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Gotowa bibliografia na temat „Uncertainty Analysis”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 7 lipca 2024

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Artykuły w czasopismach na temat "Uncertainty Analysis"

Griffin, R. L. "Uncertain about uncertainty in pest risk analysis". Acta Horticulturae, nr 1105 (grudzień 2015): 315–20. http://dx.doi.org/10.17660/actahortic.2015.1105.45.

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Lerche, I., i F. Rocha-Legoretta. "Risking Basin Analysis Results". Energy Exploration & Exploitation 21, nr 2 (kwiecień 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 i Keqin Li. "Uncertainty Theory Based Partitioning for Cyber-Physical Systems with Uncertain Reliability Analysis". ACM Transactions on Design Automation of Electronic Systems 27, nr 3 (31.05.2022): 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, nr 6 (31.12.2020): 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, nr 10 (październik 2002): 3698–700. http://dx.doi.org/10.1063/1.1505654.

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Jiang, Chunlan, Zhengwei Liu i Jinsong Wu. "Noncommutative uncertainty principles". Journal of Functional Analysis 270, nr 1 (styczeń 2016): 264–311. http://dx.doi.org/10.1016/j.jfa.2015.08.007.

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Khosravi, Faramarz, Malte Müller, Michael Glaß i 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, nr 6 (19.03.2018): 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, nr 1 (marzec 2017): 171–97. http://dx.doi.org/10.1086/692519.

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Pan, Qiuyu, i Zuqiang Meng. "Hybrid Uncertainty Calibration for Multimodal Sentiment Analysis". Electronics 13, nr 3 (5.02.2024): 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 i Meilin Wen. "Uncertainty Theory-Based Structural Reliability Analysis and Design Optimization under Epistemic Uncertainty". Applied Sciences 12, nr 6 (10.03.2022): 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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Rozprawy doktorskie na temat "Uncertainty Analysis"

Gomolka, Beth. "Service Offering Uncertainty Analysis Tool". Thesis, Linköping University, Linköping University, Department of Management and Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-19945.

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Companies that seek to venture into providing services in addition to providing products have many business issues to consider as there are many differences between providing service and product offerings. One factor that needs to be considered in service offerings is the aspect of time, as services are offered for an extended period of time, creating a unique type of relationship between the customer and the service provider. With product offerings, the point of sale is usually the end of the product provider and customer relationship. The added time aspect in the service offering brings with it the issues of uncertainty as service contracts are made for a certain period of time in the future, where things are unknown.

This thesis looked at types of uncertainties important to service offerings, especially in the manufacturing industry. The uncertainties have an impact on how service offering contracts are constructed, as they can affect the profit and costs of the service provider. The three types of uncertainties that were examined were product malfunction uncertainty, service delivery uncertainty, and customer requirement uncertainty. Using these three types of uncertainty, mathematical models were constructed to represent the cost and revenue of different contract types. The different contract types were identified through a case study with a product manufacturer in Sweden. Different probability distributions were selected to model the three types of uncertainty based on a literature review. The mathematical models were then used to construct a software program, the uncertainty simulator tool, which service contract designers can use to model how uncertainties affect cost and revenue in their contracts.

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Zomlot, Loai M. M. "Handling uncertainty in intrusion analysis". Diss., Kansas State University, 2014. http://hdl.handle.net/2097/17603.

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Doctor of Philosophy
Department of Computing and Information Sciences
Xinming Ou
Intrusion analysis, i.e., the process of combing through Intrusion Detection System (IDS) alerts and audit logs to identify true successful and attempted attacks, remains a difficult problem in practical network security defense. The primary cause of this problem is the high false positive rate in IDS system sensors used to detect malicious activity. This high false positive rate is attributed to an inability to differentiate nearly certain attacks from those that are merely possible. This inefficacy has created high uncertainty in intrusion analysis and consequently causing an overwhelming amount of work for security analysts. As a solution, practitioners typically resort to a specific IDS-rules set that precisely captures specific attacks. However, this results in failure to discern other forms of the targeted attack because an attack’s polymorphism reflects human intelligence. Alternatively, the addition of generic rules so that an activity with remote indication of an attack will trigger an alert, requires the security analyst to discern true alerts from a multitude of false alerts, thus perpetuating the original problem. The perpetuity of this trade-off issue is a dilemma that has puzzled the cyber-security community for years. A solution to this dilemma includes reducing uncertainty in intrusion analysis by making IDS-nearly-certain alerts prominently discernible. Therefore, I propose alerts prioritization, which can be attained by integrating multiple methods. I use IDS alerts correlation by building attack scenarios in a ground-up manner. In addition, I use Dempster-Shafer Theory (DST), a non-traditional theory to quantify uncertainty, and I propose a new method for fusing non-independent alerts in an attack scenario. Finally, I propose usage of semi-supervised learning to capture an organization’s contextual knowledge, consequently improving prioritization. Evaluation of these approaches was conducted using multiple datasets. Evaluation results strongly indicate that the ranking provided by the approaches gives good prioritization of IDS alerts based on their likelihood of indicating true attacks.

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Urganci, Ilksen. "Positional Uncertainty Analysis Using Data Uncertainy Engine A Case Study On Agricultural Land Parcels". Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12611409/index.pdf.

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Most of spatial data extraction and updating procedures require digitization of geographical entities from satellite imagery. During digitization, errors are introduced by factors like instrument deficiencies or user errors. In this study positional uncertainty of geographical objects, digitized from high resolution Quickbird satellite imagery, is assessed using Data Uncertainty Engine (DUE). It is a software tool for assessing uncertainties in environmental data
and generating realisations of uncertain data for use in uncertainty propagation analyses. A case study area in Kocaeli, Turkey that mostly includes agricultural land parcels is selected in order to evaluate positional uncertainty and obtain uncertainty boundaries for manually digitized fields. Geostatistical evaluation of discrepancy between reference data and digitized polygons are undertaken to analyse auto and cross correlation structures of errors. This process is utilized in order to estimate error model parameters which are employed in defining an uncertainty model within DUE. Error model parameters obtained from training data, are used to generate simulations for test data. Realisations of data derived via Monte Carlo Simulation using DUE, are evaluated to generate uncertainty boundaries for each object guiding user for further analyses with pre-defined information related to the accuracy of spatial entities. It is also aimed to assess area uncertainties affected by the position of spatial entities. For all different correlation structures and object models, weighted average positional error for this study is between 2.66 to 2.91 meters. At the end of uncertainty analysis, deformable object model produced the smallest uncertainty bandwidth by modelling cross correlation.

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Filipsson, Monika. "Uncertainty, variability and environmental risk analysis". Doctoral thesis, Linnéuniversitetet, Institutionen för naturvetenskap, NV, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-11193.

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The negative effects of hazardous substances and possible measures that can be taken are evaluated in the environmental risk analysis process, consisting of risk assessment, risk communication and risk management. Uncertainty due to lack of knowledge and natural variability are always present in this process. The aim of this thesis is to evaluate some tools as well as discuss the management of uncertainty and variability, as it is necessary to treat them both in a reliable and transparent way to gain regulatory acceptance in decision making. The catalytic effects of various metals on the formation of chlorinated aromatic compounds during the heating of fly ash were investigated (paper I). Copper showed a positive catalytic effect, while cobalt, chromium and vanadium showed a catalytic effect for degradation. Knowledge of the catalytic effects may facilitate the choice and design of combustion processes to decrease emissions, but it also provides valuable information to identify and characterize the hazard. Exposure factors of importance in risk assessment (physiological parameters, time use factors and food consumption) were collected and evaluated (paper II). Interindividual variability was characterized by mean, standard deviation, skewness, kurtosis and multiple percentiles, while uncertainty in these parameters was estimated with confidence intervals. How these statistical parameters can be applied was shown in two exposure assessments (papers III and IV). Probability bounds analysis was used as a probabilistic approach, which enables separate propagation of uncertainty and variability even in cases where the availability of data is limited. In paper III it was determined that the exposure cannot be expected to cause any negative health effects for recreational users of a public bathing place. Paper IV concluded that the uncertainty interval in the estimated exposure increased when accounting for possible changes in climate-sensitive model variables. Risk managers often need to rely on precaution and an increased uncertainty may therefore have implications for risk management decisions. Paper V focuses on risk management and a questionnaire was sent to employees at all Swedish County Administrative Boards working with contaminated land. It was concluded that the gender, age and work experience of the employees, as well as the funding source of the risk assessment, all have an impact on the reviewing of risk assessments. Gender was the most significant factor, and it also affected the perception of knowledge.
Negativa effekter orsakade av skadliga ämnen och möjliga åtgärder bedöms och utvärderas i en miljöriskanalys, som kan delas i riskbedömning, riskkommunikation och riskhantering. Osäkerhet som beror på kunskapsbrist samt naturlig variabilitet finns alltid närvarande i denna process. Syftet med avhandlingen är att utvärdera några tillvägagångssätt samt diskutera hur osäkerhet och variabilitet hanteras då det är nödvändigt att båda hanteras trovärdigt och transparent för att riskbedömningen ska vara användbar för beslutsfattande. Metallers katalytiska effekt på bildning av klorerade aromatiska ämnen under upphettning av flygaska undersöktes (artikel I). Koppar visade en positiv katalytisk effekt medan kobolt, krom och vanadin istället katalyserade nedbrytningen. Kunskap om katalytisk potential för bildning av skadliga ämnen är viktigt vid val och design av förbränningsprocesser för att minska utsläppen, men det är också ett exempel på hur en fara kan identifieras och karaktäriseras. Information om exponeringsfaktorer som är viktiga i riskbedömning (fysiologiska parametrar, tidsanvändning och livsmedelskonsumtion) samlades in och analyserades (artikel II). Interindividuell variabilitet karaktäriserades av medel, standardavvikelse, skevhet, kurtosis (toppighet) och multipla percentiler medan osäkerhet i dessa parametrar skattades med konfidensintervall. Hur dessa statistiska parametrar kan tillämpas i exponeringsbedömningar visas i artikel III och IV. Probability bounds analysis användes som probabilistisk metod, vilket gör det möjligt att separera osäkerhet och variabilitet i bedömningen även när tillgången på data är begränsad. Exponeringsbedömningen i artikel III visade att vid nu rådande föroreningshalter i sediment i en badsjö så medför inte bad någon hälsofara. I artikel IV visades att osäkerhetsintervallet i den skattade exponeringen ökar när hänsyn tas till förändringar i klimatkänsliga modellvariabler. Riskhanterare måste ta hänsyn till försiktighetsprincipen och en ökad osäkerhet kan därmed få konsekvenser för riskhanteringsbesluten. Artikel V fokuserar på riskhantering och en enkät skickades till alla anställda som arbetar med förorenad mark på länsstyrelserna i Sverige. Det konstaterades att anställdas kön, ålder och erfarenhet har en inverkan på granskningsprocessen av riskbedömningar. Kön var den mest signifikanta variabeln, vilken också påverkade perceptionen av kunskap. Skillnader i de anställdas svar kunde också ses beroende på om riskbedömningen finansierades av statliga bidrag eller av en ansvarig verksamhetsutövare.

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Söderman, Filip. "Uncertainty Analysis of the Aerodynamic Coefficients". Thesis, KTH, Flygdynamik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-223317.

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This thesis treats an error propagation analysis used to estimate the uncertainty of the aerodynamic coefficients. The propagation methods used in this analysis are a Taylor Series Method and a Monte Carlo Method. The Taylor Series Method uses the partial derivatives of each input variable whereas the Monte Carlo Method uses random and repeated samples from the probability density function of each variable. By comparing the results obtained by the different methods, the results can be validated. Coverage intervals with a coverage probability of 95% are calculated along with the percentage contribution each input variable has on the expanded uncertainty. The results showed that the uncertainty of the coefficients varied between 10% and 20% and negligible differences between the methods were observed. More accurate measurements of the dynamic pressure and the position of the center of gravity are needed in order to decrease the uncertainty.

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Johnson, David G. "Representations of uncertainty in risk analysis". Thesis, Loughborough University, 1998. https://dspace.lboro.ac.uk/2134/31941.

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Uncertainty in situations involving risk is frequently modelled by assuming a plausible form of probability distribution for the uncertain quantities involved, and estimating the relevant parameters of that distribution based on the knowledge and judgement of informed experts or decision makers. The distributions assumed are usually uni-modal (and often bell-shaped) around some most likely value, with the Normal, Beta, Gamma and Triangular distributions being popular choices.

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Walker, A. M. "Uncertainty Analysis of Zone Fire Models". University of Canterbury. Civil Engineering, 1997. http://hdl.handle.net/10092/8298.

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Zone fire models are used by practising engineers every day in New Zealand, yet the models have limitations, and the uncertainty of these models has not been well documented. Comparisons with experimental data are simply comparison and do not analyse the uncertainty of the models, nor are they validation of the models. The object of this research has been to discuss the uncertainties in components of zone models and show how uncertainty within user supplied data affects the results obtained. The zone fire model selected for analysis is the second version of CFAST. A numerical uncertainty analysis is performed, utilising sensitivity factors as the basis of the analysis. In the analysis, no assumptions are made as to the independency of the input variables. A large amount of information is appended, with a discussion of pertinent results. Several input variables were identified to resulted in discernible uncertainty in the output. Consisting of the heat release rate, radiative fraction, ambient temperature, ambient pressure, and ceiling height.

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Gallagher, Raymond. "Uncertainty modelling in quantitative risk analysis". Thesis, University of Liverpool, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367676.

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Cui, W. C. "Uncertainty analysis in structural safety assessment". Thesis, University of Bristol, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303742.

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Ghate, Devendra. "Inexpensive uncertainty analysis for CFD applications". Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:6be44a1d-6e2f-4bf9-b1e5-1468f92e21e3.

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The work presented in this thesis aims to provide various tools to be used during design process to make maximum use of the increasing availability of accurate engine blade measurement data for high fidelity fluid mechanic simulations at a reasonable computational expense. A new method for uncertainty propagation for geometric error has been proposed for fluid mechanics codes using adjoint error correction. Inexpensive Monte Carlo (IMC) method targets small uncertainties and provides complete probability distribution for the objective function at a significantly reduced computational cost. A brief literature survey of the existing methods is followed by the formulation of IMC. An example algebraic model is used to demonstrate the IMC method. The IMC method is extended to fluid mechanic applications using Principal Component Analysis (PCA) for reduced order modelling. Implementation details for the IMC method are discussed using an example airfoil code. Finally, the IMC method has been implemented and validated for an industrial fluid mechanic code HYDRA. A consistent methodology has been developed for the automatic generation of the linear and adjoint codes by selective use of automatic differentiation (AD) technique. The method has the advantage of keeping the linear and the adjoint codes in-sync with the changes in the underlying nonlinear fluid mechanic solver. The use of various consistency checks have been demonstrated to ease the development and maintenance process of the linear and the adjoint codes. The use of AD has been extended for the calculation of the complete Hessian using forward-on-forward approach. The complete mathematical formulation for Hessian calculation using the linear and the adjoint solutions has been outlined for fluid mechanic solvers. An efficient implementation for the Hessian calculation is demonstrated using the airfoil code. A new application of the Independent Component Analysis (ICA) is proposed for manufacturing uncertainty source identification. The mathematical formulation is outlined followed by an example application of ICA for artificially generated uncertainty for the NACA0012 airfoil.

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Książki na temat "Uncertainty Analysis"

1940-, Ronen Yigal, red. Uncertainty analysis. Boca Raton, Fla: CRC Press, 1988.

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Marivoet, J. Uncertainty analysis techniques. Luxembourg: Commission of the European Communities, 1987.

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Tung, Yeou-Koung. Hydrosystems engineering uncertainty analysis. New York: McGraw-Hill, 2005.

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S, Kushwaha H., i Bhabha Atomic Research Centre. Health, Safety & Environment Group., red. Uncertainty modeling and analysis. Mumbai: Health, Safety & Environment Group, Bhabha Atomic Research Centre, 2009.

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Sensitivity and uncertainty analysis. Boca Raton, Fla: Chapman & Hall/CRC, 2003.

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De Bièvre, Paul, i Helmut Günzler, red. Measurement Uncertainty in Chemical Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05173-3.

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F, Dunn Patrick, red. Uncertainty analysis for forensic science. Wyd. 2. Tucson, Ariz: Lawyers & Judges Pub., 2009.

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name, No. Measurement uncertainty in chemical analysis. Berlin: Springer, 2003.

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Paul, De Bièvre, i Günzler Helmut, red. Measurement uncertainty in chemical analysis. Berlin: Springer, 2003.

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Pointe, P. R. La, i Y. Zee Ma. Uncertainty analysis and reservoir modeling. Tulsa, OK: American Association of Petroleum Geologists, 2011.

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Części książek na temat "Uncertainty Analysis"

Borgonovo, Emanuele. "Uncertainty Quantification". W Sensitivity Analysis, 117–27. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52259-3_13.

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Yoe, Charles. "Uncertainty". W Principles of Risk Analysis, 27–46. Second edition. | Boca Raton : Taylor and Francis, CRC Press, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429021121-2.

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Yoe, Charles. "Uncertainty". W Primer on Risk Analysis, 29–53. Second edition. | Boca Raton : Taylor & Francis, CRC Press, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429021145-2.

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Ma, Y. Z. "Uncertainty Analysis". W Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling, 593–621. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17860-4_24.

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Mauskopf, Josephine, i Stephanie Earnshaw. "Uncertainty Analysis". W Budget-Impact Analysis of Health Care Interventions, 129–38. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50482-7_8.

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Rose, Adam, Fynnwin Prager, Zhenhua Chen, Samrat Chatterjee, Dan Wei, Nathaniel Heatwole i Eric Warren. "Uncertainty Analysis". W Integrated Disaster Risk Management, 87–97. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-2567-9_7.

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Curry, Steve, i John Weiss. "Uncertainty". W Project Analysis in Developing Countries, 187–99. London: Palgrave Macmillan UK, 1993. http://dx.doi.org/10.1057/9780230378506_8.

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Curry, Steve, i John Weiss. "Uncertainty". W Project Analysis in Developing Countries, 225–37. London: Palgrave Macmillan UK, 2000. http://dx.doi.org/10.1057/9780230375116_9.

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Liu, Baoding. "Uncertain Risk Analysis". W Uncertainty Theory, 115–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13959-8_3.

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Liu, Baoding. "Uncertain Reliability Analysis". W Uncertainty Theory, 125–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13959-8_4.

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Streszczenia konferencji na temat "Uncertainty Analysis"

Ye, Ruiqi, Mingxue Liao, Tianyu Cui i Pin Lv. "The simulation of open one-side uncertain probability for fusion model of data uncertainty and data relation uncertainty". W 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA). IEEE, 2018. http://dx.doi.org/10.1109/icbda.2018.8367658.

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Szekely, Pedro, Yu-Han Chang, Rajiv Maheswaran, Yan Wang, Huihui Cheng i Karan Singh. "Interactive uncertainty analysis". W the 2012 ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2166966.2167015.

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Shushkevich, Tatyana V. "Uncertainty Analysis Tools". W 2018 XIV International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering (APEIE). IEEE, 2018. http://dx.doi.org/10.1109/apeie.2018.8545801.

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Seibel, Arthur, i Josef Schlattmann. "Buckling Analysis under Uncertainty". W Second International Conference on Vulnerability and Risk Analysis and Management (ICVRAM) and the Sixth International Symposium on Uncertainty, Modeling, and Analysis (ISUMA). Reston, VA: American Society of Civil Engineers, 2014. http://dx.doi.org/10.1061/9780784413609.215.

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Chen, Wei, Ruichen Jin i Agus Sudjianto. "Analytical Uncertainty Propagation via Metamodels in Simulation-Based Design Under Uncertainty". W 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.2004-4356.

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Wilcox, R. C., i B. M. Ayyub. "Uncertainty modeling of data and uncertainty propagation for risk studies". W Fourth International Symposium on Uncertainty Modeling and Analysis. ISUMA 2003. IEEE, 2003. http://dx.doi.org/10.1109/isuma.2003.1236160.

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Song, Gyun, i Man Kim. "Uncertainty Quantification for Passive Safety System and Treatment of Model Uncertainty". W 18th International Probabilistic Safety Assessment and Analysis (PSA 2023). Illinois: American Nuclear Society, 2023. http://dx.doi.org/10.13182/psa23-41009.

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Negreiros, J., M. Painho, A. Cristina Costa, P. Cabral i F. Aguilar. "The local confidence uncertainty plume of SAKWeb©". W RISK ANALYSIS 2008. Southampton, UK: WIT Press, 2008. http://dx.doi.org/10.2495/risk080091.

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Fabbri, A. G., i C. J. Chung. "On spatial uncertainty in hazard and risk assessment". W RISK ANALYSIS 2014. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/risk140011.

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Bu, T., i S. I. Aanonsen. "Surfactant flooding uncertainty analysis". W IOR 1991 - 6th European Symposium on Improved Oil Recovery. European Association of Geoscientists & Engineers, 1991. http://dx.doi.org/10.3997/2214-4609.201411207.

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Raporty organizacyjne na temat "Uncertainty Analysis"

Worley, B. A. Deterministic uncertainty analysis. Office of Scientific and Technical Information (OSTI), grudzień 1987. http://dx.doi.org/10.2172/5534706.

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Halderman, C., i M. Dunn. ATARR Uncertainty Analysis. Fort Belvoir, VA: Defense Technical Information Center, marzec 1991. http://dx.doi.org/10.21236/ada315475.

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Coutts, D. A. Uncertainty and calibration analysis. Office of Scientific and Technical Information (OSTI), marzec 1991. http://dx.doi.org/10.2172/10188883.

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McCurley, R. Analysis of Infiltration Uncertainty. Office of Scientific and Technical Information (OSTI), październik 2003. http://dx.doi.org/10.2172/836530.

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Smith, F., i M. Phifer. ENHANCED UNCERTAINTY ANALYSIS FOR SRS COMPOSITE ANALYSIS. Office of Scientific and Technical Information (OSTI), czerwiec 2011. http://dx.doi.org/10.2172/1023276.

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Aaron J. Pawel i Dr. George L. Mesina. Uncertainty Analysis for RELAP5-3D. Office of Scientific and Technical Information (OSTI), sierpień 2011. http://dx.doi.org/10.2172/1042350.

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Alexandra E Gertman i Dr. George L Mesina. Uncertainty Analysis of RELAP5-3D. Office of Scientific and Technical Information (OSTI), lipiec 2012. http://dx.doi.org/10.2172/1056002.

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Makaruk, Hanna. Uncertainty in Experimental Data Analysis. Office of Scientific and Technical Information (OSTI), grudzień 2020. http://dx.doi.org/10.2172/1734696.

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Leyva, Nha. Uncertainty Analysis and Software Verification. Office of Scientific and Technical Information (OSTI), lipiec 2021. http://dx.doi.org/10.2172/1813900.

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Cardoni, Jeffrey N., i Donald A. Kalinich. Fukushima Daiichi unit 1 uncertainty analysis--Preliminary selection of uncertain parameters and analysis methodology. Office of Scientific and Technical Information (OSTI), luty 2014. http://dx.doi.org/10.2172/1204089.

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