• Proceedings of the CALSEC Conference
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• 1999.07a
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• pp.41-50
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• 1999

최근의 CALS 시스템 구축 가속화 및 전자상거래 활성화 분위기는 정보기술의 전략적 활용의 초점을 개별기업 위주에서 기업간 협력체제 구축과 이를 바탕으로 하는 기업간 전자적 결합(Electronic Integration)으로 옮기고 있다. 우리는 정보기술의 공유를 기반으로 새로운 기업간의 관계를 구축할 수 있으며, 이러한 관계를 전략적으로 활용함으로써 기존 사업에서의 경쟁우위 뿐만 아니라, 새로운 사업기회를 창출할 수 있다. 관련기업간의 데이터 표준화를 통한 정보의 공유는 CALS 시스템 구축의 바탕이 된다고 할 수 있는데, 정보공유의 범위는 산업별 특성과 제품별 특성에 따라 달라질 수밖에 없으며, 관련기업간의 교섭력(Bargaining Power)에 의해서도 영향을 받게된다. CALS 환경에서는 개별기업의 정보전략에 따라 수직적 협력관계에 있는 기업들간의 정보공유 정도와 수평적 협력관계에 있는 기업들간의 정보공유 정도가 결정된다고 할 수 있다. 정보공유의 범위를 설정하는 데에는 기업간에 이루어지는 업무의 성격, 공급-제조-유통으로 연결되는 관련기업간의 구조 등도 크게 영향을 미치는 것으로 파악되고 있다. 이 연구에서는 CALS 시스템 구축과정에서 우리가 고려할 수 있는 정보공유의 범위를 관련업무공유, 시스템공유, 부품 설비공유, 시설공유 등의 관점에서 분석하여 봄으로써, 각 산업별로 진행되고 있는 시범사업들의 시스템 영역의 범위를 재조명해보고자 한다. 이 연구에서 집중적으로 다루게 될 정보공유의 범위에 대한 내용은 CALS 시스템을 개발하려는 기업들의 응용서비스 개발 및 정보전략 구축에 중요한 공헌을 할 수 있을 것으로 기대된다.진" 사업에 대한 표준 설정을 위하여 노스캐롤라이나주 지방보건소의 "보건교육/건강증진" 표준체제를 예로 들었다. 다음으로, 경제적인 효율면에서 볼 때 "보건교육/건강증진" 사업에는 단기 혹은 장기 투자가치가 있는가 하는 것이다. 새로 태어나는 미국 지방 보건소의 "보건교육/건강증진" 활동은 지역 시민 및 그 단체가 광범위하고도 자연 다발적으로 참여할 때만 성공할 수 있다고 결론 지울 수 있다.한 분야별 전문가시스템 개발을 지양하고 MCRDR이론을 기반으로 한 범용성 있는 전문가시스템 개발 툴의 개발에 관한 연구를 소개한다. 후 새로운 지식을 얻는 반복적인 Explanation-based Data Mining Architecture를 제시하였다. 본 연구의 의의로는 데이타 마이닝을 통한 귀납적 지식생성에 있어 귀납적 오류의 발생을 고메인 지식을 통해 설명가능 함을 보임으로 검증하고 아울러 이러한 설명을 통해 연역적으로 새로운 가설지식을 생성시켜 이를 가설검증방식으로 검증함으로써 귀납적 접근과 연역적 접근의 통합 데이타 마이닝 접근을 제시하였다는데 있다.gical learning to give information necessary to improve the making. program and policy decision making, The objectives of the study are to develop the methodology of modeling the socioeconomic evaluation, and build up the practical socioeconomic evaluation model of the HAN projec

• Journal of Navigation and Port Research
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• v.43 no.1
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• pp.72-78
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• 2019

The second-hand ship market provides immediate access to the freight market for shipping investors. When introducing second-hand vessels, the precise estimate of the price is crucial to the decision-making process because it directly affects the burden of capital cost to investors in the future. Previous studies on the second-hand market have mainly focused on the market efficiency. The number of papers on the estimation of second-hand vessel values is very limited. This study proposes an artificial neural network model that has not been attempted in previous studies. Six factors, freight, new-building price, orderbook, scrap price, age and vessel size, that affect the second-hand ship price were identified through literature review. The employed data is 366 real trading records of Panamax second-hand vessels reported to Clarkson between January 2016 and December 2018. Statistical filtering was carried out through correlation analysis and stepwise regression analysis, and three parameters, which are freight, age and size, were selected. Ten-fold cross validation was used to estimate the hyper-parameters of the artificial neural network model. The result of this study confirmed that the performance of the artificial neural network model is better than that of simple stepwise regression analysis. The application of the statistical verification process and artificial neural network model differentiates this paper from others. In addition, it is expected that a scientific model that satisfies both statistical rationality and accuracy of the results will make a contribution to real-life practices.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.4
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• pp.225-232
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• 2024

This study focuses on the determination of the fracture mode in composite laminates using deep learning. With the increase in the use of laminated composites in numerous engineering applications, the insurance of their integrity and performance is of paramount importance. However, owing to the complex nature of these materials, the identification of fracture modes is often a tedious and time-consuming task that requires critical domain knowledge. Therefore, to alleviate these issues, this study aims to utilize modern artificial intelligence technology to automate the fractographic analysis of laminated composites. To accomplish this goal, scanning electron microscopy (SEM) images of fractured tensile test specimens are obtained from laminated composites to showcase various fracture modes. These SEM images are then categorized based on numerous fracture modes, including fiber breakage, fiber pull-out, mix-mode fracture, matrix brittle fracture, and matrix ductile fracture. Next, the collective data for all classes are divided into train, test, and validation datasets. Two state-of-the-art, deep learning-based pre-trained models, namely, DenseNet and GoogleNet, are trained to learn the discriminative features for each fracture mode. The DenseNet models shows training and testing accuracies of 94.01% and 75.49%, respectively, whereas those of the GoogleNet model are 84.55% and 54.48%, respectively. The trained deep learning models are then validated on unseen validation datasets. This validation demonstrates that the DenseNet model, owing to its deeper architecture, can extract high-quality features, resulting in 84.44% validation accuracy. This value is 36.84% higher than that of the GoogleNet model. Hence, these results affirm that the DenseNet model is effective in performing fractographic analyses of laminated composites by predicting fracture modes with high precision.

• Economic and Environmental Geology
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• v.40 no.3 s.184
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• pp.295-306
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• 2007

In this study, we predicted areas vulnerable to ground subsidence near abandoned underground coal mine at Sam-cheok City in Korea using a probability (frequency ratio) model with Geographic Information System (GIS). To extract the factors related to ground subsidence, a spatial database was constructed from a topographical map, geo-logical map, mining tunnel map, land characteristic map, and borehole data on the study area including subsidence sites surveyed in 2000. Eight major factors were extracted from the spatial analysis and the probability analysis of the surveyed ground subsidence sites. We have calculated the decision coefficient ($R^2$) to find out the relationship between eight factors and the occurrence of ground subsidence. The frequency ratio model was applied to deter-mine each factor's relative rating, then the ratings were overlaid for ground subsidence hazard mapping. The ground subsidence hazard map was then verified and compared with the surveyed ground subsidence sites. The results of verification showed high accuracy of 96.05% between the predicted hazard map and the actual ground subsidence sites. Therefore, the quantitative analysis of ground subsidence near abandoned underground coal mine would be possible with a frequency ratio model and a GIS.

• Information Systems Review
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• v.8 no.1
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• pp.63-80
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• 2006

This paper suggests a normative performance measurement model for B2B e-marketplace. The model is for the neutral and systematic sourcing B2B e-marketplace. It is based on BSC that is very powerful and sound tool among all the available performance measurement systems. In addition, we used AHP to make the model more structured and to draw weights of performance measures from experts. We derived main features and factors affecting performance of e-business companies and especially of B2B e-marketplaces based on literature. In order to make the model usefulness, we co-worked with some experts who are engaged in real Korean B2B e-marketplaces. They included venture capitalists, top managers of B2B e-marketplaces, and consultants. By their assistances, we could find important evaluating factors and define the hierarchical structure. After defining the hierarchical structure, the expert group decided weights of performance indicators through the designed questionnaires. The weight for each measure was calculated using geometric mean of the questionnaire response. We made the case studies of two real Korean B2B e-marketplaces to apply the suggested model with some adjustments. The results of the case studies were so satisfactory that the suggested performance measurement model for B2B e-marketplace is useful and applicable to the real world.

• Journal of Bio-Environment Control
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• v.23 no.3
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• pp.244-249
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• 2014

Hunter value ($L^*$, $a^*$, $b^*$) is a rapid and convenience method for compare with different skin color, but not suitable to compare quality with various grape cultivars. Color Index for Red Grapes (CIRG) is one of a simply convert method for grape skin color as numeric data, and many previously result showed that high correlation between CIRG index and various quality factors of grape. In this experiment, we measured CIRG index, total soluble solids (TSS), titratable acidity (TA), and total anthocyanin content on the grapes (Campbell Early, Heukbosuk, Kyoho) from the varaison to harvest. As a result, CIRG index and anthocyanin contents have an exponential correlation; r-square is each 0.8595 in the 'Campbell Early' and 0.8500 in 'Heukbosuk'. And TSS and CIRG index have a positive r-square, at the Campbell Early is 0.7758 and Heukbosuk is 0.7253. Also TA and CIRG index have a positive r-square, at the Campbell Early is 0.7279 and Heukbosuk is 0.6630. These results were shown that CIRG index is a reliable indicator for a none-destructive evaluation of the grape quality.

• Journal of the Korean Association of Geographic Information Studies
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• v.4 no.1
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• pp.1-17
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• 2001

This research is primarily intended to develop a decision-making support system about the natural environment in the country by integrating various formats of spatial data into a comprehensive GIS database. The first part of the research is to design GIS database as a means of combining thematic cartographic data with field survey records. Ecosystem records acquired by a field sampling have been reproduced as digital thematic maps. The database also included various natural environment related data (map for forestry, soil, national park etc.) collected from many other agencies. The second part of the research is devoted to the design of efficient man-machine interfaces. A key concern in the GUI (graphic user interface) design process was to address the need to handle and to manage large databases by non-GIS experts. The system has been checked experimentally, enabled the users to quarry the data required simply and execute spatial analysis accommodating versatile alternatives in decision-making process. Detailed visual maps can be generated over large areas quickly and easily. The system will serve to increase the scientific and objective decision-making by overcoming serious constraints suffered from the past non-GIS-based approach. It is anticipated that this research output could be used as a valuable reference in building a nationwide natural environment GIS-DB (database) in the central and local governments.

• Journal of the Korea Society of Computer and Information
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• v.17 no.10
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• pp.117-127
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• 2012

Identifying user requirements efficiently and reflecting them on the existing system is very important in a rapidly changing web and mobile environments. This study proposes the strategies to refining requirements and to prioritizing those refined requirements for changing of web and mobile application based on user requirements (e.g. mobile application comments, Q&A, reported information as discomfort factors). In order to refining the user requirements, those requirements are grouped by using the advancement of the software business of the Forum of standardization and the existing configuration-based programs. Then, we mapped them onto the PIECES framework to identifying whether the refined requirements are correctly reflected to the system in a way of valid and pure. To determine the priority of refined requirements, first, relative weights are given to software structure, requirements and categories of PIECES. Second, integration points on each requirement are counted to obtain the relative value of partial and overall score of a set of software structural requirements. In order to verifying the possibility and proving the effectiveness of proposing technique in this study, survey was conducted on changing requirements of mobile application which have been serviced at S University by targeting 15 people of work-related stakeholders.

• International Journal of Highway Engineering
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• v.10 no.4
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• pp.151-159
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• 2008

Performances of retroreflectivity vary place to place, according to traffic volumes and time lengths after striping, depending on pavement marking materials and colors. The present paper uses the nation wide data of retroreflectivity, which has been collected from freeways and then tries to develop the regression curve setting traffic volume and service life as independent variables and retroreflectivities as dependent variables. The DB system includes two year's measurement in $2005{\sim}2006$ over Korean freeway pavement marking at an interval of three months for the period. The mobile measurement system, a laserlux, was employed for the purpose. The DB has provided a lot of information about materials and performance of the specific pavement marking such as geometric features, traffic volumes, material characteristics and the installation date. This study provides the comparison of pavement marking performances under diversified conditions. Based on accumulated pavement marking performances, this study provides performance curves based on the diversified factors. The goal of the retroreflectivity modeling is to develop equations that can be used to estimate an average retroreflectivity of pavement markings as a function time since application and traffic volume. After representing the variation of retroreflectivities and estimating regression curves by linear, exponential, logarithmic and power function, the regression curve which had the highest coefficient of determination and the value similar to the last field measurement was regarded as the retroreflectivity decay model. As a result of verification, the decay model showed the signification within the 90% confidence level and especially showed the clear relation with field data according to increase of cumulative vehicle exposure. Accordingly, these models can be used to determine service lives, retroreflectivity degradation rates, and retroreflectivity of new markings.

• Journal of Digital Convergence
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• v.16 no.1
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• pp.85-92
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• 2018

In this study investigated the factors for the career preparation behaviors of social welfare major students based on Lent et al. (1994)'s Social-cognitive Career Theory so as to provide essential baseline data for establishing proper career support strategies that suit the distinctive nature of social welfare studies. The participants of this study are 132 social welfare major students from three colleges who have completed social welfare field education. This study analyzed the relationship between cognitive factor (outcome expectation), vocational interest factor (major selection satisfaction), goal factor (career decision level), and work performance factor (career preparation behavior). For analysis, SPSS 24.0 and AMOS 24.0 were used. The analysis results are as follows. First, the model's goodness of fit was found to be at a statistically ideal level with CFI=.904, TLI=.887, and RMSEA=.068. Second, the result of analyzing the correlation between the primary variables is as follows: as outcome expectation increased, major selection satisfaction grew, which then increased the career decision level and led to the improvement in career preparation behavior. These results indicate the importance of developing a customized route support program considering the perceived and interesting factors of individual students to improve their career preparation behavior for social welfare majors.