• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.10
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• pp.1785-1792
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• 2003

Methods to predict fatigue crack growth are compared in a quantitative manner for crack growth test data of 2024- T351 aluninum alloy under narrow and wide band random loading. In order to account for the effect of load ratio, crack closure model, Hater's equation and NASGRO's equation have been employed. Load interaction effect under random loading has been considered by crack closure model, Willenborg's model and Wheeler's model. The prediction method using the measured crack opening results provides the best result among the prediction methods discussed for narrow and wide band random loading data.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2009.05a
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• pp.270-273
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• 2009

This work describes a method of determining material parameters included in recrystallization and grain growth models. Focus is on the recrystallization and grain growth models of Ni-Fe base superalloy, Alloy 718. High temperature compression tests at different strain, strain rate and temperature conditions were chosen to determine the material parameters of dynamic recrystallization model. The critical strain and dynamically recrystallized grain size and fraction at various process variables were quantitated with the microstructual analysis and strain-stress relationships of the compression tests. Besides, 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 measured data obtained from other compression tests.

• Journal of Preventive Veterinary Medicine
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• v.42 no.4
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• pp.143-147
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• 2018

The present study aimed to develop growth prediction models of Listeria monocytogenes in processed meat products, such as mixed pressed hams, to perform accurate microbial risk assessments. Considering cold storage temperatures and the amount of time in the stages of consumption after opening, the growth of L. monocytogenes was determined as a function of temperature at 0, 5, 10, and $15^{\circ}C$, and time at 0, 1, 3, 6, 8, 10, 15, 20, 25, and 30 days. Based on the results of these measurements, a Baranyi model using the primary model was developed. The input parameters of the Baranyi equation in the variable temperature for polynomial regression as a secondary model were developed: $SGR=0.1715+0.0199T+0.0012T^2$, $LT=5.5730-0.3215T+0.0051T^2$ with $R^2$ values 0.9972 and 0.9772, respectively. The RMSE (Root mean squared error), $B_f$ (bias factor), and $A_f$ (accuracy factor) on the growth prediction model were determined to be 0.30, 0.72, and 1.50 in SGR (specific growth rate), and 0.10, 0.84, and 1.35 in LT (lag time), respectively. Therefore, the model developed in this study can be used to determine microorganism growth in the stages of consumption of mixed pressed hams and has potential in microbial risk assessments (MRAs).

• Biotechnology and Bioprocess Engineering:BBE
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• v.5 no.5
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• pp.382-385
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• 2000

A mathematical model was developed, based on the time dependent changes of the specific growth rate, for prediction of the typical microbial cell growth in batch cultures. This model could predict both the lag growth phase and the stationary growth phase of batch cultures, and it was tested with the batch growth of Trichoderma reesei and Lactobacillus delbrueckii.

• Ocean and Polar Research
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• v.43 no.3
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• pp.179-192
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• 2021

In this study, an integrated wave model from global to coastal scales was developed to improve the operational wave prediction performance of the Korean Meteorological Administration (KMA). In this system, the wave model was upgraded to the WaveWatch III version 6.07 with the improved parameterization of the source term. Considering the increased resolution of the wind input field and the introduction of the high-performance KMA 5^th Supercomputer, the spatial resolution of global and regional wave models has been doubled compared to the operational model. The physical processes and coefficients of the wave model were optimized for the current KMA global atmospheric forecasting system, the Korean Integrated Model (KIM), which is being operated since April 2020. Based on the sensitivity experiment results, the wind-wave growth parameter (β_max) for the global wave model was determined to be 1.33 with the lowest root mean square errors (RMSE). The value of β_max showed the lowest error when applied to regional/coastal wave models for the period of the typhoon season when strong winds occur. Applying the new system to the case of August 2020, the RMSE for the 48-hour significant wave height prediction was reduced by 13.4 to 17.7% compared to the existing KMA operating model. The new integrated wave prediction system plans to replace the KMA operating model after long-term verification.

• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.15-27
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• 2020

Accurate knowledge of land use/land cover (LULC) features and their relative changes over upon the time are essential for sustainable urban management. Urban sprawl growth has been always also a worldwide concern that needs to carefully monitor particularly in a developing country where unplanned building constriction has been expanding at a high rate. Recently, remotely sensed imageries with a very high spatial/spectral resolution and state of the art machine learning approaches sent the urban classification and growth monitoring to a higher level. In this research, we classified the Quickbird satellite imagery by object-based image analysis of Dempster-Shafer (OBIA-DS) for the years of 2002 and 2015 at Karbala-Iraq. The real LULC changes including, residential sprawl expansion, amongst these years, were identified via change detection procedure. In accordance with extracted features of LULC and detected trend of urban pattern, the future LULC dynamic was simulated by using land transformation model (LTM) in geospatial information system (GIS) platform. Both classification and prediction stages were successfully validated using ground control points (GCPs) through accuracy assessment metric of Kappa coefficient that indicated 0.87 and 0.91 for 2002 and 2015 classification as well as 0.79 for prediction part. Detail results revealed a substantial growth in building over fifteen years that mostly replaced by agriculture and orchard field. The prediction scenario of LULC sprawl development for 2030 revealed a substantial decline in green and agriculture land as well as an extensive increment in build-up area especially at the countryside of the city without following the residential pattern standard. The proposed method helps urban decision-makers to identify the detail temporal-spatial growth pattern of highly populated cities like Karbala. Additionally, the results of this study can be considered as a probable future map in order to design enough future social services and amenities for the local inhabitants.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.61-69
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• 2023

Recently, as the fishery resources are depleted, expectations for productivity improvement by 'rearing fishery' in land farms are greatly rising. In the case of land farms, unlike ocean environments, it is easy to control and manage environmental and breeding factors, and has the advantage of being able to adjust production according to the production plan. On the other hand, unlike in the natural environment, there is a disadvantage in that operation costs may significantly increase due to the artificial management for fish growth. Therefore, profit maximization can be pursued by efficiently operating the farm in accordance with the planned target shipment. In order to operate such an efficient farm and nurture fish, an accurate growth prediction model according to the target fish species is absolutely required. Most of the growth prediction models are mainly numerical results based on statistical analysis using farm data. In this paper, we present a growth prediction model from a stochastic point of view to overcome the difficulties in securing data and the difficulty in providing quantitative expected values for inaccuracies that existing growth prediction models from a statistical point of view may have. For a stochastic approach, modeling is performed by introducing a Gaussian process regression method based on water temperature, which is the most important factor in positive growth. From the corresponding results, it is expected that it will be able to provide reference values for more efficient farm operation by simultaneously providing the average value of the predicted growth value at a specific point in time and the confidence interval for that value.

• Computers and Concrete
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• v.5 no.3
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• pp.261-277
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• 2008

This paper presents methodologies for remaining life prediction of plain concrete structural components considering tension softening effect. Non-linear fracture mechanics principles (NLFM) have been used for crack growth analysis and remaining life prediction. Various tension softening models such as linear, bi-linear, tri-linear, exponential and power curve have been presented with appropriate expressions. A methodology to account for tension softening effects in the computation of SIF and remaining life prediction of concrete structural components has been presented. The tension softening effects has been represented by using any one of the models mentioned above. Numerical studies have been conducted on three point bending concrete structural component under constant amplitude loading. Remaining life has been predicted for different loading cases and for various tension softening models. The predicted values have been compared with the corresponding experimental observations. It is observed that the predicted life using bi-linear model and power curve model is in close agreement with the experimental values. Parametric studies on remaining life prediction have also been conducted by using modified bilinear model. A suitable value for constant of modified bilinear model is suggested based on parametric studies.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.2
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• pp.475-485
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• 1995

In this study, statistical analysis of fatigue data which had obtained from respective 24 fatigue crack, was examined for SiC whisker reinforced aluminium 6061 composite alloy (SiC$_{w}$/A16061) and aluminium 6061 alloy. SiC volume fraction in composite alloy is 25%. The analysis results stress intensity factor range and 0.1 mm fatigue crack initiation life for SiC$_{w}$/A16061 composite & A16061 matrix are the log-normal distribution. And regression analysis by linear model, exponential model and multiplicative model were performed to find out the relationship between fatigue crack growth rate(da/dN) and stress intensity for find out the relationship between fatigue crack growth rate(da/dN) and stress intensity factor range(.DELTA.K) in the SiC$_{w}$/A16061 composite and examine the applicability of Paris' equation to SiC$_{w}$A16061 composite. Also computer simulation was performed for fatigue life prediction of SiC$_{w}$/A16061 composite using the statistical results of this study.udy.

• 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.