• 대한원격탐사학회지
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• 제36권1호
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• pp.41-53
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• 2020

The production of near- and off-shore fisheries in South Korea is decreasing due to rapid changes in the fishing environment, particularly including higher sea temperature in recent years. To improve the competitiveness of the fisheries, it is necessary to provide fish catch information that changes spatiotemporally according to the sea state. In this study, artificial intelligence models that predict the CPUE (catch per unit effort) of mackerel, anchovies, and squid (Todarodes pacificus), which are three major fish species in the near- and off-shore areas of South Korea, on a 15-km grid and daily basis were developed. The models were trained and validated using the sea surface temperature, rainfall, relative humidity, pressure,sea surface wind velocity, significant wave height, and salinity as input data, and the fish catch statistics of Suhyup (National Federation of Fisheries Cooperatives) as observed data. The 10-fold blind test results showed that the developed artificial intelligence models exhibited accuracy with a corresponding correlation coefficient of 0.86. It is expected that the fish catch models can be actually operated with high accuracy under various sea conditions if high-quality large-volume data are available.

• 응용통계연구
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• 제32권6호
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• pp.899-908
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• 2019

수 많은 모수들을 가지고 있는 방대한 심층신경망은 매우 강력한 기계학습 방법이지만 모형의 과도한 융통성으로 인하여 과적합문제를 내포하고 있다. 드롭아웃 방법은 크기가 큰 신경망의 과적합 문제를 해결하는 다양한 방법들 중 하나이며 매우 효과적인 방법으로 알려져 있다. 드롭아웃 방법은 훈련과정에서 각각의 표본에 다른 모형을 적용하는데 이들 모형은 입력과 은닉층의 노드들을 무작위로 제거한 모형들 중에 임의로 선택된다. 본 연구에서는 임의로 선택된 모형에 둘 이상의 표본을 적용하여 모형의 가중치들에 대한 추정치의 안정성을 높이는 하이브리드 드롭아웃 방법을 제시하였다. 실제 자료를 이용한 시뮬레이션 결과 노드의 선택확률과 모형의 적합에 사용되는 표본의 수를 적절하게 선택하여 기존의 방법에 비하여 추정치의 변동성이 감소시킬 수 있었으며 동시에 검증자료에 대한 최저오차도 줄일 수 있음을 보였다.

• 경영정보학연구
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• 제13권1호
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• pp.61-74
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• 2011

소프트웨어 산업은 아직도 뿌리깊은 문제에 시달리고 있다. 어떻게 보면, 제조업과 같은 보다 성숙한 산업의 모범사례로부터 뭔가를 배워야 할 지 모른다. 제조업에서의 '린' 원칙을 소프트웨어 개발에 적용하는 것도 한 방안일 수 있다. 소프트웨어공학 문헌에서 이런 '린' 원칙은 시도해 볼만 한 것으로 언급되었다. 그러나 이들 원칙을 실제 어떻게 적용할 수 있을 가에 대한 구체적 방안을 제시한 연구는 별로 없었다 본 연구는 '린' 원칙을 소프트웨어 개발에 적용하는 방안을 제시하고자 한다. 이 방안의 핵심은 낭비제거라는 린의 관점을 구체화하기 위하여 프로젝트에서의 리드 타임과 결함을 누적 흐름도(Cumulative Flow Diagram)을 통해 관리하는 방법이다. 또한 이 방안을 실제 프로젝트 사례에 적용함으로써 타당성을 검증하고 적용 방법에 대한 이해를 돕고자 하였다.

• 한국식품조리과학회지
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• 제28권6호
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• pp.829-837
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• 2012

This study was investigated seafoods provided by school food service and students' preferences for and perceptions of seafoods. The subjects were 275 second grade(age 14-16) students of 4 middle schools in Gyeongnam. The results were as follows. The most main seafoods intake place was 'home'(65.8%). 'School food service' took meaningful ratio(20.7%) of students' seafoods intakes. In the intake amount of seafoods provided by school food service, 'all' took 22.5%(male 31.6%, female 14.1%), 'more than provided' took 1.5%(male 3.0%, female 0%). Male students ate seafoods more than female students did(p<.001). In seafoods providing frequency, '2~3 times a week' took 74.5%, '4~5 times a week' took higher ratio in males' schools, while '0~1 times a week' took higher ratio in females'(p<.05). In perceptions of seafoods, most subjects had positive perceptions as 'good for health'(3.95), 'various kinds'(3.75) except 'good peculiar smell' got smallest point(2.85). In means of learning about seafoods names, 'by looking at everyday menu' took 64.6%. In taking nutrition education, 'no nutrition education' took 69.5%. In preferences for seafoods using 5-point scale, males' preferences were higher than females'(p<.001). 48.1% of males got higher than 4 point, while 14.1% of females did. In improvement measures of seafoods, 'provide various kinds'(47.3%) took highest ratio. In preferences for seafoods by seafoods kinds, preference for 'crustacean' was highest while preferences for 'shell fish' and 'fish' were relatively low. Both male and female students highly preferred laver, shrimp, swimming crab, small octopus, fish cake and tuna canned goods. Male students' preferences were higher than female students' for most kinds of seafoods. In preferences for seafoods by cooking methods, preferences for 'grilled', 'stir fried', 'pan fried' were relatively high, 'braised', 'deep fried', 'steamed' were relatively low. Males' preferences were higher than females' for every cooking method except 'steamed'.

• 대한원격탐사학회지
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• 제34권4호
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• pp.661-670
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• 2018

This study aims to develop a methodology of convolutional neural networks (CNNs) to produce thematic maps from remote sensing data. Optimizing the image size for CNNs was studied, since the size of the image affects to accuracy, working as hyper-parameter. The selected study area is Mt. Ung, located in Dangjin-si, Chungcheongnam-do, South Korea, consisting of both coniferous forest and deciduous forest. Spatial structure analysis and the classification of forest type using CNNs was carried in the study area at a diverse range of scales. As a result of the spatial structure analysis, it was found that the local variance (LV) was high, in the range of 7.65 m to 18.87 m, meaning that the size of objects in the image is likely to be with in this range. As a result of the classification, the image measuring 15.81 m, belonging to the range with highest LV values, had the highest classification accuracy of 85.09%. Also, there was a positive correlation between LV and the accuracy in the range under 15.81 m, which was judged to be the optimal image size. Therefore, the trial and error selection of the optimum image size could be minimized by choosing the result of the spatial structure analysis as the starting point. This study estimated the optimal image size for CNNs using spatial structure analysis and found that this can be used to promote the application of deep-learning in remote sensing.

• 인터넷정보학회논문지
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• 제18권5호
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• pp.61-67
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• 2017

본 논문에서는 딥러닝의 회선신경망을 이용한 실제 소셜 네트워크 상의 이미지 분류가 얼마나 효과적인지 알아보기 위한 실험을 수행하고, 그 결과와 그를 통해 알게 된 교훈에 대해 소개한다. 이를 위해 ImageNet Large Scale Visual Recognition Challenge(ILSVRC)의 2012년 대회와 2015년 대회에서 각각 우승을 차지한 AlexNet 모델과 ResNet 모델을 이용하였다. 평가를 위한 테스트 셋으로 인스타그램에서 수집한 이미지를 사용하였으며, 12개의 카테고리, 총 240개의 이미지로 구성되어 있다. 또한, Inception V3모델을 이용하여 fine-tuning을 실시하고, 그 결과를 비교하였다. AlexNet과 ResNet, Inception V3, fine-tuned Inception V3 이 네 가지 모델에 대한 Top-1 error rate들은 각각 49.58%, 40.42%, 30.42% 그리고 5.00%로 나타났으며, Top-5 error rate들은 각각 35.42%, 25.00%, 20.83% 그리고 0.00%로 나타났다.

• 분석과학
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• 제33권2호
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• pp.98-107
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• 2020

Methamphetamine (MA) is currently the most abused illicit drug in Korea. MA is produced by chemical synthesis, and the final target drug that is produced contains small amounts of the precursor chemicals, intermediates, and by-products. To identify and quantify these trace compounds in MA seizures, a practical and feasible approach for conducting chromatographic fingerprinting with a suite of traditional chemometric methods and recently introduced machine learning approaches was examined. This was achieved using gas chromatography (GC) coupled with a flame ionization detector (FID) and mass spectrometry (MS). Following appropriate examination of all the peaks in 71 samples, 166 impurities were selected as the characteristic components. Unsupervised (principal component analysis (PCA), hierarchical cluster analysis (HCA), and K-means clustering) and supervised (partial least squares-discriminant analysis (PLS-DA), orthogonal partial least squares-discriminant analysis (OPLS-DA), support vector machines (SVM), and deep neural network (DNN) with Keras) chemometric techniques were employed for classifying the 71 MA seizures. The results of the PCA, HCA, K-means clustering, PLS-DA, OPLS-DA, SVM, and DNN methods for quality evaluation were in good agreement. However, the tested MA seizures possessed distinct features, such as chirality, cutting agents, and boiling points. The study indicated that the established qualitative and semi-quantitative methods will be practical and useful analytical tools for characterizing trace compounds in illicit MA seizures. Moreover, they will provide a statistical basis for identifying the synthesis route, sources of supply, trafficking routes, and connections between seizures, which will support drug law enforcement agencies in their effort to eliminate organized MA crime.

• 한국초등수학교육학회지
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• 제20권2호
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• pp.239-257
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• 2016

미국의 새 수학과 규준인 Common Core State Standards for Mathematics는 잘 알려진 것처럼 모든 학생이 대학에 진학하거나 직업에 종사하기위한 준비를 위해 명료하고 일관된 틀을 제공하는 것을 목표로 개발되어, 현재 40개의 주가 이 새로운 규준을 채택하고 있다. 이 규준에 관한 최근 우리나라의 선행연구들은 규준을 Cluster Heading 수준에서 소개하고 있고, 규준의 수준에서 우리나라의 교육과정과 비교하고 있다. 그러니 실제로 각 세부 규준의 내용을 상세하게 해석한 내용은 수면 아래의 모습을 보여 준다. 캘리포니아 주의 수학과 규준의 내용을 상세하게 해석한 책이 Mathematics Framework for California Public Schools이다. 이 연구는 미국 캘리포니아 주의 수학과 규준인 California Common Core State Standards(CA CCSSM)와 이 규준의 해설서라고 부르기에 적당한 Mathematics Framework for California Public Schools에서 제시한 초등학교 도형영역을 상세하게 살펴보는 것이 목적이다.

• 인터넷정보학회논문지
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• 제18권6호
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• pp.55-63
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• 2017

One of the purposes of Information Technology (IT) is to support human response to natural and social problems such as natural disasters and spread of disease, and to improve the quality of human life. Recent climate change has happened worldwide, natural disasters threaten the quality of life, and human safety is no longer guaranteed. IT must be able to support tasks related to disaster response, and more importantly, it should be used to predict and minimize future damage. In South Korea, the data related to the damage is checked out by each local government and then federal government aggregates it. This data is included in disaster reports that the federal government discloses by disaster case, but it is difficult to obtain raw data of the damage even for research purposes. In order to obtain data, information extraction may be applied to disaster reports. In the field of information extraction, most of the extraction targets are web documents, commercial reports, SNS text, and so on. There is little research on information extraction for government disaster reports. They are mostly text, but the structure of each sentence is very different from that of news articles and commercial reports. The features of the government disaster report should be carefully considered. In this paper, information extraction method for South Korea government reports in the word format is presented. This method is based on patterns and dictionaries and provides some additional ideas for tokenizing the damage representation of the text. The experiment result is F1 score of 80.2 on the test set. This is close to cutting-edge information extraction performance before applying the recent deep learning algorithms.

• 인터넷정보학회논문지
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• 제21권5호
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• pp.119-127
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• 2020

국내 전기차 (EV: Electric Vehicle) 시장이 성장함에 따라, 빠르게 증가하는 EV 충전 수요에 대응하기 위한 충전설비의 확충이 요구되고 있다. 이와 관련하여, 종합적인 설비 계획을 수립하기 위해서는 미래 시점의 충전 수요량을 예측하고 이를 바탕으로 전력설비 부하에 미치는 영향을 체계적으로 분석하는 것이 필요하다. 본 논문에서는 한국전력공사의 EV 충전 데이터를 이용하여 충전소 단위의 일별최대부하를 예측하는 LSTM(Long Short-Term Memory) 신경망 모델을 설계 및 개발한다. 이를 위해, 먼저 데이터 전처리 및 이상치 제거를 통해 정제된 데이터를 얻는다. 다음으로, 충전소 단위의 일별 특징들을 추출하여 훈련 데이터 집합을 구성하여 일별 최대 전력부하 예측 모델을 학습시킨다. 마지막으로 충전소 유형 별 테스트 집합을 이용한 성능 분석을 통해 예측 모델을 검증하고 이의 한계점을 논의한다.