• 한국전자거래학회지
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• 제22권4호
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• pp.71-101
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• 2017

미국 무역위원회(United States International Trade Commission)는 불공정 무역으로 인해 무역 질서를 해치는 경우 상계 관세(Countervailing Duties)와 반덤핑 관세(Antidumping Duties) 등을 징수하고 있다. 본 연구에서는 상기 연구 목적을 달성하기 위하여 상계 관세 및 반덤핑 관세와 관련된 데이터를 수집해 양적 분석을 수행하였다. 몇 가지 데이터 마이닝(Data mining) 기법을 활용한 본 연구의 양적 분석 결과, 미국의 상계 관세 및 반덤핑 관세 부과 경향이 우리나라의 중공업 산업의 성장률에 유의한 영향을 미친다고 잠정적으로 결론 내릴 수 있었다. 본 연구의 가장 큰 기여점은 '미국의 보호주의 무역기조가 울산지역의 주력산업의 경영성과에 부정적인 영향을 미칠 수 있다'는 직관적인 명제를 과거 데이터를 가지고 객관적으로 검증해보고 그 영향 정도를 계량화해 측정할 수 있도록 한 것이라고 할 수 있다.

• 한국수자원학회논문집
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• 제56권4호
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• pp.245-259
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• 2023

농업용 저수지는 농업용수 공급에 있어서 매우 중요한 생산기반시설로, 우리나라 농업용수의 60% 정도를 공급하고 있다. 다만, 여러 문제로 인해 농업용수의 효율적인 공급에 어려움이 발생하고 있으며, 효과적인 공급 및 관리 체계 구현을 위한 정확한 실시간 저수위 혹은 저수량 추정이 필요하다. 본 연구에서는 영상정보를 활용한 딥러닝 기반 농업용 저수지 수위 인식 모델을 제안하였다. 개발한 모델은 (1) CCTV 영상정보 자료 수집 및 분석, (2) U-Net 이미지 분할 방법을 통한 입력 자료 생성, 그리고 (3) CNN과 ResNet 모델을 통한 수위 인식 세 단계로 구성된다. 모델은 두 농업용 저수지(G저수지와 M저수지)의 영상자료와 저수위 시계열자료를 활용하여 구현하였다. 적용 결과 이미지 분할 모델의 성능은 매우 우수한 것으로 나타났으며, 수위 인식 모델의 경우 수위 분류 계급구간에 따라 성능이 상이한 것으로 나타났다. 특히 영상자료의 픽셀 변동이 클수록 정확도 80% 이상이 확보 가능한 것으로 확인되었으나, 그렇지 않은 경우, 정확도가 50% 수준인 것으로 나타났다. 본 연구에서 개발한 모델은 향후 이미지 자료가 추가로 확보될 경우, 그 활용도 및 정확도가 더 높아질 것으로 기대한다.

• 대한지역사회영양학회지
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• 제18권2호
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• pp.154-164
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• 2013

The objectives of this study were to assign reasonability to importance of weight selection issue in key performance indicator for performance evaluation of Centers for Child-care Foodservice Management (CCFSM) developed by using Balanced Scorecard (BSC), to draw key performance indicator (KPI) by perspective and to analyze differences in recognition on importance. From September 25 to October 9, 2012, we conducted a questionnaire-based study via e-mail, targeting chiefs and team leaders of nationwide 21 CCFSMs (43 persons), officials of local governments where CCFSM was established (21 persons), officials of Korea Food and Drug Administration (2 persons) and foodservice management experts (27 persons) in order to estimate the relative importance on 4 perspectives and 14 KPIs and analyzed its results by using 61 collected data. The results showed that relative importance of perspectives was estimated in order of importance as follows: business performance (0.3519), customer (0.3393), resource (0.1557), learning and growth (0.1531). Relative importance of KPIs was in order of importance as follows: Evaluation of sanitary management level in child-care foodservice facilities (0.1327), Level of customer recognition and behavior improvement (0.1153), performances of round visiting inspection on foodservice, sanitary, safety management, and foodservice consulting (0.0913). Our results showed that the recognition differences exist on the relative importance of perspectives and KPIs between officials of CCFSM, KFDA, local government and foodservice management experts. These observations will form the basis for developing evaluation systems, and it is considered that performance indicators developed on this basis will suggest direction of operation which CCFSM will have to perform.

• 지식경영연구
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• 제14권2호
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• pp.137-154
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• 2013

In an ever-tougher competitive environment caused by globalization, domestic companies are increasingly adopting business strategies aimed at continuously securing competitive advantage by taking advantage of globally-competitive foreign professionals. Despite a persistent rise of such trend, domestic researches regard migrant workers as the socially underprivileged, and delve into the issue of migrant workers from the policy, welfare, and ethical perspectives. With a growing need to deal with migrant professionals from the strategic viewpoint - to acquire professional talent in an era of global competition, it becomes essential to verify the real effectiveness of migrant professionals. Yet, there has been relatively little discussion of it. This study assumes that based on th137e integration-learning perspective on diversity, the greater the number of foreign professionals, the greater the effect on organizational innovative performance. Also could be effective in managing diversity is diversity-friendly HRM which involves eliminating discrimination against migrant professional workers and treating them fairly. Based on the data collected from 72 domestic companies, this study conducted an empirical analysis of the impact of the percentage of foreign professionals in the total workforce on organizational innovative performance and of the moderating role of diversity-friendly HRM. The results show that the proportion of foreign professionals in the entire workforce has had no significant impact on organizational innovative performance, and that the proportion of foreign professionals in the total workforce and diversity-friendly HRM have had a interaction effect on organizational innovative performance. Based on these research results, the study attempted to interpret the significance of the proportion that migrant professionals make up of the total workforce and of diversity-friendly HRM in relation to organizational innovative performance, and their implications for diversity management.

• 한국IT서비스학회지
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• 제19권1호
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• pp.103-112
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• 2020

Job seekers are making various efforts to find a good company and companies attempt to recruit good people. Job search activities through self-introduction essay are nowadays one of the most active processes. Companies spend time and cost to reviewing all of the numerous self-introduction essays of job seekers. Job seekers are also worried about the possibility of acceptance of their self-introduction essays by companies. This research builds a classification model and conducted an experiments to classify self-introduction essays into pass or fail using deep learning and decision tree techniques. Real world data were classified using stratified sampling to alleviate the data imbalance problem between passed self-introduction essays and failed essays. Documents were embedded using Doc2Vec method developed from existing Word2Vec, and they were classified using logistic regression analysis. The decision tree model was chosen as a benchmark model, and K-fold cross-validation was conducted for the performance evaluation. As a result of several experiments, the area under curve (AUC) value of PV-DM results better than that of other models of Doc2Vec, i.e., PV-DBOW and Concatenate. Furthmore PV-DM classifies passed essays as well as failed essays, while PV_DBOW can not classify passed essays even though it classifies well failed essays. In addition, the classification performance of the logistic regression model embedded using the PV-DM model is better than the decision tree-based classification model. The implication of the experimental results is that company can reduce the cost of recruiting good d job seekers. In addition, our suggested model can help job candidates for pre-evaluating their self-introduction essays.

• 대한원격탐사학회지
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• 제35권2호
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• pp.279-288
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• 2019

본 연구에서는 항공정사영상을 이용하여 SegNet 기반의 의미분할을 수행하고, 토지피복분류에서의 그 성능을 평가하였다. 의미분할을 위한 분류 항목을 4가지(시가화건조지역, 농지, 산림, 수역)로 선정하였고, 항공정사영상과 세분류 토지피복도를 이용하여 총 2,000개의 데이터셋을 8:2 비율로 훈련(1,600개) 및 검증(400개)로 구분하여 구축하였다. 구축된 데이터셋은 훈련과 검증으로 나누어 학습하였고, 모델 학습 시 정확도에 영향을 미치는 하이퍼파라미터의 변화에 따른 검증 정확도를 평가하였다. SegNet 모델 검증 결과 반복횟수 100,000회, batch size 5에서 가장 높은 성능을 보였다. 이상과 같이 훈련된 SegNet 모델을 이용하여 테스트 데이터셋 200개에 대한 의미분할을 수행한 결과, 항목별 정확도는 농지(87.89%), 산림(87.18%), 수역(83.66%), 시가화건조지역(82.67%), 전체 분류정확도는 85.48%로 나타났다. 이 결과는 기존의 항공영상을 활용한 토지피복분류연구보다 향상된 정확도를 나타냈으며, 딥러닝 기반 의미분할 기법의 적용 가능성이 충분하다고 판단된다. 향후 다양한 채널의 자료와 지수의 활용과 함께 분류 정확도 향상에 크게 기여할 수 있을 것으로 기대된다.

• Advances in Computational Design
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• 제7권2호
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• pp.113-128
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• 2022

Water consumption is strongly affected by numerous factors, such as population, climatic, geographic, and socio-economic factors. Therefore, the implementation of a reliable predictive model of water consumption pattern is challenging task. This study investigates the performance of predictive models based on multi-layer perceptron (MLP), multiple linear regression (MLR), and support vector regression (SVR). To understand the significant factors affecting water consumption, the stepwise regression (SW) procedure is used in MLR to obtain suitable variables. Then, this study also implements three predictive models based on these significant variables (e.g., SWMLR, SWMLP, and SWSVR). Annual data of water consumption in Thailand during 2006 - 2015 were compiled and categorized by provinces and distributors. By comparing the predictive performance of models with all variables, the results demonstrate that the MLP models outperformed the MLR and SVR models. As compared to the models with selected variables, the predictive capability of SWMLP was superior to SWMLR and SWSVR. Therefore, the SWMLP still provided satisfactory results with the minimum number of explanatory variables which in turn reduced the computation time and other resources required while performing the predictive task. It can be concluded that the MLP exhibited the best result and can be utilized as a reliable water demand predictive model for both of all variables and selected variables cases. These findings support important implications and serve as a feasible water consumption predictive model and can be used for water resources management to produce sufficient tap water to meet the demand in each province of Thailand.

• Journal of information and communication convergence engineering
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• 제20권4호
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• pp.303-308
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• 2022

Simulating the heat transfer in a composite material is an important topic in material science. Difficulties arise from the fact that adjacent materials cannot match perfectly, resulting in discontinuity in the temperature variables. Although there have been several numerical methods for solving the heat-transfer problem in imperfect contact conditions, the methods known so far are complicated to implement, and the computational times are non-negligible. In this study, we developed a ResNet-type deep neural network for simulating a heat transfer model in a composite material. To train the neural network, we generated datasets by numerically solving the heat-transfer equations with Kapitza thermal resistance conditions. Because datasets involve various configurations of composite materials, our neural networks are robust to the shapes of material-material interfaces. Our algorithm can predict the thermal behavior in real time once the networks are trained. The performance of the proposed neural networks is documented, where the root mean square error (RMSE) and mean absolute error (MAE) are below 2.47E-6, and 7.00E-4, respectively.

• Smart Structures and Systems
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• 제29권1호
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• pp.53-62
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• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

• 정보교육학회논문지
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• 제11권4호
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• pp.471-482
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• 2007

최근 UCC(User-created content)는 연예, 스포츠 등의 분야에서 많이 생성되어 공유되고 있지만 그 생명주기(life cycle)는 매우 짧고 이를 교육이나 학습에 활용하는 경우는 거의 드문 상황이다. 따라서 본 연구에서는 UCC 중에서 특히 창작 동영상을 활용하여 원격 교육에 적용하는 방법을 제안한다. 교사는 자신이 제작한 동영상을 원격 교육 시스템에 탑재하여 학생들로 하여금 자기 주도적 학습을 할 수 있도록 지원하며 학생들은 직접 만든 동영상으로 교사에게 지속적인 피드백을 받고 최종 평가를 받는다. 본 연구에서는 모형으로 원격 교육 시스템을 구현하고 초등학교 특기 계발교육 중 단소 실기 과목을 선정하여 원격 교육을 수행하였다. 원격 교육 후의 설문에서 학생들은 우선 단소 실기 능력이 향상되고 학습 및 평가 방법에 만족하는 것으로 나타났으며 단소 실기 능력 향상 이외에도 카메라/캠코더 조작기술 및 컴퓨터 활용 능력이 향상되었다고 응답하였다. 또한 동영상을 친구들 혹은 부모님과 함께 제작하고 많은 시간을 함께 보내면서 대부분의 학생들이 이전보다 관계가 더 좋아진 것으로 나타났다.