• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.612-616
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• 2007

This paper describes a procedure to improve road noise using DPDS estimation. We can estimate a body local stiffness at chassis mounting point where the path of road input vibration by DPDS with experiment and FE simulation. DPDS result from FE model has a good correlation with experiment data. FE model DPDS shows weak points among chassis mounting points. Body panel thickness and shape were changed to meet DPDS target. Improved DPDS of critical points makes a road noise level lower.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.7
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• pp.1439-1446
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• 2011

This paper describes the algorithm that lowers the dimension, maintains the object recognition and significantly reduces the eigenspace configuration time by combining the higher correlation feature information and Principle Component Analysis. Since the suggested method doesn't require a lot of computation than the method using existing geometric information or stereo image, the fact that it is very suitable for building the real-time system has been proved through the experiment. In addition, since the existing point to point method which is a simple distance calculation has many errors, in this paper to improve recognition rate the recognition error could be reduced by using several successive input images as a unit of recognition with K-Nearest Neighbor which is the improved Class to Class method.

• Journal of Auto-vehicle Safety Association
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• v.5 no.2
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• pp.45-50
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• 2013

The whiplash Injuries due to rear collision occur frequently. As result, in many countries, seat performance is being assessed and developed to improve head whiplash injury in rear collision of passenger car. This study compares whiplash assessment methods in each country. Using the DFSS(Design for Six Sigma) method, the correlation between influence parameters of head restraints and whiplash injury criteria is analyzed. Four control factors are used in this study. And total 11 whiplash injury criteria from NCAP(New Car Assessment Program) of Korea, Europe, China and IIHS(Insurance Institute for Highway Safety) of USA are used for output response. By the experimental design, L9 orthogonal coordinate system is configured and is tested by sled test equipment, twice. By using average assay value and ANOVA, the correlation between control factors and injury criteria has been comprehended. Optimization design of head restraint according the regional seat safety assessment was derived through the correlation.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1031-1053
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• 2016

The health conditions of in-service civil infrastructures can be evaluated by employing structural health monitoring technology. A reliable health evaluation result depends heavily on the quality of the data collected from the structural monitoring sensor network. Hence, the problem of sensor fault diagnosis has gained considerable attention in recent years. In this paper, an innovative sensor fault diagnosis method that focuses on fault detection and isolation stages has been proposed. The dynamic or auto-regressive characteristic is firstly utilized to build a multivariable statistical model that measures the correlations of the currently collected structural responses and the future possible ones in combination with the canonical correlation analysis. Two different fault detection statistics are then defined based on the above multivariable statistical model for deciding whether a fault or failure occurred in the sensor network. After that, two corresponding fault isolation indices are deduced through the contribution analysis methodology to identify the faulty sensor. Case studies, using a benchmark structure developed for bridge health monitoring, are considered in the research and demonstrate the superiority of the new proposed sensor fault diagnosis method over the traditional principal component analysis-based and the dynamic principal component analysis-based methods.

• Bulletin of the Korean Chemical Society
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• v.20 no.8
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• pp.897-904
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• 1999

We have presented the thermodynamic, structural and dynamic properties of liquid pentane isomers - normal pentane, isopentane, and neopentane - using an expanded collapsed atomic model. The thermodynamic properties show that the intermolecular interactions become weaker as the molecular shape becomes more nearly spherical and the surface area decreases with branching. The structural properties are well predicted from the site-site radial, the average end-to-end distance, and the root-mean-squared radius of gyration distribution func-tions. The dynamic properties are obtained from the time correlation functions - the mean square displacement (MSD), the velocity auto-correlation (VAC), the cosine (CAC), the stress (SAC), the pressure (PAC), and the heat flux auto-correlation (HFAC) functions - of liquid pentane isomers. Two self-diffusion coefficients of liquid pentane isomers calculated from the MSD's via the Einstein equation and the VAC's via the Green-Kubo relation show the same trend but do not coincide with the branching effect on self-diffusion. The rotational re-laxation time of liquid pentane isomers obtained from the CAC's decreases monotonously as branching increases. Two kinds of viscosities of liquid pentane isomers calculated from the SAC and PAC functions via the Green-Kubo relation have the same trend compared with the experimental results. The thermal conductivity calculated from the HFAC increases as branching increases.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.51-55
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• 2007

In this paper Statistical Energy Analysis has been considered to predict high frequency air borne interior noise. Dash panel Insulation is major part to reduce engine excitation noise. Transmission loss and absorption coefficient are considered to predict dash insulation performance. Transmission lose is derived from coupling loss factor and absorption coefficient is derived from internal damping loss factor. Material Biot properties were used to calculate each loss factors. Insulation geometry thickness distribution was hard to measure, so FeGate software was used to calculate thickness map from CAD drawing. Each predicted transmission losses between conventional insulation and light weight insulation were compared with SEA. Transmission loss measurement was performed to validate each prediction result, and it showed good correlation between prediction and measurement. Finally interior noise prediction was performed and result showed light weight insulation system can reduce 40% weight to keep similar performance with conventional insulation system, even though light weigh insulation system has lower sound transmission loss and higher absorption coefficient than conventional system.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.6
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• pp.891-897
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• 2013

The spectrum and the number of the values of the cross-correlation function between the maximal period binary sequences have been extensively studied because of their importance in communications applications. In this paper, we propose the new family of the sequences using the decimation $d=2^{m-1}(3{\cdot}2^{m}-1)$. And we find the spectrum of the cross-correlation function of the sequences and analyze the number of times each value occurs for $0{\leq}{\tau}{\leq}2^{n}-2$.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.1
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• pp.75-80
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• 2013

In this paper, We proposed a Optical Correlation for indoor positioning based LED-ID. The proposed Optical Correlation has a advantage to low-interference between spread code number sequences and LED-ID. it is applied a spread code to reduce the interference with additional information based LED-ID. The additional information is enable to detect in transmitted signal using auto correlation. Also we designed and implemented the Optical Correlation for clearly detecting the additional information. Simulations were performed to confirm the performance of BER and the power of additional information. Optical Correlation simulator to indoor positioning based LED-ID was implemented to prove a usefulness.

• Korean Journal of Radiology
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• v.22 no.11
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• pp.1764-1776
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• 2021

Objective: This study aimed to validate a deep learning-based fully automatic calcium scoring (coronary artery calcium [CAC]_auto) system using previously published cardiac computed tomography (CT) cohort data with the manually segmented coronary calcium scoring (CAC_hand) system as the reference standard. Materials and Methods: We developed the CAC_auto system using 100 co-registered, non-enhanced and contrast-enhanced CT scans. For the validation of the CAC_auto system, three previously published CT cohorts (n = 2985) were chosen to represent different clinical scenarios (i.e., 2647 asymptomatic, 220 symptomatic, 118 valve disease) and four CT models. The performance of the CAC_auto system in detecting coronary calcium was determined. The reliability of the system in measuring the Agatston score as compared with CAC_hand was also evaluated per vessel and per patient using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. The agreement between CAC_auto and CAC_hand based on the cardiovascular risk stratification categories (Agatston score: 0, 1-10, 11-100, 101-400, > 400) was evaluated. Results: In 2985 patients, 6218 coronary calcium lesions were identified using CAC_hand. The per-lesion sensitivity and false-positive rate of the CAC_auto system in detecting coronary calcium were 93.3% (5800 of 6218) and 0.11 false-positive lesions per patient, respectively. The CAC_auto system, in measuring the Agatston score, yielded ICCs of 0.99 for all the vessels (left main 0.91, left anterior descending 0.99, left circumflex 0.96, right coronary 0.99). The limits of agreement between CAC_auto and CAC_hand were 1.6 ± 52.2. The linearly weighted kappa value for the Agatston score categorization was 0.94. The main causes of false-positive results were image noise (29.1%, 97/333 lesions), aortic wall calcification (25.5%, 85/333 lesions), and pericardial calcification (24.3%, 81/333 lesions). Conclusion: The atlas-based CAC_auto empowered by deep learning provided accurate calcium score measurement as compared with manual method and risk category classification, which could potentially streamline CAC imaging workflows.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.07a
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• pp.476-476
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• 2004