• 한국환경복원기술학회지
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• 제18권1호
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• pp.127-133
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• 2015

Interests in ecosystem services have increased and a number of attempts to perform a quantitative valuation on them have been undertaken. To classify the ecosystem types landcover classification maps are generally used. However, some forest types on landcover classification maps have a number of errors. The purpose of this study is to verify the forest types on the landcover map by using a variety of field survey data and to suggest an improved method for forest type classifications. Forest types are compared by overlaying the landcover classification map with the 4th forest type map, and then they are verified by using National Forest Inventory, 3rd National Ecosystem Survey and field survey data. Misclassifications of forest types are found on the forest on the forest type map and farm and other grassland on the landcover map. Some errors of forest types occur at Daegu, Busan and Ulsan metropolitan cities and Gangwon province. The results of accuracy in comprehensive classification show that deciduous forest is 76.1%; coniferous forest is 54.0%; and mixed forest is 22.2%. In order to increase the classification accuracy of forest types a number of remote sensing images during various time periods should be used and the survey period of NFI and the National Forest Inventory and National Ecosystem Survey should be consistent. Also, examining areas with wide forest patch should be prioritized during the field survey in order to decrease any errors.

• 한국기록관리학회지
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• 제18권1호
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• pp.155-175
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• 2018

이 연구의 목적은 함평사건희생자유족회의 소장 기록물에 대한 분류체계를 마련하는 데에 있다. 이에 따라 기록물의 맥락을 기능적 출처주의를 통해 구현하며, 기록물을 효과적으로 활용할 수 있도록 유형별 특성과 생산시기별 특성을 반영한 분류표를 제시하였다. 기능분류체계 개발 방법론인 DIRKS를 사용하여 함평사건희생자유족회의 업무분석을 수행함으로써, 업무기능-업무활동-처리행위로 이어지는 업무분류표를 도출한다. 함평사건희생자유족회 소장 기록물을 유형과 생산시기별 특성을 고려하여 그 범주를 결정한다. 기록물 맵핑은 업무분류표에 해당하는 업무분류체계에 1차적으로 실행하고, 2차적으로는 업무분류에 유형분류와 시대분류를 접목한 다중분류체계에 맵핑한다. 업무주제-업무활동-처리행위-유형-시대의 형태로 이어지는 기록물 분류표를 도출한다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.433-438
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• 1999

The land use type of Korea is high-density. So, the image classification using coarse resolution satellite image may not provide land cover classification results as good as expected. The purpose of this paper is to compare the result of visual interpretation with that of digital image classification of 20 m resolution SPOT satellite image at Kwangju-eup, Kyunggi-do, Korea. Classes are forest, cultivated field, pasture, water and residential area, which are clearly discriminated in visual interpretation. Maximum likelihood classifier was used for digital image classification. Accuracy assessment was done by comparing each classification result with ground truth data obtained from field checking. The classification result from the visual interpretation presented an total accuracy 9.23 percent higher than that of the digital image classification. This proves the importance of visual interpretation for the area with high density land use like the study site in Korea.

• 동의생리병리학회지
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• 제18권6호
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• pp.1892-1895
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• 2004

The purpose of this study was to investigate the personality traits in constitutional types. The Questionnaire for the Sasang Constitution Classification Ⅱ and Eysenck Personality Questionnaire were completed by 155 university students. Statistical analyses of the Questionnaire for the Sasang Constitution Classification Ⅱ and Eysenck Personality Questionnaire scale scores and results are as follows : First, Personality differences in constitutional types are revealed significantly with respect to Eysenck Personality Questionnaire scales such as neuroticism, addiction, and criminality. Second, there is a significant difference between Eum(음) type and Yang(양) type along psychoticism and neuroticism. Third, sex difference is significant along the dimension of extraverion-intraversion.

• 한국연초학회지
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• 제4권1호
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• pp.37-42
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• 1982

Korean local and introduced varieties were classified by the cluster analysis of correlation and taxonomic distance based on nineteen growth characters. 1. Thirty six varieties can be classified into three groups(I, II, III) by WVGM (weighted variable group method) 2. Major characters for classifying cultivars were days to flowering, number of leaves, leaf length, stem diameter and width of midrib: the five characters seemed to be useful in monothetic classification. 3. Korean varieties were similar to oriental, and japanese varieties to taiwan. 4. WVGM was more accurate and meaningful than classification by WPGM (weighted paired group method) and reticulate diagram of correlation. 5. Characteristics of each group: Group I closely related to many leaves, late of maturity and broad leaf type, Group II related to medium leaves, late of maturity and narrow leaf type, Croup 19 related to few leaves, early of maturity and medium leaf type respectively.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.808-810
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• 1996

In this paper, neural network, which has learning capability, is used for fault type classification and fault section estimation for high speed relaying. The potential of the neural network approach is demonstrated by simulation using ATP. The instantaneous values of voltages and currents are used the inputs of neural networks. This approach determines the fault section directly. In this paper, back-propagation network(BPN) is used for fault type classification and fault section estimation and can use for high speed relaying because it determines fault section within a few msec.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1445-1456
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• 2003

The ASTER TIR (thermal infrared radiometer) sensor has 5 spectral bands over 8 to 12 ${\mu}$m region. Rock type classification using the ASTER TIR nighttime data was performed in the Erta Ale range of the Ethiopian Rift Valley. Erta Ale range is the most important axial volcanic chain of the Afar region. The petrographic diversity of lava erupted in this area is very important, ranging from magnesian transitional basalt to rhyolites. We tried to classify the rock types based on the spectral behavior of each volcanic rock types in thermal infrared range and estimated SiO$_{2}$ content with emission data by the ASTER TIR.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권9호
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• pp.2904-2926
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• 2022

Currently, diabetes is the most common chronic disease in the world, affecting 23.7% of the population in the Kingdom of Saudi Arabia. Diabetes may be the cause of lower-limb amputations, kidney failure and blindness among adults. Therefore, diagnosing the disease in its early stages is essential in order to save human lives. With the revolution in technology, Artificial Intelligence (AI) could play a central role in the early prediction of diabetes by employing Machine Learning (ML) technology. In this paper, we developed a diagnosis system using machine learning models for the detection of type 2 diabetes among adults, through the adoption of two different diabetes datasets: one for training and the other for the testing, to analyze and enhance the prediction accuracy. This work offers an enhanced classification accuracy as a result of employing several pre-processing methods before applying the ML models. According to the obtained results, the implemented Random Forest (RF) classifier offers the best classification accuracy with a classification score of 98.95%.

• 제어로봇시스템학회논문지
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• 제19권11호
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• pp.1043-1047
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• 2013

Smart mobile robot is a kind of Intelligent Robot. It means that operates manipulate autonomously and recognize the external environment. Smart mobile robot moving mechanism has many type and the type depend on the robot shape or purpose. Recently, research on the moving mechanism has been actively in many area. The moving mechanism divided to wheel type, crawler type, walking type, other type and the moving type choose by the kind of robot or the purpose robot. In this paper, describe the kind of moving mechanism on the smart mobile robot and the technical trend of moving mechanism of smart mobile robot.

• 한국통신학회논문지
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• 제19권10호
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• pp.1991-2000
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• 1994

본 논문에서는 필기체 문자 인식 시스템을 위하여 6형식 분류 알고리즘을 제안하였다. 입력 영상은 세선화 과정을 거친 후 잡음을 제거하는 절단화 과정을 거친 후 $64\times64$ 크기로 정규화하여 이용하였다. 6형식 분류는 신경회로망의 모델 중 다층 퍼셉트론의 학습알고리즘을 이용하여 대분류와 상세분류 과정에서 이루어진다. 특징값 추출은 부분적인 특정값으로는 Subblock Hough transform을 이용하였으며 전체적인 특징값으로는 표본화 Hough transfrom을 이용하였다. 실험은 10사람이 한 형식당 30번씩 쓴 1800자를 대상으로 하였으며 받침의 유무로 대분류한 후 각기 종모음과 횡모음의 유무로 상세분류하여 90%의 분류 성공율을 얻었다.