Insufficient Input Data Research Articles

Novel bootstrap-based ellipsoidal convex model for non-probabilistic reliability-based design optimization with insufficient input data

Performing reliability-based design optimization with insufficient input data is a significant and challenging issue as accurately quantifying epistemic uncertainty can be difficult in such scenarios. Non-probabilistic models can marginally alleviate the need for a large sample size by constructing the boundaries of uncertainty parameters. However, most of the existing non-probabilistic models account for only the available or known samples. For cases with insufficient data, the compact uncertainty domain may result in considerably hazardous results, particularly for the problems that require high reliability. Therefore, a novel bootstrap-based ellipsoidal convex model (BECM) is proposed herein to account for both known and unknown data. Herein, the bootstrap method is introduced to non-probabilistic convex models for the first time to quantify the ellipsoidal shape uncertainty caused by insufficient input data. Further, the proposed BECM is also integrated into non-probabilistic reliability-based design optimization. The entire framework of uncertainty quantification and reliability optimization design for insufficient input data is established. Marginally conservative results can finally be achieved in a rational and objective manner by partially sacrificing the ellipsoid volume. Comprehensive comparisons and detailed discussions on aspects, including the problem complexity, initial sample size, and distribution type, are also conducted. The numerical results show that the BECM displays the best performance in terms of accuracy and robustness. It can achieve an approximate balance between being conservative and taking risks. The significant advantages are also illustrated by an engineering example of linear buckling analysis of stiffened cylindrical shells.

EVALUATION OF ACCURACY OF DIGITAL TERRAIN MODELING METHODS BASED ON SPATIAL INTERPOLATION (Part 2)

Digital terrain model (DTM) is an integral part of many geodetic works. It allows accurately reproducing the earth's surface in a digital form with varying levels of detail. One of the most significant problems in creating a DTM is the accuracy of reflecting the relief. In the first part of the work, an analysis of methods that can more detailed reflect the relief was already conducted. For these purposes, the Surfer geoinformation package was selected, which offers a considerable number of methods based on the spatial interpolation algorithm. Although these methods have represented the same area with high detail, there is still a need to assess the accuracy of each method. The purpose of this article is to assess the accuracy of methods for constructing a digital terrain model based on spatial interpolation. Since model accuracy is a crucial factor for many geodetic works, it is necessary to determine which methods are the most accurate and meet the requirements of different purposes of use. The accuracy assessment of methods will improve the efficiency of geodetic works and avoid errors and additional costs. In the Surfer program, after constructing the DTM with each method, a report can be obtained containing the input values of points and the values obtained after interpolation with each method. This report is very useful for assessing the accuracy of each method since it allows understanding how well the model reflects the real relief. To assess the accuracy of each method, control points of the spatial variable Z were taken, and the values in these points were interpolated for each method. This assessment makes it possible to obtain a numerical characteristic of accuracy for each method and compare them with each other. Based on the analysis conducted, it can be concluded that problematic areas were identified on a fragment of a topographic map, where there were insufficient input data for accurate interpolation. The result is not accurate and could be better with input data obtained from geodetic instruments and technologies such as GPS, lidar, etc. The "Natural Neighbor" method performed the best in reproducing the digital elevation model on this hilly terrain. The application of the "Kriging," "Triangulation with Linear Interpolation," "Modified Shepard's Method," and "Radial Basic Function" methods provided fairly accurate interpolation. However, the "Minimum Curvature" method was found to be ineffective in hilly terrain as it did not provide accurate interpolation. After conducting an accuracy assessment, it can be concluded that high-quality input data is an extremely important element in achieving more accurate results when creating a digital elevation model. Keywords: digital elevation model, construction methods, spatial interpolation, geographic information system, Surfer.

Reliability measure approach considering mixture uncertainties under insufficient input data

Reliability measure approach considering mixture uncertainties under insufficient input data

Dual-Sensor Signals Based Exact Gaussian Process-Assisted Hybrid Feature Extraction and Weighted Feature Fusion for Respiratory Rate and Uncertainty Estimations.

Accurately estimating respiratory rate (RR) has become essential for patients and the elderly. Hence, we propose a novel method that uses exact Gaussian process regression (EGPR)-assisted hybrid feature extraction and feature fusion based on photoplethysmography and electrocardiogram signals to improve the reliability of accurate RR and uncertainty estimations. First, we obtain the power spectral features and use the multi-phase feature model to compensate for insufficient input data. Then, we combine four different feature sets and choose features with high weights using a robust neighbor component analysis. The proposed EGPR algorithm provides a confidence interval representing the uncertainty. Therefore, the proposed EGPR algorithm, including hybrid feature extraction and weighted feature fusion, is an excellent model with improved reliability for accurate RR estimation. Furthermore, the proposed EGPR methodology is likely the only one currently available that provides highly stable variation and confidence intervals. The proposed EGPR-MF, 0.993 breath per minute (bpm), and EGPR-feature fusion, 1.064 (bpm), show the lowest mean absolute error compared to the other models.

Modelling supply chain disruption analytics under insufficient data: A decision support system based on Bayesian hierarchical approach

Recent disruptions in global and local supply chains (SCs) due to manmade and natural disasters have drawn a lot of attention to academics and practitioners. Such disruptions are often characterized by a lack of insufficient data. To tackle such a data scarce supply chain ecosystem, this article examines the potential disruption risks in SCs under insufficient input data. For this, a decision support system (DSS) based on the Bayesian hierarchical approach and value at risk (VaR) reduction analysis is proposed to assess a supplier's disruption risk events probability as well as the supplier revenue impact on a company of interest. Empirical data are used to examine the DSS. The findings show that the DSS is effective in generating the suppliers’ risk profiles. The proposed DSS can be utilized by supply chain managers and practitioners to manage SC disruption risks in a more efficient manner.

A sequential single-loop reliability optimization and confidence analysis method

In practical engineering problems, it is frequently challenging to collect sufficient data to construct high-precision probabilistic models. In this case, probabilistic models typically contain cognitive uncertainty and may be biased. Confidence-based design optimization (CBDO) is an effective tool for solving optimization problems with insufficient input data. Therein, epistemic uncertainty is taken into account, and its propagation is quantified by the confidence level of reliability. However, the coupling of reliability analysis and confidence analysis (CA) results in low calculation accuracy and an unacceptable rise in computational cost. To improve the performance of CBDO, a sequential single-loop reliability optimization and confidence analysis method (SROCA) is proposed in this paper. Specifically, a decoupling strategy with a serial of cycles of complete single-loop method (CSLM) and CA is established. In each cycle, CSLM and CA are decoupled from each other. Therein, the CSLM integrates both the reliability and confidence constraints into the deterministic optimization loop, and the performance functions are not required to be evaluated in CA due to the application of a semi-analytic sensitivity method. Additionally, update strategies for both the reliability and confidence indexes are employed to neutralize the linear approximation errors based on the dimension reduction method. Finally, four mathematical and one engineering example are adopted to demonstrate the performance of the proposed SROCA. The results show that the efficiency and accuracy of implementing CBDO achieve significant improvement by the proposed method.

Gait recognition using a few gait frames.

Gait has been deemed as an alternative biometric in video-based surveillance applications, since it can be used to recognize individuals from a far distance without their interaction and cooperation. Recently, many gait recognition methods have been proposed, aiming at reducing the influence caused by exterior factors. However, most of these methods are developed based on sufficient input gait frames, and their recognition performance will sharply decrease if the frame number drops. In the real-world scenario, it is impossible to always obtain a sufficient number of gait frames for each subject due to many reasons, e.g., occlusion and illumination. Therefore, it is necessary to improve the gait recognition performance when the available gait frames are limited. This paper starts with three different strategies, aiming at producing more input frames and eliminating the generalization error cause by insufficient input data. Meanwhile, a two-branch network is also proposed in this paper to formulate robust gait representations from the original and new generated input gait frames. According to our experiments, under the limited gait frames being used, it was verified that the proposed method can achieve a reliable performance for gait recognition.

БАГАТОМІРНИЙ АНАЛІЗ ЧИННИКІВ МОДУЛЯ ЗАГАЛЬНОГО ГІДРОЕНЕРГЕТИЧНОГО ПОТЕНЦІАЛУ РІЧОК БАСЕЙНУ ПРИП’ЯТІ В МЕЖАХ УКРАЇНИ

The importance of assessing hydropower resources in recent years determines the study of the quantitative characteristics of river hydropower. Interesting and at the same time important for understanding the assessment of hydropower potential and its module is a multivariate analysis of the determining factors that determine their magnitude. This approach makes it possible to establish the impact and assess the possible relationship between natural and anthropogenic indicators on its formation. It is especially important to establish the factors that determine the magnitude of the modulus of the hydropower potential, an indicator by which it is possible to reflect the total hydropower of rivers in a spatial context. Since the magnitude of the hydropower potential and its modulus is influenced by numerous factors that may be weakly interdependent, it was decided to apply multivariate analysis to establish the most significant indicators using factor analysis. Studies have shown that hydropower, runoff indicators, catchment area, indicators of erosional activity of the catchment, and indicators of the river’s slope exert the greatest influence on the magnitude of the modules of the hydropower potential. The indicators of plowing, forest cover, and regulation indirectly affect the magnitude of the modulus of the total hydropower potential. In the course of the study, the dependences of the hydropower potential on the catchment area and the average annual water discharge, the module of the hydropower potential and the indicator of the depth of the erosional incision of the rivers were built. The connections are characterized by good degrees of correlation and can be used to calculate the magnitude of hydropower in rivers for which there are no or insufficient input data. In a conclusion, the use of factor analysis made it possible to establish a relationship between all 15 factors, according to 26 hydrological stations, and to identify the main determining factors influencing the formation and spatial distribution of the total hydropower potential module for the Pripyat basin rivers within Ukraine. According to the results of the calculation by the method of factor analysis, five main groups of factors with the corresponding factor load. The first two groups of factors accounted for more than 80% of the total variance of the distribution.

Is Support Vector Regression method suitable for predicting rate of penetration?

Drilling operations constitute the major part of the exploration costs. Drill bits are the primary part required to be changed frequently due to its quick wearing nature. In order to reduce the drilling cost, rate of bit penetration (ROP) must be estimated or optimized by using several drilling parameters. One of the most commonly used methods for estimating the optimum penetration rate is Bourgoyne and Young Method (BYM) that is eight-parameter model. However, mathematically speaking, minimum of thirty different sets of data zones, which must be taken from uniform-lithology sections (particularly shales) are needed to get accurate results. To construct the functional relationship with the data, a multiple regression analysis is performed. When there is not enough data, the accuracy of BYM decreases.Support Vector Regression (SVR), one of machine learning methods, is commonly used to increase the prediction accuracy when insufficient data is available. In this study, two kinds of regression techniques are used to predict ROP: multiple linear regression (BYM) and SVR. For the calculations, two different field datasets obtained from the literature are used. Each dataset is trained and tested individually and completely with respect to all data points, using different scenarios. The results of different predicting methods for each dataset are compared in a statistical point of view to demonstrate the effectiveness of the SVR method subjected to insufficient input data. The results show the significant effect of data selection on the accuracy of ROP prediction.SVR is used for ROP prediction for the first time by fully implementing in BYM. This study paves the way for predicting ROP when the data is insufficient.

Reliability optimization design for composite laminated plate considering multiple types of uncertain parameters

The available amount of input data for uncertain parameters of composite laminated plates can vary, and the uncertain parameters can be decomposed into strong statistical variables, sparse variables and interval variables. Therefore, a new reliability optimization design methodology for composite laminated plate considering these uncertainty types simultaneously is proposed. The uncertainty types of elastic material parameters are identified, and the corresponding distribution parameters are calculated based on the Akaike information criterion. The reliability indices considering three uncertainty types are calculated based on linear approximation models of first-ply failure functions. A two-level reliability optimization algorithm is proposed to calculate the optimum stacking sequence for composite laminated plate, which can satisfy the lightweight requirement and reliability constraints. Comparisons with the optimization results of the Monte Carlo simulation method in two examples of composite laminated plates demonstrate the effectiveness of the proposed algorithm in conditions with multiple uncertain parameters due to insufficient input data.

Принятие решений на основе вывода по прецедентам в моделировании месторождений нефти и газа

This article describes a case-based reasoning (CBR) method as a decision-making tool in modeling oil and gas fields, its existing and potential application in the industry. The main direction for the application of the CBR method is the search for analogous objects for the design of oil and gas field development. The current engineering practice involves a vaguely formalized method of analogies, which does not allow defining the object as much as possible authentically the analogue that does not cause errors. The analogue object serves not only as a source of ready-made near optimal design solutions, but also as additional information about the object of development and key decisions in modeling hydrocarbon fields.<br> This paper considers the CBR method as the main tool for finding analogue objects, the main methods of extracting precedents from the database, and gives an idea of the object of development as a precedent. Proceeding from the peculiarities of presenting the object of development as a precedent and the peculiarities of applying the methods of extracting precedents, the authors have developed the concept of searching for analogue objects. In its implementation, it will allow for a different degree of information content of precedents stored in the database and will accelerate the procedure of extracting precedents from the database. The principal novelty is that the presented conceptual scheme allows using the methods of extracting precedents in the conditions of insufficient input data, which is important for the design of oil and gas fields.

Reliability measure approach for confidence-based design optimization under insufficient input data

In most of the reliability-based design optimization (RBDO) researches, accurate input statistical model has been assumed to concentrate on the variability of random variables; however, only a limited number of data are available to quantify the input statistical model in many practical engineering applications. In other words, irreducible variability and uncertainty due to lack of knowledge exist simultaneously in random design variables, which may result in uncertainty of reliability. Therefore, the uncertainty induced by insufficient data has to be accounted for RBDO to guarantee the confidence of reliability. Using the Bayesian approach, the uncertainty of input distributions is successfully propagated to a cumulative distribution function (CDF) of reliability under reasonable assumptions, but it requires a number of function evaluations in double-loop Monte Carlo simulation (MCS). To tackle this challenge, the reliability measure approach (RMA) in confidence-based design optimization (CBDO) is proposed to handle the uncertainty of reliability following the idea of performance measure approach (PMA) in RBDO. Input distribution parameters are transformed to random variables following the standard normal distribution for the most probable point (MPP) search based on the proposed stochastic sensitivity analysis of reliability. Therefore, the reliability is approximated at MPP with respect to input distribution parameters. The proposed CBDO can treat confidence constraints employing the reliability value at the target confidence level that is approximated by MPP in standard normal space. In conclusion, CBDO can be performed in a probabilistic space of input distribution parameters corresponding to the conventional U-space in RBDO to yield the probability (confidence) that reliability is larger than the target reliability. The proposed method can significantly reduce the number of function evaluations by eliminating outer-loop MCS while maintaining acceptable accuracy. Numerical examples are used to demonstrate the effectiveness of the developed sensitivity analysis and RMA to estimate the confidence of reliability in CBDO.

Uncertainty analysis of composite laminated plate with data-driven polynomial chaos expansion method under insufficient input data of uncertain parameters

The uncertainty information related to uncertain structural, material and geometric parameters is included in the available input uncertainty data, and there are multiple uncertainty types when only insufficient input data is acquired from experimental or numerical analyses. In this study, an uncertainty analysis method for composite laminated plate is proposed using a data-driven polynomial chaos expansion (PCE) method under insufficient input data related to uncertain design parameters. An identification criterion for uncertain types of design parameters is constructed based on the Akaike information criterion and observation significance level method, and the uncertain design parameters are subsequently divided into strong statistical variables, sparse variables, and interval variables. A data-driven PCE model of composite laminated plate is constructed by simultaneously considering the three uncertainty types, in which the polynomial coefficients are calculated based on the statistical moment of both strong statistical variables and sparse variables, and the PCE coefficients are quadratic polynomial functions of interval variables. The uncertainty analysis problems of the natural frequency of 5-layer and 10-layer composite laminated plates are presented to verify the effectiveness of the proposed algorithm.

Estimation of the thickness of anthropogenic deposits in historical urban centres: An interdisciplinary methodology applied to Rome (Italy)

In historical urban centres, the superimposition of excavation and deposition activities over time has resulted in an irregular spatial distribution of anthropogenic deposits, which may reach considerable thicknesses. The detection of those thicknesses requires extensive investigations. Broad borehole and geophysical campaigns cost time and money, consequently at the urban-planning level, it is usual to shift to an estimation of thicknesses, which may be performed through map-algebra operations, that is, by subtracting from the modelled ground surface the elevation of the anthropogenic-deposit basal surface. The latter is implemented through the interpolation of point elevation data, which are generally provided by borehole logs. Despite the development of advanced spatial interpolation methodologies, previous modelling results in the literature show that if the process is affected by insufficient input data, it produces imprecise interpolation outputs. This paper reports an interdisciplinary methodology aiming at enhancing elevation datasets, in order to obtain more accurate digital elevation models. The increase in number and spatial distribution of input points is achieved through past-landscape analyses mainly based on elevation data given by borehole logs, available archaeological reports and historical topographic maps, these being generally available for historical urban centres. The methodology was tested in an urban sector of Rome, where significant activities have been performed for millennia particularly during the Roman Age. A reliable model of the basal surface of the anthrostrata led to a better estimation of the spatial distribution of such deposits and, in addition, revealed the original topographic surface, as modified by human activities.

Computation of modern anthropogenic-deposit thicknesses in urban areas: A case study in Rome, Italy

In comparison with naturally stratified deposits, anthropogenic strata often have large variability in physical and chemical characteristics, which can be determined only through field surveys and laboratory tests. The estimation of their spatial distribution, based on the modelling of their basal surface, may output imprecise results, since the interpolation process is often affected by insufficient input data, which are provided by available borehole-log data. This paper reports the results of a multitemporal-analysis methodology, which is supported by historical maps that are generally available for urban centres. The methodology allows the spatial characterization of major portions of anthropogenic deposits, which were mainly produced after the Industrial Revolution, worldwide. The first tests seemed to be satisfactory, although the selected area, which has been greatly urbanized since the end of the 19th century, is part of a city in which humans have made important modifications since ancient times.

Unified uncertainty representation and quantification based on insufficient input data

Uncertainty quantification accuracy of system performance has an important influence on the results of reliability-based design optimization (RBDO). A new uncertain identification and quantification methodology is proposed considering the strong statistical variables, sparse variables, and interval variables simultaneously. Maximum likelihood function and Akaike information criterion (AIC) methods are used to identify the best-fitted distribution types and distribution parameters of sparse variables. The interval variables are represented with evidence theory. Finally, a unified uncertainty quantification framework considering the three types of uncertain design variables is put forward, and then the failure probability of system performance is quantified with belief and plausibility measures. The Kriging metamodel and random sampling method are used to reduce the computational complexity. Three examples are illustrated to verify the effectiveness of the proposed methodology.

Conservative reliability-based design optimization method with insufficient input data

Reliability analysis and reliability-based design optimization (RBDO) require an exact input probabilistic model to obtain accurate probability of failure (PoF) and RBDO optimum design. However, often only limited input data is available to generate the input probabilistic model in practical engineering problems. The insufficient input data induces uncertainty in the input probabilistic model, and this uncertainty forces the PoF to be uncertain. Therefore, it is necessary to consider the PoF to follow a probability distribution. In this paper, the probability of the PoF is obtained with consecutive conditional probabilities of input distribution types and parameters using the Bayesian approach. The approximate conditional probabilities are obtained under reasonable assumptions, and Monte Carlo simulation is applied to calculate the probability of the PoF. The probability of the PoF at a user-specified target PoF is defined as the conservativeness level of the PoF. The conservativeness level, in addition to the target PoF, will be used as a probabilistic constraint in an RBDO process to obtain a conservative optimum design, for limited input data. Thus, the design sensitivity of the conservativeness level is derived to support an efficient optimization process. Using numerical examples, it is demonstrated that the conservativeness level should be involved in RBDO when input data is limited. The accuracy and efficiency of the proposed design sensitivity method is verified. Finally, conservative RBDO optimum designs are obtained using the developed methods for limited input data problems.

Extracting molecular potentials from incomplete spectroscopic information

We extend our recently developed inversion method to extract excited-state potentials from fluorescence line positions and line strengths. We consider a previous limitation of the method arising due to insufficient input data in cases where the relatively weaker emission data are not experimentally available. We develop a solution to this problem by ‘regenerating’ these weak transition lines via applying a model potential, e.g. a Morse potential. The result of this procedure, illustrated for the Q-branch emission from the lowest three vibrational levels of the B(1Π) state of LiRb, is shown to have an error of 0.29 cm−1 in the classically allowed region and a global error of 5.67 cm−1 for V ≤ E(ν′ = 10). The robustness of this procedure is also demonstrated by considering the statistical error in the measured line intensities.

Assessment of future water availability under the changing climate: case study of Klang River Basin, Malaysia

ABSTRACTThis study aims to assess future water deficit and availability in Klang valley in the context of climate change. A surface water budget model for Klang river basin was developed using MIKE BASIN. The parameters for the Nedbør‐Afrstrømnings (NAM) model were determined and examined by trial-and-error approach and evaluated based on correlation coefficient (R), Nash–Sutcliffe efficiency (RNS), and overall water balance error (WBer). The climate change scenario assessment used downscaled projected future rainfall data from 18 Global Circulation Models for the period 2046–2065. The assessment of water availability was carried out for Klang Gates Dam. The calibration and validation results for NAM model were satisfactory. The reservoir model verification result showed that the observed and simulated dam level were slightly dissimilar, which may be due to insufficient input data, particularly rainfall data as rainfall stations were very sparse upstream of the dam. The water deficit analysis for 2046–2065 showed that there will be water shortage in most of the months. The results of this assessment will help decision-makers evaluate the water availability and existing water infrastructure capacity in the vicinity of Klang river basin, particularly with respect to future raw and treated water yield.

Simulation of streamflow and sediment with the soil and water assessment tool in a data scarce catchment in the three gorges region, china.

The Three Gorges Region in China is currently subject to a large-scale land use change, which was induced by the construction of the Three Gorges Dam on the Yangtze River. The relocation of towns, villages, and agricultural areas is expected to affect the water balance and increase erosion rates and sediment yields in the affected catchments. Hydrologic and water quality models are frequently used to assess the impact of land use changes on water resources. In this study, the eco-hydrological Soil and Water Assessment Tool (SWAT) model is applied to the Xiangxi Catchment in the Three Gorges Region. This paper presents the calibration and validation of streamflow and sediment loads at Xingshan gauging station. The calibration of daily streamflow resulted in a satisfactory fit of simulated and observed data, which is indicated by Nash-Sutcliffe efficiency (NSE) values of 0.69 and 0.67 for the calibration (1981-1986) and validation (1988-1993) periods, respectively. In contrast, the model was not able to simulate the monthly average sediment loads correctly, as indicated by very low NSE values of 0.47 (calibration) and 0.08 (validation). This might be due to inadequate representation of spatial rainfall variability by the available climate stations, insufficient input data, uncertainties in the model structure, or uncertainties in the observed sediment loads. The discussion of these possible reasons for the incorrect prediction of sediment loads by SWAT reveals the need for further research in the field of hydrological and water quality modeling in China.