• Information Systems Review
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• v.2 no.1
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• pp.151-160
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• 2000

• The Korean Journal of Ecology
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• v.9 no.1
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• pp.41-50
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• 1986

The net primary production in the Korean peninsula was estimated by Miami model, Montreal model and Kira's model, based on 148 meteorological data. The modes in frequency distribution of the values calculated by Montreal and Miami model were found at 1,500g/m2/yr. class and at one step high class in 100g. interval, while by Kira's madel at 1,700g/m2/yr. class. The relationships between values by Miami model(X) and those by Motreal model (Ym) and Kira's model(Yk) can be expressed as follows: Ym=0.365X+944.7, Yk=0.462 X+1006.9 and Yk=1.282Ym-211.5. The total amount of the net primary production in 218,583.4km2, 98.9% of the whole area(220,951 km2) of the Korean Peninsula, was estimated as 290,691,407 tons/yr. by Miami model, 310,751,566 tons/yr by Montreal model and 352,071,901 tons/yr by Kira's model. Therefore, it is reasonable that the organic substance over 300 million-tons is added yearly in the Korean Peninsula, because only 1.1% of the whole area no calculated. In additiion, the net primary production amount of Han-river basin was estimated as ca. 38 million-tons, whether calculated with the meteorological data in level of the Korean Peninsula or with more detail data.

• Journal of Korean Society of Rural Planning
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• v.27 no.2
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• pp.69-76
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• 2021

This study focuses on the relationship between ginseng production per area and meteorological factors. Four areas of major ginseng production are considered in the study. Chungcheongnam-do and Gyengsangbuk-do are selected as the original major production places and Gyeonggi-do and Kangwon-do are selected as the new major places. The meteorological factors applied for study are the average temperature, accumulated precipitation, and integrated sunshine hours. With the data collected across four areas, we used a panel data analysis. From the results of Hausman test, the fixed effects model allowing to control individual area effect is preferable to the random effects model. Based on the results of the fixed effects model, the accumulated precipitation statistically and significantly affect the decreases in ginseng production. Changes in the average temperature negatively affect ginseng production, but the value is not statistically significant. The integrated sunshine positively affect ginseng production, but the value is not statistically significant.

• Journal of Microbiology and Biotechnology
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• v.18 no.6
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• pp.1130-1135
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• 2008

The effect of pH on anaerobic hydrogen production was investigated under various pH conditions ranging from pH 3 to 10. When the modified Gompertz equation was applied to the statistical analysis of the experimental data, the hydrogen production potential and specific hydrogen production rate at pH 5 were 1,182 ml and 112.5 ml/g biomass-h, respectively. In this experiment, the maximum theoretical hydrogen conversion ratio was 22.56%. The Haldane equation model was used to find the optimum pH for hydrogen production and the maximum specific hydrogen production rate. The optimum pH predicted by this model is 5.5 and the maximum specific hydrogen production rate is 119.6 ml/g VSS-h. These data fit well with the experimented data($r^2=0.98$).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.40
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• pp.203-217
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• 1996

This thesis describes a quantitative decision-making of production planning system. A mathmatical model of Linear Programming is used set up a production scheuling under the assumption. As the emphasis is laid on the applicability of the developed model, the linrar programming is applied to establish the production schedule for "F" furniture company which produces kitchin cabinet and OA furniture, The optimal solution is obtained by using the LP package, QBS. By the solution reduced to 14% of work force compared with the real data during all of the planning horizon. And it is also possible to reduce the work-force of the lowest level of employee by 10% for the reasonable management. There are some limitations in computerized data processing, which is only considering the economic costs without considering any external environment of case enterprise. As a result, it is shown that the LP model is very useful method of make aggregate production schedule. schedule.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.54 no.1
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• pp.38-53
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• 2018

The purpose of this study is to estimate potential yield (PY) for Korean west coast fisheries using the holistic production method (HPM). HPM involves the use of surplus production models to apply input data of catch and standardized fishing efforts. HPM compared the estimated parameters of the surplus production from four different models: the Fox model, CYP model, ASPIC model, and maximum entropy model. The PY estimates ranged from 174,232 metric tons (mt) using the CYP model to 238,088 mt using the maximum entropy model. The highest coefficient of determination ($R^2$), the lowest root mean square error (RMSE), and the lowest Theil's U statistic (U) for Korean west coast fisheries were obtained from the maximum entropy model. The maximum entropy model showed relatively better fits of data, indicating that the maximum entropy model is statistically more stable and accurate than other models. The estimate from the maximum entropy model is regarded as a more reasonable estimate of PY. The quality of input data should be improved for the future study of PY to obtain more reliable estimates.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.148-154
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• 2021

We propose the AI Smart Factory Model for integrated management of production processes in this paper .It is an integrated platform system for the production of food packaging containers, consisting of a platform system for the main producer, one or more production partner platform systems, and one or more raw material partner platform systems while each subsystem of the three systems consists of an integrated storage server platform that can be expanded infinitely with flexible systems that can extend client PCs and main servers according to size and integrated management of overall raw materials and production-related information. The hardware collects production site information in real time by using various equipment such as PLCs, on-site PCs, barcode printers, and wireless APs at the production site. MES and e-SCM data are stored in the cloud database server to ensure security and high availability of data, and accumulated as big data. It was built based on the project focused on dissemination and diffusion of the smart factory construction, advancement, and easy maintenance system promoted by the Ministry of SMEs and Startups to enhance the competitiveness of small and medium-sized enterprises (SMEs) manufacturing sites while we plan to propose this model in the paper to state funding projects for SMEs.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.81-81
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• 2022

Recently, many studies on big data based smart farming have been conducted. Research to quantify morphological characteristics using image data from various crops in smart farming is underway. Rice is one of the most important food crops in the world. Much research has been done to predict and model rice crop yield production. The number of productive tillers per plant is one of the important agronomic traits associated with the grain yield of rice crop. However, modeling the basic growth characteristics of rice requires accurate data measurements. The existing method of measurement by humans is not only labor intensive but also prone to human error. Therefore, conversion to digital data is necessary to obtain accurate and phenotyping quickly. In this study, we present an image-based method to predict leaf number and evaluate tiller number of individual rice crop using YOLOv5 deep learning network. We performed using various network of the YOLOv5 model and compared them to determine higher prediction accuracy. We ako performed data augmentation, a method we use to complement small datasets. Based on the number of leaves and tiller actually measured in rice crop, the number of leaves predicted by the model from the image data and the existing regression equation were used to evaluate the number of tillers using the image data.

• The Journal of Engineering Geology
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• v.29 no.4
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• pp.367-381
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• 2019

We investigate pore pressure conditions and reservoir compaction associated with oil and gas production using 3 different permeability models, which are all based on one-dimensional radial flow diffusion model, but differ in considering permeability evolution during production. Model 1 assumes the most simplistic constant and invariable permeability regardless of production; Model 2 considers permeability reduction associated with reservoir compaction only due to pore pressure drawdown during production; Model 3 also considers permeability reduction but due to the effects of both pore pressure drawdown and coupled pore pressure-stress process. We first derive a unified stress-permeability relation that can be used for various sandstones. We then apply this equation to calculate pore pressure and permeability changes in the reservoir due to fluid extraction using the three permeability models. All the three models yield pore pressure profiles in the form of pressure funnel with different amounts of drawdown. Model 1, assuming constant permeability, obviously predicts the least amount of drawdown with pore pressure condition highest among the three models investigated. Model 2 estimates the largest amount of drawdown and lowest pore pressure condition. Model 3 shows slightly higher pore pressure condition than Model 2 because stress-pore pressure coupling process reduces the effective stress increase due to pore pressure depletion. We compare field data of production rate with the results of the three models. While models 1 and 2 respectively overestimates and underestimates the production rate, Model 3 estimates the field data fairly well. Our result affirms that coupling process between stress and pore pressure occurs during production, and that it is important to incorporate the coupling process in the permeability modeling, especially for tight reservoir having low permeability.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.2
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• pp.166-173
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• 2007

In this paper, we propose a model-based approach to estimating production parameters of semiconductor FAB equipment. For FAB scheduling, for example, we need to know equipment's production parameters such as flow time, tact time, setup time, and down time. However, these data are not available, and they have to be estimated from material move data such as loading times and unloading times that are automatically collected in modern automated semiconductor FAB. The proposed estimation method may be regarded as a Bayes estimation method because we use additional information about the production parameters. Namely, it is assumed that the technical ranges of production parameters are known. The proposed estimation method has been applied to a LCD FAB, and found to be valid and useful.