• Journal of the Korea Safety Management & Science
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• v.2 no.2
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• pp.139-153
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• 2000

To develope a domestic machinery industry, international competition through reducing cost by increasing productivity is as important as evolving technology and increasing quality. In this study, we have studied in application of GT technology. GT is a skill that acquires high productivity using part's resemblance for multi part and small size production like mass production. We have classified group, such as design, layout and the others, to be applied systematically. The design group includes retrieval for drawings and study for GT design. The layout group includes GT layout, analysis of production process and study for composition method. And others include scheduling, standardization and standard process.

• Journal of Broadcast Engineering
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• v.23 no.1
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• pp.63-73
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• 2018

These days, VR contents are created in three ways: CG based method, game engine based method, and live action shooting method. The most universal method is live action shooting. So far, most live actions are shot with action cams. Therefore, this method is different from professional image production method for movies and TV dramas. This study tries to point out the difference between professional image production method and action cam based shooting method, and proposes an alternative. The proposed method is log shooting based HDR filming and editing. As the result of test shooting and editing, the proposed method was able to obtain more color information than conventional action cam based shooting method and thereby to implement high-definition images which are hard in an action cam.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.4
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• pp.700-707
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• 2009

The application of renewable energy in electric power systems is growing rapidly in order to make provision for the inequality of the climate, the dwindling supplies of coal, oil and natural gas and a further rise in oil prices. Solar cell generators(SCG) is one of the fastest growing renewable energy. This paper presents a methodology on probabilistic production cost simulation of a power system including SCGs. The generated power by SCGs is variable due to the random variation of solar radiation. In order to solve this problem, the SCGs is modeled as multi-state operational model in this paper. Probabilistic production cost of a power system can be calculated by proposed method considering SCGs with multi-state. The results show that the impacts of SCGs added to a power system can be analyzed in view point of production cost using the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.350-353
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• 1996

In this paper, based on the performance analysis of serial production lines with quality inspection machines, we develope an buffer size optimization method to maximize the production rate. The total sum of buffer sizes are given and a constant, and under this constraint, using the linear approximation method, we suggest a closed form solution for the optimization problem with an acceptable error. Also, we show that the upstream and downstream buffers of the worst performance machine have a significant effect on the production rate. Finally, the suggested methods are validated by simulations.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.157-158
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• 2021

Recently, there have been a growing number of cases using precast concrete construction methods to efficiently carry out construction projects. In order to efficiently carry out PC construction, it is necessary to establish a production plan of PC components that effectively reflect various design alternatives during the initial design stage. Because the production plan of PC components is based on productivity of PC members, the use of PC production simulations that can effectively predict productivity for design alternatives is necessary. Therefore, this paper propose a method to efficiently generate design alternatives which is necessary to perform to production simulations using parametric modeling techniques and BIM.

• 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.

• International journal of advanced smart convergence
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• v.7 no.1
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• pp.42-47
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• 2018

Recently, as the demand for limited resources continues to rise and problems of resource depletion rise worldwide, the importance of renewable energy is gradually increasing. In order to solve these problems, various methods such as energy conservation and alternative energy development have been suggested, and biogas, which can utilize the gas produced from biomass as fuel, is also receiving attention as the next generation of innovative renewable energy. New and renewable energy using biogas is an energy production method that is expected to be possible in large scale because it can supply energy with high efficiency in compliance with energy supply method of recycling conventional resources. In order to more efficiently produce and manage these biogas, a biogas plant has emerged. In recent years, a large number of biogas plants have been installed and operated in various locations. Organic wastes corresponding to biogas production resources in a biogas plant exist in a wide variety of types, and each of the incoming raw materials is processed in different processes. Because such a process is required, the case where the biogas plant process is inefficiently operated is continuously occurring, and the economic cost consumed for the operation of the biogas production relative to the generated biogas production is further increased. In order to solve such problems, various attempts such as process analysis and feedback based on the feedstock have been continued but it is a passive method and very limited to operate a medium/large scale biogas plant. In this paper, we propose "CNN-based production yield prediction algorithm for increasing process efficiency of biogas plant" for efficient operation of biogas plant process. Based on CNN-based production yield forecasting, which is one of the deep-leaning technologies, it enables mechanical analysis of the process operation process and provides a solution for optimal process operation due to process-related accumulated data analyzed by the automated process.

• Asian-Australasian Journal of Animal Sciences
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• v.23 no.10
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• pp.1261-1267
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• 2010

Milk production traits are important economic traits for dairy cattle. The aim of the present study was to refine the position of previously detected quantitative trait loci (QTL) on bovine chromosome 6 affecting milk production traits in Chinese Holstein dairy cattle. A daughter design with 918 daughters from 8 elite sire families and 14 markers spanning the previously identified QTL region were used in the analysis. We employed a combined linkage and linkage disequilibrium analysis (LDLA) approach with two options for calculating the IBD probabilities, one was based on haplotypes of all 14 markers (named Method 1) and the other based on haplotypes with sliding windows of 5 markers (named Method 2). For milk fat yield, the two methods revealed a highly significant QTL located within a 6.5 cM interval (Method 1) and a 4.0 cM interval (Method 2), respectively. For milk protein yield, a highly significant QTL was detected within a 3.0 cM interval (Method 1) or a 2.5 cM interval (Method 2). These results confirmed the findings of our previous study and other studies, and greatly narrowed down the QTL positions.

• Nuclear Engineering and Technology
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• v.56 no.8
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• pp.2906-2915
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• 2024

Radiocarbon (¹⁴C), with a radioactive half-life of approximately 5730 years, poses a long-term environmental contamination risk when released into the atmosphere. The quantification analysis of its release estimates plant-specific generation rates based on factors such as plant power, core neutron flux distribution, and the volume of water exposed to this flux. Utilizing the improved estimation method, the ¹⁴C production rate for several Korean Pressurized Water Reactors (PWRs) was calculated. Also, improvements in measurement methods through sampling have also been made. These enhancements include the verification of the absorption method versus the mixing method. The results of this study indicate that plant-specific ¹⁴C production rates range from 0.213 to 0.317 TBq/yr, which are comparable to the global range observed in PWRs. Furthermore, the study evaluated a quenching correction curve for a liquid scintillation counter using two quenching correction methods: the external standard method and the internal standard method. The accuracy of these methods with 72 samples was validated with an average relative error within ±2.5%. The relative error of the mixing method, when compared to the direct absorption method, was found to be within ±20%. This finding underscores the validity of the improved measurement technique.

• Korean Journal of Computational Design and Engineering
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• v.11 no.2
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• pp.148-155
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• 2006

This paper presents a new method for assessing the efficiency of production process plans using tolerance chart to lower production cost. The tolerance chart is used to predict the accuracy of a part that is to be produced following the process plan, and to carry out the quantitative measurement on the efficiency of the process plan. By comparing the values of design tolerances and their corresponding resultant tolerances calculated using the tolerance chart, the process plan that is incapable of satisfying the design requirements and the faulty production operations can be identified. Similarly, the process plan that imposes unnecessarily high accuracy and wasteful production operations can also be identified. For the latter, a quantitative measure on the efficiency of the process plan is introduced. The higher the unnecessary cost of the production, the poor is the efficiency of the process plan. A coefficient is introduced for measuring the process plan efficiency. The coefficient also incorporates two weighting factors to reflect the difficulty of manufacturing operations and number of dimensional tolerances involved. To facilitate the identification of the machining operations and the machined surfaces, which are related to the unnecessarily tight resultant tolerances caused by the process plan, a rooted tree representation of the tolerance chart is introduced, and its use is demonstrated. An example is presented to illustrate the new method. This research introduces a new quantitative process plan evaluation method that may lead to the optimization of process plans.