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Do individual characteristics influence the beta-elliptic modeling errors during ellipse drawing movements?

COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2022年第7期25卷 783-793页

作者： Dhieb, Thameur Rezzoug, Nasser Boubaker, Houcine Ben Ayed, Mounir Alimi, Adel M. Univ Sousse ISITCom Networks & Multimedia Dept Sousse Tunisia Univ Sfax REGIM Lab REs Grp Intelligent Machines Sfax Tunisia Univ Bordeaux Ctr Bordeaux Sud Ouest CNRS UMR5218 InriaEquipe Projet AUCTUS Inria IMS Talence France Univ Toulon & Var Fac Sports Sci Toulon France Univ Sfax Fac Sci Sfax Comp Sci & Commun Dept Sfax Tunisia Univ Johannesburg Fac Engn & Built Environm Dept Elect & Elect Engn Sci Johannesburg South Africa

This paper investigates whether age, gender, and degree of familiarity with writing have an influence on the Beta-elliptic model errors during hand-drawing on a graphical tablet. A database of elliptical hand drawing movements was built within a sample of 99 participants aged between 19 and 85 years. Using the Beta-elliptic model, the velocity profile was modeled by overlapped Beta functions and the drawing trajectory was segmented between velocity extrema and each segment geometry was modeled by elliptic arcs. Average absolute and relative geometric, curvature and curvilinear velocity errors were 0.27 mm, 0.68%, 4.54 mm, 0.48%, 4.68 mm/s, and 8.79% respectively. Statistical analyses revealed not significant or low correlation between modeling errors and age and movement velocity, and no significant or low error differences according to gender or degree of familiarity with writing.

关键词： Beta-elliptic model handwriting aging modeling errors

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Dynamic force identification considering modeling errors using modal expansion method and relevant vector regression algorithm

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AEROSPACE SCIENCE AND TECHNOLOGY 2024年 154卷

作者： Zhou, Xinyuan Gao, Zehan Zhang, Yahui Dalian Univ Technol State Key Lab Struct Anal Optimizat & CAE Software Dalian 116024 Liaoning Peoples R China Aerosp Sci & Ind Def Technol Res & Test Ctr Beijing 100854 Peoples R China

Accurate identification and estimation of external forces on launch vehicles, aircraft, and other in-service engineering structures plays a vital role in structural design and health monitoring. Considering structural modeling errors, this paper develops a novel force identification method based on modal expansion and relevant vector regression (RVR). Initially, the incomplete and noisy experimental modal data are used to modify the stiffness and mass matrices of the finite element (FE) model, reducing the impact of modeling errors on force identification accuracy. Subsequently, to account for noise disturbances and potential information about unknown initial conditions in the measured acceleration responses, the external forces and the unknown initial conditions are respectively expanded using trigonometric functions and the mode shapes based on the function fitting technique. On this basis, the force identification equation is established for the updated model. The RVR algorithm is integrated into modal expansion and force identification. Firstly, the mode shapes of the FE model are utilized to expand the incomplete and noisy experimental mode shapes to their full degree of freedom, significantly enhancing the accuracy of the model updating. Secondly, by combining the measured acceleration responses, the base coefficients can be solved sparsely, effectively identifying the dynamic forces even when unknown initial conditions are present.

关键词： Force identification modeling errors Relevant vector regression Modal expansion Function fitting technique Unknown initial conditions

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modeling errors-preserving constrained sensitivity analysis

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NUCLEAR ENGINEERING AND DESIGN 2020年 365卷 110729-110729页

作者： Huang, Dongli Abdel-Khalik, Hany Purdue Univ Sch Nucl Engn 205 Gates Rd W Lafayette IN 47907 USA

This manuscript addresses a key technical gap in cross-section adjustment and uncertainty analysis research employed in support of neutronic model validation and data assimilation. The key assumption in this body of research is that cross-sections, in condensed group format, are responsible for the key sources of uncertainties in responses of interest, e.g., multiplication factor, flux and power distribution, etc. In reality, the condensed group cross-sections are contaminated not only by epistemic uncertainties, propagated from ENDF, but also by the various modeling assumptions and approximations, typically customizing the condensed cross-sections to the reactor type and flux spectra. Thus, any reliable procedure employed to identify or adjust for key sources of uncertainties must be able to distinguish between epistemic uncertainties and modeling errors. This manuscript proposes a new algorithm that adds new types of constraints to conventional sensitivity analysis procedure to ensure that uncertainty analysis and cross-section adjustment studies are not contaminated by the modeling errors. The proposed approach will directly benefit research and development efforts in many areas of analysis relying on uncertainty and sensitivity methods, such as criticality safety, core monitoring, and analysis of first-of-a-kind reactor and fuel concept technologies. Demonstrative examples using a CANDU-6 core model during steady state and transient conditions are employed to exemplify application of the proposed methodology.

关键词： Uncertainty Analysis modeling errors Cross-section Adjustments Model Validation

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Impacts of modeling errors and Randomness on Topology Identification of Electric Distribution Network

Impacts of Modeling Errors and Randomness on Topology Identi...

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IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Fei, Wanghao Wu, Di Jiang, John N. Univ Oklahoma Sch Elect & Comp Engn Norman OK 73019 USA North Dakota State Univ Dept Elect & Comp Engn Fago ND USA

ISBN: (纸本)9781538635964

A few models have been established to study the relationship between SCADA voltages in the cyber layer and the topology of electric distribution networks in the physical layer. While these models could identify the topology of distribution networks under some assumptions, one of the issues that havent been deeply studied is whether the topology of distribution networks could be identified when violating these assumptions. We define the violation of these assumptions as the impact of modeling errors which may have different characteristics comparing to other errors. When the model of using SCADA voltage correlation to identify the topology of distribution networks is constructed, a few assumptions are made such as uniformed L/R ratio and the uncorrelated injective power which is a very strict restriction in practice. This paper focuses on understanding the impact of the identification result when violating some of these assumptions that used to established the model. Specifically, 4 cases are presented in this paper while each case contains whether violates the assumption of uniformed L/R ratio and uncorrelated injective power or not. The analysis results show that violating each individual assumptions could cause inaccurate identification result. Also, the results show that the errors could be decreased by increasing the sample size of the SCADA voltage. Understanding the impact of errors is useful to better understand weakness of the model and find the way of decreasing errors.

关键词： distribution networks identification modeling errors cyber-physical system

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Sensitivity analysis of parametric uncertainties and modeling errors in computational-mechanics models by using a generalized probabilistic modeling approach

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2017年 167卷 394-405页

作者： Arnst, M. Goyal, K. Univ Liege Aerosp & Mech Engn Allee Decouverte 9 B-4000 Liege Belgium Thapar Univ Sch Math Patiala Punjab India

Engineering analyses of structures may be confronted with many sources of uncertainty, which may be of different types, such as parametric uncertainties versus modeling errors, and which may pertain to different structural components when complex structures are analyzed. Soize, Generalized probabilistic approach of uncertainties in computational dynamics using random matrices and polynomial chaos decompositions, Int. J. Numer. Meth. Eng., 81:939-970, 2010 has recently introduced a generalized probabilistic modeling approach, which can individually represent parametric uncertainties and modeling errors and which can individually represent sources of uncertainty pertaining to different structural components of complex structures. In this paper, we propose to augment this generalized probabilistic modeling approach with a stochastic sensitivity analysis in order to quantify and gain insight into separate impacts of distinct sources of uncertainty on quantities of interest. We demonstrate the proposed methodology by applying it to two computational-mechanics models involving uncertainty. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Uncertainty quantification Stochastic sensitivity analysis Random matrix Random field Parametric uncertainties modeling errors

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Swarming Movement of Dynamical Multi-Agent Systems with modeling errors 20

Swarming Movement of Dynamical Multi-Agent Systems with Mode...

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20th IEEE International Conference on Computational Science and Engineering (CSE) / 15th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (EUC)

作者： Wang, Fusheng Yang, Hongyong Ludong Univ Sch Informat & Elect Engn Yantai 264025 Peoples R China

ISBN: (纸本)9781538632215

Swarming movement of dynamical multi-agent systems with modeling errors is studied. Supposing multi-agent systems have multiple leader agents and their topology is dynamically changed with jointly-connected, swarming algorithm of multi-agent systems with model uncertain parameters is proposed. The moving stability of multi-agent systems with time-varying delays is investigated by applying Matrix theory and Lyapunov-Krasovskii function. Many stability conditions are given for swarming movement of multi agent systems with dynamical topologies. Finally, simulations are used to verify the effectiveness of the conclusion.

关键词： Swarming movement multi-agent systems modeling errors dynamical topologies

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TOWARDS A FRAMEWORK TO DETECT modeling AND SEMANTIC errors IN EVENT GRAPHS 21

TOWARDS A FRAMEWORK TO DETECT MODELING AND SEMANTIC ERRORS I...

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International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS)

作者： Osais, Yahya E. King Fahd Univ Petr & Minerals Dept Comp Engn Dhahran 31261 Saudi Arabia

ISBN: (纸本)9781538671450;9781538671443

An event graph is a visual representation of a discrete-event simulation model. It shows the events that occur in the model and the scheduling relationships between them. It also shows the impact of events on the state of the simulation model. On the other hand, event graphs have no formal semantics. This can lead to modeling and execution errors. For example, if a deadlock in the event graph is not detected while constructing the model, the execution of the model will halt when the deadlock is encountered. Deadlocks can he easily detected by inspecting the corresponding event graph of a simulation problem. This paper represents the first step towards building a framework for assisting in detecting modeling and semantic errors in event graphs. The author is going to point out some common modeling issues with event graphs. Several examples will he given to illustrate the power of event graphs and their shortcomings.

关键词： discrete-event simulation event graphs modeling errors semantics code synthesis

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Accounting for environmental variability, modeling errors, and parameter estimation uncertainties in structural identification

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JOURNAL OF SOUND AND VIBRATION 2016年 374卷 92-110页

作者： Behmanesh, Iman Moaveni, Babak WSP Parsons Brinckerhoff Bldg Struct New York NY USA Tufts Univ Dept Civil & Environm Engn Medford MA 02155 USA

This paper presents a Hierarchical Bayesian model updating framework to account for the effects of ambient temperature and excitation amplitude. The proposed approach is applied for model calibration, response prediction and damage identification of a footbridge under changing environmental/ambient conditions. The concrete Young's modulus of the footbridge deck is the considered updating structural parameter with its mean and variance modeled as functions of temperature and excitation amplitude. The identified modal parameters over 27 months of continuous monitoring of the footbridge are used to calibrate the updating parameters. One of the objectives of this study is to show that by increasing the levels of information in the updating process, the posterior variation of the updating structural parameter (concrete Young's modulus) is reduced. To this end, the calibration is performed at three information levels using (1) the identified modal parameters, (2) modal parameters and ambient temperatures, and (3) modal parameters, ambient temperatures, and excitation amplitudes. The calibrated model is then validated by comparing the model-predicted natural frequencies and those identified from measured data after deliberate change to the structural mass. It is shown that accounting for modeling error uncertainties is crucial for reliable response prediction, and accounting only the estimated variability of the updating structural parameter is not sufficient for accurate response predictions. Finally, the calibrated model is used for damage identification of the footbridge. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Hierarchical Bayesian model updating Effects of changing environmental conditions modeling errors Response prediction Damage identification

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Nonlinear Difference Imaging Approach to Three-Dimensional Electrical Impedance Tomography in the Presence of Geometric modeling errors

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2016年第9期63卷 1956-1965页

作者： Liu, Dong Kolehmainen, Ville Siltanen, Samuli Laukkanen, Anne-Maria Seppanen, Aku Univ Eastern Finland Dept Appl Phys Kuopio 70211 Finland Univ Helsinki Dept Math & Stat FIN-00014 Helsinki Finland Univ Tampere Speech & Voice Res Lab FIN-33101 Tampere Finland

Objective: To evaluate the recently proposed nonlinear difference imaging approach to electrical impedance tomography (EIT) in realistic 3-D geometries. Methods: In this paper, the feasibility of nonlinear difference approach-based EIT is tested using simulation studies in 3-D geometries of thorax and larynx, and with an experimental study of a thorax-shaped water tank. All test cases exhibit severe modeling errors due to uncertainty in the boundary shape of the body. Results: In all test cases, the conductivity change reconstructed with nonlinear difference imaging outperforms the conventional reconstructions qualitatively and quantitatively. Conclusion: The results demonstrate that the nonlinear difference reconstructions tolerate geometrical modeling errors at least to the same extent as the conventional linear approach and produce quantitatively more accurate information on the conductivity change. Significance: Physiological processes that produce changes in the electrical conductivity of the body can be monitored with difference imaging based on EIT. The wide popularity of linearized difference imaging in EIT is mainly based on its good tolerance for the ubiquitous modeling errors, which are predominantly caused by inexact knowledge of the body geometry. However, the linearized difference imaging produces only qualitative information on the conductivity change, and the feasibility of the estimates also depends on the selection of the linearization point which ideally should be equal to the conductivity of the initial state. Based on the findings of this paper, these problems can be avoided by nonlinear difference imaging, and potentially the approach can enable quantitative imaging of conductivity change in medical applications.

关键词： Difference imaging electrical impedance tomography (EIT) inverse problems modeling errors

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Effective structural impact detection and localization using convolutional neural network and Bayesian information fusion with limited sensors

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2025年 224卷

作者： Fu, Yuguang Wang, Zixin Maghareh, Amin Dyke, Shirley Jahanshahi, Mohammad Shahriar, Adnan Zhang, Fan Nanyang Technol Univ Sch Civil & Environm Engn Singapore 639798 Singapore Purdue Univ Lyles Sch Civil Engn W Lafayette IN 47907 USA Univ Texas San Antonio Dept Mech Engn San Antonio TX 78249 USA

Due to their unpredictable nature, many impact events (e.g., overheight vehicles striking on bridges) go unnoticed or get reported many hours later. However, they can induce structural failures or hidden damage that accelerates the structure's long-term degradation. Therefore, prompt impact detection and localization strategies are essential for early warning of impact events and rapid maintenance of structures. Most existing impact detection strategies are developed for aircraft composite panels utilizing high-rate synchronized measurement from densely deployed sensors. Limited efforts have been made for infrastructure or human habitats which generally require large-scale but low-rate measurement. In particular, due to harsh environments (e.g., deep space habitats under meteoroids), structural impact localization must be robust to limited sensors (e.g., sensor damage during impacts) and multi-source errors (e.g., measurement errors). In this study, an effective impact detection and localization strategy is proposed using a limited number of vibration measurements, especially in harsh environments (e.g. in deep space). Convolutional neural networks are trained for each sensor node and are fused using Bayesian theory to improve the accuracy of impact localization. Special considerations are paid to evaluate the effect of both measurement error and modeling error in the analysis. The proposed strategy is illustrated using 1D structure, and further validated in 3D geodesic dome structure numerically. The results demonstrate that it can detect and localize impact events accurately and robustly on structures.

关键词： Impact detection Impact localization Deep learning Information fusion Measurement errors modeling errors

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