• Proceedings of the KSME Conference
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• 2004.04a
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• pp.19-24
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• 2004

A number of creep data were collected and filed for type 316LN stainless steels through literature survey and experimental data produced in KAERI. Using these data, polynomial equations for predicting creep life were obtained for Larson Miller (L-M), Qrr-Sherby-Dorn (O-S-D) and Manson-Haferd (M-H) parametric methods. In order to find out the suitability for them, the relative standard error (RSE) and standard error of estimate (SEE) values were obtained by statistical process of creep data. The O-S-D parameter showed better fitting to creep-rupture data than the L-M or the M-H parameters, and the three parametric methods did not generate the large difference in the SEE and the RSE values.

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.104-111
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• 2006

The non-linear properties of rubber material which are described as strain energy function are important parameter to design and evaluate of rubber spring. These are determined by material tests which are uni-axial tension and bi-axial tension. The computer simulation using the nonlinear element analysis program executed to predict and evaluate the load capacity and stiffness for chevron spring. In order to investigate the heat-aging effects on the rubber material properties, the acceleration test were carried out. Compression set results changes as the threshold are used for assessment of the useful life and time to threshold value were plotted against reciprocal of absolute temperature to give the Arrhenius plot. By using the compression set test, several useful life prediction for rubber material were proposed.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1997.11a
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• pp.302-305
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• 1997

Treeing due to partial discharge(PD) is one of the main causes of breakdown of the insulating materials and reduction of tile insulation life. Therefore the necessity for establishing a method to diagnose the aging of insulation materials and to predict the breakdown of insulation has become important. From this viewpoint, our studies diagnose insulation degradation using the method of computer sensing system, which has the advantages of PD and acoustic emission(AE) sensing system. To use advantages of these two methods can be used effectively to search for treeing location and PD in some materials. In analysis method of degradation. using statically operator such as the center of gravity (G). the gradient of the discharge distribution(C), we have analyzed far tole prediction of life which we can be obtained the time, occurred of many pulse of small discharge amplitude.

• Journal of the Korean Society of Safety
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• v.12 no.2
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• pp.57-64
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• 1997

Fatigue crack propagation behaviors and life prediction for SUS 321 plate and its electron beam weld metal were investigated using compact tension specimens. The larger the stress ratio is, the faster the crack propagates, but the variation of crack propagation rate decreases. The effect of stress ratio is greater in the slow crack propagation area than in the faster one. The crack propagation rate of electron beam weld metal is faster than that of base metal because of hardening, weld defect and residual stress in welding area. The crack propagation rate of transverse weld metal has a lower than that of base metal due to the effect of residual stress, but in the time of passing through welding area, has a higher rate. The crack propagation rate using $\Delta$K$_{eff}$ can be well plotted regardless of stress ratio. The fatigue life prediction method of considering crack closure more exactly predicts fatigue life than conventional one. conventional one.e.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.611-620
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• 2021

The maintenance industry is mainly progressing based on condition-based maintenance after corrective maintenance and preventive maintenance. In condition-based maintenance, maintenance is performed at the optimum time based on the condition of equipment. In order to find the optimal maintenance point, it is important to accurately understand the condition of the equipment, especially the remaining useful life. Thus, using simulation data (C-MAPSS), a prediction model is proposed to predict the remaining useful life of a turbofan engine. For the modeling process, a C-MAPSS dataset was preprocessed, transformed, and predicted. Data pre-processing was performed through piecewise RUL, moving average filters, and standardization. The remaining useful life was predicted using principal component analysis and the k-NN method. In order to derive the optimal performance, the number of principal components and the number of neighbor data for the k-NN method were determined through 5-fold cross validation. The validity of the prediction results was analyzed through a scoring function while considering the usefulness of prior prediction and the incompatibility of post prediction. In addition, the usefulness of the RUL prediction model was proven through comparison with the prediction performance of other neural network-based algorithms.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.3
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• pp.136-142
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• 2019

Nuclear power plant concrete structures are in contact with the coast, and durability due to chloride attack is very important because it is used as cooling water by taking seawater. For this purpose, a 3-year long-term saltwater immersion test was carried out to evaluate chloride ion diffusion coefficient and age apponent (m) The m values of the foundation with 4,000 class was 0.35 ~ 0.39, similar to KCI or ACI suggested values. essential service water constructions and tunnels of 5,000 class were 0.44 ~ 0.53 and 6,000 class, and 0.62 of reactor containment buildings were similar to the proposed values of FIB. As a result of the prediction of the service life with the measured age coefficient, all the safety related concrete structures of the nuclear power plants satisfied the service life of more than 60 years.

• The Journal of Information Systems
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• v.27 no.4
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• pp.115-128
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• 2018

Purpose The purpose of this study is to predict future behaviors of business process. Specifically, this study tried to predict the last activities of process instances. It contributes to overcoming the limitations of existing approaches that they do not accurately reflect the actual behavior of business process and it requires a lot of effort and time every time they are applied to specific processes. Design/methodology/approach This study proposed a novel approach based using deep learning in the form of dynamic recurrent neural networks. To improve the accuracy of our prediction model based on the approach, we tried to adopt the latest techniques including new initialization functions(Xavier and He initializations). The proposed approach has been verified using real-life data of a domestic small and medium-sized business. Findings According to the experiment result, our approach achieves better prediction accuracy than the latest approach based on the static recurrent neural networks. It is also proved that much less effort and time are required to predict the behavior of business processes.

• Journal of the Korea Safety Management & Science
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• v.15 no.1
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• pp.109-120
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• 2013

As information-oriented industry has been developed and electronic devices has come to be smaller, lighter, multifunctional, and high speed, the components used to the devices need to be much high density and should have find pattern due to high integration. Also, diverse reliability problems happen as user environment is getting harsher. For this reasons, establishing and securing products and components reliability comes to key factor in company's competitiveness. It makes accelerated test important to check product reliability in fast way. Out of fine pattern failure modes, failure of Electrochemical Migration(ECM) is kind of degradation of insulation resistance by electro-chemical reaction, which it comes to be accelerated by biased voltage in high temperature and high humidity environment. In this thesis, the accelerated life test for failure caused by ECM on fine pattern substrate, $20/20{\mu}m$ pattern width/space applied by Semi Additive Process, was performed, and through this test, the investigation of failure mechanism and the life-time prediction evaluation under actual user environment was implemented. The result of accelerated test has been compared and estimated with life distribution and life stress relatively by using Minitab software and its acceleration rate was also tested. Through estimated weibull distribution, B10 life has been estimated under 95% confidence level of failure data happened in each test conditions. And the life in actual usage environment has been predicted by using generalized Eyring model considering temperature and humidity by developing Arrhenius reaction rate theory, and acceleration factors by test conditions have been calculated.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.464-469
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• 2023

As modern society develops, the number of diseases caused by bad smells is increasing. As it can harm people's health, it is important to predict in advance the extent to which bad smells may occur, inform the public about this, and take preventive measures. In this paper, we propose a hybrid neural network structure of CNN and LSTM that can be used to detect or predict the occurrence of odors, which are most required in manufacturing or real life, using odor complex sensors. In addition, the proposed learning model uses a complex odor sensor to receive four types of data, including hydrogen sulfide, ammonia, benzene, and toluene, in real time, and applies this data to the inference model to detect and predict the odor state. The proposed model evaluated the prediction accuracy of the training model through performance indicators based on accuracy, and the evaluation results showed an average performance of more than 94%.

• Journal of the Korea Concrete Institute
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• v.11 no.6
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• pp.89-100
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• 1999

Service life of concrete structures that are exposed to the environmental attack is largely influenced by the corrosion of reinforcing bare due to the chloride contamination. Chloride ions penetrate continuously into concrete from the environment, and chloride diffusion velocity is governed by a mechanical steady stage. In this study, a method is developed to predict corrosion initiation of reinforcing bars in the sea-shore structures, based on governing equations that take into account the diffusing of chloride ions and a mechanical steady state. As a result of this study, Corrosion Prediction System (CPS) is developed, and it can be used to determine an optimal time for repair and rehabilitation actions need to be taken. Futhermore, CPS assists the concrete mixing structures by predicting of chloride concentrations in concrete mixture, exposed to salt concentrations and service environment.