• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.1085-1092
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• 2002

수십만명의 대입응시자와 대학입학을 준비하는 수백만명의 초중고등학교 학생들에게 공정한 경쟁의 규칙과 측정방법을 마련하여 적응하는 것은 매우 중요하다. 현행 대학입학 수학능력 시험에서 각 영역별 표준점수는 응시계열인 인문계열, 자연계열, 예체능계열로 나누어 각 계열의 평균과 표준편차를 사용하여 계산한다. 따라서 동일한 점수도 어느 응시계열에 속하는가에 따라 표준점수가 달라지게 되며, 상이한 표준점수를 사용하여 대등한 경쟁을 하는 경우가 있어 불공정성이 제기된다. 비록 변환표준점수로 조정하여 계열간 불공정이 어느 정도 조정되지만, 자신의 점수에 비하여 집단의 평균점수가 낮을수록 변환표준점수가 증가하게 되므로 계열선택의 영향이 없다고 보기 어렵다. 이러한 결과로 유리한 계열로 대거 이동하는 현상이 나타나고 있다. 본 연구는 대학입학에 필수적인 대학입학 수학능력시험에서 계열간 실력차이를 보정하여 공정한 경쟁을 가능하게 하는 표준점수 계산방법을 제시하였다. 또한 모든 과목이 선택과목이 되는 2005학년도부터 시행될 수학능력시험에서 과목간 표준점수를 보정하는 방법을 제시하였다 본 연구는 결론을 도출하는데 있어 응시자들간 표준점수의 차이는 응시과목에 따라 달라지지 않는다는 과목의 동질성을 가정하였다. 응시과목의 동질성 가정하에서 집단간의 표준점수를 보정하는 방식은 동일한 시험문제로 각 집단이 시험을 보는 경우 집단간의 차이만큼을 표준점수에 합하여 보정하고, 각 집단이 고유하게 응시하는 시험과목은 공통과목의 차이만큼을 각 집단에 보정하여 주는 것이다. 과목간에 표준점수를 보정하는 방식은 해당과목에 응시한 응시자들이 다른 과목에서 획득한 표준점수의 평균치로보정하는 것이다.하기 위해서, 기업간 프로세스 협업(collaboration) 부분의 데이터 및 서식, 이를 취급하는 기능과 프로세스에 대란 분석을 통해 업무 프로세스 모델링 방법론과 관련한 모델링 지침 및 메타모델을 이용한 표준 업무 프로세스 모델을 개발하여 기업간 업무 프로세스 표준화에 대한 체계적인 관리에 대한 방안을 연구하고자 한다.의Bullwhip effect를 감소시킬 수 있는 장점이 있다. 동시에 이것은 향후 e-Business 시스템 구축을 위한 기본 인프라 역할을 수행할 수 있게 된다. 많았고 년도에 따른 변화는 보이지 않았다. 스키손상의 발생빈도는 초기에 비하여 점차 감소하는 경향을 보였으며, 손상의 특성도 부위별, 연령별로 다양한 변화를 나타내었다.해가능성을 가진 균이 상당수 검출되므로 원료의 수송, 김치의 제조 및 유통과정에서 병원균에 대한 오염방지에 유의하여야 할 것이다. 확인할 수 있었다. 이상의 결과에 의하면 고농도의 유기물이 함유된 음식물쓰레기는 Hybrid Anaerobic Reactor (HAR)를 이용하여 HRT 30일 정도에서 충분히 직접 혐기성처리가 가능하며, 이때 발생된 $CH_{4}$를 회수하여 이용하면 대체에너지원으로 활용 가치가 높은 것으로 판단된다./207), $99.2\%$(238/240), $98.5\%$(133/135) 및 $100\%$ (313)였다. 각각 두 개의 요골동맥과 우내흉동맥에서 부분협착이나 경쟁혈류가 관찰되었다. 결론: 동맥 도관만을 이용한 Off pump CABG를 시행하여 감염의 위험성을 증가시키지 않으면서 영구적인 신경학적 합병증을 일으

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.987-993
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• 2002

최근 들어 전자상거래의 급속한 발달로 전 세계적으로 수송 물동량이 급격히 증대되고 있고, 이로 인해 택배사업이 대단히 활성화되고 있다. 출발지와 목적지가 서로 상이한 무수히 만은 수송 요구가 들어오면 수송 요구화물의 신속한 집배송을 위한 배차계획 및 수송계획을 세우는 것이 택배회사의 주요 업무이다. 이러한 배차 계획 및 수송 계획을 어떻게 수립하느냐에 따라 전체 수송비용뿐만 아니라 고객들의 서비스 수준에 상당한 영향을 미치게 된다. 그러나 이러한 운영적 차원에서의 의사결정 이전에 훨씬 중요하게 고려해야 할 내용이 택배네트워크의 설계 문제이다. 이러한 택배네트워크의 설계에는 터미널 개수 및 위치를 결정하는 전략적 문제와 영업소들을 터미널에 할당하는 전술적 문제로 구분될 수 있다. 현재 우리 국내에는 크고 작은 수많은 택배사업자들이 있으나, 그 중에서 비교적 규모가 큰 주요 택배회사들은 대부분 전국에 걸쳐 다수의 터미널을 설치하여 두고 수송화물의 집배송을 위한 물류거점으로 운영하고 있다. 이와 같은 터미널 위치 및 개수가 정해진 상태에서 전국에 걸쳐 분포되어 있는 영업소들을 어떤 터미널에 할당하여 처리되도록 하느냐의 여부는 수송비용 측면에서뿐만 아니라 고객들에 대한 서비스 측면에서 대단히 중요한 의사결정 중의 하나이다. 본 연구에서는 비용과 시간을 고려하여 전국에 걸쳐 분포되어 있는 영업소들을 어떤 터미널에 할당해야 하는지를 결정하기 위한 수리적 모형을 제시하고, 이에 대한 탐색적 해법을 제시하며, 국내의 택배회사 사례를 대상으로 모형을 적용해 보고자 한다.무가 많이 발생하는 유통 분야의 프랜차이즈 산업을 대상으로 기업정보시스템 구현 및 경쟁력 강화를 뒷받침하기 위해서, 기업간 프로세스 협업(collaboration) 부분의 데이터 및 서식, 이를 취급하는 기능과 프로세스에 대란 분석을 통해 업무 프로세스 모델링 방법론과 관련한 모델링 지침 및 메타모델을 이용한 표준 업무 프로세스 모델을 개발하여 기업간 업무 프로세스 표준화에 대한 체계적인 관리에 대한 방안을 연구하고자 한다.의Bullwhip effect를 감소시킬 수 있는 장점이 있다. 동시에 이것은 향후 e-Business 시스템 구축을 위한 기본 인프라 역할을 수행할 수 있게 된다. 많았고 년도에 따른 변화는 보이지 않았다. 스키손상의 발생빈도는 초기에 비하여 점차 감소하는 경향을 보였으며, 손상의 특성도 부위별, 연령별로 다양한 변화를 나타내었다.해가능성을 가진 균이 상당수 검출되므로 원료의 수송, 김치의 제조 및 유통과정에서 병원균에 대한 오염방지에 유의하여야 할 것이다. 확인할 수 있었다. 이상의 결과에 의하면 고농도의 유기물이 함유된 음식물쓰레기는 Hybrid Anaerobic Reactor (HAR)를 이용하여 HRT 30일 정도에서 충분히 직접 혐기성처리가 가능하며, 이때 발생된 $CH_{4}$를 회수하여 이용하면 대체에너지원으로 활용 가치가 높은 것으로 판단된다./207), $99.2\%$(238/240), $98.5\%$(133/135) 및 $100\%$ (313)였다. 각

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.1017-1028
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• 2006

Japanese railroad companies continued growing by developing diversification based on a railroad enterprise. However, after entering in the 1990s, the diversification model of a railroad company reached the management limit. Under economic depression, A decrease in the birthrate and aging progressed and passenger transport changed to the downward tendency. Nevertheless, since railroad investment was expanded, railroad achievements got worse and price competitiveness with JR East Japan became weak. But the achievements of a diversification section got worse compared with the railroad enterprise. Therefore, group management was thought as important and enterprise reorganization was developed.

• Tunnel and Underground Space
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• v.29 no.3
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• pp.184-196
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• 2019

This study aims to extract a 3D image of micro-cracks generated by hydraulic fracturing tests, using the deep learning method and X-ray computed tomography images. The pixel-level cracks are difficult to be detected via conventional image processing methods, such as global thresholding, canny edge detection, and the region growing method. Thus, the convolutional neural network-based encoder-decoder network is adapted to extract and analyze the micro-crack quantitatively. The number of training data can be acquired by dividing, rotating, and flipping images and the optimum combination for the image augmentation method is verified. Application of the optimal image augmentation method shows enhanced performance for not only the validation dataset but also the test dataset. In addition, the influence of the original number of training data to the performance of the deep learning-based neural network is confirmed, and it leads to succeed the pixel-level crack detection.

• Smart Media Journal
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• v.9 no.4
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• pp.66-72
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• 2020

As the online education market expands, educational contents with various presentation methods are being developed and released. In addition, it is imperative to develop content that reflects the usability and user environment of users who use this educational content. However, for qualitative growth of contents that will support quantitative expansion of markets, existing model analysis methods are urgently needed at a time when development direction of newly developed contents is secured. In this process of content development, a typical model for setting development goals is needed, as the rules of the prototype affect the entire development process and the final development outcome. It can also provide a positive benefit that screens the issue of performance dualization between processes due to the absence of communication between a single entity or between a number of entities. In the case of AR-based educational content which is effective to secure data necessary for development by securing samples of similar categories because there are not enough ready-made samples released. Therefore, a big data statistical analysis service is needed that can easily collect data and make decisions using big data. In this paper, we would like to design analysis services that enable the selection and detection of intuitive multidimensional factors and attributes, and propose big data-based statistical analysis services that can assist cooperative activities within an organization or among many companies.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.1-12
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• 2022

Traditional methods of measuring corneal ulcers were difficult to present objective basis for diagnosis because of the subjective judgment of the medical staff through photographs taken with special equipment. In this paper, we propose a method to detect the ulcer area on a pixel basis in corneal ulcer images using a semantic segmentation model. In order to solve this problem, we performed the experiment to detect the ulcer area based on the DeepLab model which has the highest performance in semantic segmentation model. For the experiment, the training and test data were selected and the backbone network of DeepLab model which set as Xception and ResNet, respectively were evaluated and compared the performances. We used Dice similarity coefficient and IoU value as an indicator to evaluate the performances. Experimental results show that when 'crop & resized' images are added to the dataset, it segment the ulcer area with an average accuracy about 93% of Dice similarity coefficient on the DeepLab model with ResNet101 as the backbone network. This study shows that the semantic segmentation model used for object detection also has an ability to make significant results when classifying objects with irregular shapes such as corneal ulcers. Ultimately, we will perform the extension of datasets and experiment with adaptive learning methods through future studies so that they can be implemented in real medical diagnosis environment.

• Journal of Environmental Impact Assessment
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• v.31 no.4
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• pp.214-225
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• 2022

This study emphasized the soil of environmental impact assessment to devise measures to minimize the negative impact of project implementation on the environment. As a series of efforts for impact assessment procedures, a national inventory-based database was established for urban development projects, and three machine learning model performance evaluation as well as soil pollutant concentration distribution mapping were conducted. Here, nine soil pollutants were mapped to the metropolitan area of South Korea using the Random Forest model, which showed the best performance. The results of this study found that concentrations of Zn, F, and Cd were relatively concerned in Seoul, where urbanization is the most active. In addition, in the case of Hg and Cr6+, concentrations were detected below the standard, which was derived from a lack of pollutants such as industrial and industrial complexes that affect contents of heavy metals. A significant correlation between land cover and pollutants was inferred through the spatial distribution mapping of soil pollutants. Through this, it is expected that efficient soil management measures for minimizing soil pollution and planning decisions regarding the location of the project site can be established.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.11-17
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• 2022

In this paper, a model that can increase the efficiency of work in arranging interior furniture by applying augmented reality technology was studied. In the existing system to which augmented reality is currently applied, there is a problem in that information is limitedly provided depending on the size and nature of the company's product when outputting the image of furniture. To solve this problem, this paper presents an AR labeling algorithm. The AR labeling algorithm extracts feature points from the captured images and builds a database including indoor location information. A method of detecting and learning the location data of furniture in an indoor space was adopted using the CNN technique. Through the learned result, it is confirmed that the error between the indoor location and the location shown by learning can be significantly reduced. In addition, a study was conducted to allow users to easily place desired furniture through augmented reality by receiving detailed information about furniture along with accurate image extraction of furniture. As a result of the study, the accuracy and loss rate of the model were found to be 99% and 0.026, indicating the significance of this study by securing reliability. The results of this study are expected to satisfy consumers' satisfaction and purchase desires by accurately arranging desired furniture indoors through the design and implementation of AR labels.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4D
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• pp.687-693
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• 2006

Image co-registration is the process of overlaying two images of the same scene, one of which represents a reference image, while the other is geometrically transformed to the one. In order to improve efficiency and effectiveness of the co-registration approach, the author proposed a pre-qualified area matching algorithm which is composed of feature extraction with canny operator and area matching algorithm with cross correlation coefficient. For refining matching points, outlier detection using studentized residual was used and iteratively removes outliers at the level of three standard deviation. Throughout the pre-qualification and the refining processes, the computation time was significantly improved and the registration accuracy is enhanced. A prototype of the proposed algorithm was implemented and the performance test of 3 Landsat images of Korea. showed: (1) average RMSE error of the approach was 0.435 pixel; (2) the average number of matching points was over 25,573; (3) the average processing time was 4.2 min per image with a regular workstation equipped with a 3 GHz Intel Pentium 4 CPU and 1 Gbytes Ram. The proposed approach achieved robustness, full automation, and time efficiency.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.6
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• pp.555-567
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• 2023

The cracks in the tunnel are currently determined through visual inspections conducted by inspectors based on images acquired using tunnel imaging acquisition systems. This labor-intensive approach, relying on inspectors, has inherent limitations as it is subject to their subjective judgments. Recently research efforts have actively explored the use of deep learning to automatically detect tunnel cracks. However, most studies utilize public datasets or lack sufficient objectivity in the analysis process, making it challenging to apply them effectively in practical operations. In this study, we selected test datasets consisting of images in the same format as those obtained from the actual inspection system to perform an objective evaluation of deep learning models. Additionally, we introduced ensemble techniques to complement the strengths and weaknesses of the deep learning models, thereby improving the accuracy of crack detection. As a result, we achieved high recall rates of 80%, 88%, and 89% for cracks with sizes of 0.2 mm, 0.3 mm, and 0.5 mm, respectively, in the test images. In addition, the crack detection result of deep learning included numerous cracks that the inspector could not find. if cracks are detected with sufficient accuracy in a more objective evaluation by selecting images from other tunnels that were not used in this study, it is judged that deep learning will be able to be introduced to facility safety inspection.