• International Journal of Automotive Technology
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• v.8 no.5
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• pp.605-614
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• 2007

This paper presents the research results for the reduction of a gear whine noise based on experimental and analytic methods. The test vehicle has a whine noise problem at the passenger seats in a sport utility vehicle. To identify the transfer path of the interior noise due to the axle system, a vibration path analysis, modal analysis and operational deflection shape analysis are systematically employed. By using these various methods, it has been found that the interior noise generated by the axle system was airborne noise. To reduce and predict the whine generated by the axle system, structural modifications for the axle system are performed by using FEM and BEM techniques. The structural modification of the axle cover is suggested for the reduction of whine noise.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.240-242
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• 1994

Spectroscopic ellipsometry(SE) was employed to characterize the Si/$Si_{1-x}Ge_x$ heterostructure. The dielectric function spectrum of $Si_{1-x}Ge_x$ at an arbitrary x value in the spectral range of $1.5{\sim}4.5\;eV$ was computed by EMA (effective medium approximation) model using the available optical constants measured at a number of fixed x values of Ge composition. The thickness and the Ge composition of $Si_{1-x}Ge_x$ measured by SE was compared with those measured by RBS. DC bias effect on the $E_2$ peak of dielectric function spectra was studied.

• Composites Research
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• v.20 no.3
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• pp.63-66
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• 2007

Structural analysis of automotive engine cover under vibration excitation is performed by finite element analysis (FEA) in order to identify the critical area of the structure. Assembly load due to the tightening of the bolts as well as the vibration excitation were considered to describe the actual loading condition. Natural frequencies of the system were extracted considering the damping effect of the structure. Dynamic analysis was performed based on the extracted natural frequency of the system. Experimental modal analysis (EMA) and measurement of strains were performed to verify the results of the analysis. Analysis results correlated closely with the experimental results. Analysis and experiments showed that contribution of the assembly load should not be ignored to predict the structural failure of the engine cover.

• Steel and Composite Structures
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• v.51 no.6
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• pp.713-727
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• 2024

Damage detection in structures using the change of modal parameters (modal shapes and natural frequencies) has achieved satisfactory results. However, as modal shapes and natural frequencies alone may not provide enough information to accurately detect damages. Therefore, a hybrid singular value decomposition and deep belief network approach is developed to effectively identify damages in aluminum plate structures. Firstly, damage locations are determined using singular value decomposition (SVD) to reveal the singularities of measured displacement modal shapes. Secondly, using experimental modal analysis (EMA) to measure the natural frequencies of damaged aluminum plates as inputs, deep belief network (DBN) is employed to search damage severities from the damage evaluation database, which are calculated using finite element method (FEM). Both simulations and experimental investigations are performed to evaluate the performance of the presented hybrid method. Several damage cases in a simply supported aluminum plate show that the presented method is effective to identify multiple damages in aluminum plates with reasonable precision.

• Journal of Animal Science and Technology
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• v.55 no.1
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• pp.13-18
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• 2013

This study was conducted to estimate the Genomic Estimated Breeding Value (GEBV) using Genomic Best Linear Unbiased Prediction (GBLUP) method in Hanwoo (Korean native cattle) population. The result is expected to adapt genomic selection onto the national Hanwoo evaluation system. Carcass weight (CW), eye muscle area (EMA), backfat thickness (BT), and marbling score (MS) were investigated in 552 Hanwoo progeny-tested steers at Livestock Improvement Main Center. Animals were genotyped with Illumina BovineHD BeadChip (777K SNPs). For statistical analysis, Genetic Relationship Matrix (GRM) was formulated on the basis of genotypes and the accuracy of GEBV was estimated with 10-fold Cross-validation method. The accuracies estimated with cross-validation method were between 0.915~0.957. In 534 progeny-tested steers, the maximum difference of GEBV accuracy compared to conventional EBV for CW, EMA, BT, and MS traits were 9.56%, 5.78%, 5.78%, and 4.18% respectively. In 3,674 pedigree traced bulls, maximum increased difference of GEBV for CW, EMA, BT, and MS traits were increased as 13.54%, 6.50%, 6.50%, and 4.31% respectively. This showed that the implementation of genomic pre-selection for candidate calves to test on meat production traits could improve the genetic gain by increasing accuracy and reducing generation interval in Hanwoo genetic evaluation system to select proven bulls.

• Korean Journal of Agricultural Science
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• v.39 no.3
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• pp.357-364
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• 2012

Genomic breeding value (GEBV) has recently become available in the beef cattle industry. Genomic selection methods are exceptionally valuable for selecting traits, such as marbling, that are difficult to measure until later in life. One method to utilize information from sparse marker panels is the Bayesian model selection method with RJMCMC. The accuracy of prediction varies between a multiple SNP model with RJMCMC (0.47 to 0.73) and a least squares method (0.11 to 0.41) when using SNP information, while the accuracy of prediction increases in the multiple SNP (0.56 to 0.90) and least square methods (0.21 to 0.63) when including a polygenic effect. In the multiple SNP model with RJMCMC model selection method, the accuracy ($r^2$) of GEBV for marbling predicted based only on SNP effects was 0.47, while the $r^2$ of GEBV predicted by SNP plus polygenic effect was 0.56. The accuracies of GEBV predicted using only SNP information were 0.62, 0.68 and 0.73 for CWT, EMA and BF, respectively. However, when polygenic effects were included, the accuracies of GEBV were increased to 0.89, 0.90 and 0.89 for CWT, EMA and BF, respectively. Our data demonstrate that SNP information alone is missing genetic variation information that contributes to phenotypes for carcass traits, and that polygenic effects compensate genetic variation that whole genome SNP data do not explain. Overall, the multiple SNP model with the RJMCMC model selection method provides a better prediction of GEBV than does the least squares method (single marker regression).

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.12
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• pp.1071-1086
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• 2016

The safety of aircraft can be threatened by environmental factors, such as icing, turbulence, and lightning strike. Due to its adverse effects on aircraft structure and electronic components of aircraft, lightning strike is one of the biggest hazards on aircraft safety. Lightning strike can inject high voltage electric current to the aircraft, which may generate strong magnetic field and extreme hot spots, leading to severe damage of structure or other equipment in aircraft. In this work, mechanism of lightning strike and associated direct and indirect effects of lightning on aircraft were studied. First, on the basis of aircraft lightning regulations provided by Aerospace Recommended Practice (ARP), we considered different lightning waveform and zones of an aircraft. A coupled thermal-electrical computational model of ABAQUS was then used for simulating flow of heat and electric current caused by a lightning strike. A study on fuel tank, with and without lightning protection system, was also conducted using the computational model. Finally, electric current flow on two full scale airframes was analyzed using the EMA3D code.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.7
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• pp.1057-1067
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• 2020

Objective: This study evaluated the effect of pedigree errors (PEs) on the accuracy of estimated breeding value (EBV) and genetic gain for carcass traits in Korean Hanwoo cattle. Methods: The raw data set was based on the pedigree records of Korean Hanwoo cattle. The animals' information was obtained using Hanwoo registration records from Korean animal improvement association database. The record comprised of 46,704 animals, where the number of the sires used was 1,298 and the dams were 38,366 animals. The traits considered were carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT), and marbling score (MS). Errors were introduced in the pedigree dataset through randomly assigning sires to all progenies. The error rates substituted were 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, and 80%, respectively. A simulation was performed to produce a population of 1,650 animals from the pedigree data. A restricted maximum likelihood based animal model was applied to estimate the EBV, accuracy of the EBV, expected genetic gain, variance components, and heritability (h²) estimates for carcass traits. Correlation of the simulated data under PEs was also estimated using Pearson's method. Results: The results showed that the carcass traits per slaughter year were not consistent. The average CWT, EMA, BFT, and MS were 342.60 kg, 78.76 ㎠, 8.63 mm, and 3.31, respectively. When errors were introduced in the pedigree, the accuracy of EBV, genetic gain and h² of carcass traits was reduced in this study. In addition, the correlation of the simulation was slightly affected under PEs. Conclusion: This study reveals the effect of PEs on the accuracy of EBV and genetic parameters for carcass traits, which provides valuable information for further study in Korean Hanwoo cattle.

• Journal of Animal Science and Technology
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• v.44 no.5
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• pp.593-598
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• 2002

This experiment was prosecuted to estimate the carcass traits and their relationships in Hanwoo steers. The mean carcass weight(CWT), back fat thickness (BFT), meat, bone, intra- muscular fat (IMF) and eye muscle area (EMA) of steers were 300.27 kg, 9.23 mm, 58.99%, 12.92%, 28.08% and 74.00 $cm^2$, respectively. The pHB(before aging), pHA(after aging), cook loss before aging($CL_B$) and cook loss after aging ($CL_A$) were 5.32, 5.60, 16.43% and 19.21%, respectively. It was also found that the MS and FC were 3.67 and 3.17, respectively. The EMA and quantity index (QIX) were positively correlated with CWT, meat% and bone% but negatively correlated with IMF%. The BFT was positively correlated with CWT and meat% but negatively correlated with bone% and IMF%. The pH (both before and after aging) was negatively correlated with CL (both before and after aging), water loss during freezing (WLF) and FC. The muscle score(MS) was negatively correlated with CL but positively correlated with WLF and FC. The ARM, HRD, CHW and CTM increased with the increasing of fat% and sharply decreased with the increasing of meat% in the carcass.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.10
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• pp.1117-1125
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• 2007

In this paper, a study has been performed on the design of small loop probe and analysis of induced electromagnetic wave signal from a smartcard for contactless cryptographic analysis. Probes for cryptographic analysis are different from conventional EM probes, because the purpose of proposed probe is to obtain the information for secret key analysis of cryptographic system. The waveform of induced voltage on probe must be very close to radiated waveform from IC chip on smartcard because electromagnetic attack makes an attempt to analyze the radiated waveform from smartcard. In order to obtain secret key information, we need to study about cryptographic analysis using electromagnetic waves, an approximate model of source, characteristic of probe for cryptographic analysis, measurement of electromagnetic waves and calibration of probes. We measured power consumption signal on a smartcard chip and electromagnetic wave signal using proposed probe and compared with two signals of EMA point of view. We verified experimently the suitability of the proposed small loop probe for contactless cryptographic analysis by applying ARIA algorithm.