• Ocean and Polar Research
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• v.39 no.4
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• pp.301-318
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• 2017

The production of Korean offshore fisheries has been gradually decreasing due to the severe depletion of offshore fisheries resources caused by excessive fishing efforts. The production of the offshore fisheries in 2016 was the lowest since 1975. So the federal and local governments in Korea adopted and implemented various fisheries management plans and policies in order to restore fisheries resources. However, these plans and polices have not been successful in re-establishing fisheries resources. Thus, in order to accurately diagnose the situation with regard to offshore fisheries, this study sought to estimate not only the return to scale by fishing gear of offshore fisheries, but marginal productivity of individual fishing gear based on production factors derived from offshore fisheries production functions. The study was organized in the following manner. First of all, this study estimates production functions of offshore fisheries. The Cobb-Douglas and the translog production functions are adopted as offshore fisheries production functions. Specifically, the functions are estimated by crew, vessels, and offshore resource as production factors. The offshore resource is estimated by the Clarke Yoshimoto Pooley model based on the surplus production model. Secondly, the fisheries production functions are extended to the fixed-effect model and the random-effect model with panel data. Thirdly, this study analyzes the return to scale of offshore fisheries and the marginal productivity of the production factors from the estimated offshore fisheries production function. In conclusion, this study suggests plans and countermeasures for productivity improvement by group (labor intensive or technology intensive) based on the characteristics of individual offshore fishing gear.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.17 no.3
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• pp.213-223
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• 1999

This study provides a improved model of digital basemap production that can efficiently identify and correct the various errors generated in digital map production process. In order to fulfill the requirements that the new model calls for, this study provides a typology of errors by analyzing the errors in digital basemap data. Computer programs for automatic error searching and for checking the correctness of the digital codes in the data have also been developed. Exsiting visual error-checking process has also been analyzed and more systematic process is suggested. As a result, it is found that the improved model of digital basemap production suggested in this study contributes to improving the quality of the digital map database by providing a systematic method for efficient error-searching and correction of digital map data.

• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.57-65
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• 2023

Various studies to enhance the accuracy of rice production forecasting are focused on improving the accuracy of the models. In contrast, there is a relative lack of research regarding the data itself, which the prediction models are applied to. When applying the same dependent variable and prediction model to two different sets of rice production data composed of distinct features, discrepancies in results can occur. It is challenging to determine which dataset yields superior results under such circumstances. To address this issue, by identifying potential influential features within the data before applying the prediction model and centering the modeling around these, it is possible to achieve stable prediction results regardless of the composition of the data. In this study, we propose a method to adjust the composition of the data's features in order to select optimal base variables, aiding in achieving stable and consistent predictions for rice production. This method makes use of the Korea Meteorological Administration's ASOS data. The findings of this study are expected to make a substantial contribution towards enhancing the utility of performance evaluations in future research endeavors.

• Ocean and Polar Research
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• v.34 no.2
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• pp.229-237
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• 2012

Using results from an Earth System model, we investigated change in primary production in the East China Sea, under a global warming scenario. As global warming progresses, the vertical stratification of water becomes stronger, and nutrient supply from the lower part to the upper part is reduced. Consequently, so is the primary production. In addition to the warming trend, there is strong decadal to interdecadal scale variability, and it takes a few decades before the warming trend surpasses natural variability. Thus, it would be very hard to investigate the global warming trend using data of several years' length.

• Korean Management Science Review
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• v.24 no.1
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• pp.91-111
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• 2007

In this paper an effective two-phase approach adopting modified p-median mathematical model is proposed for grouping machines and parts in cellular manufacturing(CM). Unlike the conventional methods allowing machines and parts to be improperly assigned to cells and families, the proposed approach seeks to find the proper block diagonal solution where all the machines and parts are properly assigned to their most associated cells and families in term of the actual machine processing and part moves. Phase 1 uses the modified p-median formulation adopting new inter-machine similarity coefficient based on the non-binary production data-based part-machine incidence matrix(PMIM) that reflects both the operation sequences and production volumes for the parts to find machine cells. Phase 2 apollos iterative reassignment procedure to minimize inter-cell part moves and maximize within-cell machine utilization by reassigning improperly assigned machines and parts to their most associated cells and families. Computational experience with the data sets available on literature shows the proposed approach yields good-quality proper block diagonal solution.

• Journal of Environmental Science International
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• v.22 no.9
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• pp.1171-1180
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• 2013

Recent climate change has led to fluctuations in agricultural production, and as a result national food supply has become an important strategic factor in economic policy. As such, in this study, panel data was collected to analyze the effects of seven meteorological elements and using the Lagrange multipliers method, the fixed-effects model for the production of five types of food crop and the seven meteorological elements were analyzed. Results showed that the key factors effecting increases in production of rice grains were average temperature, average relative humidity and average ground surface temperature, while wheat and barley were found to have positive correlations with average temperature and average humidity. The implications of this study are as follow. First, it was confirmed that the meteorological elements have profound effects on the production of food crops. Second, when compared to existing studies, the study was not limited to one food crop but encompassed all five types, and went beyond other studies that were limited to temperature and rainfall to include various meterological elements.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.58-58
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• 2022

With the development of advanced technology, automation of agricultural work is spreading. In association with the 4th industrial revolution-based technology, research on field smart farm technology is being actively conducted. A state-of-the-art unmanned automated agricultural production demonstration complex was established in Naju-si, Jeollanam-do. For the operation of the demonstration area platform, it is necessary to build a sophisticated, advanced, and intelligent field smart farming model. For the operation of the unmanned automated agricultural production demonstration area platform, we are building data on the growth of soybean for smart cultivated crops and conducting research to determine the optimal time for agricultural work. In order to operate an unmanned automation platform, data is collected to discover digital factors for water management immediately after planting, water management during the growing season, and determination of harvest time. A subsurface drip irrigation system was established for smart water management. Irrigation was carried out when the soil moisture was less than 20%. For effective water management, soil moisture was measured at the surface, 15cm, and 30cm depth. Vegetation indices were collected using drones to find key factors in soybean production prediction. In addition, major growth characteristics such as stem length, number of branches, number of nodes on the main stem, leaf area index, and dry weight were investigated. By discovering digital factors for effective decision-making through data construction, it is expected to greatly enhance the efficiency of the operation of the unmanned automated agricultural production demonstration area.

• Asian Journal of Business Environment
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• v.14 no.1
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• pp.23-30
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• 2024

Purpose: The purpose of this research is to explore the macroeconomic model through both static and dynamic equations. The primary objective of this study is to investigate the variations in the elasticity of substitution across changing economic variables within the framework of the Allen-Uzawa production functions. Research, design, data and methodology: The data were drawn from the World Bank's annual central statistical office database from 2010 to 2021 in the United States of America. The level of expenditures and of the public finance sector, macroeconomic data like output, inflation rates, and labor are examined. Results: This study demonstrates the interaction of two equations, clarifying that the macroeconomic model is practical to determining the stability of both static and dynamic equation systems analytically. The Allen-Uzawa equations allow for the verification of macroeconomic model properties, and study results demonstrate an increase in the range of capital uses as a form of mechanization. A constant elasticity of substitution function is derived from the macroeconomic variables. Conclusion: The macroeconomic model, though the analysis of the static and dynamic Allen - Uzawa model, not only facilitates the examination of long-term trends in crucial endogenous variables but also overcomes challenges commonly associated with other mathematical methods. Overall, the analysis promotes economic growth, investment, and employment. The levels of expenditures and the public finance sector, along with macroeconomic data such as output, inflation rates, and labor, are examined.

• Biotechnology and Bioprocess Engineering:BBE
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• v.6 no.5
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• pp.352-358
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• 2001

Because algal cells are so efficient at absorbing incoming light energy, providing more light energy to photobioreactors would simply decrease energy conversion efficiency. Furthermore, the algal biomass productivity in photobioreactor is always proportional to the total photosynthetic rate. In order to optimize the productivity of algal photobioreactors (PBRs), the oxygen production rate should be estimated. Based on a simple model of light penetration depth and algal photosynthesis, the oxygen production rate in high-density microalgal cultures could be calculated. The estimated values and profiles of oxygen production rate by this model were found to be in accordance with the experimental data. Optimal parameters for PBR operations were also calculated using the model.

• IE interfaces
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• v.24 no.3
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• pp.267-273
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• 2011

In the shipbuilding industry, since production processes are so complicated that the data collection for decision making cannot be fully automated, most of production planning and controls are based on the information provided only by field workers. Therefore, without sufficient information it is very difficult to manage the whole production process efficiently. Job status is one of the most important information used for evaluating the remaining processing time in production control, specifically, in block assembly shop. Currently, it is checked by a production manager manually and production planning is modified based on that information, which might cause a delay in production control, resulting in performance degradation. Motivated by these remarks, in this paper we propose an efficient algorithm for identifying job status in block assembly shop for shipbuilding. The algorithm is based on the multi-layer perceptron neural network model using two key factors for input parameters. We showed the superiority of the algorithm by using a numerical experiment, based on real data collected from block assembly shop.