• 해양환경안전학회:학술대회논문집
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• 해양환경안전학회 2017년도 추계학술발표회
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• pp.49-49
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• 2017

• 한국태양에너지학회:학술대회논문집
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• 한국태양에너지학회 2009년도 추계학술발표대회 논문집
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• pp.251-258
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• 2009

Building-Integrated Photovoltaics (BIPV) is a photovoltaic (PV) technology which can be incorporated into the roofs walls of both commercial and domestic buildings to provide a source of electricity. BIPV systems can operate as a multi-functional building components, which generates electricity and serves as part of building envelope. It can be regarded as a new architectural elements, adding to the building's aesthetics. Applying PV modules on roof has an advantage over wall applications as they seem to receive more solar radiation on PV modules. There are various types of PV applications on building roofs: attached, on-top and integrated. This paper describes the classification and characteristics of PV applications on roofs.

• 한국컴퓨터정보학회논문지
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• 제25권2호
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• pp.213-219
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• 2020

본 연구는 스마트폰 과의존을 진단하고 예측하기 위하여 할 수 있는 분류분석 방법과 스마트폰 과의존 분류율에 영향을 미치는 중요변수를 규명하고자 시도되었다. 이를 위해 인공지능의 방법인 기계학습 분석 기법 중 의사결정트리, 랜덤포레스트, 서포트벡터머신의 분류율을 비교하였다. 자료는 한국정보화진흥원에서 제공한 '2018년 스마트폰 과의존 실태조사'에 응답한 25,465명의 데이터였고, R 통계패키지(ver. 3.6.2)를 사용하여 분석하였다. 분석한 결과, 3가지 분류분석 기법은 정분류율이 유사하게 나타났으며, 모델에 대한 과적합 문제가 발생되지 않았다. 3가지 분류분석 방법 중 서포트벡터머신의 분류율이 가장 높게 나타났고, 다음으로 의사결정트리 기법, 랜덤포레스트 기법 순이었다. 스마트폰 이용 유형 중 분류율에 영향을 미치는 상위 3개 변수는 생활서비스형, 정보검색형, 여가추구형이었다.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2005년도 지반공학 공동 학술발표회
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• pp.154-162
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• 2005

In this paper, site response analyses were performed based on equivalent linear technique using the shear wave velocity profiles of 162 sites collected around the Korean peninsula. The site characteristics, particularly the shear wave velocities and the depth to the bedrock, are compared to those in the western United States. The results show that the site-response coefficients based on the mean shear velocity of the top 30m ($V_{S30}$) suggested in the current code underestimates the motion in short-period ranges and overestimates the motion in mid-period ranges. Also the current Korean code based on UBC is required to be modified considering site characteristics in Korea for the reliable estimation of site amplification. From the results of numerical estimations, new regression curves were derived between site coefficients ($F_a$ and $F_v$) and the fundamental site periods, and site coefficients were grouped based on site periods in the regions of shallow bedrock. The standard deviations of the proposed method was reasonable compared to site classification based on $V_{S30}$. Finally, new site classification system is recommended based on site periods for regions of shallow bedrock depth in Korea.

• 한국멀티미디어학회논문지
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• 제20권7호
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• pp.994-1003
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• 2017

Ground-glass opacity nodules(GGNs) in chest CT images are associated with lung cancer, and have a different malignant rate depending on existence of solid component in the nodules. In this paper, we propose a method to classify pure GGNs and part-solid GGNs using multiview images and texture analysis in pulmonary GGNs with solid components of 5mm or smaller. We extracted 1521 features from the GGNs segmented from the chest CT images and classified the GGNs using a SVM classification model with selected features that classify pure GGNs and part-solid GGNs through a feature selection method. Our method showed 85% accuracy using the SVM classifier with the top 10 features selected in the multiview images.

• 한국융합학회논문지
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• 제11권11호
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• pp.9-17
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• 2020

본 논문에서는 개인의 성향을 추출하기 위한 딥러닝 기반의 SNS 리뷰 분석 방법을 제안한다. 기존의 SNS 리뷰 분석 방법은 대부분이 가장 높은 가중치를 기반으로 처리되기 때문에 여러 관심사에 대한 다양한 의견을 반영하지 못하는 문제점이 있다. 이를 해결하기 위해 제안된 방법은 음식을 대상으로 한 SNS의 리뷰에서 사용자의 개인적인 성향을 추출하기 위한 방법이다. YOLOv3 모델을 사용하여 분류체계를 작성하고, BiLSTM 모델을 통해 감성분석을 수행한 후 집합 알고리즘을 통해 다양한 개인적 성향을 추출한다. 실험 결과, YOLOv3 모델의 경우 Top-1 88.61%, Top-5 90.13%의 성능을 보여주었으며, BiLSTM 모델의 경우 90.99%의 정확도를 보여주었다. 또한, SNS 리뷰 분류에서의 개인 성향에 대한 다양성을 히트맵을 통해 시각화하여 확인하였다. 향후에는 다양한 분야에서의 개인 성향을 추출하여 사용자 맞춤 서비스나 마케팅 등에 활용될 것으로 기대된다.

• Industrial Engineering and Management Systems
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• 제10권4호
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• pp.272-278
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• 2011

Decision tree as a classification tool is being used successfully in many areas such as medical diagnosis, customer churn prediction, signal detection and so on. The main advantage of decision tree classifiers is their capability to break down a complex structure into a collection of simpler structures, thus providing a solution that is easy to interpret. Since decision tree is a top-down algorithm using a divide and conquer induction process, there is a risk of reaching a local optimal solution. This paper proposes a procedure of optimally determining thresholds of the chosen variables for a decision tree using an adaptive particle swarm optimization (APSO). The proposed algorithm consists of two phases. First, we construct a decision tree and choose the relevant variables. Second, we find the optimum thresholds simultaneously using an APSO for those selected variables. To validate the proposed algorithm, several artificial and real datasets are used. We compare our results with the original CART results and show that the proposed algorithm is promising for improving prediction accuracy.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.6-8
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• 2007

In this paper, We propose reasoning system for object recognition and space classification using not only visual features but also contextual information. It is necessary to perceive object and classify space in real environments for mobile robot. especially vision based. Several visual features such as texture, SIFT. color are used for object recognition. Because of sensor uncertainty and object occlusion. there are many difficulties in vision-based perception. To show the validities of our reasoning system. experimental results will be illustrated. where object and space are inferred by bi -directional rules even with partial and uncertain information. And the system is combined with top-down and bottom-up approach.

• 한국의료질향상학회지
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• 제12권2호
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• pp.84-92
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• 2006

Background : To examine the difference between ICD-10 and The Korean standard classification of disease(oriental medicine), and to aim at improve the practical use as statistical data. It is one of the reason of disease classification. On that account we convert the many to many correspondence presenting classification of oriental medicine into many to one correspondence. Method : The study tracked out 155 patients discharged from the university hospital which is located in Gyeonggi Province and managing hospital and oriental medicine hospital from July to October this year. The period of this study was from August 1 to November 18. We compared correspondence between the two services' diagnosis(hospital services and oriental medicine hospital services) at the same time and attempted many to one correspondence classification. That is for production of statistical data. Result : We investigated the group which have had medical treatment experience of two kinds of services at the same time. The result of this investigation was that the same oriental medicine diagnosis used differently in western medicine diagnosis. 44.5% was accorded with western medicine diagnosis. Correspondence of the western medicine diagnose with the top of the Korean standard classification of disease(oriental medicine) list's western medicine diagnosis was 13.5%. For many to one correspondence classification for statistics, one western medicine diagnosis was selected for one oriental medicine diagnosis. In case of the main diagnosis(I sign) was not enough to explain oriental medicine diagnosis' characteristic, we chose multiple other diagnosis, so other diagnosis(II sign) about patient's cause of disease could be selected for supplement after we examined the patient's records. The statistics was possible with this many to one correspondence. Conclusion : The result of this study about correspondence between western medicine diagnoses and those of oriental medicine confirms that The Korean standard classification of disease(oriental medicine) is hard to be standardized with western medicine diagnosis. Therefore, according to this study, we use new many to one correspondence classification, multiple oriental medicine diagnoses with one ICD-10, which can be used by statistical data.

• Journal of the Korean Data and Information Science Society
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• 제12권2호
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• pp.113-124
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• 2001

의사결정나무모형 가운데 하나인 CHAID, 로지스틱 회귀모형, 이들을 이용한 각각의 베깅모형 등 4가지 예측분류모형에 대한 오분류율과 훈련시간을 표본크기별로 계산하고, 이들 모형에 대한 모의실험 비교를 통하여 주어진 알고리즘들의 효율성을 평가하였다. 베깅 의사결정나무모형은 오분류율은 낮았으나 상대적으로 훈련시간이 가장 길었다.