• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.138-142
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• 2018

This study used HILS to test an expansion joint, which is vulnerable to the water hammer effect. The operation data for the HIL simulator was the length rate of the expansion joint by the water hammer, which was used for life prediction based on the vibration durability. For the vibration durability test, the internal pressure of the expansion joint was assumed to be a factor of the durability life, and the lifetime prediction model equation was obtained by curve fitting the lifetime data at each pressure. During the test, the major failure modes of crack and water leakage occurred on the surface of the bellows part. The lifetime prediction model typically follows an inverse power law model. The pressure is a stress factor, and the model is effective in only a specific environment. Therefore, another stress factor such as temperature will be added and considered for a mixed lifetime prediction model in the future.

• Journal of the Korean Society for Railway
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• v.8 no.5
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• pp.439-443
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• 2005

The biggest challenge bridge agencies face is the maintenance of bridges, keeping them safe and serviceable, with limited funds. To maintain the bridges effectively, there is and urgent need to predict their remaining life from a system reliability viewpoint. In this paper, a model using lifetime functions to evaluate the overall system probability of survival of a rail road bridge is proposed. In this model, the rail load bridge is modeled as a system. Using the model, the lifetime of the rail road bridge is predicted.

• Journal of the Korean Society of Safety
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• v.34 no.1
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• pp.8-13
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• 2019

Weight and elongation changes of IV and HIV insulations were measured simultaneously at several given temperature of $80^{\circ}C$, $90^{\circ}C$ and $100^{\circ}C$. And the lifetime was predicted using the Arrhenius model. Based on the initial weight values, a 50% elongation reduction was seen at 6.96% for the IV insulation and 10.29% for the HIV insulation. The activation energy from the slope of the lifetime regression equation was calculated as 92.895 kJ/mol(0.9632 eV) for the IV insulation and 95.213 kJ/mol(0.9873 eV) for the HIV insulation. Also, the expected lifetime at the operating temperature of $30^{\circ}C$ to $90^{\circ}C$ is 2.02 to 94.32 years, and longer lifetime was predicted on HIV insulated wires than on IV insulated wires. As a result, it was found that the thermal characteristics of the HIV insulated wires were about 12.44% better than those of IV insulated wires under the same conditions of use.

• Animal Bioscience
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• v.37 no.4
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• pp.622-630
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• 2024

Objective: Pig breeders cannot obtain phenotypic information at the time of selection for sow lifetime productivity (SLP). They would benefit from obtaining genetic information of candidate sows. Genomic data interpreted using deep learning (DL) techniques could contribute to the genetic improvement of SLP to maximize farm profitability because DL models capture nonlinear genetic effects such as dominance and epistasis more efficiently than conventional genomic prediction methods based on linear models. This study aimed to investigate the usefulness of DL for the genomic prediction of two SLP-related traits; lifetime number of litters (LNL) and lifetime pig production (LPP). Methods: Two bivariate DL models, convolutional neural network (CNN) and local convolutional neural network (LCNN), were compared with conventional bivariate linear models (i.e., genomic best linear unbiased prediction, Bayesian ridge regression, Bayes A, and Bayes B). Phenotype and pedigree data were collected from 40,011 sows that had husbandry records. Among these, 3,652 pigs were genotyped using the PorcineSNP60K BeadChip. Results: The best predictive correlation for LNL was obtained with CNN (0.28), followed by LCNN (0.26) and conventional linear models (approximately 0.21). For LPP, the best predictive correlation was also obtained with CNN (0.29), followed by LCNN (0.27) and conventional linear models (approximately 0.25). A similar trend was observed with the mean squared error of prediction for the SLP traits. Conclusion: This study provides an example of a CNN that can outperform against the linear model-based genomic prediction approaches when the nonlinear interaction components are important because LNL and LPP exhibited strong epistatic interaction components. Additionally, our results suggest that applying bivariate DL models could also contribute to the prediction accuracy by utilizing the genetic correlation between LNL and LPP.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.8
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• pp.1446-1452
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• 1994

In this paper, Weibull distribution is applied to the lifetme distribution of a device. The method of Bayesian estimate used to estimate requiring parameter in order to predict lifetime of device using accelerated lifetime test data, namely failure time of device. The method of Bayesian estimate needs prior information in order to estimate parameter. But this paper proposed the method of parameter estimate without prior information. As stress is temperature, Arrhenius model is applied and the method of linear estimate is applied to predict lifetime of device at the state of normal operation.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.8
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• pp.49-56
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• 1993

In this paper, we present a new lifetime prediction model for PMOSFET by using the correlation between transconductance degradation and substrate current influence. The suggested model is applied to a different channel structured PMOSFET, dgm/gm of both SC-PMOSFET and BC-PMOSFET appear with one straigth line about Qbib, therefore, this model is independent of channel structure. The suggested model is applied to a different drain structured SC-PMOSFET. Unlike S/D structured SC-PMOSFET, dgm/gm of LDD structured SC-PMOSFET appears with one straight line about Qb, therefore, this model is dependent of drain structure.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.12
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• pp.1379-1385
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• 2014

Even the rough prediction of the product test time before the lifetime test of mechanical component begins would be of use in estimating cost and deciding how to keep up with the test. The reliability predictions of mechanical components are difficult because failure or degradation mechanisms are complicated, and few plausible databases are available for lifetime prediction. Therefore, this study conducted lifetime predictions of elastomeric U seals that were respectively installed in a hydraulic actuator and a pneumatic actuator using lifetime models and a field database based on failure physics and an actual test database obtained from the NSWC handbook. To validate the results, the predicted failure rates were compared with the actual lifetime test results acquired in the lab durability tests. Finally, this study discussed an engineering procedure to determine the coefficients in the failure rate models and analyzed the sensitivity of each influential parameter on the seal lifetime.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.9
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• pp.30-37
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• 1993

In this paper, we present a simple and general lifetime prediction model for PMOSFET by using the correlation between transconductance degradation and gate current influence to solve a problem that that I$_{b}$ is dependent on drain structure. The suggested model is applied to a different channel, drain structured PMOSFET. For all PMOSFETs, dg$_{m}$/g$_{m}$ of PMOSFET appears with one straight line about Q$_{g}$, therefore, this model using I$_{g}$ is consistent with experiment result independently of channel, drain structure. It is, therefore, proposed that a model using I$_{g}$ has a general applicability for PMOSFET's.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.3
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• pp.259-265
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• 2016

Recently, life prediction models for Pb-based and Pb-free solders used in chip resistor assemblies under thermal cycling have been introduced. The models suggest that the field lifetimes of Pb-free solders would be better than those of Pb-based solders when used for chip resistors under thermal cycling conditions, while the lifetime of the chip assemblies under accelerated test conditions show a reverse relationship. In this study, the prediction models were verified by applying the model to another research case. Finite element models were built, thermal cycling conditions were applied, and the energy densities were calculated. Finally, life prediction analysis was conducted for the cases where Pb-based and Pb-free solders were used. The prediction results were then compared with the test data of the case. It was verified that the predictions of the developed life cycle models are on the practical scale.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.4
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• pp.173-179
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• 2023

This paper analyzes the time-dependent dielectric breakdown(TDDB) degradation mechanism for each stress region of Peri devices manufactured by 4th generation VNAND process, and presents a complementary lifetime prediction model that improves speed and accuracy in a wider reliability evaluation region compared to the conventional model presented. SiON dielectric nMOSFETs were measured 10 times each under 5 constant voltage stress(CVS) conditions. The analysis of stress-induced leakage current(SILC) confirmed the significance of the field-based degradation mechanism in the low electric field region and the current-based degradation mechanism in the high field region. Time-to-failure(TF) was extracted from Weibull distribution to ascertain the lifetime prediction limitations of the conventional E-model and 1/E-model, and a parallel complementary model including both electric field and current based degradation mechanisms was proposed by extracting and combining the thermal bond breakage rate constant(k) of each model. Finally, when predicting the lifetime of the measured TDDB data, the proposed complementary model predicts lifetime faster and more accurately, even in the wider electric field region, compared to the conventional E-model and 1/E-model.