Mr. Seung-Seop Jin Profile

Seung-Seop Jin

at KAIST

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Publications (3)

Proceedings Article | 20 April 2016 Paper

Likelihood-free Bayesian computation for structural model calibration: a feasibility study

Seung-Seop Jin, Hyung-Jo Jung

Proceedings Volume 9803, 980323 (2016) https://doi.org/10.1117/12.2222029

KEYWORDS: Diagnostics, Bayesian inference, Calibration, Systems modeling, Statistical modeling, Tolerancing, Inverse problems, Error analysis, Monte Carlo methods, Biology, Genetics

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Proceedings Article | 9 March 2014 Paper

Performance enhancement of piezoelectric energy harvesting system using a corrugated cantilever beam

Jeongsu Park, In-ho Kim, SeungSeop Jin, Jeong-Hoi Koo, Hyung-Jo Jung

Proceedings Volume 9057, 90571Y (2014) https://doi.org/10.1117/12.2046579

KEYWORDS: Ferroelectric polymers, Energy harvesting, Beam shaping, Prototyping, Civil engineering, Manufacturing, Numerical simulations, Bridges, Buildings, Mechanical engineering

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Proceedings Article | 9 March 2014 Paper

Vibration-based structural health monitoring using adaptive statistical method under varying environmental condition

Seung-Seop Jin, Hyung-Jo Jung

Proceedings Volume 9064, 90640T (2014) https://doi.org/10.1117/12.2046088

KEYWORDS: Principal component analysis, Data modeling, Damage detection, Environmental sensing, Statistical analysis, Structural health monitoring, Environmental monitoring, Statistical modeling, Systems modeling, Statistical methods

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It is well known that the dynamic properties of a structure such as natural frequencies depend not only on damage but also on environmental condition (e.g., temperature). The variation in dynamic characteristics of a structure due to environmental condition may mask damage of the structure. Without taking the change of environmental condition into account, false-positive or false-negative damage diagnosis may occur so that structural health monitoring becomes unreliable. In order to address this problem, an approach to construct a regression model based on structural responses considering environmental factors has been usually used by many researchers. The key to success of this approach is the formulation between the input and output variables of the regression model to take into account the environmental variations. However, it is quite challenging to determine proper environmental variables and measurement locations in advance for fully representing the relationship between the structural responses and the environmental variations. One alternative (i.e., novelty detection) is to remove the variations caused by environmental factors from the structural responses by using multivariate statistical analysis (e.g., principal component analysis (PCA), factor analysis, etc.). The success of this method is deeply depending on the accuracy of the description of normal condition. Generally, there is no prior information on normal condition during data acquisition, so that the normal condition is determined by subjective perspective with human-intervention. The proposed method is a novel adaptive multivariate statistical analysis for monitoring of structural damage detection under environmental change. One advantage of this method is the ability of a generative learning to capture the intrinsic characteristics of the normal condition. The proposed method is tested on numerically simulated data for a range of noise in measurement under environmental variation. A comparative study with conventional methods (i.e., fixed reference scheme) demonstrates the superior performance of the proposed method for structural damage detection.

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