Journal articles: 'Uncertainty Analysis'

1

Griffin, R. L. "Uncertain about uncertainty in pest risk analysis." Acta Horticulturae, no. 1105 (December 2015): 315–20. http://dx.doi.org/10.17660/actahortic.2015.1105.45.

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Lerche, I., and F. Rocha-Legoretta. "Risking Basin Analysis Results." Energy Exploration & Exploitation 21, no. 2 (April 2003): 81–164. http://dx.doi.org/10.1260/014459803322362459.

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The work presented here uses a basin analysis code, developed for Excel, to handle burial history, fluid flow, fracturing, overpressure development with time, erosion events, kerogen breakdown to oil and gas, hydrocarbon volumetrics for both oil and gas including source retention, migration loss, and area changes with time of source rocks for each formation. The code is remarkably fast, requiring about 0.2 seconds on a laptop to perform all the above calculations for ten formations as well as producing pictorial representations of all variables with space and time. The code seamlessly interfaces with the Monte Carlo risking program Crystal Ball so that a total uncertainty analysis can be done with as many uncertain inputs as required and as many outputs of interest as needed without increasing the computer time needed. A thousand Crystal Ball runs take only about 200 seconds, allowing one to investigate many possible scenarios extremely quickly. We show here with four basic examples how one goes about identifying which parameters in the input (ranging from uncertain data, uncertain thermal history, uncertain permeability, uncertain fracture coefficients for rocks, uncertain geochemistry kinetics, uncertain kerogen amounts and types per formation, through to uncertain volumetric factors) are causing the greatest contributions to uncertainty in any and all outputs. The relative importance, relative contributions and relative sensitivity are examined to show when it is necessary to know more about the underlying distributions of uncertain parameters, when it is necessary to know more about the dynamic range of a parameter to narrow its contribution to the total uncertainty, and which parameters are necessary to first focus on to narrow their uncertainty in order to improve the dynamical, thermal or hydrocarbon outputs. An interface of such a coupled pair of very fast Excel codes with an Excel economics package can also now easily be undertaken so that one ties scientific uncertainty and economic uncertainty together for hydrocarbon exploration and identifies the global parameters dominantly influencing the combined economic/basin analysis system.

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Chen, Si, Guoqi Xie, Renfa Li, and Keqin Li. "Uncertainty Theory Based Partitioning for Cyber-Physical Systems with Uncertain Reliability Analysis." ACM Transactions on Design Automation of Electronic Systems 27, no. 3 (May 31, 2022): 1–19. http://dx.doi.org/10.1145/3490177.

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Reasonable partitioning is a critical issue for cyber-physical system (CPS) design. Traditional CPS partitioning methods run in a determined context and depend on the parameter pre-estimations, but they ignore the uncertainty of parameters and hardly consider reliability. The state-of-the-art work proposed an uncertainty theory based CPS partitioning method, which includes parameter uncertainty and reliability analysis, but it only considers linear uncertainty distributions for variables and ignores the uncertainty of reliability. In this paper, we propose an uncertainty theory based CPS partitioning method with uncertain reliability analysis. We convert the uncertain objective and constraint into determined forms; such conversion methods can be applied to all forms of uncertain variables, not just for linear. By applying uncertain reliability analysis in the uncertainty model, we for the first time include the uncertainty of reliability into the CPS partitioning, where the reliability enhancement algorithm is proposed. We study the performance of the reliability obtained through uncertain reliability analysis, and experimental results show that the system reliability with uncertainty does not change significantly with the growth of task module numbers.

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Kim, Eung Seok. "Analysis of Runoff According to Application of SWMM-LID Element Technology (II): Parameter Uncertainty Analysis." Journal of the Korean Society of Hazard Mitigation 20, no. 6 (December 31, 2020): 445–50. http://dx.doi.org/10.9798/kosham.2020.20.6.445.

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This study quantitatively analyzed the degree of uncertainty associated with runoff based on the sensitivity analysis of runoff parameters using Low Impact Development (LID) element technology of study (I). Uncertainty was analyzed for parameter uncertainty, uncertainty of runoff, and uncertainty about the degree of parameter and runoff. Parameter uncertainty indices showed lower uncertainty indices as a whole and uncertainty indices of peak runoff were higher than that of total runoff in runoff uncertainty. The reason for this is that the LID element technology itself is intended to store low-frequency small-scale rainfall, so that the uncertainty index of peak rainfall seems to be highly uncertain. As a result of the analysis of uncertainty degree associated with runoff, it was found that the uncertainty of storage depth of bio retention cell and rain garden was low, while the heaviness parameters of rain barrel had the highest uncertainty index. In future experiments and research, it is necessary to modify the parameter range suitable for Korea, which will be helpful for urban development, reduction of nonpoint source pollution, and designing of low frequency rainfall storage facilities.

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Longtin, Jon P. "The uncertainty tree: Reducing the uncertainty of uncertainty analysis." Review of Scientific Instruments 73, no. 10 (October 2002): 3698–700. http://dx.doi.org/10.1063/1.1505654.

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Jiang, Chunlan, Zhengwei Liu, and Jinsong Wu. "Noncommutative uncertainty principles." Journal of Functional Analysis 270, no. 1 (January 2016): 264–311. http://dx.doi.org/10.1016/j.jfa.2015.08.007.

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Khosravi, Faramarz, Malte Müller, Michael Glaß, and Jürgen Teich. "Simulation-based uncertainty correlation modeling in reliability analysis." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 232, no. 6 (March 19, 2018): 725–37. http://dx.doi.org/10.1177/1748006x18758720.

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Due to destructive effects like temperature and radiation, today’s embedded systems have to deal with unreliable components. The intensity of these effects depends on uncertain aspects like environmental or usage conditions such that highly safety-critical systems are pessimistically designed for worst-case mission profiles. These uncertain aspects may affect several components simultaneously, implying correlation across uncertainties in their reliability. This paper enables a state-of-the-art uncertainty-aware reliability analysis technique to consider multiple arbitrary correlations; in other words, components’ reliability is affected by several uncertain aspects to different degrees. This analysis technique combines reliability models such as binary decision diagrams with a Monte Carlo simulation, and derives the uncertainty distribution of the system’s reliability with insights on the mean, quantile intervals, and so on. The proposed correlation method aims at generating correlated samples from the uncertainty distribution of components’ reliability such that the shape and statistical properties of each individual distribution remain unchanged. Experimental results confirm that the proposed correlation model enables the employed uncertainty-aware analysis to accurately calculate uncertainty at system level.

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Hansen, Lars Peter. "Uncertainty in Economic Analysis and the Economic Analysis of Uncertainty." KNOW: A Journal on the Formation of Knowledge 1, no. 1 (March 2017): 171–97. http://dx.doi.org/10.1086/692519.

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Pan, Qiuyu, and Zuqiang Meng. "Hybrid Uncertainty Calibration for Multimodal Sentiment Analysis." Electronics 13, no. 3 (February 5, 2024): 662. http://dx.doi.org/10.3390/electronics13030662.

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In open environments, multimodal sentiment analysis (MSA) often suffers from low-quality data and can be disrupted by noise, inherent defects, and outliers. In some cases, unreasonable multimodal fusion methods can perform worse than unimodal methods. Another challenge of MSA is effectively enabling the model to provide accurate prediction when it is confident and to indicate high uncertainty when its prediction is likely to be inaccurate. In this paper, we propose an uncertain-aware late fusion based on hybrid uncertainty calibration (ULF-HUC). Firstly, we conduct in-depth research on the issue of sentiment polarity distribution in MSA datasets, establishing a foundation for an uncertain-aware late fusion method, which facilitates organic fusion of modalities. Then, we propose a hybrid uncertainty calibration method based on evidential deep learning (EDL) that balances accuracy and uncertainty, supporting the reduction of uncertainty in each modality of the model. Finally, we add two common types of noise to validate the effectiveness of our proposed method. We evaluate our model on three publicly available MSA datasets (MVSA-Single, MVSA-Multiple, and MVSA-Single-Small). Our method outperforms state-of-the-art approaches in terms of accuracy, weighted F1 score, and expected uncertainty calibration error (UCE) metrics, proving the effectiveness of the proposed method.

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Zhou, Shuang, Jianguo Zhang, Qingyuan Zhang, Ying Huang, and Meilin Wen. "Uncertainty Theory-Based Structural Reliability Analysis and Design Optimization under Epistemic Uncertainty." Applied Sciences 12, no. 6 (March 10, 2022): 2846. http://dx.doi.org/10.3390/app12062846.

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Reliability analysis and trade-offs between safety and cost with insufficient data represent an inevitable problem during the early stage of structural design. In this paper, efficient uncertainty theory-based reliability analysis and a design method are proposed under epistemic uncertainty. The factors influencing the structure are regarded as uncertain variables. Based on this, a new metric termed uncertain measure is employed to define an uncertainty reliability indicator (URI) for estimating the reliable degree of structure. Two solving methods, namely, the crisp equivalent analytical method and uncertain simulation (US) method, are introduced to calculate the URI and acquire reliability. Thereafter, a URI-based design optimization (URBDO) model is constructed with target reliability constraints. To solve the URBDO model and obtain optimal solutions, crisp equivalent programming and a genetic-algorithm combined US approach are developed. Four physical examples are solved to verify the adaptability and advantage of the established model and corresponding solving techniques.

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Pollack-Johnson, Bruce, and Matthew J. Liberatore. "Project Planning under Uncertainty Using Scenario Analysis." Project Management Journal 36, no. 1 (March 2005): 15–26. http://dx.doi.org/10.1177/875697280503600103.

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An important component of risk management relates to project schedule uncertainty. To address this issue, a scenario (i.e., macro-level approach) for modeling and analyzing projects with significant uncertainty in their network structure and/or durations of some activities is presented. This approach requires that a set of project network scenarios is able to be identified, each with an assessed probability of occurrence. These scenarios might differ according to the results of uncertain events that could occur during the course of the project, uncertain activity durations (whether independent or dependent), finite loops where repeated activities can have different durations, or a combination of these. Advantages of our approach include the use of standard methods and software, as well as greater accessibility to, and likelihood of, the use of uncertainty analysis in project planning. Several examples are used to illustrate the suggested approach.

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12

Strichartz, Robert S. "Uncertainty principles in harmonic analysis." Journal of Functional Analysis 84, no. 1 (May 1989): 97–114. http://dx.doi.org/10.1016/0022-1236(89)90112-2.

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Tazid Ali, Tazid Ali, and Rubab F. Nomani. "Uncertainty modeling : A case study in cost-benefit analysis." International Journal of Scientific Research 2, no. 12 (June 1, 2012): 115–17. http://dx.doi.org/10.15373/22778179/dec2013/38.

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14

Song, Qi, and Xue Liang Hou. "Uncertainty Analysis of 220kV Substation Project." Applied Mechanics and Materials 584-586 (July 2014): 2689–94. http://dx.doi.org/10.4028/www.scientific.net/amm.584-586.2689.

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Project investment is often accompanied by the unpredictable risk. To reduce the impact of uncertain factors on the economic efficiency, it is necessary to measure the risk level after the project is put into operation. Combing relevant theories of uncertainty analysis with the case of a 220kV substation in Hebei, China, the thesis analyzed the break even and sensitivity to get scientific results. Based on the above results, rational suggestions on sensitivity measures were put forward to provide the reference to uncertainty analysis of the other similar projects.

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Biró, András, and Vsevolod F. Lev. "Uncertainty in finite planes." Journal of Functional Analysis 281, no. 3 (August 2021): 109026. http://dx.doi.org/10.1016/j.jfa.2021.109026.

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Maass, Peter, Chen Sagiv, Nir Sochen, and Hans-Georg Stark. "Do Uncertainty Minimizers Attain Minimal Uncertainty?" Journal of Fourier Analysis and Applications 16, no. 3 (October 20, 2009): 448–69. http://dx.doi.org/10.1007/s00041-009-9099-4.

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17

YUAN, Yahui. "New Method for Uncertainty Analysis in Multidisciplinary System—Collaborative Uncertainty Analysis." Journal of Mechanical Engineering 45, no. 07 (2009): 174. http://dx.doi.org/10.3901/jme.2009.07.174.

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Khan, Imran Ahemad, Kazi Syed Zakiuddin, and Punit Fulzele. "Harmonic Analysis of Simply Supported Square Plate for Uncertain Parameters." Journal of Physics: Conference Series 2251, no. 1 (April 1, 2022): 012012. http://dx.doi.org/10.1088/1742-6596/2251/1/012012.

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Abstract Many Mechanical structures are manufacture by using plates. However Harmonic Analysis of plate is growing research subject in today’s era. In the present research paper vibrational response of simply supported square plate with uncertain parameters are studied. In designing any real life mechanical system uncertainty phenomenon plays vital role. Thus it becomes important to study the effect of uncertainty for different frequency domain in mechanical system. In present research work square plate as structural element is selected. On this plate mass, stiffness and both collectively mass and stiffness uncertainty are taken. The plate boundary condition selected is simply supported. For determining the plate dynamic response FEM tool is used. While doing harmonic analysis the behavior of all uncertain parameters plates are compare with bare plate. In the FRF plot of plate with stiffness uncertainty, it is found that it has drastic changes in dynamic response. Thus this shows that uncertain parameters have adverse effects and it changes the dynamic behavior of the structure.

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19

G.V., Suresh, and Srinivasa Reddy E.V. "Uncertain Data Analysis with Regularized XGBoost." Webology 19, no. 1 (January 20, 2022): 3722–40. http://dx.doi.org/10.14704/web/v19i1/web19245.

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Uncertainty is a ubiquitous element in available knowledge about the real world. Data sampling error, obsolete sources, network latency, and transmission error are all factors that contribute to the uncertainty. These kinds of uncertainty have to be handled cautiously, or else the classification results could be unreliable or even erroneous. There are numerous methodologies developed to comprehend and control uncertainty in data. There are many faces for uncertainty i.e., inconsistency, imprecision, ambiguity, incompleteness, vagueness, unpredictability, noise, and unreliability. Missing information is inevitable in real-world data sets. While some conventional multiple imputation approaches are well studied and have shown empirical validity, they entail limitations in processing large datasets with complex data structures. In addition, these standard approaches tend to be computationally inefficient for medium and large datasets. In this paper, we propose a scalable multiple imputation frameworks based on XGBoost, bootstrapping and regularized method. XGBoost, one of the fastest implementations of gradient boosted trees, is able to automatically retain interactions and non-linear relations in a dataset while achieving high computational efficiency with the aid of bootstrapping and regularized methods. In the context of high-dimensional data, this methodology provides fewer biased estimates and reflects acceptable imputation variability than previous regression approaches. We validate our adaptive imputation approaches with standard methods on numerical and real data sets and shown promising results.

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20

Bommer, Julian J. "Uncertainty about the uncertainty in seismic hazard analysis." Engineering Geology 70, no. 1-2 (October 2003): 165–68. http://dx.doi.org/10.1016/s0013-7952(02)00278-8.

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Wang, Lei, Chuang Xiong, and Qinghe Shi. "An adaptive collocation method for structural fuzzy uncertainty analysis." Engineering Computations 37, no. 9 (April 24, 2020): 2983–98. http://dx.doi.org/10.1108/ec-10-2018-0464.

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Purpose Considering that uncertain factors widely exist in engineering practice, an adaptive collocation method (ACM) is developed for the structural fuzzy uncertainty analysis. Design/methodology/approach ACM arranges points in the axis of the membership adaptively. Through the adaptive collocation procedure, ACM can arrange more points in the axis of the membership where the membership function changes sharply and fewer points in the axis of the membership where the membership function changes slowly. At each point arranged in the axis of the membership, the level-cut strategy is used to obtain the cut-level interval of the uncertain variables; besides, the vertex method and the Chebyshev interval uncertainty analysis method are used to conduct the cut-level interval uncertainty analysis. Findings The proposed ACM has a high accuracy without too much additional computational efforts. Originality/value A novel ACM is developed for the structural fuzzy uncertainty analysis.

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Ibrahim, Ahmed Abdul, Hai Bo ZHOU, Chao Long ZHANG, and Ji An DUAN. "Analysis of the Footprint of Uncertainty of a Parallelogram Membership Function." International Journal of Artificial Intelligence & Mathematical Sciences 1, no. 1 (September 9, 2022): 1–14. http://dx.doi.org/10.58921/ijaims.v1i1.17.

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The general effectiveness of fuzzy representation depends heavily on the membership functions. The bestfootprint of uncertainty for a specific fuzzy system is highly dependent on the nature of the problem that the fuzzysystem is supposed to solve. As a result, a proper footprint of uncertainty size selection is needed to improve the fuzzysystem’s efficiency. This research intends to investigate the impact of a unique linear parallelogram membershipfunction’s footprint of uncertainty on nonlinear system modeling and control. The proposed type-2 membershipfunction has a crisp membership degree at the end points of the footprint of uncertainty and uncertain values inbetween. When dealing with data whose membership degree is certain at the boundary but uncertain in-between, theproposed membership function having its highest uncertainty at the midpoint of the membership function width isadvantageous. Tuning the parameters of the proposed MF will provide a variety of triangular and quadrilateralfootprints of uncertainty shapes that will better capture the training data’s uncertainties. The gradient descent learningalgorithm was used to tune the consequent parameters of the evaluated interval type-2 fuzzy system. The performanceresults demonstrated the effect of the footprint of uncertainty on linear parallelogram membership function-based fuzzysystem’s capability in prediction, identification, and control tasks.

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Wang, Zan, Shengwen Qi, and Bowen Zheng. "Uncertainty Analysis of the Storage Efficiency Factor for CO2 Saline Resource Estimation." Energies 17, no. 6 (March 8, 2024): 1297. http://dx.doi.org/10.3390/en17061297.

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Carbon capture and sequestration (CCS) is a promising technology for reducing CO2 emissions to the atmosphere. It is critical to estimate the CO2 storage resource before deploying the CCS projects. The CO2 storage resource is limited by both the formation pore volume available to store CO2 and the maximum allowable pressure buildup for safe injection. In this study, we present a workflow for estimating the volume- and pressure-limited storage efficiency factor and quantifying the uncertainty in the estimates. Thirteen independent uncertain physical parameters characterizing the storage formation are considered in the Monte Carlo uncertainty analysis. The uncertain inputs contributing most to the overall uncertainty in the storage efficiency factor are identified. The estimation and uncertainty quantification workflow is demonstrated using a publicly available dataset developed for a prospective CO2 storage site. The statistical distributions of the storage efficiency factor for the primary storage formation and the secondary storage formation located in deeper depth are derived using the proposed workflow. The effective-to-total porosity contributes most to the overall uncertainty in the estimated storage efficiency factor at the study site, followed by the maximum allowable pressure buildup, the net-to-gross thickness ratio, the irreducible water saturation, and the permeability. While the significant uncertain input variables identified are tailored to the characteristics of the study site, the statistical methodology proposed can be generalized and applied to other storage sites. The influential uncertain inputs identified from the workflow can provide guidance on future data collection needs for uncertainty reduction, improving the confidence in the CO2 saline storage resource estimates.

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Lawson, Tony. "Uncertainty and Economic Analysis." Economic Journal 95, no. 380 (December 1985): 909. http://dx.doi.org/10.2307/2233256.

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Possolo, Antonio. "Copulas for uncertainty analysis." Metrologia 47, no. 3 (April 20, 2010): 262–71. http://dx.doi.org/10.1088/0026-1394/47/3/017.

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Schmitz, Tony L., Jason E. Action, David L. Burris, John C. Ziegert, and W. Gregory Sawyer. "Wear-Rate Uncertainty Analysis." Journal of Tribology 126, no. 4 (October 1, 2004): 802–8. http://dx.doi.org/10.1115/1.1792675.

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Wear due to relative motion between component surfaces is one of the primary modes of failure for many engineered systems. Unfortunately, it is difficult to accurately predict component life due to wear as reported wear rates generally exhibit large scatter. This paper analyzes a reciprocating tribometer in an attempt to understand the instrument-related sources of the scatter in measured wear rates. To accomplish this, an uncertainty analysis is completed for wear-rate testing of a commercially available virgin polytetrafluoroethylene pin on 347 stainless steel counterface. It is found that, for the conditions selected in this study, the variance in the experimental data can be traced primarily to the experimental apparatus and procedure. Namely, the principal uncertainty sources were found to be associated with the sample mass measurement and volume determination.

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Marshall, John M. "Welfare analysis under uncertainty." Journal of Risk and Uncertainty 2, no. 4 (December 1989): 385–403. http://dx.doi.org/10.1007/bf00356863.

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Ronen, Y. "The inverse uncertainty analysis." Annals of Nuclear Energy 15, no. 10-11 (January 1988): 511–12. http://dx.doi.org/10.1016/0306-4549(88)90067-9.

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Paulter, N. G., and D. R. Larson. "Pulse parameter uncertainty analysis." Metrologia 39, no. 2 (April 2002): 143–55. http://dx.doi.org/10.1088/0026-1394/39/2/4.

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Steele, W. G., R. A. Ferguson, R. P. Taylor, and H. W. Coleman. "Computer-assisted uncertainty analysis." Computer Applications in Engineering Education 5, no. 3 (1997): 169–79. http://dx.doi.org/10.1002/(sici)1099-0542(1997)5:3<169::aid-cae4>3.0.co;2-b.

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Monte, Luigi, Lars Håkanson, Ulla Bergström, John Brittain, and Rudie Heling. "Uncertainty analysis and validation of environmental models: the empirically based uncertainty analysis." Ecological Modelling 91, no. 1-3 (November 1996): 139–52. http://dx.doi.org/10.1016/0304-3800(95)00185-9.

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Shakshuki, Elhadi, Kumaraswamy Ponnambalam, and Tassew Wodaj. "Risk analysis in environmental systems." Canadian Journal of Civil Engineering 29, no. 1 (February 1, 2002): 1–7. http://dx.doi.org/10.1139/l01-072.

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Uncertainty is an inherent feature of environmental systems, which makes probabilistic models important. Environmental risk assessment is an important but time consuming task. For large-scale systems, use of linear systems with uncertainty information on parameters and inputs is one of the few possible methods to assess risk. To estimate risk, it is necessary to have at least the first two moments of output variables. This paper describes an efficient method developed for second-moment analysis of linear systems with uncertain coefficients. The main objective is to provide the means and the variances of the output and to provide efficient formulation and automation of the moment equations. This method is demonstrated in two real-world applications of environmental modeling.Key words: uncertainty, second-moment methods, risk analysis, reliability, linear systems.

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Jaming, Philippe, and Alexander M. Powell. "Uncertainty principles for orthonormal sequences." Journal of Functional Analysis 243, no. 2 (February 2007): 611–30. http://dx.doi.org/10.1016/j.jfa.2006.09.001.

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Alexandre, R., Y. Morimoto, S. Ukai, C. J. Xu, and T. Yang. "Uncertainty principle and kinetic equations." Journal of Functional Analysis 255, no. 8 (October 2008): 2013–66. http://dx.doi.org/10.1016/j.jfa.2008.07.004.

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Ibrahim, Mohammad Nizam, Zainal Hisham Che Soh, Nor Shahanim Mohamad Hadis, and Ali Othman. "Uncertainty and sensitivity analysis applied to a voltage series operational amplifier." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 3 (March 10, 2021): 1347. http://dx.doi.org/10.11591/ijeecs.v21.i3.pp1347-1355.

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This paper investigates the applied uncertainty and sensitivity analysis to resistors used in voltage series operational amplifier circuit. Two resistors bands are considered which are the gold band (5% uncertainty) and the silver band (10% uncertainty). To generate resistors uncertainty sample points, the SIMLAB uncertainty and sensitivity tool is used. A total of sample points based on Sobol’ technique has been created for each resistor band. The voltage series amplifier is modelled in MATLAB/Simulink. A MATLAB script has been written to execute Monte-Carlo simulations for reading the resistor sample points, updating and executing the voltage series model and finally calculating the voltage gain. The result of uncertainty analysis shows that the produced voltage gain is uncertain within the range of for the gold band and for the silver band with respect to a reference voltage gain. The result of sensitivity analysis shows that each resistor, although their values are different, contributes equally contribution to the uncertainty of voltage gain.

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Peykani, Pejman, and Mir Saman Pishvaee. "Performance Evaluation of Hospitals under Data Uncertainty: An Uncertain Common-Weights Data Envelopment Analysis." Healthcare 12, no. 6 (March 7, 2024): 611. http://dx.doi.org/10.3390/healthcare12060611.

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In the context of healthcare systems, the performance evaluation of hospitals plays a crucial role in assessing the quality of healthcare systems and facilitating informed decision-making processes. However, the presence of data uncertainty poses significant challenges to accurate performance measurement. This paper presents a novel uncertain common-weights data envelopment analysis (UCWDEA) approach for evaluating the performance of hospitals under uncertain environments. The proposed UCWDEA approach addresses the limitations of traditional data envelopment analysis (DEA) models by incorporating the uncertainty theory (UT) to model the inherent uncertainty in input and output data. Also, by utilizing a common set of weights (CSW) technique, the UCWDEA method provides a more robust and reliable assessment of hospital performance. The main advantages of the proposed UCWDEA approach can be succinctly summarized as follows. Firstly, it allows for the comparison of all hospitals on a consistent basis to calculate a realistic efficiency score, rather than an overly optimistic efficiency score. Secondly, the uncertain common-weights DEA approach exhibits linearity, enhancing its applicability. Thirdly, it possesses the capability to extend its utility under various other prevalent uncertainty distributions. Moreover, it enhances the discriminatory power of results, facilitates the ranking of hospitals in the presence of data uncertainty, and aids in identifying the sensitivity and stability levels of hospitals towards data uncertainty. Notably, in order to showcase the pragmatic application and efficacy of the uncertain common-weights DEA model, a genuine dataset has been utilized to evaluate the efficiency of 20 public hospitals in Tehran, all of which are affiliated with the Iran University of Medical Sciences. The results of the experiment demonstrate the efficacy of the UCWDEA approach in assessing and ranking hospitals amidst uncertain conditions. In summary, the research outcomes can offer policymakers valuable insights regarding hospital performance amidst data uncertainty. Additionally, it can provide practical recommendations on optimizing resource allocation, benchmarking performance, and formulating effective policies to augment the overall efficiency and effectiveness of healthcare services.

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Wu, Hangfei, Baoguo Liu, Yanxu Liu, and Wei Feng. "Natural Characteristics Analysis of a Dual-Rotor System with Nonparametric Uncertainty." Applied Sciences 12, no. 24 (December 8, 2022): 12573. http://dx.doi.org/10.3390/app122412573.

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In order to evaluate the impacts of parameter uncertainty and nonparametric uncertainty on the natural characteristics of a dual-rotor system, a nonparametric probabilistic method based on random matrix theory is proposed. In this paper, a nonparametric Riccati whole transfer model is derived based on the maximum entropy principle and the random matrix theory. It is used to model a dual-rotor system with nonparametric uncertainty, as well as to calculate the natural characteristics of the system. Furthermore, the impacts of parameter uncertainty and nonparametric uncertainty on the natural characteristics at the intermediate support element and at the disk-shaft element are discussed using numerical simulations, and the results are compared with related references. The results show that at the same level of uncertainties, the effect of nonparametric uncertainty is often more significant than that of parameter uncertainty. The effects of uncertainties also increase with the level of uncertainties. The results of this paper provide a theoretical basis for the design of uncertain dual-rotor and multi-rotor systems.

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Moges, Edom, Yonas Demissie, Laurel Larsen, and Fuad Yassin. "Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis." Water 13, no. 1 (December 25, 2020): 28. http://dx.doi.org/10.3390/w13010028.

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Despite progresses in representing different processes, hydrological models remain uncertain. Their uncertainty stems from input and calibration data, model structure, and parameters. In characterizing these sources, their causes, interactions and different uncertainty analysis (UA) methods are reviewed. The commonly used UA methods are categorized into six broad classes: (i) Monte Carlo analysis, (ii) Bayesian statistics, (iii) multi-objective analysis, (iv) least-squares-based inverse modeling, (v) response-surface-based techniques, and (vi) multi-modeling analysis. For each source of uncertainty, the status-quo and applications of these methods are critiqued in gauged catchments where UA is common and in ungauged catchments where both UA and its review are lacking. Compared to parameter uncertainty, UA application for structural uncertainty is limited while input and calibration data uncertainties are mostly unaccounted. Further research is needed to improve the computational efficiency of UA, disentangle and propagate the different sources of uncertainty, improve UA applications to environmental changes and coupled human–natural-hydrologic systems, and ease UA’s applications for practitioners.

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Goodell, John W., Richard J. McGee, and Frank McGroarty. "Election uncertainty, economic policy uncertainty and financial market uncertainty: A prediction market analysis." Journal of Banking & Finance 110 (January 2020): 105684. http://dx.doi.org/10.1016/j.jbankfin.2019.105684.

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Xu, Ning, Jing Fang Guo, and Jin Fang Han. "Measurements and Mathematical Characterization of Uncertain Information." Applied Mechanics and Materials 530-531 (February 2014): 591–96. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.591.

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This paper, the problems of mathematical characteristics and measurements for three kind of uncertainty information are descussed by means of the logical analysis method. Firstly, By virtue of the analysis for the importance of uncertain information research in scientific development, a research chain: uncertainty informationinformation theorycomplexity is presented. Secondly, the mathematical characterization and measurements for three kind of uncertainty information are obtained in terms of the characteristic analysis for uncertain information .

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MENG, Debiao. "An Improvement Method for Uncertainty Analysis in Multidisciplinary System -Modified Collaborative Uncertainty Analysis." Journal of Mechanical Engineering 47, no. 19 (2011): 129. http://dx.doi.org/10.3901/jme.2011.19.129.

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Graf, Wolfgang, and Jan Uwe Sickert. "Time-Dependent Fuzzy Stochastic Reliability Analysis of Structures." Applied Mechanics and Materials 104 (September 2011): 45–54. http://dx.doi.org/10.4028/www.scientific.net/amm.104.45.

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The paper reviews the development of reliability assessment in structural analysis under consideration of the non-traditional uncertainty model fuzzy randomness. Starting froma discussion of sources of variability and imprecision, uncertainty models are introduced. On this basis, numerical approaches are displayed for uncertain structural analysis and reliability assessment. Thereby, variations in time are considered which results in a time-dependent reliability measure. Capacity and applicability of the approaches are demonstrated by means of an example.

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Shen, Y., and N. A. Duffie. "An Uncertainty Analysis Method for Coordinate Referencing in Manufacturing Systems." Journal of Engineering for Industry 117, no. 1 (February 1, 1995): 42–48. http://dx.doi.org/10.1115/1.2803276.

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Accurate and consistent transformations between design and manufacturing coordinate systems are essential for high quality part production. Fixturing and coordinate measurement are common coordinate referencing techniques which are used to locate points or measurement points on workpiece reference surfaces to establish these coordinate transformations. However, uncertainty sources such as geometric form deviations in workpiece surfaces, tolerances on fixture locators, and errors in coordinate measurements exist. A result is that coordinate transformations established using the locating and measurement points are in herently uncertain. An uncertainty analysis method for coordinate referencing is presented in this paper. The uncertainty interval concept is used to describe essential characteristics of uncertainty sources in coordinate referencing and coordinate transformation relationships. The method is applied to estimating uncertainties in simple and compound coordinate transformation obtained using coordinate referencing in an experimental mold manufacturing system. Results of Monte Carlo simulations are used to show that the uncertainty analysis method gives a consistent and high percentage of coverage in evaluating coordinate referencing in the examples studied.

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Wang, Juan, Changming Hu, Zuqiang Liu, and Li Li. "Study on Structural Reliability Analysis Method Based on Chance Theory." Buildings 13, no. 5 (May 9, 2023): 1245. http://dx.doi.org/10.3390/buildings13051245.

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Many factors influence structural reliability in practice engineering. Some factors can be measured to obtain lots of data, but others are difficult to acquire statistical data. In view of this situation, a new structural reliability analysis method is proposed using chance theory, which is composed of probability theory and uncertainty theory and can reflect random uncertainty and cognitive uncertainty simultaneously. The performance function of a structural mechanical element is defined, and when it is a random uncertain variable, the chance distribution is established. Then the calculated method of failure measures and reliability measures for the structural mechanical element is put forward. Furthermore, considering the series system and parallel system, the performance function of the structural system is proposed, and the calculated method of failure measure is determined by theoretical proof. The results can provide a new approach to analyzing structural reliability under the uncertain circumstance of lack of statistical data.

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Bansal, Ashish, and Ajay Kumar. "Heisenberg uncertainty inequality for Gabor transform." Journal of Mathematical Inequalities, no. 3 (2016): 737–49. http://dx.doi.org/10.7153/jmi-10-60.

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Dejeu, M. F. E. "An Uncertainty Principle for Integral Operators." Journal of Functional Analysis 122, no. 1 (May 1994): 247–53. http://dx.doi.org/10.1006/jfan.1994.1067.

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Pérez, Carlos, and Richard L. Wheeden. "Uncertainty Principle Estimates for Vector Fields." Journal of Functional Analysis 181, no. 1 (April 2001): 146–88. http://dx.doi.org/10.1006/jfan.2000.3711.

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Wang, Yishin, and Hwong-wen Ma. "Analysis of uncertainty in material flow analysis." Journal of Cleaner Production 170 (January 2018): 1017–28. http://dx.doi.org/10.1016/j.jclepro.2017.09.202.

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Prein, Andreas Franz, Andreas Gobiet, and Heimo Truhetz. "Analysis of uncertainty in large scale climate change projections over Europe." Meteorologische Zeitschrift 20, no. 4 (August 1, 2011): 383–95. http://dx.doi.org/10.1127/0941-2948/2011/0286.

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Awachat, Prashant, Vinayak Dakre, Pranav Charkha, and Siva Reddy Vundela. "Robust Analysis of Various Measurement Uncertainty Parameters in Rebound Hammer Test." Indian Journal Of Science And Technology 16, no. 32 (August 29, 2023): 2560–67. http://dx.doi.org/10.17485/ijst/v16i32.1689.

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Journal articles on the topic 'Uncertainty Analysis'

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