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Journal articles on the topic 'Identification uncertainty'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Younce, R. C., and C. E. Rohrs. "Identification with nonparametric uncertainty." IEEE Transactions on Automatic Control 37, no. 6 (1992): 715–28. http://dx.doi.org/10.1109/9.256327.

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2

Baliuta, S. M., P. O. Chernenko, Iu V. Kuievda, and V. P. Kuevda. "IDENTIFICATION OF MATHEMATICAL MODEL OF TURBINE GENERATOR UNIT IN PRESENCE OF UNCERTAINTY." Tekhnichna Elektrodynamika 2021, no. 1 (2021): 32–39. http://dx.doi.org/10.15407/techned2021.01.032.

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An identification procedure of mathematical model of turbine generator unit in the presence of uncertainty is studied for using in the interconnected robust control automated system. The procedure is based on “worst-case” identification approach. The controlled object is modelled by the matrix transfer function with additive uncertainty. The identification consists of two stages: first is to identify transfer function with nominal parameters with the use of prediction error minimization algorithm, second – to determine weight function in additive uncertainty model using finding the worst-case

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3

Hogg, Michael A., David K. Sherman, Joel Dierselhuis, Angela T. Maitner, and Graham Moffitt. "Uncertainty, entitativity, and group identification." Journal of Experimental Social Psychology 43, no. 1 (2007): 135–42. http://dx.doi.org/10.1016/j.jesp.2005.12.008.

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4

Giacomini, Raffaella, Toru Kitagawa, and Alessio Volpicella. "Uncertain identification." Quantitative Economics 13, no. 1 (2022): 95–123. http://dx.doi.org/10.3982/qe1671.

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Uncertainty about the choice of identifying assumptions is common in causal studies, but is often ignored in empirical practice. This paper considers uncertainty over models that impose different identifying assumptions, which can lead to a mix of point‐ and set‐identified models. We propose performing inference in the presence of such uncertainty by generalizing Bayesian model averaging. The method considers multiple posteriors for the set‐identified models and combines them with a single posterior for models that are either point‐identified or that impose nondogmatic assumptions. The output

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5

Choi, Eunice U., and Michael A. Hogg. "Self-uncertainty and group identification: A meta-analysis." Group Processes & Intergroup Relations 23, no. 4 (2019): 483–501. http://dx.doi.org/10.1177/1368430219846990.

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A key prediction of uncertainty-identity theory is that under conditions of high self-uncertainty, people will identify more strongly with their group. This has been supported by numerous studies. To quantify this relationship, a meta-analysis was conducted on 35 studies from 30 papers ( N = 4,657). The relationship between self-uncertainty and group identification varied significantly as a function of how psychologically real the uncertainty was, as reflected in how uncertainty was operationalized and how the study was conducted. Self-uncertainty operationalized as social identity uncertainty

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6

Ye, Yun, Marija Jankovic, Gül E. Kremer, and Jean-Claude Bocquet. "Managing uncertainty in potential supplier identification." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 28, no. 4 (2014): 339–51. http://dx.doi.org/10.1017/s0890060414000511.

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AbstractAs a benefit of modularization of complex systems, original equipment manufacturers (OEMs) can choose suppliers in a less constricted way when faced with new or evolving requirements. However, new suppliers usually add uncertainties to the system development. Because suppliers are tightly integrated into the design process in modular design and therefore greatly influence the outcome of the OEM's products, the uncertainty along with requirements satisfaction of the suppliers and their modules should be controlled starting from potential supplier identification. In addition, to better s

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7

Milanese, Mario, and Michele Taragna. "H∞ identification of “soft” uncertainty models." Systems & Control Letters 37, no. 4 (1999): 217–28. http://dx.doi.org/10.1016/s0167-6911(99)00022-5.

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8

Wu, S. Q., and S. S. Law. "Statistical moving load identification including uncertainty." Probabilistic Engineering Mechanics 29 (July 2012): 70–78. http://dx.doi.org/10.1016/j.probengmech.2011.09.001.

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9

Hogg, Michael A. "Managing Self-Uncertainty Through Group Identification." Psychological Inquiry 20, no. 4 (2009): 221–24. http://dx.doi.org/10.1080/10478400903333452.

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10

Corrêa, G. O., and I. Postlethwaite. "Model Uncertainty And Linear System Identification." IFAC Proceedings Volumes 18, no. 3 (1985): 115–20. http://dx.doi.org/10.1016/b978-0-08-032575-0.50022-9.

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11

Tøffner-Clausen, Steen. "Identification for Control: Quantification of Uncertainty." IFAC Proceedings Volumes 28, no. 16 (1995): 169–73. http://dx.doi.org/10.1016/s1474-6670(17)45173-1.

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12

Darwish, Saad M., Adel A. El-Zoghabi, and Oday A. Hassen. "A Modified Walk Recognition System for Human Identification Based on Uncertainty Eigen Gait." International Journal of Machine Learning and Computing 4, no. 4 (2014): 346–53. http://dx.doi.org/10.7763/ijmlc.2014.v4.435.

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13

Costa, M. M., and J. C. W. A. Costa. "Identification and evaluation of uncertainty contributions to optical current sensor measurement uncertainty." Journal of Physics: Conference Series 2606, no. 1 (2023): 012033. http://dx.doi.org/10.1088/1742-6596/2606/1/012033.

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Abstract Optical current sensors can replace traditional current sensor due to their several advantages, as electromagnetic immunity, low dimensions, low cost and others. One of the current main challenges is to develop optical current sensors with good accuracy and stability. This paper presents identification and evaluation of uncertainty contributions to measurement uncertainty evaluation of optical current sensors based on Faraday effect.

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14

Jung, Jiin, Michael A. Hogg, and Gary J. Lewis. "Identity uncertainty and UK–Scottish relations: Different dynamics depending on relative identity centrality." Group Processes & Intergroup Relations 21, no. 6 (2016): 861–73. http://dx.doi.org/10.1177/1368430216678329.

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Drawing on uncertainty-identity theory, we investigated how people respond differently to identity uncertainty at a superordinate (i.e., UK) or subgroup (i.e., Scottish) level depending on the subjective self-conceptual centrality of subgroup relative to superordinate group; altering superordinate and subgroup identification and attitude toward subgroup relations to the superordinate group in the context of Scotland’s bid for independence from the UK ( N = 115). Hierarchical regression analyses confirmed our prediction. Where the subgroup was self-conceptually more central than the superordina

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15

Nagai, Shinji, and Hidetoshi Iijima. "Uncertainty Identification of Supersonic Wind-Tunnel Testing." Journal of Aircraft 48, no. 2 (2011): 567–77. http://dx.doi.org/10.2514/1.c031159.

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16

Manko, G. I., and E. P. Chistokletov. "Use of information uncertainty in identification tasks." Computer Modeling: Analysis, Control, Optimization 5, no. 1 (2019): 30–35. http://dx.doi.org/10.32434/2521-6406-2019-5-1-30-35.

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17

Burns, John A., Eugene M. Cliff, and Terry L. Herdman. "Identification of dynamical systems with structured uncertainty." Inverse Problems in Science and Engineering 26, no. 2 (2017): 280–321. http://dx.doi.org/10.1080/17415977.2017.1316500.

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18

Schnute, Jon. "DATA UNCERTAINTY, MODEL AMBIGUITY, AND MODEL IDENTIFICATION." Natural Resource Modeling 2, no. 2 (1987): 159–212. http://dx.doi.org/10.1111/j.1939-7445.1987.tb00034.x.

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19

Reid, Scott A., and Michael A. Hogg. "Uncertainty Reduction, Self-Enhancement, and Ingroup Identification." Personality and Social Psychology Bulletin 31, no. 6 (2005): 804–17. http://dx.doi.org/10.1177/0146167204271708.

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20

Eşkinat, Eşref. "Identification and Uncertainty Modeling for Multivariable Processes." IFAC Proceedings Volumes 33, no. 10 (2000): 749–54. http://dx.doi.org/10.1016/s1474-6670(17)38629-9.

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21

Koh, C. G., and L. M. See. "Identification and Uncertainty Estimation of Structural Parameters." Journal of Engineering Mechanics 120, no. 6 (1994): 1219–36. http://dx.doi.org/10.1061/(asce)0733-9399(1994)120:6(1219).

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22

Barreto, J. P., and L. E. Muñoz. "Low uncertainty method for inertia tensor identification." Mechanical Systems and Signal Processing 68-69 (February 2016): 207–16. http://dx.doi.org/10.1016/j.ymssp.2015.04.029.

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23

Wagener, Thorsten, and Hoshin V. Gupta. "Model identification for hydrological forecasting under uncertainty." Stochastic Environmental Research and Risk Assessment 19, no. 6 (2005): 378–87. http://dx.doi.org/10.1007/s00477-005-0006-5.

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24

Fonseca, José R., Michael I. Friswell, John E. Mottershead, and Arthur W. Lees. "Uncertainty identification by the maximum likelihood method." Journal of Sound and Vibration 288, no. 3 (2005): 587–99. http://dx.doi.org/10.1016/j.jsv.2005.07.006.

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25

Chun-Bo Feng and Ying Zhang. "Unbiased identification of systems with nonparametric uncertainty." IEEE Transactions on Automatic Control 40, no. 5 (1995): 933–36. http://dx.doi.org/10.1109/9.384234.

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26

de Oliveira, Elcio. "Critical Metrological Evaluation of Fuel Analyses by Measurement Uncertainty." Metrology and Measurement Systems 18, no. 2 (2011): 235–48. http://dx.doi.org/10.2478/v10178-011-0006-4.

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Critical Metrological Evaluation of Fuel Analyses by Measurement UncertaintyIt is now widely recognized that the evaluation of the uncertainty associated with a result is an essential part of any quantitative analysis. One way to use the estimation of measurement uncertainty as a metrological critical evaluation tool is the identification of sources of uncertainty on the analytical result, knowing the weak steps, in order to improve the method, when it is necessary. In this work, this methodology is applied to fuel analyses and the results show that the relevant sources of uncertainty are: bey

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27

Wang, Jiajia, Hao Chen, Jing Ma, and Tong Zhang. "Research on application method of uncertainty quantification technology in equipment test identification." MATEC Web of Conferences 336 (2021): 02026. http://dx.doi.org/10.1051/matecconf/202133602026.

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This paper introduces the concepts of equipment test qualification and uncertainty quantification, and the analysis framework and process of equipment test uncertainty quantification. It analyzes the data uncertainty, model uncertainty and environmental uncertainty, and studies the corresponding uncertainty quantification theory to provide technical reference for the application of uncertainty quantification technology in the field of test identification.

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28

Eisenbach, Markus, Andreas Gebhardt, Dustin Aganian, and Horst-Michael Gross. "Improving Re-Identification by Estimating and Utilizing Diverse Uncertainty Types for Embeddings." Algorithms 17, no. 10 (2024): 430. http://dx.doi.org/10.3390/a17100430.

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In most re-identification approaches, embedding vectors are compared to identify the best match for a given query. However, this comparison does not take into account whether the encoded information in the embedding vectors was extracted reliably from the input images. We propose the first attempt that illustrates how all three types of uncertainty, namely model uncertainty (also known as epistemic uncertainty), data uncertainty (also known as aleatoric uncertainty), and distributional uncertainty, can be estimated for embedding vectors. We provide evidence that we do indeed estimate these typ

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29

Vigsø, Michael, Rune Brincker, and Christos Georgakis. "Evaluating the Effect of Modelling Errors in Load Identification Using Classical Identification Methods." Shock and Vibration 2019 (October 21, 2019): 1–14. http://dx.doi.org/10.1155/2019/9490760.

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Load identification, or input identification as the more general term, is a field of study that requires a wide set of disciplines, which suffers from uncertainties caused by the challenges within each discipline. When making load identification, several different approaches exist. For all (or at least most) methods, however, some sort of system model is required. This model may be simple or complex, depending on the system at hand. Typically, if the identification process is vibration fed, the system model will be created from modal parameters. These parameters, however, are often subject to

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30

Welzbacher, Peter, Anja Geipl, Benjamin Kraus, Steffen Puchtler, and Eckhard Kirchner. "A FOLLOW-UP ON THE METHODICAL FRAMEWORK FOR THE IDENTIFICATION, ANALYSIS AND CONSIDERATION OF UNCERTAINTY IN THE CONTEXT OF THE INTEGRATION OF SENSORY FUNCTIONS BY MEANS OF SENSING MACHINE ELEMENTS." Proceedings of the Design Society 3 (June 19, 2023): 141–50. http://dx.doi.org/10.1017/pds.2023.15.

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AbstractWhen integrating sensing machine elements for in-situ measurements in technical systems, special attention must be paid to uncertainty to ensure the reliability of the provided information. Therefore, a methodical framework for the identification, analysis and consideration of uncertainty was already developed in prior research, which still offers room for improvement regarding the included methods and tools. Therefore, in this contribution, the initially proposed methods and tools are adapted and extended to enhance their efficiency and applicability and to reduce their error pronenes

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31

Zhou, Tong, and Hidenori Kimura. "Simultaneous identification of nominal model, parametric uncertainty and unstructured uncertainty for robust control." Automatica 30, no. 3 (1994): 391–402. http://dx.doi.org/10.1016/0005-1098(94)90117-1.

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32

Manski, Charles F., Alan H. Sanstad, and Stephen J. DeCanio. "Addressing partial identification in climate modeling and policy analysis." Proceedings of the National Academy of Sciences 118, no. 15 (2021): e2022886118. http://dx.doi.org/10.1073/pnas.2022886118.

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Numerical simulations of the global climate system provide inputs to integrated assessment modeling for estimating the impacts of greenhouse gas mitigation and other policies to address global climate change. While essential tools for this purpose, computational climate models are subject to considerable uncertainty, including intermodel “structural” uncertainty. Structural uncertainty analysis has emphasized simple or weighted averaging of the outputs of multimodel ensembles, sometimes with subjective Bayesian assignment of probabilities across models. However, choosing appropriate weights is

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33

Badiul Alam, Abul Hasan Md, Junichiro Takeuchi, and Toshihiko Kawachi. "Identification and Uncertainty Assessment of Model Parameters of Distributed Runoff Model Using Bayesian Inference." Journal of Rainwater Catchment Systems 13, no. 1 (2007): 1–10. http://dx.doi.org/10.7132/jrcsa.kj00004675580.

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34

Chen, Liang-Kuang, and A. Galip Ulsoy. "Identification of a Driver Steering Model, and Model Uncertainty, From Driving Simulator Data." Journal of Dynamic Systems, Measurement, and Control 123, no. 4 (2001): 623–29. http://dx.doi.org/10.1115/1.1409554.

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For active safety systems that function while the driver is still in the control loop, driver uncertainty can affect system performance significantly. In this paper, an approach to obtain both the driver model and its uncertainty from driving simulator data is presented. The structured uncertainty is used to represent the driver’s time-varying behavior, and the unstructured uncertainty for unmodeled dynamics. The uncertainty models can represent both the uncertainty within one driver and the uncertainty across multiple drivers. The structured uncertainty suggests that an estimation and adaptat

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35

Razali, Muhammad Najib, Rohaya Abdul Jalil, Kamalahasan Achu, and Hishamuddin Mohd Ali. "Identification of Risk Factors in Business Valuation." Journal of Risk and Financial Management 15, no. 7 (2022): 282. http://dx.doi.org/10.3390/jrfm15070282.

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It is widely accepted that risk and uncertainty are integral parts of the property valuation process. Uncertainty in property valuation is derived from the characteristics of property itself. The issue pertaining to risk and uncertainty in property valuations is currently one of the key concerns in global valuation practice to date in addressing the decision of risk and uncertainty in valuation, especially for business purposes or in the current term known as business valuation. The judgment and experience still depend on the expertise of the individual valuers alone. The valuation methods use

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36

Schmitt, Antje, Kathrin Rosing, Stephen X. Zhang, and Michael Leatherbee. "A Dynamic Model of Entrepreneurial Uncertainty and Business Opportunity Identification: Exploration as a Mediator and Entrepreneurial Self-Efficacy as a Moderator." Entrepreneurship Theory and Practice 42, no. 6 (2017): 835–59. http://dx.doi.org/10.1177/1042258717721482.

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This study focuses on the identification of business opportunities when entrepreneurs’ perceived level of environmental uncertainty changes. We suggest that within persons, exploration mediates this relationship and entrepreneurial self-efficacy moderates whether entrepreneurs explore more or less with increasing uncertainty. To test our moderated mediation model we conducted a monthly field study with 121 early-stage entrepreneurs. Multilevel regression analyses reveal that an increase in the level of perceived uncertainty within entrepreneurs predicted the identification of opportunities thr

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37

Mishra, Sudeep, Neelam Richhariya, Rachana Rani, and Lalitesh K Thakur. "Uncertanity of Measurement During Estimation of 23 Organophosphorus Pesticides Residue Present in Bottle Gourd." Journal of Forensic Chemistry and Toxicology 5, no. 2 (2019): 131–36. http://dx.doi.org/10.21088/jfct.2454.9363.5219.6.

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46The study presents the assessment of uncertainty calculation generated, within the analysis of selected 23 organophosphrus pesticides residues of bottle guard. The samples were prepared by using a modified quick, easy, cheap, effective, rugged and safe (QuEChERS) analytical protocol. Multiresidue method used for analysis of samples consisted of (i) acetonitrile extraction, (ii) PSA/C18 clean-up and (iii) identification/quantification of residues by GC utilizing either (nitrogen–phosphorus) or mass-selective detectors (quadrupole analyzer) were evaluated. Major sources like weighing of standa

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38

Lindblom, E., U. Jeppsson, and G. Sin. "Identification of behavioural model input data sets for WWTP uncertainty analysis." Water Science and Technology 81, no. 8 (2019): 1558–68. http://dx.doi.org/10.2166/wst.2019.427.

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Abstract Uncertainty analysis is important for wastewater treatment plant (WWTP) model applications. An important aspect of uncertainty analysis is the identification and proper quantification of sources of uncertainty. In this contribution, a methodology to identify an ensemble of behavioural model representations (combinations of input data, model structure and parameter values) is presented and evaluated. The outcome is a multivariate conditional distribution of input data that is used for generating samples of likely inputs (such as Monte Carlo input samples) to perform WWTP model uncertai

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39

Munoz, Albert, Mathew Todres, and Laura Rook. "Empowering Organisations to Gain From Uncertainty: a Conceptualisation of Antifragility Through Leveraging Organisational Routines in Uncertain Environments." Australasian Business, Accounting and Finance Journal 15, no. 3 (2021): 23–42. http://dx.doi.org/10.14453/aabfj.v15i3.3.

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Economic shifts, disruptive innovations, and competitive rivalries continuously reshape the operating environment of organisations. Such uncertainty impacts organisations and raises significant challenges. While many organisations tend to respond to uncertainty by adopting loss minimisation strategies, others see uncertainty as an opportunity to achieve gains. The latter view is exemplified in Taleb’s (2012) concept of ‘antifragility’, a property of systems that gain when exposed to uncertainty. For organisations, the challenge lies in the identification and execution of fundamental artefacts

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40

Gabriele, S., C. Valente, and F. Brancaleoni. "An Interval Uncertainty Based Method for Damage Identification." Key Engineering Materials 347 (September 2007): 551–56. http://dx.doi.org/10.4028/www.scientific.net/kem.347.551.

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The problem of damage identification in presence of uncertainties is faced up in the framework of interval analysis. A method previously developed by the authors in the context of model updating and global minimization for dynamic problems is applied to identify the damage in framed structures. The inclusion property of the interval analysis is exploited to find the bounds of the physical solutions. Model parameters, experimental measures and modelling errors are considered as possible sources of uncertainty. The advantages of the interval approach are discussed through numerical simulations i

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41

Li, Xuefeng, Mohamed Ichchou, Christophe Droz, Abdel-Malek Zine, and Noureddine Bouhaddi. "Algebraic wavenumber identification method in presence of uncertainty." MATEC Web of Conferences 360 (2022): 00005. http://dx.doi.org/10.1051/matecconf/202236000005.

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This paper presents an algebraic wavenumber identification method to identify wavenumbers under stochastic conditions. Stochastic condition results from the introduction of small perturbation which is referred to the uncertainty of measurements points’ coordinates caused by the operation faults or problems with experimental errors. The proposed method is compared with two popular alternatives, namely: inhomogeneous wave correlation method and inverse convolution method which are both capable of extracting the bending wavenumbers of a meta-structure. A good performance is observed for the ident

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42

CHIANG, Shen, Yasuto TACHIKAWA, and Kaoru TAKARA. "AN APPROACH TO UNCERTAINTY IDENTIFICATION IN HYDROLOGICAL MODELING." PROCEEDINGS OF HYDRAULIC ENGINEERING 49 (2005): 211–16. http://dx.doi.org/10.2208/prohe.49.211.

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43

Knudsen, Torben. "SUBSPACE IDENTIFICATION - REDUCING UNCERTAINTY ON THE STOCHASTIC PART." IFAC Proceedings Volumes 35, no. 1 (2002): 235–40. http://dx.doi.org/10.3182/20020721-6-es-1901.00450.

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44

Reynders, Edwin P. B. "Uncertainty quantification in data-driven stochastic subspace identification." Mechanical Systems and Signal Processing 151 (April 2021): 107338. http://dx.doi.org/10.1016/j.ymssp.2020.107338.

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45

Zhou, Tong. "Model uncertainty in frequency response based system identification." Systems & Control Letters 32, no. 3 (1997): 121–33. http://dx.doi.org/10.1016/s0167-6911(97)00073-x.

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46

Zhao, Feifei, and Yingxia Li. "Comparison and Uncertainty Analysis on Source Identification Methods." Procedia Environmental Sciences 2 (2010): 749–60. http://dx.doi.org/10.1016/j.proenv.2010.10.086.

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47

Lin, Tsung-Chih, Chia-Hao Kuo, and Valentina Emilia Balas. "Real-time fuzzy system identification using uncertainty bounds." Neurocomputing 125 (February 2014): 195–216. http://dx.doi.org/10.1016/j.neucom.2013.05.011.

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48

Grant, Fiona, and Michael A. Hogg. "Self-uncertainty, social identity prominence and group identification." Journal of Experimental Social Psychology 48, no. 2 (2012): 538–42. http://dx.doi.org/10.1016/j.jesp.2011.11.006.

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49

Yan, B. F., A. Miyamoto, and E. Brühwiler. "Wavelet transform-based modal parameter identification considering uncertainty." Journal of Sound and Vibration 291, no. 1-2 (2006): 285–301. http://dx.doi.org/10.1016/j.jsv.2005.06.005.

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50

Häggblom, Kurt E. "MIMO Uncertainty Model Identification of Time-Delay Systems." IFAC Proceedings Volumes 45, no. 15 (2012): 385–90. http://dx.doi.org/10.3182/20120710-4-sg-2026.00114.

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