• Journal of Microbiology
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• v.43 no.1
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• pp.38-43
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• 2005

Lactic acid production parameter optimization using Lactobacillus amylovorus NRRL B-4542 was performed using the design of experiments (DOE) available in the form of an orthogonal array and a software for automatic design and analysis of the experiments, both based on Taguchi protocol. Optimal levels of physical parameters and key media components namely temperature, pH, inoculum size, moisture, yeast extract, $MgSO_4{\cdot}7H_20$, Tween 80, and corn steep liquor (CSL) were determined. Among the physical parameters, temperature contributed higher influence, and among media components, yeast extract, $MgSO_4{\cdot}7H_20$, and Tween 80 played important roles in the conversion of starch to lactic acid. The expected yield of lactic acid under these optimal conditions was 95.80% and the actual yield at optimum conditions was 93.50%.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.4
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• pp.188-196
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• 2001

This paper presents and efficient algorithm for the loss minimization by automatic sectionalizing switch operation in distribution systems. Ant colony algorithm is multi-agent system in which the behaviour of each single agent, called artificial ant, is inspired by the behaviour of real ants. Ant colony algorithm is suitable for combinatiorial optimization problem as network reconfiguration because it use the long term memory, called pheromone, and heuristic information with the property of the problem. The proposed methodology with some adoptions have been applied to improve the computation time and convergence property. Numerical examples demonstrate the validity and effectiveness of the proposed methodology using a KEPCO's distribution system.

• The Journal of Economics, Marketing and Management
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• v.4 no.1
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• pp.1-8
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• 2016

The purpose of this paper is to discuss the methodology of optimizing delivery route scheduling using a capacity integer linear programming problem model developed to a previous case study. The methodology suggests a two-stage decision: the first, automatic, where the manager will obtain guidance generated by the solution of the linear programming model, later they could use post-optimization techniques to fine tune to the best operational solution. This study has the goal to reduce the size of service companies' ground transportation fleets, aiming not only to reduce costs and increase competitive advantages but also to lower levels of air pollution and its consequences, traffic and, therefore, the levels of carbon dioxide, allowing for a reduction in envir onmental disasters.

• Journal of Korean Society for Atmospheric Environment
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• v.15 no.2
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• pp.121-138
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• 1999

This study was carried out to evaluate the performance of a sampling and analytical methodology for the measurement of selected volatile organic compounds (VOCs) in the ambient air. VOCs were determined by the adsorbent tube sampling and automatic thermal desorption coupled with GC/FID and GC/MSD analysis. Target analytes were aromatic VOCs, including BTEX, 1,3,5-and 1,2,4,-trimethylbenzenes(TMBs), and naphthalene. The methodology was investigatedwith a wide range of performance criteria such as repeatability, linearity, lower detection limits, collection efficiency, thermal conditioning, breakthrough volume and calibration methods using internal and external standards. standards. Stability of samples collected on adsorbent tubes during storage was also investigated. In addition, the sampling and analytical method developed during this study was applied to real samples duplicately collected in various indoor and outdoor environments. Precisions for the duplicate samples and distributed volume samples appeared to be well comparable with the performance criteria recommended by USEPA TO-17. The audit accuracy was estimated by inter-lab comparison of both duplicate samples and standard materials between the two independent labs. The overall precision and accuracy of the method were estimated to be within 30% for major aromatic VOCs such as BTEX. This study demonstrated that the adsorbent sampling and thermal desorption method can be reliably applied for the measurement of BTEX in ppb levels frequently occurred in common indoor and ambient environments.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.231-234
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• 2008

Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in load demand data. The main aim of our work is to forecast customers' contract information from capacity of daily power consumption patterns. According to the result, we try to evaluate the contract information's suitability. The proposed our approach consists of three stages: (i) data preprocessing: noise or outlier is detected and removed (ii) cluster analysis: SOMs clustering is used to create load patterns and the representative load profiles and (iii) classification: we applied the K-NNs classifier in order to predict the customers' contract information base on power consumption patterns. According to the our proposed methodology, power load measured from AMR(automatic meter reading) system, as well as customer indexes, were used as inputs. The output was the classification of representative load profiles (or classes). Lastly, in order to evaluate KNN classification technique, the proposed methodology was applied on a set of high voltage customers of the Korea power system and the results of our experiments was presented.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.166-170
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• 1993

This paper introduces API(Application Programming Interface) development technology for improving design efficiency which is concerned with special product design environment and development lead time of company's own. Even though most companies commercial CAD/CAM/CAE procucts. For reducing procuct development cycles and improving design efficiency. We have to automatize design processes through the standadizarion and parameterization and develop the specialized utilities as a infrastructure. The proposed API development methodology provides improved automatic 2D,3D modeling procedures and useful user interfaces at a small fraction of the cost and design effort.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.26-31
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• 1994

The analysis of acoustic emission signals generated during machining has been proposed as a technique for studying both the fundamentals of the cutting process and process and as a methodology for detecting tool fracture on line. In this study, AE signals detected during End Milling were applied as the experimental test to sensing tool fracture on the CNC vertical milling machine. Because automatic monitoring of the cutting condition is one of the most important technologies in machining, the in-process detection of cutting tool life including fracture has been investigated by performing experimental test.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.468-473
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• 2002

This paper presents the methodology for measuring eccentricity of the cylindricaliy machined part using CCS(Cylindrical Capacity Spindle Sensor) signal in the CNC turning process. In order to investigate the relationships between CCS orbits and eccentricities, the initial conditions for various eccentricity state and machining process is applied to the experimental strategy. AS a result, it is considered the linearities of CCS signal and magnitude of eccentricity of machined cylindrical surfaces based on the possibility as a automatic detection apparatus for the CNC lathe.

• International Journal of Naval Architecture and Ocean Engineering
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• v.1 no.2
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• pp.78-88
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• 2009

In shipbuilding industry, the manufacturing technology has advanced at an unprecedented pace for the last decade. As a result, many automatic systems for cutting, welding, etc. have been developed and employed in the manufacturing process and accordingly the productivity has been increased drastically. Despite such improvement in the manufacturing technology, however, development of an automatic system for fabricating a curved hull plate remains at the beginning stage since hardware and software for the automation of the curved hull fabrication process should be developed differently depending on the dimensions of plates, forming methods and manufacturing processes of each shipyard. To deal with this problem, it is necessary to create a "plug-in" framework, which can adopt various kinds of hardware and software to construct a full automatic fabrication system. In this paper, a framework for automatic fabrication of curved hull plates is proposed, which consists of four components and related software. In particular the software module for computing fabrication information is developed by using the ooCBD development methodology, which can interface with other hardware and software with minimum effort. Examples of the proposed framework applied to medium and large shipyards are presented.

• Korean Journal of Agricultural Science
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• v.49 no.3
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• pp.619-627
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• 2022

In Korea, apples and pears are among the most important agricultural products to farmers who seek to earn money as income. Generally, farmers make decisions at various stages to maximize their income but they do not always know exactly which option will be the best one. Many previous studies were conducted to solve this problem by predicting farmers' income structure, but researchers are still exploring better approaches. Currently, machine learning technology is gaining attention as one of the new approaches for farmers' income prediction. The machine learning technique is a methodology using an algorithm that can learn independently through data. As the level of computer science develops, the performance of machine learning techniques is also improving. The purpose of this study is to predict the income structure of apples and pears using the automatic machine learning solution H2O.AI and to present some implications for apple and pear farmers. The automatic machine learning solution H2O.AI can save time and effort compared to the conventional machine learning techniques such as scikit-learn, because it works automatically to find the best solution. As a result of this research, the following findings are obtained. First, apple farmers should increase their gross income to maximize their income, instead of reducing the cost of growing apples. In particular, apple farmers mainly have to increase production in order to obtain more gross income. As a second-best option, apple farmers should decrease labor and other costs. Second, pear farmers also should increase their gross income to maximize their income but they have to increase the price of pears rather than increasing the production of pears. As a second-best option, pear farmers can decrease labor and other costs.