• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.670-672
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• 2021

색소 세포성 모반과 악성 흑색종은 형태가 유사하지만 유해성의 측면에서 악성 흑색종은 암으로써 무해한 색소 세포성 모반에 비해 위험한 질환이다. 이에 기반하여 기존 연구에서 색소 세포성 모반과 악성 흑색종을 구분하기 위한 연구가 있었지만, 데이터를 취득하는 과정에서 많은 cost 가 필요하였다. 본 연구에서는 이를 개선하기 위해 두 병변의 dermatoscopic 영상을 분류 학습의 데이터로 사용하여 연구를 진행하였다. 학습을 위한 데이터는 오픈소스 dermatoscopic 데이터셋인 HAM10000을 사용하였으며 모델은 CNN 에서 개선된 MobileNetV2 를 사용하였다. 실험 결과, MobileNetV2 를 사용한 학습은 3-layer CNN 에 비해 15 분의 1 가량 적은 파라미터를 가졌으며, 검증 성능과 테스트 성능에서 93%에 근사하는 성능을 보였다. 본 연구는 이전 연구에 비해 cost 측면에서 큰 개선을 이루었으며, 상용화 가능한 분류 기법을 발견했다는 점을 시사한다.

• Korean Journal of Plant Taxonomy
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• v.50 no.1
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• pp.56-79
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• 2020

Baekdudaegan, the largest mountain range in eastern Asia, is a biodiversity hotspot in Korea that may have served as a glacial refugium. This study presents the flora of vascular plants on Deok-chi upto the Yuk-sim-nyeong area of the Baekdudaegan conservation area. The survey area was divided into four subareas and fieldwork was conducted for a total of twelve days in 2015. Voucher specimens were collected during the survey and were deposited at Daejeon University. A list of vascular plants was prepared based on the voucher specimens. The results of the survey showed that a total of 441 taxa, consisting of 100 families, 265 genera, 398 species, 9 subspecies, 32 varieties, and 2 forms, were found in the survey area. There was one endangered species, Aconitum coreanum, in the Bonghwasan Mt. area. Sixteen endemic taxa, 74 floristic regional indicator plants, as designated by the Ministry of the Environment, and eleven naturalized plants were distributed. The results of this study can serve as basic information to establish conservation and management plans for the Baekdudaegan conservation area.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2231-2238
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• 2011

In this paper, the extract method of eye and eye-line from picture of a person is proposed. Most of existing papers are to extract the position of eyeball but in this paper, by extracting not only the position of eyeball but also eye-line, it can be applied to the face application program variously. The experimental data of the input picture is a full face photograph taken by smart phone, basically the picture is limited to the face of one person and back ground can be taken from every where and no restriction of race. The proposed method is to extract face candidated area by using Harr Classifier and set up the candidate area of eye position from face candidate area. To extract high value from eye candidate area using dilate operation, and proposed the method to classify eye and eyelash by local thresholding of the picture. After that, using thresholding image from eyemapC that Hsu's suggested, and separated the area with eye and without eye. Finally extract the contour of eye and detect eye-line using optimum ellipse estimation.

• Journal of the Korea Society of Computer and Information
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• v.13 no.3
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• pp.179-187
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• 2008

In this paper, we apply a set of algorithms to classily normal and cancer nucleus from uterine cervical pap-smear images. First, we use lightening compensation algorithm to restore color images that have defamation through the process of obtaining $1{\times}400$ microscope magnification. Then, we remove the background from images with the histogram distributions of RGB regions. We extract nucleus areas from candidates by applying histogram brightness, Kapur method, and our own 8-direction contour tracing algorithm. Various binarization, cumulative entropy, masking algorithms are used in that process. Then, we are able to recognize normal and cancer nucleus from those areas by using three morphological features - directional information, the size of nucleus, and area ratio - with fuzzy membership functions and deciding rules we devised. The experimental result shows our method has low false recognition rate.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.81-88
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• 2022

The major educational goal of reading part, which occupies important portion in Korean language in Korean SAT, is to evaluated whether a given text can be fully understood. Therefore given questions in the exam must be able to solely solvable by given text. In this paper we developed a datatset based on Korean SAT's reading part in order to evaluate whether a deep learning language model can classify if the given question is true or false, which is a binary classification task in NLP. In result, by applying language model solely according to the passages in the dataset, we were able to acquire better performance than 59.2% in F1 score for human performance in most of language models, that KoELECTRA scored 62.49% in our experiment. Also we proved that structural limit of language models can be eased by adjusting data preprocess.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.7C
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• pp.675-686
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• 2005

In this paper, we propose an efficient face region detection technique for the content-based video summarization. To segment video, shot changes are detected from a video sequence and key frames are selected from the shots. We select one frame that has the least difference between neighboring frames in each shot. The proposed face detection algorithm detects face region from selected key frames. And then, we provide user with summarized frames included face region that has an important meaning in dramas or movies. Using Bayes classification rule and statistical characteristic of the skin pixels, face regions are detected in the frames. After skin detection, we adopt the projection method to segment an image(frame) into face region and non-face region. The segmented regions are candidates of the face object and they include many false detected regions. So, we design a classifier to minimize false lesion using CART. From SGLD matrices, we extract the textual feature values such as Inertial, Inverse Difference, and Correlation. As a result of our experiment, proposed face detection algorithm shows a good performance for the key frames with a complex and variant background. And our system provides key frames included the face region for user as video summarized information.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.789-795
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• 2020

Many deaths have been reported due to the worldwide pandemic of COVID-19. In order to prevent the further spread of COVID-19, it is necessary to quickly and accurately read images of suspected patients and take appropriate measures. To this end, this paper introduces a deep learning-based COVID-19 chest X-ray reading technique that can assist in image reading by providing medical staff whether a patient is infected. First of all, in order to learn the reading model, a sufficient dataset must be secured, but the currently provided COVID-19 open dataset does not have enough image data to ensure the accuracy of learning. Therefore, we solved the image data number imbalance problem that degrades AI learning performance by using a Stacked Generative Adversarial Network(StackGAN++). Next, the DenseNet-based classification model was trained using the augmented data set to develop the reading model. This classification model is a model for binary classification of normal chest X-ray and COVID-19 chest X-ray, and the performance of the model was evaluated using part of the actual image data as test data. Finally, the reliability of the model was secured by presenting the basis for judging the presence or absence of disease in the input image using Grad-CAM, one of the explainable artificial intelligence called XAI.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.273-280
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• 2020

Automatic Static Analysis Tools help developers to quickly find potential defects in source code with less effort. However, the tools reports a large number of false positive warnings which do not have to fix. In our study, we proposed an artificial neural network-based warning classification method using topic models of source code blocks. We collect revisions for fixing bugs from software change management (SCM) system and extract code blocks modified by developers. In deep learning stage, topic distribution values of the code blocks and the binary data that present the warning removal in the blocks are used as input and target data in an simple artificial neural network, respectively. In our experimental results, our warning classification model based on neural network shows very high performance to predict label of warnings such as true or false positive.

• Journal of Dental Rehabilitation and Applied Science
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• v.29 no.3
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• pp.236-248
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• 2013

In this study, 37 subjects with normal facial shape and normal occlusion are classified and reference value for such classification was investigated. Difference in position of maxillary incisor was studied according to the cl assification. Moreover, by investigating correlation between factors affecting forehead morphology and positio n of maxillary incisor, following results have been obtained. 1. Morphology of forehead can be classified as angular type, round type, straight type, and concave type. 2. There were no specific reference value for evaluation of forehead morphology but possibilities of evaluating forehead morphology using S value and forehead length (Tri-Gla) still remain. 3. There were no correlation between forehead morphology and position of maxillary incisor. 4. Forehead inclination and Andrew analysis show statistically significant negative correlation. That is, as forehead inclination increases, maxillary incisor is positioned posteriorly and this relationship can be shown as following equation, Andrew analysis = -0.39*Forehead inclination.

• Proceedings of the ESK Conference
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• 1993.10a
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• pp.189-204
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• 1993

산업화로 인한 급속한 경제성장과 증가하는 여성들의 사회진출은 의생활에서 보다 편 하고 간소화된 생활양식을 추구하게 되고, 이에따라 의복의 구입에 있어서도 맞춤복보다 는 시간, 경제면에서 유리한 기성복을 선호하게 되었다. 불특정다수를 위한 기성복의 생산에 있어서 개개인의 체형에 보다 적합성이 좋은 제품의 개발을 위해 다수인의 다항 목계측치에 근거한 치수체계의 설정은 무엇보다 중요한 문제가 된다. 우리나라에서는 1979년 공업진흥청에 의해 제 1차 국민표준체위조사가 실시되었으며, 1986년에는 제 2차 조사가 실시되어 그 결과를 토대로 한국 공업규격의 의류치수 규격이 제정 발표되었다. 그러나 이것은 연령에 따른 신체적 변화를 고려한 체형분류가 되어 있지 않으며, 제품 호수에 따라 동일한 간격을 둔 체계였다. 1992년에는 제 3차 국민표준체위조사가 실시 되어 그 결과가 발표되었다. 의복은 인체계측에 의한 신체 각 부위의 치수와 형태를 기초로 2차원적인 소재에 적절한 원형을 사용하여 재단, 봉제과정을 거쳐 3차원의 입체 로 구성하고 인체에 대응시킨 것이므로 기성복에 대하여 소비자의 치수적합성을 만족시 키기 위해서는 체형의 특징을 고려해야 할 것이다. 특히, 성장이 완료된 성인 여성은 출산후 중년에 접어들면서 신체적 특성이 변하여 체형이 바뀌는 경향이 있으므로 전 여성에대한 의류치수규격보다는 연령의 구분과 체형의 분류가 필요하다고 본다. 더우기 의류업체들의 판매대상의 세분화는 이를 더욱 뒷받침해주고 있다. 따라서 체형의 분류는 의복 치수 규격에 적용되어 의복의 종류에 따라 대다수의 불특정 다수 에게 보다 잘 맞는 의복 치수를 제공할 것이며, 그 치수의 정확성을 증대시키게 된다. 김성득(1991)은 소비자의 기성복에 대한 구매확률을 높이기 위해서는 규격치를 등간격 으로 하기 보다는 소비자의 분포밀도가 높은 곳일수록 규격치 간격들을 좁게 설정함으 로써 생산자 입장에서 총손실을 줄이고, 상대적 비용절감효과를 갖게할 수 있다고 하였다. 따라서 본 연구에서는 성인 여성기성복의 치수적합성을 높이기위하여 출산 후 중년에 접어 들면서 체형이 변화되는 것을 고려하여 연령을 분류하고(18세-34세, 30세-51세), 각 연령 집단에 따른 체형을 각각 3가지로 분류하였다. 이에 따라 의복 생산시의 총손실을 줄이기위한 상의, 하의생산시 필요한 부위별 최적규격치 간격을 제시하였다.