Relevant bibliographies by topics / Initialization uncertainty

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
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Academic literature on the topic 'Initialization uncertainty'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Initialization uncertainty"

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Celemin, Carlos E., and Miguel A. Melgarejo. "A Proposal to Speed up the Computation of the Centroid of an Interval Type-2 Fuzzy Set." Advances in Fuzzy Systems 2013 (2013): 1–17. http://dx.doi.org/10.1155/2013/158969.

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This paper presents two new algorithms that speed up the centroid computation of an interval type-2 fuzzy set. The algorithms include precomputation of the main operations and initialization based on the concept of uncertainty bounds. Simulations over different kinds of footprints of uncertainty reveal that the new algorithms achieve computation time reductions with respect to the Enhanced-Karnik algorithm, ranging from 40 to 70%. The results suggest that the initialization used in the new algorithms effectively reduces the number of iterations to compute the extreme points of the interval cen
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Akil, Nicolas, Guillaume Artigue, Michaël Savary, Anne Johannet, and Marc Vinches. "Uncertainty Estimation in Hydrogeological Forecasting with Neural Networks: Impact of Spatial Distribution of Rainfalls and Random Initialization of the Model." Water 13, no. 12 (2021): 1690. http://dx.doi.org/10.3390/w13121690.

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Neural networks are used to forecast hydrogeological risks, such as droughts and floods. However, uncertainties generated by these models are difficult to assess, possibly leading to a low use of these solutions by water managers. These uncertainties are the result of three sources: input data, model architecture and parameters and their initialization. The aim of the study is, first, to calibrate a model to predict Champagne chalk groundwater level at Vailly (Grand-Est, France), and, second, to estimate related uncertainties, linked both to the spatial distribution of rainfalls and to the par
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Payne, Mark R., Manuel Barange, William W. L. Cheung, et al. "Uncertainties in projecting climate-change impacts in marine ecosystems." ICES Journal of Marine Science 73, no. 5 (2015): 1272–82. http://dx.doi.org/10.1093/icesjms/fsv231.

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Abstract Projections of the impacts of climate change on marine ecosystems are a key prerequisite for the planning of adaptation strategies, yet they are inevitably associated with uncertainty. Identifying, quantifying, and communicating this uncertainty is key to both evaluating the risk associated with a projection and building confidence in its robustness. We review how uncertainties in such projections are handled in marine science. We employ an approach developed in climate modelling by breaking uncertainty down into (i) structural (model) uncertainty, (ii) initialization and internal var
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Hidasi, Balazs, and Domonkos Tikk. "Initializing Matrix Factorization Methods on Implicit Feedback Databases." JUCS - Journal of Universal Computer Science 19, no. (12) (2013): 1834–53. https://doi.org/10.3217/jucs-019-12-1834.

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The implicit feedback based recommendation problem--when only the user history is available but there are no ratings--is a much harder task than the explicit feedback based recommendation problem, due to the inherent uncertainty of the interpretation of such user feedbacks. Recently, implicit feedback problem is being received more attention, as application oriented research gets more attractive within the field. This paper focuses on a common matrix factorization method for the implicit problem and investigates if recommendation performance can be improved by appropriate initialization of the
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Koziol, Conrad P., Joe A. Todd, Daniel N. Goldberg, and James R. Maddison. "fenics_ice 1.0: a framework for quantifying initialization uncertainty for time-dependent ice sheet models." Geoscientific Model Development 14, no. 9 (2021): 5843–61. http://dx.doi.org/10.5194/gmd-14-5843-2021.

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Abstract. Mass loss due to dynamic changes in ice sheets is a significant contributor to sea level rise, and this contribution is expected to increase in the future. Numerical codes simulating the evolution of ice sheets can potentially quantify this future contribution. However, the uncertainty inherent in these models propagates into projections of sea level rise is and hence crucial to understand. Key variables of ice sheet models, such as basal drag or ice stiffness, are typically initialized using inversion methodologies to ensure that models match present observations. Such inversions of
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Koster, Randal D., Anthony M. DeAngelis, Siegfried D. Schubert, and Andrea M. Molod. "Asymmetry in Subseasonal Surface Air Temperature Forecast Error with Respect to Soil Moisture Initialization." Journal of Hydrometeorology 22, no. 10 (2021): 2505–19. http://dx.doi.org/10.1175/jhm-d-21-0022.1.

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AbstractSoil moisture (W) helps control evapotranspiration (ET), and ET variations can in turn have a distinct impact on 2-m air temperature (T2M), given that increases in evaporative cooling encourage reduced temperatures. Soil moisture is accordingly linked to T2M, and realistic soil moisture initialization has, in previous studies, been shown to improve the skill of subseasonal T2M forecasts. The relationship between soil moisture and evapotranspiration, however, is distinctly nonlinear, with ET tending to increase with soil moisture in drier conditions and to be insensitive to soil moistur
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Hong, Euntae, and Jongwoo Lim. "Visual-Inertial Odometry with Robust Initialization and Online Scale Estimation." Sensors 18, no. 12 (2018): 4287. http://dx.doi.org/10.3390/s18124287.

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Visual-inertial odometry (VIO) has recently received much attention for efficient and accurate ego-motion estimation of unmanned aerial vehicle systems (UAVs). Recent studies have shown that optimization-based algorithms achieve typically high accuracy when given enough amount of information, but occasionally suffer from divergence when solving highly non-linear problems. Further, their performance significantly depends on the accuracy of the initialization of inertial measurement unit (IMU) parameters. In this paper, we propose a novel VIO algorithm of estimating the motional state of UAVs wi
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Albrecht, Jan Christoph, Anna Lucia Romero Müller, and Florian Rüttger. "Dealing with uncertainty regarding the initialization and budgeting of digital transformation projects." Procedia Computer Science 256 (2025): 1575–82. https://doi.org/10.1016/j.procs.2025.02.293.

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Xu, Qiuhui, Shenfang Yuan, and Tianxiang Huang. "Multi-Dimensional Uniform Initialization Gaussian Mixture Model for Spar Crack Quantification under Uncertainty." Sensors 21, no. 4 (2021): 1283. http://dx.doi.org/10.3390/s21041283.

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Guided Wave (GW)-based crack monitoring method as a promising method has been widely studied, as this method is sensitive to small cracks and can cover a wide monitoring range. Online crack quantification is difficult as the initiation and growth of crack are affected by various uncertainties. In addition, crack-sensitive GW features are influenced by time-varying conditions which further increase the difficulty in crack quantification. Considering these uncertainties, the Gaussian mixture model (GMM) is studied to model the probability distribution of GW features. To further improve the accur
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Mozyrsky, Dima, and Vladimir Privman. "Quantum Signal Splitting that Avoids Initialization of the Targets." Modern Physics Letters B 11, no. 29 (1997): 1277–83. http://dx.doi.org/10.1142/s0217984997001523.

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The classical signal splitting and copying are not possible in quantum mechanics. Specifically, one cannot copy the basis up and down states of the input (I) two-state system (qubit, spin) into the copy (C) and duplicate-copy (D) two-state systems if the latter systems are initially in an arbitrary state. We consider instead a quantum evolution in which the basis states of I at time t are duplicated in at least two of the systems I, C, D, at time t+Δt. In essence, the restriction on the initial target states is exchanged for uncertainty as to which two of the three qubits retain copies of the
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Dissertations / Theses on the topic "Initialization uncertainty"

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Weisman, Ryan Michael 1984. "Nonlinear Transformations and Filtering Theory for Space Operations." Thesis, 2012. http://hdl.handle.net/1969.1/148211.

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Decisions for asset allocation and protection are predicated upon accurate knowledge of the current operating environment as well as correctly characterizing the evolution of the environment over time. The desired kinematic and kinetic states of objects in question cannot be measured directly in most cases and instead are inferred or estimated from available measurements using a filtering process. Often, nonlinear transformations between the measurement domain and desired state domain distort the state domain probability density function yielding a form which does not necessarily resemble the
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Book chapters on the topic "Initialization uncertainty"

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Jendoubi, Siwar, and Aurélien Baelde. "SSFuzzyART: A Semi-Supervised Fuzzy ART Through Seeding Initialization." In Information Processing and Management of Uncertainty in Knowledge-Based Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08974-9_58.

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Cachi, Paolo Gabriel, Sebastián Ventura, and Krzysztof Jozef Cios. "Fast Convergence of Competitive Spiking Neural Networks with Sample-Based Weight Initialization." In Information Processing and Management of Uncertainty in Knowledge-Based Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50153-2_57.

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Mir-Fuentes, Arnau, Arnau Mir, Felipe Antunes-Santos, F. Javier Fernandez, and Carlos Lopez-Molina. "A Framework for Active Contour Initialization with Application to Liver Segmentation in MRI." In Information Processing and Management of Uncertainty in Knowledge-Based Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08974-9_21.

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Toumanidis, Lazaros, Panagiotis Kasnesis, Christos Chatzigeorgiou, Michail Feidakis, and Charalampos Patrikakis. "ActiveCrowds: A Human-in-the-Loop Machine Learning Framework." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210090.

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A widespread practice in machine learning solutions is the continuous use of human intelligence to increase their quality and efficiency. A common problem in such solutions is the requirement of a large amount of labeled data. In this paper, we present a practical implementation of the human-in-the-loop computing practice, which includes the combination of active and transfer learning for sophisticated data sampling and weight initialization respectively, and a cross-platform mobile application for crowdsourcing data annotation tasks. We study the use of the proposed framework to a post-event
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Singh, Uday Pratap, Sanjeev Jain, Akhilesh Tiwari, and Rajeev Kumar Singh. "Nature-Inspired-Based Adaptive Neural Network Approximation for Uncertain System." In Handbook of Research on Emergent Applications of Optimization Algorithms. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2990-3.ch019.

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In this chapter, we focus and studied on some important nature inspired optimization methods like Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Cuckoo Search (CS), Bat Algorithm (BA) and Flower Pollination Algorithm (FPA) are used for assign initial weights of Back-Propagation Neural Network (BPN). Success of neural networks are sturdily depends on different parameters and initialization weight is one, these nature inspired methods are used for optimization of mean square error (MSE) and mean absolute percentage error (MAPE) are used as test functions. The proposed method is based
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Labiod, Salim, Hamid Boubertakh, and Thierry Marie Guerra. "Indirect Adaptive Fuzzy Control for a Class of Uncertain Nonlinear Systems with Unknown Control Direction." In Contemporary Theory and Pragmatic Approaches in Fuzzy Computing Utilization. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-1870-1.ch010.

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In this paper, the authors propose two indirect adaptive fuzzy control schemes for a class of uncertain continuous-time single-input single-output (SISO) nonlinear dynamic systems with known and unknown control direction. Within these schemes, fuzzy systems are used to approximate unknown nonlinear functions and the Nussbaum gain technique is used to deal with the unknown control direction. This paper first presents a singularity-free indirect adaptive control algorithm for nonlinear systems with known control direction, and then this control algorithm is generalized for the case of unknown co
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Conference papers on the topic "Initialization uncertainty"

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Sherman, Jason A. F., Natalie M. Isenberg, John D. Siirola, and Chrysanthos E. Gounaris. "Recent Advances of PyROS: A Pyomo Solver for Nonconvex Two-Stage Robust Optimization in Process Systems Engineering." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.142058.

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In this work, we present recent algorithmic and implementation advances of the nonconvex two-stage robust optimization solver PyROS. Our advances include extensions of the scope of PyROS to models with uncertain variable bounds, improvements to the formulations and/or initializations of the various subproblems used by the underlying cutting set algorithm, and extensions to the pre-implemented uncertainty set interfaces. The effectiveness of PyROS is demonstrated through the results of an original benchmarking study on a library of over 8,500 small-scale instances, with variations in the nonlin
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Fu, Peng, Shuaixin Li, Tianyou Yu, Xiaozhou Zhu, Wen Yao, and Xiaoqian Chen. "An Initialization Method for Monocular Visual-Inertial Odometry Based on Uncertainty State." In 2022 4th International Conference on Communications, Information System and Computer Engineering (CISCE). IEEE, 2022. http://dx.doi.org/10.1109/cisce55963.2022.9851127.

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Budzien, Joanne, James Byerly, Rob Aulwes, Rao Garimella, Angela Herring, and Jon Woodring. "Linking Material Models Between Codes: Establishing Thermodynamic Consistency." In ASME 2022 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/vvs2022-86808.

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Abstract One increasingly important workflow for multiphysics simulations is linking simulation codes that have different physics models and different regimes for which they have been optimized. The science question for this scoping work was evaluating the compatibility of physics models on both sides of a link to ensure a smooth simulation continuation was possible. The VVUQ aspects were establishing the most important physics aspects for a credible simulation. The most important aspect was determined to be thermodynamic consistency such that nothing unphysical would be encountered during the
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Hu, Zhen, and Sankaran Mahadevan. "Adaptive Surrogate Modeling for Multidisciplinary Reliability Analysis Under Time-Dependent Uncertainty." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67383.

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Multidisciplinary systems will remain in transient states when time-dependent interactions are present among the coupling variables. This brings significant challenges to time-dependent multidisciplinary system reliability analysis. This paper develops an adaptive surrogate modeling approach (ASMA) for multidisciplinary system reliability analysis under time-dependent uncertainty. The proposed framework consists of three modules, namely initialization, uncertainty propagation, and three-level global sensitivity analysis (GSA). The first two modules check the quality of the surrogate models and
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Kachuma, Dick, Jean-Claude Hild, and Irina Belushko. "Simplified Initialization of Reservoir Simulation Models with Continuously Varying Tilted Contacts and Complex Fluid Distributions." In SPE Reservoir Simulation Conference. SPE, 2021. http://dx.doi.org/10.2118/203966-ms.

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Abstract We tackle reservoir simulation model initialization in situations where the current free water level has risen significantly above the original paleo-contact. In such cases, traditional initialization based on primary drainage capillary-gravity equilibrium is insufficient because it does not consider the reimbibition that takes place after primary oil migration. Usage of traditional initialization would require a generation of several thousands of regions to have a satisfactory representation of such phenomenon in the model. We apply this initialization to a reservoir with a complex i
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Santoso, Ryan, Xupeng He, Marwa Alsinan, Hyung Kwak, and Hussein Hoteit. "Uncertainty Quantification and Optimization of Deep Learning for Fracture Recognition." In SPE Middle East Oil & Gas Show and Conference. SPE, 2021. http://dx.doi.org/10.2118/204863-ms.

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Abstract Automatic fracture recognition from borehole images or outcrops is applicable for the construction of fractured reservoir models. Deep learning for fracture recognition is subject to uncertainty due to sparse and imbalanced training set, and random initialization. We present a new workflow to optimize a deep learning model under uncertainty using U-Net. We consider both epistemic and aleatoric uncertainty of the model. We propose a U-Net architecture by inserting dropout layer after every "weighting" layer. We vary the dropout probability to investigate its impact on the uncertainty r
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Yonebayashi, Hideharu, Takaaki Uetani, and Hiromi Kaido. "Managing to Reproduce Original Carbonate Core Condition: Is Core Initialized Realistically on Three-Dimensional Geometry?" In SPE International Conference and Exhibition on Formation Damage Control. SPE, 2022. http://dx.doi.org/10.2118/208860-ms.

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Abstract In this study, we established initial water saturation (Swi) using three techniques: (1) the dynamic displacement technique, (2) the porous plate technique, and (3) the vacuum saturation technique. A unique heterogeneous carbonate reservoir rock sample (1.5-inch diameter and 3-inches long) was used repeatedly to compare the techniques without an uncertainty of different cores. After establishing Swi by each initialization technique, the cross sections were scanned using a micro-CT scanner. The image data was processed to estimate the cross sectional fluid distribution in XY-direction.
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Bayerl, Mathias, Pascale Neff, Torsten Clemens, Martin Sieberer, Barbara Stummer, and Andras Zamolyi. "Accelerating Mature Field EOR Evaluation Using Machine Learning Uncertainty Workflows Integrating Subsurface And Economics." In Abu Dhabi International Petroleum Exhibition & Conference. SPE, 2021. http://dx.doi.org/10.2118/208194-ms.

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Abstract Field re-development planning for tertiary recovery projects in mature fields traditionally involves a comprehensive subsurface evaluation circle, including static/dynamic modeling, scenario assessment and candidate selection based on economic models. The aforementioned sequential approach is time-consuming and includes the risk of delaying project maturation. This work introduces a novel approach which integrates subsurface geological and dynamic modeling as well as economics and uses machine learning augmented uncertainty workflows to achieve project acceleration. In the elaborated
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Zhang, Fuli, Olga Brezhneva, and Amit Shukla. "Optimal Sensor Placement Using Chaotic Monkey Search Algorithm." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46863.

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The optimal sensor placement (OSP) problem is integral to modern large scale structures for their health monitoring. Evolutionary algorithms for the OSP problem are attractive as they can result in global optima without gradient information. In this paper, a modification of the Monkey Algorithm with a chaotic search strategy and adaptive parameters is proposed. It includes chaotic initialization, variable search step length, and adaptive watching time. The performance of the proposed chaotic Monkey Algorithm (cMA) is compared with the original Monkey Algorithm. Convergence property of cMA is e
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Noviyanto, E. "Probabilistic History Matching and Prediction of Production Performance by Waterflood: A Case Study of 70 Years Old Oil Field." In Indonesian Petroleum Association 44th Annual Convention and Exhibition. Indonesian Petroleum Association, 2021. http://dx.doi.org/10.29118/ipa21-e-68.

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This paper presents the application of probabilistic history matching and prediction workflow in a real field case in Indonesia. The main objective of this novel approach is to capture the subsurface uncertainty for better reservoir understanding to be able to manage its risk and make a better decision for further field development. The field is very complex, with updated geological concept of multi-level reservoirs that has more than a hundred of wells and has been producing for 70 years. Existing multi-realization of static reservoir model was built to determine range of probabilistic cases
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Reports on the topic "Initialization uncertainty"

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Torres, Marissa, Michael-Angelo Lam, Levi Cass, Matt Malej, and Fengyan Shi. Getting started with FUNWAVE-TVD : troubleshooting guidance and recommendations. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48631.

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This technical note reviews some common initialization errors when first getting started with the numerical wave model, FUNWAVE-TVD (Fully Nonlinear Wave model–Total Variation Diminishing), and provides guidance for correcting these errors. Recommendations for troubleshooting the source or cause of instabilities in an application of the model as well as recognizing the difference between physical and numerical instabilities are also outlined and discussed. In addition, a quick start troubleshooting guide is provided in the Appendix. This guidance is particularly useful for novice to intermedia
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