• Economic and Environmental Geology
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• v.55 no.5
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• pp.541-550
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• 2022

A pre-existing landform is created by weathering and erosion along the bedrock fault and the weak zone. A neotectonic landform is formed by neotectonic movements such as earthquakes, volcanoes, and Quaternary faults. It is difficult to clearly distinguish the landform in the actual field because the influence of the tectonic activity in the Korean Peninsula is relatively small, and the magnitude of surface processes (e.g., erosion and weathering) is intense. Thus, to better understand the impact of tectonic activity and distinguish between pre-existing landforms and neotectonic landforms, it is necessary to understand the development process of pre-existing landforms depending on the bedrock characteristics. This study used a two-dimensional numerical landscape evolution model (LEM) to study the spatio-temporal development of landscape according to the different erodibility under the same factors of climate and the uplift rate. We used hill-slope indices (i.e., relief, mean elevation, and slope) and channels (i.e., longitudinal profile, normalized channel steepness index, and stream order) to distinguish the difference according to different bedrocks. As a result of the analysis, the terrain with high erosion potential shows low mean elevation, gentle slope, low stream order, and channel steepness index. However, the value of the landscape with low erosion potential differs from that with high erodibility. In addition, a knickpoint came out at the boundary of the bedrock. When researching the actual topography, the location around the border of difference in bedrock has only been considered a pre-existing factor. This study suggested that differences in bedrock and various topographic indices should be comprehensively considered to classify pre-existing and active tectonic topography.

• The Journal of the Korea Contents Association
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• v.6 no.12
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• pp.105-112
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• 2006

In this study, we propose a simple preprocessing method for classification of basal cell carcinoma (BCC), which is one of the most common skin cancer. The preprocessing step consists of data clipping with a half Hanning window and dimension reduction with principal components analysis (PCA). The application of the half Hanning window deemphasizes the peak near $1650cm^{-1}$ and improves classification performance by lowering the false negative ratio. Classification results with various classifiers are presented to show the effectiveness of the proposed method. The classifiers include maximum a posteriori probability (MAP), k-nearest neighbor (KNN), probabilistic neural network (PNN), multilayer perceptron(MLP), support vector machine (SVM) and minimum squared error (MSE) classification. Classification results with KNN involving 216 spectra preprocessed with the proposed method gave 97.3% sensitivity, which is very promising results for automatic BCC detection.

• Journal of KIISE
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• v.45 no.1
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• pp.61-68
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• 2018

The identification of genes that contribute to the prediction of prognosis in patients with cancer is one of the challenges in providing appropriate therapies. To find the prognostic genes, several classification models using gene expression data have been proposed. However, the prediction accuracy of cancer prognosis is limited due to the heterogeneity of cancer. In this paper, we integrate microarray data with biological network data using a modified PageRank algorithm to identify prognostic genes. We also predict the prognosis of patients with 6 cancer types (including breast carcinoma) using the K-Nearest Neighbor algorithm. Before we apply the modified PageRank, we separate samples by K-Means clustering to address the heterogeneity of cancer. The proposed algorithm showed better performance than traditional algorithms for prognosis. We were also able to identify cluster-specific biological processes using GO enrichment analysis.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.1
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• pp.64-70
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• 2007

Raman spectroscopy is known to have strong potential for providing noninvasive dermatological diagnosis of skin cancer. According to the previous work, various well known methods including maximum a posteriori probability (MAP) and multilayer perceptron networks (MLP) showed competitive results. Since even the small errors often leads to a fatal result, we investigated the method that reduces classification error perfectly by screening out some ambiguous patterns. Those ambiguous patterns can be examined by routine biopsy. We incorporated an ambiguous pattern class in MAP, linear classifier using minimum squared error (MSE), MLP and reduced coulomb energy networks (RCE). The experiments involving 216 confocal Raman spectra showed that every methods could perfectly classify BCC by screening out some ambiguous patterns. The best results were obtained with MSE. According to the experimental results, MSE gives perfect classification by screening out 8.8% of test patterns.

• The Journal of the Korean dental association
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• v.27 no.8 s.243
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• pp.697-702
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• 1989

악성종양은 상피성의 암종(carcinoma)과 결체직성의 육종(sarcoma)으로 분류된다. 최근 암환자의 발생은 점차 증가되는 추세이며, 우리나라에서도 사망원인 중 뇌졸증 다음으로 암이 차지하는 비율이 높다. 이러한 암은 침윤성(infiltrative)인 성장으로 인하여 인접 정상조직을 급격히 파괴시키며, 해당 임파절을 따라 신체 다른 부위로 전이(metastasis)된다. 임파절에 전이되기 전에 조기발견된 암은 70%의 5년 생존률을 보이는 반면, 임파절에 전이된 후에 발견된 암은 30%의 5년 생존률을 보인다. 따라서 암의 성공적인 치료를 위해서는 이의 조기발견이 매우 중요하다고 할수 있다. 그러나 불행하게도 많은 환자가 이미 병소가 상당히 진행된 상태에서 내원하기 때문에 이의 근치가 어려우며, 예후 또한 좋지 않다. 이러한 암을 조기발견하기 위해서는 환자 자신의 관심뿐만 아니라 치과의사의 세심한 검진이 필요하리라 생각된다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.757-759
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• 2003

암의 치료법은 같은 종류의 암이라 해도 그 하부 클래스에 따라 매우 다르기 때문에 암의 클래스를 예측하는 것은 그 정확한 치료를 위하여 매우 중요하다. 유전자 발현 데이터를 이용한 암의 분류에 있어 기존의 연구들은 각 데이터를 하나의 클러스터에 소속시키는 하드 분할(hard partition)에 의한 분할 방식을 사용하는 하드 클러스터링을 사용하였다. 하지만 일반적으로 유전자 발현 암 데이터와 같은 실세계의 데이터는 쉽게 나뉘어지기 힘들거나 클러스터 간의 경계가 분명하지 않기 때문에 하드 클러스터링 기법은 주어진 데이터의 성질을 손실시킬 수 있는데 반해, 퍼지 클러스터링 기법은 각 데이터가 소속 정도에 따라 여러 개의 클러스터에 속할 수 있도록 분할하기 때문에 이러한 손실을 최소화할 수 있다. 따라서 본 논문에서는 퍼지 클러스터링의 대표적인 방법인 fuzzy c-means 클러스터링을 적용하여 암의 클래스를 예측하고, 다양한 하드 클러스터링 방법과 비교함으로써 퍼지 클러스터링의 성능을 검증하였다.

• ICROS
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• v.16 no.3
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• pp.21-27
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• 2010

본 논문에서는 지금까지 이루어진 전립선 암 진단 및 치료를 위한 로봇기술 적용 사례 관련 연구들을 조사하여 체계적인 분류 및 분석 작업을 수행함으로써 현재 기술 동향을 파악하고, 앞으로 나아갈 연구 방향을 제시하고자 한다.

• Journal of Service Research and Studies
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• v.3 no.1
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• pp.87-93
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• 2013

Breast cancer is known as a disease that occurs in a lot of developed countries. However, in recent years, the incidence of Korea's modern woman is increased steadily. As well known, breast cancer usually occurs in women over 50. In the case of Korea, however, the incidence of 40s with young women is increased steadily than the West. Therefore, it is a very urgent task to build a manual to the accurate diagnosis of breast cancer in adult women in Korea. In this paper, we show how using data mining techniques to predict breast cancer. Data mining refers to the process of finding regular patterns or relationships among variables within the database. To this, sophisticated analysis using the model, you will find useful information that is easily revealed. In this paper, through experiments Deicion Tree Naive Bayes analysis techniques were compared using analysis techniques to diagnose breast cancer. Two algorithms was analyzed by applying C4.5 algorithm. Deicison Tree classification accuracy was fairly good. Naive Bayes classification method showed better accuracy compared to the Decision Tree method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.134-136
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• 2021

Research related to early diagnosis of breast cancer using artificial intelligence algorithms has been actively conducted in recent years. Although various algorithms that classify breast cancer based on a few publicly available ultrasound breast cancer images have been published, these methods show various limitations such as, processing speed and accuracy suitable for the user's purpose. To solve this problem, in this paper, we propose a multi-stage transfer learning where ResNet model trained on ImageNet is transfer learned to microscopic cancer cell line images, which was again transfer learned to classify ultrasound breast cancer images as benign and malignant. The images for the experiment consisted of 250 breast cancer ultrasound images including benign and malignant images and 27,200 cancer cell line images. The proposed multi-stage transfer learning algorithm showed more than 96% accuracy when classifying ultrasound breast cancer images, and is expected to show higher utilization and accuracy through the addition of more cancer cell lines and real-time image processing in the future.

• The Science & Technology
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• v.33 no.3 s.370
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• pp.74-75
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• 2000

대구효성가톨릭대 양재호교수는 인체 피부가 다이옥신에 민감하다는 사실에 착안하여 인체 피부세포를 모델로 사용하여 인체에서도 암을 유발한다는 사실을 증명한 과학자이다. 양교수의 연구결과는 다이옥신이 인체 세포에서 암을 일으킬 수 있음을 증명한 최초의 보고서로 평가되고 있으며 97년 세계보건기구 산하의 암연구기구도 다이옥신을 인체발암물질로 분류하는데 중요한 자료로 활용하고 있다.