemented in Matlab environment using Intlab toolbox. Results yielded by the proposed method are compared with those ones yielded by the Monte Carlo Simulation. The 14-bus and 1768-bus test systems are used.

Comparison of Damping Mechanisms for Transverse Waves in Solar Coronal Loops

Abstract We present a method to assess the plausibility of alternative mechanisms to explain the damping of magnetohydrodynamic transverse waves in solar coronal loops. The considered mechanisms are resonant absorption of kink waves in the Alfvén continuum, phase mixing of Alfvén waves, and wave leakage. Our methods make use of Bayesian inference and model comparison techniques. We first infer the values for the physical parameters that control the wave damping, under the assumption of a particular mechanism, for typically observed damping timescales. Then, the computation of marginal likelihoods and Bayes factors enable us to quantify the relative plausibility between the alternative mechanisms. We find that, in general, the evidence is not large enough to support a single particular damping mechanism as the most plausible one. Resonant absorption and wave leakage offer the most probable explanations in strong damping regimes, while phase mixing is the best candidate for weak/moderate damping. When applied to a selection of 89 observed transverse loop oscillations, with their corresponding measurements of damping timescales and taking into account data uncertainties, we find that positive evidence for a given damping mechanism is only available in a few cases.

Using a Software Tool in Forecasting: a Case Study of Sales Forecasting Taking into Account Data Uncertainty

Abstract Forecasting is one of the logistics activities and a sales forecast is the starting point for the elaboration of business plans. Forecast accuracy affects the business outcomes and ultimately may significantly affect the economic stability of the company. The accuracy of the prediction depends on the suitability of the use of forecasting methods, experience, quality of input data, time period and other factors. The input data are usually not deterministic but they are often of random nature. They are affected by uncertainties of the market environment, and many other factors. Taking into account the input data uncertainty, the forecast error can by reduced. This article deals with the use of the software tool for incorporating data uncertainty into forecasting. Proposals are presented of a forecasting approach and simulation of the impact of uncertain input parameters to the target forecasted value by this case study model. The statistical analysis and risk analysis of the forecast results is carried out including sensitivity analysis and variables impact analysis.

신뢰값 기반 대용량 트리플 처리를 위한 스파크 환경에서의 RDFS 온톨로지 추론

Recently, due to the development of the Internet and electronic devices, there has been an enormous increase in the amount of available knowledge and information. As this growth has proceeded, studies on large-scale ontological reasoning have been actively carried out. In general, a machine learning program or knowledge engineer measures and provides a degree of confidence for each triple in a large ontology. Yet, the collected ontology data contains specific uncertainty and reasoning such data can cause vagueness in reasoning results. In order to solve the uncertainty issue, we propose an RDFS reasoning approach that utilizes confidence values indicating degrees of uncertainty in the collected data. Unlike conventional reasoning approaches that have not taken into account data uncertainty, by using the in-memory based cluster computing framework Spark, our approach computes confidence values in the data inferred through RDFS-based reasoning by applying methods for uncertainty estimating. As a result, the computed confidence values represent the uncertainty in the inferred data. To evaluate our approach, ontology reasoning was carried out over the LUBM standard benchmark data set with addition arbitrary confidence values to ontology triples. Experimental results indicated that the proposed system is capable of running over the largest data set LUBM3000 in 1179 seconds inferring 350K triples.

Mapping a new future: using spatial multiple criteria analysis to identify novel habitats for assisted colonization of endangered species

AbstractMany species with restricted ranges or poor dispersal ability will occupy suboptimal habitats under climate change. The practice of translocating populations to more suitable areas outside the known historical range (assisted colonization) has been advanced as a solution to reduce the risk of extinction in the wild. Due to the high‐risk and bureaucratic complexities of assisted colonization, it is imperative that a systematic process is used to select release sites that have a reduced likelihood of translocation failure. Here we demonstrate how a spatially explicit, three‐stage multiple criteria analysis (MCA) can be used to identify potentially suitable sites for assisted colonization of an endangered species. We employ this method as an initial screening process, prior to final selection of sites for assisted colonization of the critically endangered western swamp tortoise (Pseudemydura umbrina). This species occurs naturally in two small reserves in southwestern Australia, and is currently threatened by a shift to a drier climate and consequent changes to hydrological regimes. A literature review, characteristics of remnant P. umbrina habitat, and expert knowledge were used to create a composite index of habitat suitability, mapped across the entire south‐west bioregion of Australia. The most suitable sites were 150 to 250 km south of the known range of P. umbrina, in areas of high landscape connectivity and low human population density. A subset of sites were examined in further detail and ranked using weighted summation. Careful use of MCA, taking into account data uncertainties and differences in expert opinion, can be a valuable tool when evaluating novel habitats for threatened species.

A Probabilistic Framework for Peptide and Protein Quantification from Data-Dependent and Data-Independent LC-MS Proteomics Experiments

A probability-based quantification framework is presented for the calculation of relative peptide and protein abundance in label-free and label-dependent LC-MS proteomics data. The results are accompanied by credible intervals and regulation probabilities. The algorithm takes into account data uncertainties via Poisson statistics modified by a noise contribution that is determined automatically during an initial normalization stage. Protein quantification relies on assignments of component peptides to the acquired data. These assignments are generally of variable reliability and may not be present across all of the experiments comprising an analysis. It is also possible for a peptide to be identified to more than one protein in a given mixture. For these reasons the algorithm accepts a prior probability of peptide assignment for each intensity measurement. The model is constructed in such a way that outliers of any type can be automatically reweighted. Two discrete normalization methods can be employed. The first method is based on a user-defined subset of peptides, while the second method relies on the presence of a dominant background of endogenous peptides for which the concentration is assumed to be unaffected. Normalization is performed using the same computational and statistical procedures employed by the main quantification algorithm. The performance of the algorithm will be illustrated on example data sets, and its utility demonstrated for typical proteomics applications. The quantification algorithm supports relative protein quantification based on precursor and product ion intensities acquired by means of data-dependent methods, originating from all common isotopically-labeled approaches, as well as label-free ion intensity-based data-independent methods.

Consistent dating for Antarctic and Greenland ice cores

We are hereby presenting a new dating method based on inverse techniques, which aims at calculating consistent gas and ice chronologies for several ice cores. The proposed method yields new dating scenarios simultaneously for several cores by making a compromise between the chronological information brought by glaciological modeling (i.e., ice flow model, firn densification model, accumulation rate model), and by gas and ice stratigraphic constraints. This method enables us to gather widespread chronological information and to use regional or global markers (i.e., methane, volcanic sulfate, Beryllium-10, tephra layers, etc.) to link the core chronologies stratigraphically. Confidence intervals of the new dating scenarios can be calculated thanks to the probabilistic formulation of the new method, which takes into account both modeling and data uncertainties. We apply this method simultaneously to one Greenland (NGRIP) and three Antarctic (EPICA Dome C, EPICA Dronning Maud Land, and Vostok) ices cores, and refine existent chronologies. Our results show that consistent ice and gas chronologies can be derived for depth intervals that are well-constrained by relevant glaciological data. In particular, we propose new and consistent dating of the last deglaciation for Greenland and Antarctic ice and gas records.

Physiological-Model-Constrained Noninvasive Reconstruction of Volumetric Myocardial Transmembrane Potentials

Personalized noninvasive imaging of subject-specific cardiac electrical activity can guide and improve preventive diagnosis and treatment of cardiac arrhythmia. Compared to body surface potential (BSP) recordings and electrophysiological information reconstructed on heart surfaces, volumetric myocardial transmembrane potential (TMP) dynamics is of greater clinical importance in exhibiting arrhythmic details and arrythmogenic substrates inside the myocardium. This paper presents a physiological-model-constrained statistical framework to reconstruct volumetric TMP dynamics inside the 3-D myocardium from noninvasive BSP recordings. General knowledge of volumetric TMP activity is incorporated through the modeling of cardiac electrophysiological system, and is used to constrain TMP reconstruction. This physiological system is reformulated into a stochastic state-space representation to take into account model and data uncertainties, and nonlinear data assimilation is developed to estimate volumetric myocardial TMP dynamics from personal BSP data. Robustness of the presented framework to practical model and data errors is evaluated. Comparison of epicardial potential reconstructions with classical regularization-based approaches is performed on computational phantom regarding right bundle branch blocks. Further, phantom experiments on intramural focal activities and an initial real-data study on postmyocardial infarction demonstrate the potential of the framework in reconstructing local arrhythmic details and identifying arrhythmogenic substrates inside the myocardium.

Non-uniqueness in surface-wave inversion and consequences on seismic site response analyses

Surface-wave tests are based on the solution of an inverse problem for shear-wave velocity profile identification from the experimentally measured dispersion curve. The main criticisms for these testing methodologies are related to the inverse problem solution and arise from the possible equivalence of different shear-wave velocity profiles. In this paper, some implications of solution non-uniqueness for seismic response studies are investigated using both numerical simulations and experimental data. A Monte Carlo approach for the inversion problem has been used to obtain a set of equivalent shear-wave velocity models. This selection is based on a statistical test which takes into account both data uncertainty and model parameterization. This set of solutions (i.e., soil profiles) is then used to evaluate the seismic response with a conventional one-dimensional analysis. It is shown that equivalent profiles with respect to surface-wave testing are equivalent also with respect to site amplification, thus countering the criticism related to inversion uncertainty for the engineering use of surface-wave tests.

Robust updating of uncertain damping models in structural dynamics for low- and medium-frequency ranges

This paper deals with the robust updating of uncertain computational models in the context of structural dynamics in the low- and medium-frequency ranges of composite sandwich panels for which experimental results are available. The uncertain computational model is constructed using the non-parametric probabilistic approach which takes into account model and data uncertainties. The formulation of the robust updating problem includes the effects of uncertainties and consists in minimizing a cost function with respect to an admissible set of updating parameters. Updating is performed in two steps using several cost functions and experimental results. The results of the robust updating problem show that the method proposed is efficient for updating the uncertain computational model in both low- and medium-frequency ranges.

Robust Design Optimization in Computational Mechanics

The motivation of this paper is to propose a methodology for analyzing the robust design optimization problem of complex dynamical systems excited by deterministic loads but taking into account model uncertainties and data uncertainties with an adapted nonparametric probabilistic approach, whereas only data uncertainties are generally considered in the literature by using a parametric probabilistic approach. The possible designs are represented by a numerical finite element model whose design parameters are deterministic and belong to an admissible set. The optimization problem is formulated for the stochastic system as the minimization of a cost function associated with the random response of the stochastic system including the variability of the stochastic system induced by uncertainties and the bias corresponding to the distance of the mean random response to a given target. The gradient and the Hessian of the cost function with respect to the design parameters are explicitly calculated. The complete theory and a numerical application are presented.

Design Optimization With an Uncertain Vibroacoustic Model

This paper deals with the design optimization problem of a structural-acoustic system in the presence of uncertainties. The uncertain vibroacoustic numerical model is constructed by using a recent nonparametric probabilistic model, which takes into account model uncertainties and data uncertainties. The formulation of the design optimization problem includes the effect of uncertainties and consists in minimizing a cost function with respect to an admissible set of design parameters. The numerical application consists in designing an uncertain master structure in order to minimize the acoustic pressure in a coupled internal cavity, which is assumed to be deterministic and excited by an acoustic source. The results of the design optimization problem, solved with and without the uncertain numerical model, show significant differences.

Determination of the Risk Function for Survivors Using Beta Pert Method

Determination of the Risk Function for Survivors Using Beta Pert Method In emergency at sea the most important is to start the rescue action as fast as possible, whereas the decision to terminate the action, in case the survivors have not been found, is made by the coordinator on the basis of his practical experience. To estimate the level of risk it is necessary to face the problem of lack of credible statistical data. The paper presents a new concept which allow to calculate the risk, taking into account data uncertainty. The method and probability model are presented and discussed.

Integration of data uncertainty in linear regression and process optimization

AbstractData uncertainties provide important information that should be taken into account along with the actual data. In fact, with the development of measurement instrumentation methods and metrology, one is very often able to rigorously specify the uncertainty associated with each measured value. The use of this piece of information, together with raw measurements, should—in principle—lead to more sound ways of performing data analysis, empirical modeling, and subsequent decision making. In this paper, we address the issues of using data uncertainty in the task of model estimation and, when it is already available, we show how the integration of measurement and actuation uncertainty can be achieved in the context of process optimization. Within the scope of the first task (model estimation), we make reference to several methods designed to take into account data uncertainties in linear multivariate regression (multivariate least squares, maximum likelihood principal component regression), and others whose potential to deal with noisy data is well known (partial least squares, principal component regression, and ridge regression), as well as modifications of previous methods that we developed, and compare their performance. MLPCR2 tends to achieve better predictive performance than all the other tested methods. The potential benefits of including measurement and actuation uncertainties in process optimization are also illustrated. © 2005 American Institute of Chemical Engineers AIChE J, 2005