• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
• /
• pp.262-266
• /
• 1999

Several commercial companies that plan to provide improved panchromatic and/or multi-spectral remote sensor data in the near future are suggesting that merge datasets will be of significant value. This study evaluated the utility of one major merging process-process components analysis and its inverse. The 6 bands of 30$\times$30m Landsat TM data and the 10$\times$l0m SPOT panchromatic data were used to create a new 10$\times$10m merged data file. For the image classification, 6 bands that is 1st, 2nd, 3rd, 4th, 5th and 7th band may be used in conjunction with supervised classification algorithms except band 6. One of the 7 bands is Band 6 that records thermal IR energy and is rarely used because of its coarse spatial resolution (120m) except being employed in thermal mapping. Because SPOT panchromatic has high resolution it makes 10$\times$10m SPOT panchromatic data be used to classify for the detailed classification. SPOT as the Landsat has acquired hundreds of thousands of images in digital format that are commercially available and are used by scientists in different fields. After the merged, the classifications used supervised classification and neural network. The method of the supervised classification is what used parallelepiped and/or minimum distance and MLC(Maximum Likelihood Classification) The back-propagation in the multi-layer perception is one of the neural network. The used method in this paper is MLC(Maximum Likelihood Classification) of the supervised classification and the back-propagation of the neural network. Later in this research SPOT systems and images are compared with these classification. A comparative analysis of the classifications from the TM and merged SPOT/TM datasets will be resulted in some conclusions.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
• /
• 제9권3호
• /
• pp.301-310
• /
• 2003

본 논문에서는 고해상도 위성영상의 효과적인 분류를 위한 감독분류 시스템을 설계하고 구현하였다. 구현된 시스템은 분류의 정확도 향상을 위한 훈련데이타의 효율적인 선택을 위해서 다양한 인터페이스와 통계자료를 제공한다. 또한, 다양한 위성영상 포맷의 지원과 새로운 감독분류 알고리즘의 확장을 용이하게 하기 위하여 시스템을 모듈화 하였으며, 분광 특성을 고려한 분류의 적용이 가능하다. 분류 알고리즘으로는 평행육면체 분류, 최소거리 분류, 마하라노비스 거리 분류, 최대우도 분류, 퍼지 분류의 감독분류기법을 이용하여 고해상도 위성영상의 처리를 지원한다. 본 시스템의 적용은 고해상도 IKONOS 위성영상을 입력으로 하고, 그 결과를 분석하여 봄으로써 시스템의 응용 가능성을 보여준다.

• 디지털융복합연구
• /
• 제8권3호
• /
• pp.39-48
• /
• 2010

정보화 사회에 있어서 피해자는 범죄원인의 제공자로서 특성과 보호대상으로서의 특성이 공존하고 있다. 이러한 상반된 특성이 피해자학의 변천과 그 피해자학의 발전을 가져왔다. 그 결과 피해자화에 따른 피해의 분류가 일반화되었다. 즉 범죄로 인한 직접피해를 제1차 피해라고 하며, 그리고 제 1차 피해로 인하여 수적으로 발생되는 피해를 제2차 및 제3차 피해로 구분할 수 있었다. 본 논문은 이와 같은 피해분류의 원인에 따라 각국의 범죄피해자 대책을 살펴보고 한국의 문제점올 고찰하였다.

• Communications for Statistical Applications and Methods
• /
• 제31권3호
• /
• pp.279-289
• /
• 2024

Familial searching is a useful technique in a forensic investigation. Using genetic information, it is possible to identify individuals, determine familial relationships, and obtain racial/ethnic information. The total number of shared alleles (TNSA) and likelihood ratio (LR) methods have traditionally been used, and novel data-mining classification methods have recently been applied here as well. However, it is difficult to apply these methods to identify familial relationships above the third degree (e.g., uncle-nephew and first cousins). Therefore, we propose to apply a stacking ensemble machine learning algorithm to improve the accuracy of familial relationship identification. Using real data analysis, we obtain superior relationship identification results when applying meta-classifiers with a stacking algorithm rather than applying traditional TNSA or LR methods and data mining techniques.

• 한국멀티미디어학회논문지
• /
• 제23권2호
• /
• pp.146-154
• /
• 2020

Negative emotion causes stress and lack of attention concentration. The classification of negative emotion is important to recognize risk factors. To classify emotion status, various methods such as questionnaires and interview are used and it could be changed by personal thinking. To solve the problem, we acquire multi modal bio-signals such as electrocardiogram (ECG), skin temperature (ST), galvanic skin response (GSR) and extract features. The neural network (NN), the deep neural network (DNN), and the deep belief network (DBN) is designed using the multi modal bio-signals to analyze emotion status. As a result, the DBN based on features extracted from ECG, ST and GSR shows the highest accuracy (93.8%). It is 5.7% higher than compared to the NN and 1.4% higher than compared to the DNN. It shows 12.2% higher accuracy than using only single bio-signal (GSR). The multi modal bio-signal acquisition and the deep learning classifier play an important role to classify emotion.

• Journal of Korean Neurosurgical Society
• /
• 제61권5호
• /
• pp.618-624
• /
• 2018

Objective : We evaluated the clinical manifestation and surgical results following operative treatment of cubital tunnel syndrome (CuTS) caused by anconeus epitrochlearis (AE) muscle. Methods : Among 142 patients who underwent surgery for CuTS from November 2007 to October 2015, 12 were assigned to the AE group based on discovery of AE muscle; 130 patients were assigned to the other group. We analyzed retrospectively; age, sex, dominant hand, symptom duration, and weakness in hand. Severity of the disease was evaluated using the Dellon classification and postoperative symptom were evaluated using disability of arm shoulder and hand (DASH) and visual analogue scale (VAS) scores. Surgery consisted of subfascial anterior transposition following excision of AE muscle. Results : AE muscle was present in 8.5% of all patients, and was more common in patients who were younger and with involvement of their dominant hand; the duration of symptom was shorter in patients with AE muscle. All patients showed postoperative improvement in symptoms according to DASH and VAS scores. Conclusion : The possibility of CuTS caused by AE muscle should be considered when younger patients have rapidly aggravated and activity-related cubital tunnel symptoms with a palpable mass in the cubital tunnel area. Excision of AE muscle and anterior ulnar nerve transposition may be considered effective surgical treatment.

• Journal of Preventive Medicine and Public Health
• /
• 제39권6호
• /
• pp.485-492
• /
• 2006

Objectives : To measure the distribution of electrocardiographic ST segment depression, and evaluate its relationships with cardiovascular risk factors based on the cross-sectional studies within a rural Korean community Methods : This study analyzed 1,343 persons, over 40 years old, who participated in a baseline survey during 2002-2005; the exclusion criteria included: a past history of myocardial infarction and angina pectoris, and specific conduction abnormalities. A Standard 12 leads ECG was recorded using an FCP-2101 (Fukuda Denshi Co.). The ST segment depression was retrospectively measured by a physician, according to the Minnesota code classification. Results : ST segment depression was found in 3.6 and 6.4% of male and female participants, respectively. After adjusting for age, gender, smoking, physical activity and obesity differences, high blood pressure showed significant relations with ST depression in females (male ORs=2.67, 95% CI=0.85-8.50; female ORs=2.62, 95% CI=1.29-5.32) Conclusions : As an ischemic ECG sign, ST depression was related to hypertension in female participants. This relationship remained significant, even after cases with left ventricular hypertrophy were removed.

• Journal of Yeungnam Medical Science
• /
• 제31권1호
• /
• pp.13-16
• /
• 2014

Congenital abnormalities of the coronary arteries are found in 0.6% to 1.3% of patients in coronary angiography. Dual left anterior descending coronary artery (LAD) is a rare coronary anomaly and is incidentally detected during coronary angiography. We report a case of a 65-year-old female with a rare coronary anomaly who was diagnosed with dual LAD via coronary computed tomography and coronary angiography. The imaging studies revealed dual LAD originating from the left main stem and right coronary sinus. These angiographic findings were considered to be consistent with the type IV variety of dual LAD by Spindola-Franco classification. Recognition of dual LAD is important to prevent errors of interpretation of the coronary angiogram and for optimal surgery.

• The Korean Journal of Physiology and Pharmacology
• /
• 제24권1호
• /
• pp.89-99
• /
• 2020

Although microscopic analysis of tissue slides has been the basis for disease diagnosis for decades, intra- and inter-observer variabilities remain issues to be resolved. The recent introduction of digital scanners has allowed for using deep learning in the analysis of tissue images because many whole slide images (WSIs) are accessible to researchers. In the present study, we investigated the possibility of a deep learning-based, fully automated, computer-aided diagnosis system with WSIs from a stomach adenocarcinoma dataset. Three different convolutional neural network architectures were tested to determine the better architecture for tissue classifier. Each network was trained to classify small tissue patches into normal or tumor. Based on the patch-level classification, tumor probability heatmaps can be overlaid on tissue images. We observed three different tissue patterns, including clear normal, clear tumor and ambiguous cases. We suggest that longer inspection time can be assigned to ambiguous cases compared to clear normal cases, increasing the accuracy and efficiency of histopathologic diagnosis by pre-evaluating the status of the WSIs. When the classifier was tested with completely different WSI dataset, the performance was not optimal because of the different tissue preparation quality. By including a small amount of data from the new dataset for training, the performance for the new dataset was much enhanced. These results indicated that WSI dataset should include tissues prepared from many different preparation conditions to construct a generalized tissue classifier. Thus, multi-national/multi-center dataset should be built for the application of deep learning in the real world medical practice.

• 임상간호연구
• /
• 제14권1호
• /
• pp.161-172
• /
• 2008

Purpose: This study was to classify patient severity score for hemodialysis patients. Method: The subject of this study was 1,575 patients. To study the severity of the patients, we used t-test and ANOVA. The congruity was measured by Kappa coefficient and the severity in each medical facility was analyzed by ANOVA. Result: The results showed that there was a significant difference according to the levels of medical center (F=171.187, p<.0001). Categorizing the severity of the patients in each medical facility, group II and III of the secondary medical institution had higher ratio than the primary medical institution. There was not a single patient coming under group IV in both of the primary or secondary medical institutions. However, the tertiary medical institutions had more subjects in group II and III than the primary and secondary medical institutions. The group IV with the highest severity had 11 patients(1.5%), demonstrating that the tertiary medical institution had higher severity patients than the primary or secondary medical institutions. Conclusion: The results of this study appropriately reflects the repayment system of medical expenses by the government. Also, it provides the fundamental information to develop nursing fee system taken into account of the systemic differences among the primary, secondary and tertiary medical institutions.