The Experts below are selected from a list of 858 Experts worldwide ranked by ideXlab platform
Maryam Kouchaki - One of the best experts on this subject based on the ideXlab platform.
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vicarious moral licensing the influence of others past moral actions on moral behavior
Journal of Personality and Social Psychology, 2011Co-Authors: Maryam KouchakiAbstract:This article investigates the effect of others' prior nonprejudiced behavior on an individual's subsequent behavior. Five studies supported the hypothesis that people are more willing to express prejudiced attitudes when their group members' past behavior has established nonprejudiced credentials. Study 1a showed that participants who were told that their group was more moral than similar other groups were more willing to describe a job as better suited for Whites than for African Americans. In Study 1b, when given information on group members' prior nondiscriminatory behavior (selecting a Hispanic applicant in a prior Task), participants subsequently gave more discriminatory ratings to the Hispanic applicant for a position stereotypically suited for majority members (Whites). In Study 2, moral self-concept mediated the effect of others' prior nonprejudiced actions on a participant's subsequent prejudiced behavior such that others' past nonprejudiced actions enhanced the participant's moral self-concept, and this inflated moral self-concept subsequently drove the participant's prejudiced ratings of a Hispanic applicant. In Study 3, the moderating role of identification with the credentialing group was tested. Results showed that participants expressed more prejudiced attitudes toward a Hispanic applicant when they highly identified with the group members behaving in nonprejudiced manner. In Study 4, the credentialing Task was dissociated from the participants' own Judgmental Task, and, in addition, identification with the credentialing group was manipulated rather than measured. Consistent with prior studies, the results showed that participants who first had the opportunity to view an in-group member's nonprejudiced hiring decision were more likely to reject an African American man for a job stereotypically suited for majority members. These studies suggest a vicarious moral licensing effect.
Abrahamsson Thomas - One of the best experts on this subject based on the ideXlab platform.
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Automated modal parameter estimation using correlation analysis and bootstrap sampling
'Elsevier BV', 2018Co-Authors: Yaghoubi Nasrabadi Vahid, Khorsand Vakilzadeh Majid, Abrahamsson ThomasAbstract:The estimation of modal parameters from a set of noisy measured data is a highly Judgmental Task, with user expertise playing a significant role in distinguishing between estimated physical and noise modes of a test-piece. Various methods have been developed to automate this procedure. The common approach is to identify models with different orders and cluster similar modes together. However, most proposed methods based on this approach suffer from high-dimensional optimization problems in either the estimation or clustering step. To overcome this problem, this study presents an algorithm for autonomous modal parameter estimation in which the only required optimization is performed in a three-dimensional space. To this end, a subspace-based identification method is employed for the estimation and a non-iterative correlation-based method is used for the clustering. This clustering is at the heart of the paper. The keys to success are correlation metrics that are able to treat the problems of spatial eigenvector aliasing and nonunique eigenvectors of coalescent modes simultaneously. The algorithm commences by the identification of an excessively high-order model from frequency response function test data. The high number of modes of this model provides bases for two subspaces: one for likely physical modes of the tested system and one for its complement dubbed the subspace of noise modes. By employing the bootstrap resampling technique, several subsets are generated from the same basic dataset and for each of them a model is identified to form a set of models. Then, by correlation analysis with the two aforementioned subspaces, highly correlated modes of these models which appear repeatedly are clustered together and the noise modes are collected in a so-called Trashbox cluster. Stray noise modes attracted to the mode clusters are trimmed away in a second step by correlation analysis. The final step of the algorithm is a fuzzy c-means clustering procedure applied to a three-dimensional feature space to assign a degree of physicalness to each cluster. The proposed algorithm is applied to two case studies: one with synthetic data and one with real test data obtained from a hammer impact test. The results indicate that the algorithm successfully clusters similar modes and gives a reasonable quantification of the extent to which each cluster is physical
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Towards an Automatic Modal Parameter Estimation Framework: Mode Clustering
'Springer Science and Business Media LLC', 2015Co-Authors: Khorsand Vakilzadeh Majid, Yaghoubi Nasrabadi Vahid, Johansson Anders, Abrahamsson ThomasAbstract:The estimation of modal parameters from a set of measured data is a highly Judgmental Task, with user expertise playing a significant role for distinguishing between physical and spurious modes. However, it can be very tedious especially in situations when the data is difficult to analyze. This study presents a new algorithm for mode clustering as a preliminary step in a multi-step algorithm for performing physical mode selection with little or no user interaction. The algorithm commences by identification of a high-order model from estimated frequency response functions to collect all the important characteristics of the structure in a so-called library of modes. This often results in the presence of spurious modes which can be detected on the basis of the hypothesis that spurious modes are estimated with a higher level of uncertainty comparing to physical modes. Therefore, we construct a series of data using a simple random sampling technique in order to obtain a set of linear systems using subspace identification. Then, their similar modes are grouped together using a new correlation criterion, which is called Modal Observability Correlation (MOC). An illustrative example shows the efficiency of the proposed clustering technique and also demonstrates its capability to dealing with inconsistent data
Yaghoubi Vahid - One of the best experts on this subject based on the ideXlab platform.
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Automated Modal Parameter Estimation Using Correlation Analysis and Bootstrap Sampling
'Elsevier BV', 2017Co-Authors: Yaghoubi Vahid, Vakilzadeh, Majid K., Abrahamsson, Thomas J. S.Abstract:The estimation of modal parameters from a set of noisy measured data is a highly Judgmental Task, with user expertise playing a significant role in distinguishing between estimated physical and noise modes of a test-piece. Various methods have been developed to automate this procedure. The common approach is to identify models with different orders and cluster similar modes together. However, most proposed methods based on this approach suffer from high-dimensional optimization problems in either the estimation or clustering step. To overcome this problem, this study presents an algorithm for autonomous modal parameter estimation in which the only required optimization is performed in a three-dimensional space. To this end, a subspace-based identification method is employed for the estimation and a non-iterative correlation-based method is used for the clustering. This clustering is at the heart of the paper. The keys to success are correlation metrics that are able to treat the problems of spatial eigenvector aliasing and nonunique eigenvectors of coalescent modes simultaneously. The algorithm commences by the identification of an excessively high-order model from frequency response function test data. The high number of modes of this model provide bases for two subspaces: one for likely physical modes and one for its complement dubbed the subspace of noise modes. By employing the bootstrap resampling technique, several subsets are generated from the same basic dataset and for each of them a model is identified to form a set of models. Then, by correlation analysis, highly correlated modes of these models which appear repeatedly are clustered together and the noise modes are collected in a so-called Trashbox cluster. Stray noise modes attracted to the mode clusters are trimmed away by correlation analysis. The final step is a fuzzy c-means clustering procedure
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System identification of large-scale linear and nonlinear structural dynamicmodels
Chalmers University of Technology Göteborg, 2016Co-Authors: Yaghoubi VahidAbstract:System identification is a powerful technique to build a model from measurement data by using methods from different fields such as stochastic inference, optimization and linear algebra. It consists of three steps: collecting data, constructing a mathematical model and estimating its parameters. The available data often do not contain enough information or contain too much noise to enable an estimation of all uncertain model parameters with a good-enough precision. These are examples of challenges in the field of system identification. To construct a mathematical model, one should decide upon a model structure and then estimate its associated parameters. This model structure could be built with clear physical interpretation of its parameters like a parameterized finite element model, or be built just to fit to test data like general state-space or modal model. Each such model class has its own identification challenges. For the former, the complexity of finite element models can create an obstacle because of their time-consuming simulation. Furthermore, if a linear model does not represent the data with reasonable accuracy, nonlinear models need to be engaged and their modeling and parameterization impose even bigger challenges. For the latter, selecting a proper model order is a challenge and the physical relevance of identified states is an important issue. Deciding upon the physical relevance of states is presently a highly Judgmental Task and to instead do a classification based on physical relevance in an automated fashion is a formidable challenge. In-depth studies of such modeling and computational challenges are presented here and proper tools are suggested. They specifically target problems encountered in identification of large-scale linear and nonlinear structures. An experimental design strategy is proposed to increase the information content of test data for linear structures. By combining some new correlation metrics with a bootstrap data resampling technique, an automated procedure is developed that gives a proper model order that represent test data. The procedure’s focus is on the physical relevance of identified states and on uncertainty quantification of parameter estimates. A method for stochastic parameter calibration of linear finite element models is developed by using a damping equalization method. Bootstrapping is used also here to estimate the uncertainty on the model parameters and response predictions. For identification of nonlinear systems a method is developed in which the information content of the data is increased by incorporating multiple harmonics of the response spectra. The parameter uncertainty is here estimated by employing a cross-validation technique. A fast higher-order time-integration method is developed which combines the well-known pseudo-force method with exponential time-integration methods. High-order-hold interpolation schemes are derived to increase the methods stability. As an alternative, to speed up the computations for large-scale linear models, a surrogate model for frequency response functions is developed based on sparse Polynomial Chaos Expansion
Paul F. M. Krabbe - One of the best experts on this subject based on the ideXlab platform.
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Quantification of Health by Scaling Similarity Judgments
2014Co-Authors: Er M. M. Arons, Paul F. M. KrabbeAbstract:Objective: A new methodology is introduced to scale health states on an interval scale based on similarity responses. It could be well suited for valuation of health states on specific regions of the health continuum that are problematic when applying conventional valuation techniques. These regions are the top-end, bottom-end, and states around ‘dead’. Methods: Three samples of approximately 500 respondents were recruited via an online survey. Each sample received a different Judgmental Task in which similarity data were elicited for the top seven health states in the dementia quality of life instrument (DQI). These states were ‘111111 ’ (no problems on any domain) and six others with some problems (level 2) on one domain. The Tasks presented two (dyads), three (triads), or four (quads) DQI health states. Similarity data were transformed into interval-level scales with metric and non-metric multidimensional scaling algorithms. The three response Tasks were assessed for their feasibility and comprehension. Results: In total 532, 469, and 509 respondents participated in the dyads, triads, and quads Tasks respectively. After the scaling procedure, in all three response Tasks, the best health state ‘111111 ’ was positioned at one end of the health-state continuum and state ‘111211 ’ was positioned at the other. The correlation between the metric scales ranged from 0.73 to 0.95, while the non-metric scales ranged from 0.76 to 1.00, indicating strong to near perfect associations. There were no apparent differences in the reported difficulty of the response Tasks, but the triads had the highest number of drop-outs
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Judgmental Tasks used in measurement methods.
2013Co-Authors: Paul F. M. KrabbeAbstract:Schematic representation of the Judgmental Task for three health states by: A = conventional monadic measurement (SG, TTO) by a sample of the general population; B = conventional discrete choice Task (paired comparison) by a sample of the general population; C = multi-attribute preference response model for individual patients (3 patients in this example, each assessing 2 nearby located health states).
Yaghoubi Nasrabadi Vahid - One of the best experts on this subject based on the ideXlab platform.
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Automated modal parameter estimation using correlation analysis and bootstrap sampling
'Elsevier BV', 2018Co-Authors: Yaghoubi Nasrabadi Vahid, Khorsand Vakilzadeh Majid, Abrahamsson ThomasAbstract:The estimation of modal parameters from a set of noisy measured data is a highly Judgmental Task, with user expertise playing a significant role in distinguishing between estimated physical and noise modes of a test-piece. Various methods have been developed to automate this procedure. The common approach is to identify models with different orders and cluster similar modes together. However, most proposed methods based on this approach suffer from high-dimensional optimization problems in either the estimation or clustering step. To overcome this problem, this study presents an algorithm for autonomous modal parameter estimation in which the only required optimization is performed in a three-dimensional space. To this end, a subspace-based identification method is employed for the estimation and a non-iterative correlation-based method is used for the clustering. This clustering is at the heart of the paper. The keys to success are correlation metrics that are able to treat the problems of spatial eigenvector aliasing and nonunique eigenvectors of coalescent modes simultaneously. The algorithm commences by the identification of an excessively high-order model from frequency response function test data. The high number of modes of this model provides bases for two subspaces: one for likely physical modes of the tested system and one for its complement dubbed the subspace of noise modes. By employing the bootstrap resampling technique, several subsets are generated from the same basic dataset and for each of them a model is identified to form a set of models. Then, by correlation analysis with the two aforementioned subspaces, highly correlated modes of these models which appear repeatedly are clustered together and the noise modes are collected in a so-called Trashbox cluster. Stray noise modes attracted to the mode clusters are trimmed away in a second step by correlation analysis. The final step of the algorithm is a fuzzy c-means clustering procedure applied to a three-dimensional feature space to assign a degree of physicalness to each cluster. The proposed algorithm is applied to two case studies: one with synthetic data and one with real test data obtained from a hammer impact test. The results indicate that the algorithm successfully clusters similar modes and gives a reasonable quantification of the extent to which each cluster is physical
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Towards an Automatic Modal Parameter Estimation Framework: Mode Clustering
'Springer Science and Business Media LLC', 2015Co-Authors: Khorsand Vakilzadeh Majid, Yaghoubi Nasrabadi Vahid, Johansson Anders, Abrahamsson ThomasAbstract:The estimation of modal parameters from a set of measured data is a highly Judgmental Task, with user expertise playing a significant role for distinguishing between physical and spurious modes. However, it can be very tedious especially in situations when the data is difficult to analyze. This study presents a new algorithm for mode clustering as a preliminary step in a multi-step algorithm for performing physical mode selection with little or no user interaction. The algorithm commences by identification of a high-order model from estimated frequency response functions to collect all the important characteristics of the structure in a so-called library of modes. This often results in the presence of spurious modes which can be detected on the basis of the hypothesis that spurious modes are estimated with a higher level of uncertainty comparing to physical modes. Therefore, we construct a series of data using a simple random sampling technique in order to obtain a set of linear systems using subspace identification. Then, their similar modes are grouped together using a new correlation criterion, which is called Modal Observability Correlation (MOC). An illustrative example shows the efficiency of the proposed clustering technique and also demonstrates its capability to dealing with inconsistent data
