• Industrial Engineering and Management Systems
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• v.2 no.1
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• pp.9-27
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• 2003

In a wafer fabrication factory, the completion time of an order is affected by many factors related to the specifics of the order and the status of the system, so is difficult to predict precisely. The level of influence of each factor on the order completion time may also depend on the production system characteristics, such as the rules for releasing and dispatching. This paper presents a method to identify those factors that significantly impact upon the order completion time under various combinations of scheduling rules. Computer simulations and statistical analyses were used to develop effective due date assignment models for improving the due date related performances. The first step of this research was to select the releasing and dispatching rules from those that were cited so frequently in related wafer fabrication factory researches. Simulation and statistical analyses were combined to identify the critical factors for predicting order completion time under various combinations of scheduling rules. In each combination of scheduling rules, two efficient due date assignment models were established by using the regression method for accurately predicting the order due date. Two due date assignment models, called the significant factor prediction model (SFM) and the key factor prediction model (KFM), are proposed to empirically compare the due date assignment rules widely used in practice. The simulation results indicate that SFM and KFM are superior to the other due date assignment rules. The releasing rule, dispatching rule and due date assignment rule have significant impacts on the due date related performances, with larger improvements coming from due date assignment and dispatching rules than from releasing rules.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.4
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• pp.46-56
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• 2021

Predicting the incident clearance time is important for eliminating the high transportation costs and congestion from non-repetitive congestion caused by incidents. In this study, the factors influencing the clearance time suitable for domestic road conditions were analyzed, using a training dataset for predicting the incident clearance time using artificial neural networks. In a previous study, the under-prediction problem for high incident clearance time was used. In the present study, over-sampling training data applied using the SMOGN technique was obtained and applied to the model as a solution. As a result, the DNN model applying the SMOGN technique could compensate for the limitations of the previously developed prediction model by predicting the clearance time with the highest accuracy among the models developed in the research process with MAE = 18.3 minutes.

• Journal of Integrative Natural Science
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• v.13 no.1
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• pp.34-40
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• 2020

This study aims to create a model for predicting the number of extinguishment manpower to put out forest fires by taking into account the climate, the situation, and the extent of the damage at the time of the forest fires. Past research has been approached to determine the cause of the forest fire or to predict the occurrence of a forest fire. How to deal with forest fires is also a very important part of how to deal with them, so predicting the number of extinguishment manpower is important. Therefore predicting the number of extinguishment manpower that have been put into the forest fire is something that can be presented as a new perspective. This study presents a model for predicting the number of extinguishment manpower inputs considering the scale of the damage with forest fire on a scale bigger than 0.1 ha as data based on the forest fire annual report(Korea Forest Service; KFS) from 2015 to 2018 using the moderated multiple regression analysis. As a result, weather factors and extinguished time considering the damage show that affect forest fire extinguishment manpower.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.304-307
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• 2013

Various programs have been developed to predict the energy consumption of a building as a result of recent increased social interest in the environmental friendliness of construction as measured by energy efficiency. The goal of environmental-friendliness, which is achieved by predicting the energy consumption of a building, can be realized in the design stage by applying a variety of technologies, planning factors and planning systems. However, most energy analyzing engines are only suitable for use in the advanced stages of design because of the large amount of design information that must be entered. Thus, because the simulation programs currently used are not suitable for use in the early stages of design, this study suggests a prediction logic that provides an overview of the energy consumption of a building according to its size, scope, and purpose by analyzing statistics collected by government agencies.

• Composites Research
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• v.37 no.4
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• pp.275-285
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• 2024

This paper presents research trends in predicting the deformation of carbon fiber reinforced thermoplastic (CFRTP) composites during thermoforming. Various thermoforming variables that must be considered during the CFRTP thermoforming stages are investigated, and factors influencing process-induced deformation are analyzed. Key material behavior models, such as crystallinity and viscoelastic, which are important for predicting thermoforming deformation, are also examined. Additionally, trends in predicting CFRTP thermoforming deformation using finite element analysis with material behavior models and machine learning techniques are analyzed. In summary, more precise prediction techniques for thermoforming deformation can be developed by associating them with material behavior models and considering thermoforming variables.

• Journal of the Korean Society of Tobacco Science
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• v.11 no.1
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• pp.27-39
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• 1989

This study was conducted to determine the time and methods of predicting tobacco yield. by analysis of climatic factors in the period of tobacco season during 8 years from 1979 to 1986 at the Daegu district, south eastern part of Korean peninsular. The results obtained are summarised as follows: 1. Climatic factors of each month which have influence on tobacco yield were the amount of rainfall in May and sunshine hours in July. Among climatic factors at tobacco growth stages, the precipitation yield. But these meteorological factors had different effect on variety. 2. Between tobacco yields and climatic factors by even values of each month, tobacco yield was estimated by equations, flue cured tobacco :Y=190.6-5.230X1+ 0.474$\times$2 + 0.142X3(Xl : Minimum temperature of April, X2: Precipitation during May, X3:Sunshine duration on July), air cured tobacco : Y= 195.3-0.447Xl + 0.363$\times$2 + 0.l12$\times$3(Xl :Maximum temperature of May, X2:Precipitation during May. X3: Sunshine duration on July). While between tobacco yield and climatic factors at different growth stage, predicting equation of yield could be derived, flue cured tobacco : Y=205.8+0.510Xl +0.289$\times$2 + 0.305$\times$3 (Xl :Average temperature during the early growth stage, X2 :Precipitation during the early and maximum growth stage, X3 : Sunshine hours during the leaf and tips maturing stage), air cured tobacco Y=194.T-0.498Xl 10.615$\times$2+0.121$\times$3(Xl ;Maximum temperature during the transplanting time, X2 : Precipitation during the maximum growth stage, X3 : Sunshine hours during the leaf and tips maturing stage).

• Journal of Korean Academy of Nursing
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• v.43 no.6
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• pp.801-811
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• 2013

Purpose: To investigate changes in Quality of life (QOL) and related factors in patients with thyroid cancer undergoing Radioactive Iodine remnant ablation (RAI). Methods: Data were collected longitudinally 3 times for 6 months (2 weeks post-surgery, post RAI, 3 months post RAI) in a hospital located in Seoul. Questionnaires were used to measure levels of physical symptoms, anxiety, depression, and QOL. Ninety-eight patients with thyroid cancer who had RAI were included in the analysis. Data were analyzed using SPSS (18.0). Results: Findings for the three data collection times respectfully were: mean scores for physical symptoms, 0.53, 1.21 and 0.62, patients with depression, 47%, 36.7% and 37.7%, patients with anxiety, 18.4 %, 19.4% and 20.4%, mean scores for QOL, 7.06, 7.01 and 7.28. QOL score was highest 3 months post RAI (p=.031). In the stepwise multiple regression analysis, depression and fatigue were predicting factors for low QOL at all data collection times. Dysponia was a predicting factor for low QOL post RAI and 3 months post RAI. Conclusion: To increase QOL, it is necessary to provide information in advance regarding physical & psychological symptoms and to develop nursing intervention programs to decrease depression and fatigue.

• Journal of the Korean Home Economics Association
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• v.43 no.9 s.211
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• pp.41-57
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• 2005

The purpose of this study was to examine the change of couple relationship factors predicting marital satisfaction and divorce intention over time tv comparing the couples' first year with their present year of marriage. The couple relationship factors consisted of affection, ambivalence, affectional expression, the expression of negativity. The study subjects 355 married women having preschool children aged 7 years old and elementary school students in the 6th grade. Data were analyzed by SPSSWIN with the method of MANOVA. The results of this study showed that couples in happy groups without divorce intention became less affectionate and demonstrated less affectional expression, and more ambivalence and expression of negativity over time. However, the amount of change was not as large as that of the unhappy groups. The findings of this research indicated that the decline of affection and affectional expression and the increase of ambivalence and expression of negativity were probably, as normative, a natural consequence of the transition from the first year of marriage to a more mature relationship. Therefore, the change over time was not important. However, the amount and aspects of change were the main points which researchers and practitioners should pal attention to in the future.

• Culinary science and hospitality research
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• v.12 no.3 s.30
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• pp.49-60
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• 2006

The purpose of this study was to measure the factors influencing on the perception of helpfulness of marking the country of origin in predicting the quality and safety of pork. A total of 239 questionnaires were completed. A multinomial logit model is specified in order to estimate which factors influence the probability that a consumer perceives the country of origin as helpful in assessing food quality and food safety. The estimations were carried out using the logistic procedure of SAS. The results are as follows. The proportional odds assumptions of models were not violated at p<0.05. The effects of age, income, children, occupation and respondents informed on the importance of the country of origin in pork quality model were statistically significant. The effects of age, children, occupation and trust on the importance of the country of origin in pork safety model were statistically significant. The results from this study could be useful in developing marketing and health promotion strategies as well as government trade policies.

• Child Health Nursing Research
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• v.7 no.4
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• pp.483-493
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• 2001

This study is attempted to define risk factor of youth runaway impulse and to structure forecast model through an extensive analysis of the factors influencing the runaway impulse of youth. The subjects were 610 high school students in Seoul and Kyunggido. The collected data was analysed by SAS. The differences between the runaway impulse group and the non-runaway impulse group were subject to chi-square and t-test. Also logistic regression analysis was conducted on the basis of purposeful selection method for constructing the forecast model. The findings are as follows : the major predicting factors of youth runaway impulse are sex(odds ratio=1.886, p=.009), existence of friends of the opposit sex(odds ratio=2.011, p=.007), anti-social personality(odds ratio= 4.953, p=.000), depressive trend(odds ratio= 2.695, p=.000), family structure(odds ratio= 5.381, p=.000), marital relationship(odds ratio =1.893, p=.009) and also between parents and youth(odds ratio=3.877, p=.000), emotional abuse(odds ratio=1.963, p=.003), authoritative controlled rearing(odds ratio=2.135, p=.005) and stress from school(odds ratio=1.924, p=.008). Therefore, the forecast model will be contribute to the nursing intervention for prevention of runaway youth.