• Journal of the Regional Association of Architectural Institute of Korea
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• v.20 no.6
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• pp.17-23
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• 2018

The purpose of this study is to suggest 100MPa class ultra high strength concrete mix design model applying neural network theory, in order to minimize an effort wasted by trials and errors method until now. Mix design model was applied to each of the 70 data using binary binder, ternary binder and quaternary binder. Then being repeatedly applied to back-propagation algorithm in neural network model, optimized connection weight was gained. The completed mix design model was proved, by analyzing and comparing to value predicted from mix design model and value measured from actual compressive strength test. According to the results of this study, more accurate value could be gained through the mix design model, if error rate decreases with the test condition and environment. Also if content of water and binder, slump flow, and air content of concrete apply to mix design model, more accurate and resonable mix design could be gained.

• Journal of the military operations research society of Korea
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• v.6 no.1
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• pp.93-101
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• 1980

An effectively designed budget system in the poor resources environment necessarily has three design criteria : (i) to be both planning-oriented and control-oriented, (ii) to be both rationalistic and realistic, (iii) to be sensitive to the variations of resources environment. PPB system is an extreme (planning-oriented and rationalistic) and conventional OEB/OUB system is the other extreme (control-oriented and incrementalistic). Generally, the merits of rationalism are limited because of the infeasibility of applications. Hence, mixtures of the two extremes such as MBO, ZBB, and RZBB have been examined and applied during the last decade. The classical mathematical models of capital budgeting are the starting points of the development of the Budget-Mix Model introduced in this paper. They are modified by the followings: (i) technological-resource constraints, (ii) bounded-variable constraint, (iii) the exchange rules. Special emphasis is laid on the above (iii), because we need more efficient interresource exchanges in the budget-mix process. The Budget-Mix Model is not based on optimization, but a heuristic approach which assures a satisficing solution. And the application fields of this model range between the incremental Nonzero-Base Budgeting and the rational Zero-Base Budgeting. In this thesis, the author suggests 'the budget- mix concept' and a budget-mix model. Budget-mix is a decision process of making program-mix and resource-mix together. For keeping this concept in the existing organization realistic, we need the development of quantitative models describing budget-mix situations.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.223-224
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• 2021

Recently, the demand for high fluidity concrete has been increased due to skyscrapers. However, it has its own limits. First of all, high fluidity concrete has large variation and through trial & error it costs lots of money and time. Neural network model has repetitive learning process which can solve the problem while training the data. Therefore, the purpose of this study is to predict optimum mix design of 40MPa, 60MPa high fluidity concrete by using neural network model and verifying compressive strength by applying real data. As a result, comparing collective data and predicted compressive strength data using MATLAB, 40MPa mix design error rate was 1.2%~1.6% and 60MPa mix design error rate was 2%~3%. Overall 40MPa mix design error rate was less than 60MPa mix design error rate.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1990.04a
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• pp.100-110
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• 1990

An optimal raw mix model in stainless steel making is developed. The key raw materials in stainless steel making are stainless steel scrap, steel scrap, and alloy materials like Fe-Ni, Fe-Cr. Among those raw materials, the alloy metals are very expensive as well as rapidly price-changing items. Consequently, it is very important to develop an minimal cost raw mix scheme while the produced stainless steel satisfies the required specification in it's composition. The linear programming model is employed to determine the minimal cost raw mix scheme. Compared with the method being used, the developed linear programming model gives much faster and better solution (lower cost raw mix plan). Together with the linear programming model, the database is also developed, which includes the following: 1) data for raw materials, such as compositions, costs, densities, available inventory levels, and so on, 2) the required specifications process. The developed optimal raw mix model will be implemented in VAX computer.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.1278-1281
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• 2022

현재 Action classification model은 computational resources의 제약으로 인해 video전체의 frame으로 학습하지 못한다. Model에 따라 다르지만, 대부분의 경우 하나의 action을 학습시키기 위해 보통 많게는 32frame, 적게는 8frame으로 model을 학습시킨다. 본 논문에서는 이 한계를 극복하기 위해 하나의 video의 많은 frame들을 mix-up과정을 거쳐 한장의 frame에 여러장의 frame 정보를 담고자 한다. 이 과정에서 video의 시간에 따른 변화(temporal- dynamics)를 손상시키지 않기 위해 linear mix-up이라는 방법을 제안하고 그 성능을 증명하며, 여러장의 frame을 mix-up시켜 모델의 성능을 향상시키는 가능성에 대해 논하고자 한다.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.11a
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• pp.253-254
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• 2012

The purpose of this study is to suggest and verify high-strength concrete mix design model applying neural network theory, in order to minimize effort and time wasted by using trial and error method utill now. There are 7 input and 2 output to predict mix design. 40 data of mix design were learned with back-propagation algorithm. Then they are repeatedly learned back-propagation in neural network theory. Also, to verify predicted model, we analyzed and compared value predicted from 60MPa mix design with value measured by actual compressive strength test.

• Bulletin of the Korean Mathematical Society
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• v.49 no.2
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• pp.319-327
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• 2012

The equations of a multifluid interpenetration mix model are analyzed. The model is an intermediate mix model in the sense that it is defined by partial pressures but only a single global pressure and a single global temperature. It none-the-less avoids the stability difficulty. It is shown that the model is hyperbolic so that it is mathematically stable.

• Journal of the Korean association of regional geographers
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• v.15 no.2
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• pp.226-249
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• 2009

This study aims to establish the regional mix types and models of place marketing strategy. For this purpose, eighty seven cases of place marketing in Gwangju-Jeonnam region during the last two years are reviewed, Twenty seven types of regional mix are abstracted according to space, theme, subject, target, and factor. There are five spatial types(urban mix, zoning mix, zoning urban mix, package urban mix, zoning mix, and space package mix), eight thematic types(culture mix, history mix, tourism mix, industry mix, administration mix, environment mix, transportation mix, and PR mix), five subject types(central government led public mix, local government led public mix, enterprise led private mix, civil society led private mix, and private public partnership mix), four target types(resident mix, tourist mix, enterprise mix, and common mix), and five factor types (organization mix, image mix, point mix, target mix, and channel mix). In the basis of these types, the twenty two primary model of regional mix, and the one hundred twenty six secondary model of regional mix are established.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.14 no.1
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• pp.58-64
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• 1989

This paper constructs a stochastic model for computer performance evaluation which has several parameters such as the kinds of instruction mix of benchmark programs, distribution and frequency of instruction mix. It shows, by applying the model to the performance evaluation of the Intel 8086/8088 microprocessor, that this model could be utilited not only for performance evaluation of existing computer systems but also for estimation of nonexisting systems.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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• pp.289-296
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• 2022

In this paper, we developed a deep learning-based recyclable object detection model. The model is developed based on YOLOv5 that is a one-stage detector. The deep learning model detects and classifies the recyclable object into 7 categories: paper, carton, can, glass, pet, plastic, and vinyl. We propose two methods for recyclable object detection models to solve problems during training. Bounding Box CutMix solved the no-objects training images problem of Mosaic, a data augmentation used in YOLOv5. Standardized Distance-based IoU replaced DIoU using a normalization factor that is not affected by the center point distance of the bounding boxes. The recyclable object detection model showed a final mAP performance of 0.91978 with Bounding Box CutMix and 0.91149 with Standardized Distance-based IoU.