• 수산경영론집
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• 제39권2호
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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.

• Journal of Welding and Joining
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• 제24권4호
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• pp.39-49
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• 2006

The austenite grain growth model in low alloyed steel HAZ without precipitates was proposed by analyzing isothermal grain growth behavior. Steels used in this study were designed to investigate the effect of alloying elements. Meanwhile, a systematic procedure was proposed to prevent inappropriate neglect of initial grain size (D0) and misreading both time exponent and activation energy for isothermal grain growth. It was found that the time exponent was almost constant, irrespectively of temperature and alloying elements, and activation energy increased with the addition of alloying elements. From quantification of the effect of alloying elements on the activation energy, an isothermal grain growth model was presented. Finally, combining with the additivity rule, the austenite grain size in the CGHAZ was predicted.

• Journal of Welding and Joining
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• 제20권5호
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• pp.78-86
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• 2002

A metallurgical model for the prediction of prior austenite grain size considering the dissolution kinetics of M$_3$C precipitates at the heat affected zone of SA508-cl.3 was proposed. The isothermal kinetics of grain growth and dissolution were respectively described by well-known equation, $dD/dT=M({\Delta}F_{eff})^M$ and Whelan's analytical model. The isothermal grain growth experiments were carried out for measure the kinetic parameters of grain growth. The precipitates of the base metal and the specimens exposed to thermal cycle were examined by TEM-carbon extraction replica method. The model was assessed by the comparison of BUE simulation experiments and showed good consistencies. However, there was no difference between the model considering and ignoring $M_3C$ precipitates. It seems considered that pinning force exerted by $M_3C$ Precipitates was lower than driving force for grain growth due to large size and small fraction of precipitates, and mobility of grain boundary was low in the lower temperature range.

• 한국생산제조학회지
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• 제8권2호
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• pp.126-136
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• 1999

We studied on crack growth retardation in single overloading condition. Crack tip branching which as the second mechanism on crack growth retardation was examined. Crack tip branching was observed to kinked type and forked type. It was found that the branching angle range was from 25 to 53 degree. The variations of crack driving force with branching angle were calculated with finite element method The variation of {{{{ KAPPA _I}}}}, {{{{ KAPPA _II}}}} and total crack driving force(K) were examined respectively So {{{{ KAPPA _I}}}}, {{{{ KAPPA _II}}}} and K mean to mode I, II and total crack driving force. Present model(Willenborg's model) for crack growth retardation prediction was modified to take into consideration the effects of crack tip branching When we predicted retardation with modified model. it was confirmed that predicted and experimental results coincided with well each other.

• 한국도로학회논문집
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• 제15권3호
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• pp.93-101
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• 2013

PURPOSES : This study is to investigate the relationship of socioeconomic characteristics and road network structure with traffic growth patterns. The findings is to be used to tweak traffic forecast provided by traditional four step process using relevant socioeconomic and road network data. METHODS: Comprehensive statistical analysis is used to identify key explanatory variables using historical observations on traffic forecast, actual traffic counts and surrounding environments. Based on statistical results, a multiple regression model is developed to predict the effects of socioeconomic and road network attributes on traffic growth patterns. The validation of the proposed model is also performed using a different set of historical data. RESULTS : The statistical analysis results indicate that several socioeconomic characteristics and road network structure cleary affect the tendency of over- and under-estimation of road traffics. Among them, land use is a key factor which is revealed by a factor that traffic forecast for urban road tends to be under-estimated while rural road traffic prediction is generally over-estimated. The model application suggests that tweaking the traffic forecast using the proposed model can reduce the discrepancies between the predicted and actual traffic counts from 30.4% to 21.9%. CONCLUSIONS : Prediction of road traffic growth patterns based on surrounding socioeconomic and road network attributes can help develop the optimal strategy of road construction plan by enhancing reliability of traffic forecast as well as tendency of traffic growth.

• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 2003년도 춘계학술발표대회 개요집
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• pp.43-45
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• 2003

The empirical model for predicting the prior austenite grain size in low-alloy steel weld HAZ was developed through examining the effect of alloying element. The test alloys were made by vacuum induction melting. Grain growth behaviors were observed and analyzed by isothermal grain growth test and subsequent metallography. As a result, it was found that the grain growth might be controlled by grain boundary diffusion and the empirical model for grain growth was presented.

• Agribusiness and Information Management
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• 제7권1호
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• pp.1-9
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• 2015

The purpose of this study is to develop a growth prediction model that can predict growth and development information influencing the production of citrus fruits: the growth model algorithm that can predict floral leaf ratio, number of fruit sets, fruit width, and overweight depending on the main period of growth and development with consideration of the applied weather factors. Every year, large scale of manpower was mobilized to investigate the production of outdoor-grown citrus fruits, but it was limited to recycling the data without an observation supporting system to systemize the database. This study intends to create a systematical database based on the basic data obtained through the observation supporting system in application of an algorithm according to the accumulated long term data and prepare a base for its continuous improvement and development. The importance of the observed data is increasingly recognized every year, and the citrus fruit observation supporting system is important for utilizing an effective policy and decision making according to various applications and analysis results through an interconnection and an integration of the investigated statistical data. The citrus fruit is a representative crop having a great ripple effect in Jeju agriculture. An early prediction of the growth and development information influencing the production of citrus fruits may be helpful for decision making in supply and demand control of agricultural products.

• Journal of Forest and Environmental Science
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• 제39권4호
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• pp.195-202
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• 2023

In this study, we sought to compare and evaluate the accuracy and predictive performance of machine learning algorithms for estimating the growth of individual Larix kaempferi trees in Gangwon Province, Korea. We employed linear regression, random forest, XGBoost, and LightGBM algorithms to predict tree growth using monitoring data organized based on different thinning intensities. Furthermore, we compared and evaluated the goodness-of-fit of these models using metrics such as the coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). The results revealed that XGBoost provided the highest goodness-of-fit, with an R² value of 0.62 across all thinning intensities, while also yielding the lowest values for MAE and RMSE, thereby indicating the best model fit. When predicting the growth volume of individual trees after 3 years using the XGBoost model, the agreement was exceptionally high, reaching approximately 97% for all stand sites in accordance with the different thinning intensities. Notably, in non-thinned plots, the predicted volumes were approximately 2.1 m³ lower than the actual volumes; however, the agreement remained highly accurate at approximately 99.5%. These findings will contribute to the development of growth prediction models for individual trees using machine learning algorithms.

• 한국신뢰성학회지:신뢰성응용연구
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• 제14권4호
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• pp.225-229
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• 2014

A one-shot device is defined as a product, system, weapon, or equipment that can be used only once. After use, the device is destroyed or must undergo extensive rebuild. Determining the reliability of a one-shot device poses a unique challenge to the manufacturers and users due to the destructive nature and costs of the testing. This paper presents a reliability growth prediction for a one-shot system. It is assumed that 1) test duration is discrete(i.e. trials or rounds); 2) trials are statistically independent; 3) the number of failures for a given system configuration is distributed according to a binomial distribution; and 4) the cumulative expected number of failures through any sequence of configurations is given by AMSAA model. When the system development is represented by three configurations and the number of trials and failures during configurations are given, the AMSAA model parameters and reliability at configuration 3 are estimated by using a reliability growth analysis software. Further, if the reliability growth predictions do not meet the target reliability, the sample size of an additional test is determined for achieving the target reliability.

• 소성∙가공
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• 제20권7호
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• pp.491-497
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• 2011

This work describes a method for determining material parameters included in recrystallization and grain growth models of metallic materials. The focus is on the recrystallization and grain growth models of Ni-Fe based superalloy, Alloy 718. High temperature compression test data at different strain, strain rate and temperature conditions were chosen to determine the material parameters of the model. The critical strain and dynamically recrystallized grain size and fraction at various process conditions were generated from the microstructural analysis and strain-stress relationships of the compression tests. Also, isothermal heat treatments were utilized to fit the material constants included in the grain growth model. Verification of the determined material parameters is carried out by comparing the average grain size data obtained from other compression tests of the Alloy 718 specimens with the initial grain size of $59.5{\mu}m$.