Input Uncertainty Research Articles

Malmquist productivity index for two-stage network systems under data uncertainty: A real-world case study.

The measurement of productivity change in decision-making units (DMUs) is crucial for assessing their performance and supporting efficient decision-making processes. In this paper, we propose a new approach for measuring productivity change using the Malmquist productivity index (MPI) within the context of two-stage network data envelopment analysis (TSNDEA) under data uncertainty. The two-stage network structure represents a realistic model for DMUs in various fields, such as insurance companies, bank branches, and mutual funds. However, traditional DEA models do not adequately address the issue of data uncertainty, which can significantly impact the accuracy of productivity measurements. To address this limitation, we integrate the MPI methodology with an uncertain programming framework to tackle data uncertainty in the productivity change measurement process. Our proposed approach enables the evaluation of productivity change by capturing both technical efficiency and technological progress over time. By incorporating fuzzy mathematical programming into the DEA framework, we model the inherent uncertainty in input and output data more effectively, enhancing the robustness and reliability of productivity measurements. The utilization of the proposed approach provides decision-makers with a comprehensive analysis of productivity change in DMUs, allowing for better identification of efficiency improvements or potential areas for enhancement. The findings from our study can enhance the decision-making process and facilitate more informed resource allocation strategies in real-world applications.

Output-Constrained Adaptive Composite Nonsingular Terminal Sliding Mode Attitude Control for a Class of Spacecraft Systems with Mismatched Disturbances and Input Uncertainties

Output-Constrained Adaptive Composite Nonsingular Terminal Sliding Mode Attitude Control for a Class of Spacecraft Systems with Mismatched Disturbances and Input Uncertainties

ODESMAN: Optimizing Decision-Making in Complex Environments: Integrating Neutrosophic and Fuzzy Logic for Advanced System Modeling

Within the domain of complex systems, inherent uncertainties, and ambiguities that traditional models frequently find difficult to handle pose a constant challenge to decision-making. To dramatically improve decision-making frameworks, this study presents a novel methodology called ODESMAN, which synergistically integrates fuzzy logic with neutrosophic sets. Neutrosophic sets, on the other hand, allow one to express the degrees of truth, untruth, and indeterminacy as shifts rather than fixed points. Therefore, their use is more elegant than the existing methods offered. The implementation of fuzzy logic into such sets may provide a high level of effectiveness in managing uncertainty, which can be predicted and quantified. For example, the model allows accounting for uncertainty in the system inputs and processes up to 20%, the variability of truth values 10-50%, and the overall uncertainty 15-30%. The application of the model in practice, specifically in the emergency response, and the supply chain system permitted achieving a 40% increase in flexibility capacity and a 25% improvement in decision-making approaches compared to the traditional frameworks. Therefore, the practical strength and broad utility of the model can be proved, which validates its efficiency and allows broad implementation of this complex theoretical framework into the existing systems.

Uncertainty quantization of meteorological input and model parameters for hydrological modelling using a Bayesian‐based integrated approach

AbstractThe traditional treatment of uncertainty in hydrological modelling primarily attributes it to model parameters, but rarely systematically considers meteorological input errors, especially in quantifying the impact of meteorological input errors on parameter uncertainty. This study developed a Bayesian‐based integrated approach to quantitatively investigate uncertainties in meteorological inputs (precipitation and temperature) and model parameters as well as the variation in parameter uncertainty due to meteorological input errors. Additionally, we analysed the propagation from these uncertainties to runoff response in snowmelt and non‐snowmelt periods. The applicability and advantages of this approach were presented by applying of the Soil and Water Assessment Tool to the Shitoukoumen Reservoir Catchment. Differential Evolution Adaptive Metropolis‐Markov Chain Monte Carlo was applied for the straightforward Bayesian inference the uncertainties of meteorological inputs and model parameters. On this basis, multilevel factorial analysis technology was used to quantitatively investigate the specific impact of the model parameters' individual and interactive effects due to meteorological input errors. Finally, the impact of meteorological input errors and model parameter uncertainty on the model performance were analysed and quantified systematically. The results showed that the meteorological input errors could affect the random characteristics of multiple model parameters. Moreover, meteorological input errors could further affect the model parameters' effects on annual average runoff. Overall, the above results have significant implications in enhancing hydrological model to simulate/predict runoff and understanding hydrological processes during different periods.

Evaluation and Refinement of a Novel Data-Driven Inverse Integrated Assessment Model focusing on Primary Energy

Climate Integrated Assessment Models (IAMs) typically focus on energy and land use to project emissions, and can be used by simple climate models to project temperature outcomes. However, the decision-makers might also be interested in the reverse perspective: given a desired temperature rise, what is the corresponding energy structure? A climate scenario database includes various models, assumptions, discrete values, and incomplete data. To answer the question, machine learning (ML) techniques, Specifically Random Forest (RF), were employed to create an inverse emulator and predict primary energy sources (fossil, oil, renewable) for the mid-20st century (2050), taking into account temperature projections for the end of the century (2100) with reference to the IPCC AR6 dataset. Two methods were employed to select the emulator’s input variables: a systematic and a manual selection approach. The uncertainties in the study, including input, parameter, and implementation uncertainties, were addressed using the Monte Carlo method. Finally, two cases were analyzed in detail to explore the potential of the emulator.

Evaluation of wind resource uncertainty on energy production estimates for offshore wind farms

Wind farm design generally relies on the use of historical data and analytical wake models to predict farm quantities, such as annual energy production (AEP). Uncertainty in input wind data that drive these predictions can translate to significant uncertainty in output quantities. We examine two sources of uncertainty stemming from the level of description of the relevant meteorological variables and the source of the data. The former comes from a standard practice of simplifying the representation of the wind conditions in wake models, such as AEP estimates based on averaged turbulence intensity (TI), as opposed to instantaneous. Uncertainty from the data source arises from practical considerations related to the high cost of in situ measurements, especially for offshore wind farms. Instead, numerical weather prediction (NWP) modeling can be used to characterize the more exact location of the proposed site, with the trade-off of an imperfect model form. In the present work, both sources of input uncertainty are analyzed through a study of the site of the future Vineyard Wind 1 offshore wind farm. This site is analyzed using wind data from LiDAR measurements located 25 km from the farm and NWP data located within the farm. Error and uncertainty from the TI and data sources are quantified through forward analysis using an analytical wake model. We find that the impact of TI error on AEP predictions is negligible, while data source uncertainty results in 0.4%–3.7% uncertainty over feasible candidate hub heights for offshore wind farms, which can exceed interannual variability.

BEPU analysis of an Upgraded ICE facility test by TRACE/DAKOTA coupling

The Ingress of Coolant Event (ICE) in the plasma chamber is one of the safety issues in fusion nuclear plants. The best estimate thermal-hydraulic system codes adopted to perform deterministic safety analysis should be validated against the phenomena typical of accidental transients in fusion installations. TRACE (TRAC/RELAP Advanced Computational Engine), best estimate thermal-hydraulic system code developed by USNRC, has been adopted to simulate an ICE. The calculated results have been compared to the experimental data obtained in one test performed in the upgraded Integrated ICE facility at JAERI. In this updated configuration the pressure suppression system is connected to the top of the plasma chamber instead of the bottom of the vacuum vessel. The facility nodalization has been developed in the SNAP environment/architecture. To qualify the code and the nodalization, an accuracy evaluation has been performed both from a qualitative and quantitative point of view. Then, considering the presence of some uncertainties in the input-deck development, an uncertainty analysis has been carried out. The probabilistic method to propagate the input uncertainties has been selected and the analysis has been carried out with the DAKOTA toolkit coupled with TRACE code in SNAP. In the uncertainty analysis, some relevant statistical parameters have been considered to characterize the dispersion of the results and the correlation between the uncertain input parameters selected and the PC pressure chosen as figure of merit.

Quantitative risk ranking of mycotoxins in milk under climate change scenarios

Mycotoxins are toxic fungal metabolites that may occur in crops. Mycotoxins may carry-over into bovine milk if bovines ingest mycotoxin-contaminated feed. Due to climate change, there may be a potential increase in the prevalence and concentration of mycotoxins in crops. However, the toxicity to humans and the carry-over rate of mycotoxins from feed to milk from bovines varies considerably. This research aimed to rank emerging and existing mycotoxins under different climate change scenarios based on their occurrence in milk and their toxicity to humans. The quantitative risk ranking took a probabilistic approach, using Monte-Carlo simulation to take account of input uncertainties and variabilities. Mycotoxins were ranked based on their hazard quotient, calculated using estimated daily intake and tolerable daily intake values. Four climate change scenarios were assessed, including an Irish baseline model in addition to best-case, worst-case, and most likely scenarios, corresponding to equivalent Intergovernmental Panel on Climate Change (IPCC) scenarios. This research prioritised aflatoxin B1, zearalenone, and T-2 and HT-2 toxin as potential human health hazards for adults and children compared to other mycotoxins under all scenarios. Relatively lower risks were found to be associated with mycophenolic acid, enniatins, and deoxynivalenol. Overall, the carry-over rate of mycotoxins, the milk consumption, and the concentration of mycotoxins in silage, maize, and wheat were found to be the most sensitive parameters (positively correlated) of this probabilistic model. Though climate change may impact mycotoxin prevalence and concentration in crops, the carry-over rate notably affects the final concentration of mycotoxin in milk to a greater extent. The results obtained in this study facilitate the identification of risk reduction measures to limit mycotoxin contamination of dairy products, considering potential climate change influences.

Accounting for Machine Learning Prediction Errors in Design

Abstract Machine learning is gaining prominence in mechanical design, offering cost-effective surrogate models to replace computationally expensive models. Nevertheless, concerns persist regarding the accuracy of these models, especially when applied to safety-critical products. To address this challenge, this study investigates methods to account for model prediction errors by incorporating epistemic uncertainty within surrogate models while managing aleatory uncertainty in input variables. The paper clarifies key aspects of modeling coupled epistemic and aleatory uncertainty when using surrogate models derived from noise-free training data. Specifically, the study concentrates on quantifying the impacts of coupled uncertainty in mechanical design through the development of numerical methods based on the concept of the most probable point. This method is particularly relevant for mechanical component design, where failure prevention holds paramount importance, and the probability of failure is low. It is applicable to design problems characterized by probability distributions governing aleatory and epistemic uncertainties in model inputs and predictions. The proposed method is demonstrated using shaft and beam designs as two illustrative examples. The results demonstrate the method's effectiveness in quantifying and mitigating the influence of coupled uncertainty in the design process.

Trajectory tracking control for underactuated autonomous vehicles via adaptive dynamic programming

In this paper, the trajectory tracking control problem for underactuated autonomous vehicles moving in 2-D space is studied. Firstly, the trajectory tracking error dynamics with the equivalent point being the origin are given. Since both model and input uncertainties of vehicles are considered, a robust control problem is formulated by giving the extended trajectory tracking error dynamics. Through showing that a robust control law is equivalent to an optimal control law for a nominal system under some conditions, the robust control problem is transformed into an equivalent optimal control problem and a cost function composed of uncertainty bounds and discount factor is constructed. In order to solve the optimal control problem, a policy iteration-based ADP algorithm is proposed, in which two neural networks, called critic and actor networks, are applied respectively to describe the unknown value function and control law. The weights of the two networks are tuned through the gradient descent method at each iteration and the optimality and convergence properties are provided in the sequel. At last, simulation results are given to illustrate the validity of the proposed algorithm.

Eigenvibrations of Kirchhoff Rectangular Random Plates on Time-Fractional Viscoelastic Supports via the Stochastic Finite Element Method.

The present work's main objective is to investigate the natural vibrations of the thin (Kirchhoff-Love) plate resting on time-fractional viscoelastic supports in terms of the Stochastic Finite Element Method (SFEM). The behavior of the supports is described by the fractional order derivatives of the Riemann-Liouville type. The subspace iteration method, in conjunction with the continuation method, is used as a tool to solve the non-linear eigenproblem. A deterministic core for solving structural eigenvibrations is the Finite Element Method. The probabilistic analysis includes the Monte-Carlo simulation and the semi-analytical approach, as well as the iterative generalized stochastic perturbation method. Probabilistic structural response in the form of up to the second-order characteristics is investigated numerically in addition to the input uncertainty level. Finally, the probabilistic relative entropy and the safety measure are estimated. The presented investigations can be applied to the dynamics of foundation plates resting on viscoelastic soil.

A comprehensive review and sensitivity analysis of the factors affecting the performance of buildings equipped with Variable Refrigerant Flow system in Middle East climates

Variable Refrigerant Flow (VRF) systems are becoming increasingly popular in commercial and residential buildings due to their flexibility and efficiency. Building design and operational parameters play an important role in the process of predicting cooling loads, and these variables contain a variety of uncertainties that must be taken into account when seeking to obtain a reliable prediction. It is common practice to oversize the Heating, Ventilation, and Air Conditioning (HVAC) system installed in a building in order to reduce uncertainties in its performance. However, oversizing is undesirable due to additional capital and operating costs, inefficient space utilization, and excessive emissions of greenhouse gases. This study provided a comprehensive literature review on previous uncertainly analysis studies from six perspectives: classification of uncertainty factors, sensitivity analysis methods, building energy model for uncertainty analysis, sampling method, the most effective parameter, and distribution of uncertainty parameters., and then developed a framework to investigate the effects of uncertainty of operational and design parameters on annual cooling load, total energy consumption, HVAC electricity use per conditioned floor area, VRF system cost, and the cooling-to-total electricity consumption ratio (R) in residential buildings equipped with VRF systems located in the Middle East (ME) region (Kuwait City, Tehran, and Istanbul). Artificial Neural Network (ANN) model was also developed to predict output parameters in residential building. Latin Hypercube Sampling (LHS) simulation was also applied to generate near-random samples of uncertainty parameter values from a multidimensional distribution. It should be mentioned that a correlation was derived in this study to predict VRF cost. The study evaluated thirteen uncertainty parameters related to building characteristics, occupants, building energy systems, VRF variables, and lighting conditions. Several methods were used to evaluate the effects of input uncertainty parameters on the output variables, namely, the Standardized Regression Coefficient (SRC), Partial Correlation Coefficient (PCC), and Spearman Rank Correlation Coefficient (SRCC) or Pearson Product-Moment Correlation Coefficient (PPMCC). The results indicated that VRF cost and HVAC electricity use per area have a greater coefficient of variation compared with other output parameters. The density distribution of output parameters indicated that uncertainty parameters had the greatest impact on output parameters in Kuwait (hyper arid climate), whereas in Istanbul (humid subtropical climate), they were the least. Among the variables examined in the Sensitivity Analysis (SA), cooling setpoint had the greatest impact on residential building energy consumption in ME climates. Finally, this paper highlights emerging trends and offers recommendations for advancing future research in the realm of building energy uncertainty analysis.

Stochastic Economic Ranking Addresses Risk and Uncertainty for Decision-Makers

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 213385, “Stochastic Economic Ranking: A Prudent Way To Address Risk and Uncertainty for Decision-Makers,” by Mahesh Narayanan, SPE, Mehaboob Abdulazeez, SPE, and Khadija Bukhamsin, SPE, Saudi Aramco, et al. The paper has not been peer reviewed. _ Project screening and ranking is inevitable even when an organization faces no resource bottlenecks. In project economic predictions, the stochastic approach is prevalent and proves to be the most natural approach to address technical and commercial uncertainties. In contrast with a deterministic approach, a stochastic Monte Carlo simulation helps in understanding the range of possible outcomes to enable effective decision-making. The objective of the complete paper is to present an integrated stochastic work flow that addresses the subsurface, surface, and cost uncertainties to screen, rank, and grade opportunities early in the derisking process. Introduction The proposed work flow simultaneously can address all technical and commercial uncertainties with a Monte Carlo model and predict a range of possible economic outcomes with customized economic analysis tools. In other words, it can achieve complete unbiased sample randomness in expected project outcomes. The work flow also creates a stage-gate decision-tree approach and reviews economics as a series of staged investments. This helps to expose incremental capital in the life cycle of a project responsibly and help with course correction where necessary. Value Addition: Stochastic Approach A common practice in the oil and gas industry is to simply examine low, middle, and high cases. This is a limited view of possible scenarios and provides a false sense of precision regarding expected project outcomes. The term “stochastic” refers to the property of being well-described by a random probability distribution. A stochastic approach features the following advantages: - A stochastic model addresses uncertainties in inputs with complete unbiased randomness in the outcomes. - By running thousands of calculations and using many different estimates of future economic conditions, stochastic models predict a range of possible future investment results, showing the potential upside and downside of each. - Once the work flow is followed and the model is set up, multiple realizations can be created with a single click. Proposed Integrated Stochastic Methodology In the world of project economics, classical stochastic economics is a tool to address uncertainty and risk. In simple terms, a mean or deterministic view of a production profile, along with one view of a schedule, is taken and blended with probabilistic distribution of cost inputs. This data is then taken through a Monte Carlo model to create multiple scenarios to predict a range of possible economic outcomes. This narrow range of economic outcomes only covers the uncertainty in cost but does not cover variability in well performance and time.

Complexity prediction model: a model for multi-object complexity in consideration to business uncertainty problems

In a competitive environment, the ability to rapidly and successfully scale up new business models is critical. However, research shows that many new business models fail. This research looks at hybrid methods for minimizing constraints and maximizing opportunities in large data sets by examining the multivariable that arise in user behavior. E-metric data is being used as assessment material. The analytical hierarchy process (AHP) is used in the multi-criteria decision making (MCDM) approach to identify problems, compile references, evaluate alternatives, and determine the best alternative. The multi-objectives genetic algorithm (MOGA) role analyzes and predicts data. The method is being implemented to expand the information base of the strategic planning process. This research examines business sustainability along two critical dimensions. First, consider the importance of economic, environmental, and social evaluation metrics. Second, the difficulty of gathering information will be used as a predictor for making long-term business decisions. The results show that by incorporating the complexity features of input optimization, uncertainty optimization, and output value optimization, the complexity prediction model (MPK) achieves an accuracy of 89%. So that it can be used to forecast future business needs by taking into account aspects of change and adaptive behavior toward the economy, environment, and social factors.

Flexible Robust Control Strategy for Synchronization of Uncertain Non-Linear Systems with Control Input Non-Linearity

A robust global fuzzy sliding mode controller is designed in this paper for the synchronization of non-linear systems with control input non-linearities (CIN) and uncertainties. A consistent global fuzzy sliding mode control (GFSMC) law is developed, which guarantees the suppression of the reaching phase and the presence of the sliding phase from the initial time. Chattering phenomenon, which is characteristic of customary sliding mode control (SMC), avoided by the on-line fuzzy regulation of the sliding surface in the controller, when the system is subject to disturbances and CIN. Finite-time boundedness (FTB) properties are designed with adequate conditions, which are entrenched in terms of linear matrix inequalities (LMIs) with the help of cost and Lyapunov functions. Numerical simulations for the synchronization problem of the chaotic modified Colpitt’s system and Duffing system clearly show the good performance of the proposed control scheme. The present work provides a regular procedure to design GFSMC for a class of non-linear systems with CIN.

A novel hybrid adaptive framework for support vector machine-based reliability analysis: A comparative study

This study presents an innovative hybrid Adaptive Support Vector Machine - Monte Carlo Simulation (ASVM-MCS) framework for reliability analysis in complex engineering structures. These structures often involve highly nonlinear implicit functions, making traditional gradient-based first or second order reliability algorithms and Monte Carlo Simulation (MCS) time-consuming. The application of surrogate models has proven effective in addressing computational challenges associated with a large number of simulations. Support Vector Machine (SVM), as an emerging machine learning method suitable for small-sample scenarios, offers a well-established theoretical foundation and presents an effective model substitution approach for reliability analysis in engineering structures. However, the existing literature lacks a comprehensive and thorough comparative analysis of SVM's hybrid adaptive modeling approach, encompassing initial sampling methods and learning functions, with regards to both computational efficiency and accuracy. Additionally, there is a gap in adaptive modeling methods capable of accommodating diverse types of input uncertainty, the nonlinearity of limit state functions, and various application scenarios. In response to these gaps, this article introduces the ASVM-MCS framework, which addresses these challenges by considering different types of input variables and various failure modes. Moreover, this study provides a comprehensive evaluation of the ASVM-MCS framework's performance, including its initial sampling methods and learning functions, across a range of application scenarios, such as scenarios involving only random variables, mixed variables, and the reliability of series-parallel systems.

Evaluating LIMU System Quality with Interval Evidence and Input Uncertainty

Evaluating LIMU System Quality with Interval Evidence and Input Uncertainty

Performance Evaluation of Nord2000, RTN-96 and CNOSSOS-EU against Noise Measurements in Central Jutland, Denmark

This article aims to assess the performance of Nord2000, RTN-96, and CNOSSOS-EU, the Nordic and European noise prediction standards, in predicting daily LAeq24h and Lden levels (dBA), by comparing them with measurements gathered over 76 days from the E45 motorway in Helsted, Central Jutland, Denmark. In addition, the article investigates the potential viability of utilizing Confidence-Weighting Average (CWA) for data fusion to enhance noise estimation accuracy. The results showed highly positive Spearman’s correlations (RS), reflecting strong agreements between observed and predicted data, Nord2000 = 0.85–0.98, CNOSSOS-EU = 0.79–0.92 and RTN-96 = 0.86–0.91. Model differences, RMSE = 0.4–3.3 dBA (Nord2000), 1.4 = 2.8 dBA (CNOSSOS) and 1.3–4.2 dBA (RTN-96), were mainly due to underlying model parametrization and uncertainties in model inputs. Overall, Nord2000 outperformed CNOSSOS and RTN-96 in reproducing observed noise levels. Moreover, CNOSSOS agreed well with the measured data and exhibited a high potential for noise mapping and health assessments. Likewise, the CWA is found to be a promising, forward-looking data fusion approach to improve noise estimates’ accuracy. More research is required to further evaluate the models in greater detail over a larger geographical area and across varied temporal scales (e.g., hourly, yearly).

Comparison of volume and centroid uncertainty of a cylindrical segment by two different methods

Some solids are seldom studied analytically, unlike spheres, cylinders, and parallelepipeds. When researchers are interested in them, approximations by discretised meshes and numerical calculations are proposed. One of those is the cylindrical segment, which has applications in novel tilt-based industrial hydrometers. More specifically, the cylindrical segment has two means of being characterized: by measuring the radius, the middle height, and the cutting angle; and by measuring the radius and both the minimum height and the maximum height. In this study, one equation from the literature about the cylindrical segment is corrected, and the two measurement forms are compared, along with their impact on the uncertainty of the volume and barycenter of this solid. Particular results depend on specific input uncertainties, measurements, and objectives. Still, for most cases, it turned out that measuring the minimum and maximum heights generally provides lower uncertainty values for the evaluated calculations, especially the centroids.

Uncertainty analysis of heat transfer of TMSR-SF0 simulator

The TMSR-SF0 simulator is an integral effect thermal-hydraulic experimental system for the development of thorium molten salt reactor (TMSR) program in China. The simulator has two heat transport loops with liquid FLiNaK. In literature, the 95% level confidence uncertainties of the thermophysical properties of FLiNaK are recommended, and the uncertainties of density, heat capacity, thermal conductivity and viscosity are ±2%, ±10, ±10% and ±10% respectively. In order to investigate the effects of thermophysical properties uncertainties on the molten salt heat transport system, the uncertainty and sensitivity analysis of the heat transfer characteristics of the simulator system are carried out on a RELAP5 model. The uncertainties of thermophysical properties are incorporated in simulation model and the Monte Carlo sampling method is used to propagate the input uncertainties through the model. The simulation results indicate that the uncertainty propagated to core outlet temperature is about ±10°C with a confidence level of 95% in a steady-state operation condition. The result should be noted in the design, operation and code validation of molten salt reactor. In addition, more experimental data is necessary for quantifying the uncertainty of thermophysical properties of molten salts.