• Journal of Applied Reliability
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• v.18 no.3
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• pp.250-259
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• 2018

Purpose: Previously, missile reliability prediction is based on theoretical failure prediction model. It has shown that the predicted reliability is inadequate to real field data. Although an MTTF based reliability prediction method using real field data has recently been studied to overcome this issue. In this paper, we present a more realistic method, considering MTBF concept, to predict missile reliability. Methods: In this paper we proposed a modified survival model. This model is considering MTBF as its core concept, and failed missiles in the model are to be repaired and redeployed. We compared the modified model (MTBF) and the previous model (MTTF) in terms of fitness against the real failure data. Results: The reliability prediction result of MTBF based model is closer to fields failure data set than that of MTTF based model. Conclusion: The proposed MTBF concept is more fitted to real failure data of missile than MTTF concept. The methodology of this study can be applied to analyze field failure data of other similar missiles.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.2
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• pp.1-13
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• 2002

In this paper, we compares the business failure prediction accuracy among Linear Programming Discriminant Analysis(LPDA) model, Multivariate Discriminant Analysis (MDA) model and logit analysis model. The Data for 417 companies analyzed were gathered from KIS-FAS Published by Korea Information Service in 1999. The result of comparison for four time horizons shows that LPDA Is advantageous in prediction accuracy over the other two models when over all tilt ratio and business failure accuracy are considered simultaneously.

• Geomechanics and Engineering
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• v.23 no.1
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• pp.51-59
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• 2020

In this paper, the WEKA platform was used to mine and analyze measured data of floor failure depth and a prediction system of floor failure depth was developed with Java. Based on the standardization and discretization of 35-set measured data of floor failure depth in China, the grey correlation degree analysis on five factors affecting the floor failure depth was carried out. The correlation order from big to small is: mining depth, working face length, floor failure resistance, mining thickness, dip angle of coal seams. Naive Bayes model, neural network model and decision tree model were used for learning and training, and the accuracy of the confusion matrix, detailed accuracy and node error rate were analyzed. Finally, artificial neural network was concluded to be the optimal model. Based on Java language, a prediction system of floor failure depth was developed. With the easy operation in the system, the prediction from measured data and error analyses were performed for nine sets of data. The results show that the WEKA prediction formula has the smallest relative error and the best prediction effect. Besides, the applicability of WEKA prediction formula was analyzed. The results show that WEKA prediction has a better applicability under the coal seam mining depth of 110 m~550 m, dip angle of coal seams of 0°~15° and working face length of 30 m~135 m.

• Proceedings of the Fisheries Business Administration Society of Korea Conference
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• 2007.12a
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• pp.167-184
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• 2007

The objectives of this paper are to identify the causes of the corporate distress and to develop a distress prediction model with the financial information in fishery industry. In this study, the corporate distress is defined as economic failure and technical insolvency. Economic failure occurs by reduction, shut-down, or change of the business and technical insolvency results from failure to pay the financial debt of companies. The 33 distressed firms from 1991 to 2003 were composed by 14 economic failure companies, 15 technical insolvency companies. 4 companies applied to the both cases. The analysis of distress prediction of fishery companies were accomplished according to the distress definition. The analysis was carried out as two steps. The first step was the univariate analysis, which was used for checking the prediction power of individual financial variable. The t-test is used to identify the differences in financial variables between the distressed group and the non-distressed group. The second step was to develop distress prediction model with logistic regression. The variables showed the significant difference in univariate analysis were selected as the prediction variables. The financial ratios, used in the logistic regression model, were selected by backward elimination method. To test stability of the distress prediction model, the whole sample was divided as three sub-samples, period 1(1990$\sim$1993), period 2(1994$\sim$1997), period 3(1998$\sim$2002). The final model built from whole sample appled each three sub-samples. The results of the logistic analysis were as follows. the growth, profitability, stability ratios showed the significant effect on the distress. the some different result was found in the sub-sample (economic failure and technical insolvency). The growth and the profitability were important to predict the economic failure. The profitability and the activity were important to predict technical insolvency. It means that profitability is the really important factor to the fishery companies.

• The Journal of Fisheries Business Administration
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• v.39 no.2
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• pp.61-79
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• 2008

The objectives of this paper are to identify the causes of the corporate distress and to develop a distress prediction model with the financial information in fishery industry. In this study, the corporate distress is defined as economic failure and technical insolvency. Economic failure occurs by reduction, shut - down, or change of the business and technical insolvency results from failure to pay the financial debt of companies. The 33 distressed firms from 1991 to 2003 were composed by 14 economic failure companies, 15 technical insolvency companies. 4 companies applied to the both cases. The analysis of distress prediction of fishery companies were accomplished according to the distress definition. The analysis was carried out as two steps. The first step was the univariate analysis, which was used for checking the prediction power of individual financial variable. The t - test is used to identify the differences in financial variables between the distressed group and the non - distressed group. The second step was to develop distress prediction model with logistic regression. The variables showed the significant difference in univariate analysis were selected as the prediction variables. The financial ratios, used in the logistic regression model, were selected by backward elimination method. To test stability of the distress prediction model, the whole sample was divided as three sub-samples, period 1(1990 - 1993), period 2(1994 - 1997), period 3(1998 - 2002). The final model built from whole sample appled each three sub - samples. The results of the logistic analysis were as follows. the growth, profitability, stability ratios showed the significant effect on the distress. the some different result was found in the sub - sample (economic failure and technical insolvency). The growth and the profitability were important to predict the economic failure. The profitability and the activity were important to predict technical insolvency. It means that profitability is the really important factor to the fishery companies.

• Geomechanics and Engineering
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• v.19 no.3
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• pp.217-227
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• 2019

This study focuses on an improved prediction model to determine the limiting grouting pressure of compaction grouting considering the ground surface upheaval, which is caused by the three-dimensional conical shearing failure. The 2D-dimensional failure curve in Zou and Xia (2016) was improved to a three-dimensional conical shearing failure for compaction grouting through coordinate rotation. The process of compaction grouting was considered as the cavity expansion in infinite Mohr-Coulomb (M-C) soil mass. The prediction model of limiting grouting pressure of compaction grouting was proposed with limit equilibrium principle, which was validated by comparing the results in El-Kelesh et al. (2001) and numerical method. Furthermore, using the proposed prediction model, the vertical and horizontal grouting tube techniques were adopted to deal with the subgrade settlement in Shao-huai highway at Hunan Provence of China. The engineering applicability and effectiveness of the proposed model were verified by the field test. The research on the prediction model for the limiting grouting pressure of compaction grouting provides practical example to the rapid treatment technology of subgrade settlement.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.5 s.236
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• pp.754-761
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• 2005

The 40\% of wall thickness criterion which has been used as a plugging rule of steam generator tubes is applicable only to a single cracked tube. In the previous studies performed by authors, several global failure prediction models were introduced to estimate the failure loads of steam generator tubes containing two adjacent parallel axial through-wall cracks. These models were applied for thin plates with two parallel cracks and the COD base model was selected as the optimum one. The objective of this study is to verify the applicability of the proposed optimum global failure prediction model for real steam generator tubes with two parallel axial through-wall cracks. For the sake of this, a series of plastic collapse tests and finite element analyses have been carried out fur the steam generator tubes with two machined parallel axial through-wall cracks. Thereby, it was proven that the proposed optimum failure prediction model can be used as the best one to estimate the failure load quite well. Also, interaction effects between two adjacent cracks were assessed through additional finite element analyses to investigate the effect on the global failure behavior.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.11
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• pp.1813-1821
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• 2004

Until now, the 40% of wall thickness criterion, which is generally used for the plugging of steam generator tubes, has been applied only to a single cracked geometry. In the previous study by the authors, a total number of 9 local failure prediction models were introduced to estimate the coalescence load of two collinear through-wall cracks and, then, the reaction force model and plastic zone contact model were selected as the optimum ones. The objective of this study is to estimate the coalescence load of two collinear through-wall cracks in steam generator tube by using the optimum local failure prediction models. In order to investigate the applicability of the optimum local failure prediction models, a series of plastic collapse tests and corresponding finite element analyses for two collinear through-wall cracks in steam generator tube were carried out. Thereby, the applicability of the optimum local failure prediction models was verified and, finally, a coalescence evaluation diagram which can be used to determine whether the adjacent cracks detected by NDE coalesce or not has been developed.

• Journal of the Korean Society of Safety
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• v.23 no.6
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• pp.7-13
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• 2008

It is important to prevent roll failure in hot rolling process for reducing maintenance cost and production loss. The relationship between rolling pass schedule and the work roll wear profile will be presented. The roll wear pattern is related with roll catastrophic failure. The irregular and deep roll wear pattern should be removed by On-line Roll Grinder(ORG) for roll failure prevention. In this study, a computer roll wear prediction model under real process working condition is developed and evaluated with hot rolling pass schedule. The method of building wear calculation functions for center portion abrasion and marginal abrasion respectively was used to develop a work roll wear prediction mathematical model. The three type rolling schedule are evaluated by wear prediction model. The optimum roll grinding methods is suggested for schedule tree rolling technique.

• Journal of the Earthquake Engineering Society of Korea
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• v.28 no.2
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• pp.113-119
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• 2024

Existing reinforced concrete buildings with seismically deficient column details affect the overall behavior depending on the failure type of column. This study aims to develop and validate a machine learning-based prediction model for the column failure modes (shear, flexure-shear, and flexure failure modes). For this purpose, artificial neural network (ANN), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models were used, considering previously collected experimental data. Using four machine learning methodologies, we developed a classification learning model that can predict the column failure modes in terms of the input variables using concrete compressive strength, steel yield strength, axial load ratio, height-to-dept aspect ratio, longitudinal reinforcement ratio, and transverse reinforcement ratio. The performance of each machine learning model was compared and verified by calculating accuracy, precision, recall, F1-Score, and ROC. Based on the performance measurements of the classification model, the RF model represents the highest average value of the classification model performance measurements among the considered learning methods, and it can conservatively predict the shear failure mode. Thus, the RF model can rapidly predict the column failure modes with simple column details.