• Journal of KIISE:Software and Applications
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• v.26 no.11
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• pp.1314-1323
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• 1999

컴퓨터의 사용이 일반화됨에 따라 데이타를 생성하고 수집하는 것이 용이해졌다. 이에 따라 데이타로부터 자동적으로 유용한 지식을 얻는 기술이 필요하게 되었다. 데이타 마이닝에서 얻어진 지식은 정확성과 이해성을 충족해야 한다. 본 논문에서는 데이타 마이닝을 위하여 퍼지 결정트리에 기반한 효율적인 퍼지 규칙을 생성하는 알고리즘을 제안한다. 퍼지 결정트리는 ID3와 C4.5의 이해성과 퍼지이론의 추론과 표현력을 결합한 방법이다. 특히, 퍼지 규칙은 속성 축에 평행하게 판단 경계선을 결정하는 방법으로는 어려운 속성 축에 평행하지 않는 경계선을 갖는 패턴을 효율적으로 분류한다. 제안된 알고리즘은 첫째, 각 속성 데이타의 히스토그램 분석을 통해 적절한 소속함수를 생성한다. 둘째, 주어진 소속함수를 바탕으로 ID3와 C4.5와 유사한 방법으로 퍼지 결정트리를 생성한다. 또한, 유전자 알고리즘을 이용하여 소속함수를 조율한다. IRIS 데이타, Wisconsin breast cancer 데이타, credit screening 데이타 등 벤치마크 데이타들에 대한 실험 결과 제안된 방법이 C4.5 방법을 포함한 다른 방법보다 성능과 규칙의 이해성에서 보다 효율적임을 보인다.Abstract With an extended use of computers, we can easily generate and collect data. There is a need to acquire useful knowledge from data automatically. In data mining the acquired knowledge needs to be both accurate and comprehensible. In this paper, we propose an efficient fuzzy rule generation algorithm based on fuzzy decision tree for data mining. We combine the comprehensibility of rules generated based on decision tree such as ID3 and C4.5 and the expressive power of fuzzy sets. Particularly, fuzzy rules allow us to effectively classify patterns of non-axis-parallel decision boundaries, which are difficult to do using attribute-based classification methods.In our algorithm we first determine an appropriate set of membership functions for each attribute of data using histogram analysis. Given a set of membership functions then we construct a fuzzy decision tree in a similar way to that of ID3 and C4.5. We also apply genetic algorithm to tune the initial set of membership functions. We have experimented our algorithm with several benchmark data sets including the IRIS data, the Wisconsin breast cancer data, and the credit screening data. The experiment results show that our method is more efficient in performance and comprehensibility of rules compared with other methods including C4.5.

• Korean Journal of Remote Sensing
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• v.11 no.3
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• pp.83-100
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• 1995

The land-use has changed rapidly during the last two decades in accordance with urbanization in the Seoul Metropolitan Region. As a result of these changes, the local climate has undergone changes as well. This study intends to define the land-use changes, and then to show how they have brought in significant changes in the local climates. Land-use changes in the study area so repidly that up-to date maps and documents are not available at present. Therefore, Landsat data for land-use classification and NOAA AVHRR thermal data for the temperature fields were analyzed. Additionary, to visualize the effect of the land-use on the local climate, computer-enhanced brightness temperatures, Green Belt and city boundaries were overlaid on land-use patterns obtained from satellite images using GIS techniques. The results of analysis demonstrate that Green Space in the Seoul Metropolitan Region decreased from 94% to 62% while urban land-use increased ten times, from 4% to 39% for the period of 1972-1992. The resulting disappearance of biomass caused by land-use changes may have implications for the local-and micro-climate. The results show that the local climate of the study area became drier and warmer. This study also suggests a need for further studies of man's effects on local climate to minimize adverse influences and hazardous pollution and efficacious ways for urban planning.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.2
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• pp.135-148
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• 2015

This research is carried out for the land cover change detection in the Nakdong River basin before and after the 4 major rivers restoration project using the LiDAR DEM(Digital Elevation Model) and the multi-temporal Landsat imagery. Firstly the river basin polygon is generated by using the levee boundaries extracted from the LiDAR DEM, and the four river basin imagery are generated from the multi-temporal Landsat-5 TM(Thematic Mapper) and Landsat-8 OLI(Operational Land Imager) imagery by using the generated river basin polygon. Then the main land covers such as river, grass and bare soil are separately generated from the generated river basin imagery by using the image classification method, and the ratio of each land cover in the entire area is calculated. The calculated land cover changes show that the areas of grass and bare soil in the entire area have been significantly changed because of the seasonal change, while the area of the river has been significantly increased because of the increase of the water storage. This paper contributes to proposing an efficient methodology for the land cover change detection in the Nakdong River basin using the LiDAR DEM and the multi-temporal satellite imagery taken before and after the 4 major rivers restoration project.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.1
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• pp.177-185
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• 2009

Cluster analysis or clustering is a kind of unsupervised learning method in which a set of data points is divided into a given number of homogeneous groups. Fuzzy clustering method, one of the most popular clustering method, allows a point to belong to all the clusters with different degrees, so produces more intuitive and natural clusters than hard clustering method does. Even more some of fuzzy clustering variants have noise-immunity. In this paper, we improved the Possibilistic Fuzzy C-Means (PFCM), which generates a membership matrix as well as a typicality matrix, using Gath-Geva (GG) method. The proposed method has a focus on the boundaries of clusters, which is different from most of the other methods having a focus on the centers of clusters. The generated membership values are suitable for the classification-type applications. As the typicality values generated from the algorithm have a similar distribution with the values of density function of Gaussian distribution, it is useful for Gaussian-type density estimation. Even more GG method can handle the clusters having different numbers of data points, which the other well-known method by Gustafson and Kessel can not. All of these points are obvious in the experimental results.

• The Korean Journal of Petroleum Geology
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• v.12 no.1
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• pp.34-42
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• 2006

Ichnology is the study of traces made by various organisms, which includes classification and description of traces, and interpretation of sedimentary process, behavior of organism and depositional environment based on traces and organism behavior. Ichnofacies, which is defined as the association of several traces related together with substrate characteristics and sedimentary processes, is closely related to depositional environment. Ichnology has been applied to sedimentology (to understand physical characteristics of depositional environment, sedimentation pattern and event bed), sequence stratigraphy (to recognize sequence boundaries and biostratigraphic discontinuities such as MFS, TSE and RSE), oil exploration (providing of many information without big cost) and palaeocology. Preliminary ichnological study on the Ganghwa intertidal flat shows that dominant ichofacies are changing with season and location, suggesting that their seasonal variation would be a good indicator to understand the seasonal change of sedimentary processes, the small- scale change of sedimentary environment and the preservation potential of such units. Ichnology on intertidal flat in western coast of Korea has a great potential to apply its results to petroleum geology as well as sedimentology.

• Current Optics and Photonics
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• v.4 no.1
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• pp.31-43
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• 2020

The feasibility of the application of terahertz electromagnetic waves in the diagnosis of prostate cancer was examined. Four samples of incomplete cancerous prostatic paraffin-embedded tissues were examined using terahertz spectral imaging (TPI) system and the results obtained by comparing the absorption coefficient and refractive index of prostate tumor, normal prostate tissue and smooth muscle from one of the paraffin tissue masses examined were reported. Three hundred and sixty cases of absorption coefficients from one of the paraffin tissues examined were used as raw data to classify these three tissues using the Principal Component Analysis (PCA) and Least Squares Support Vector Machine (LS-SVM). An excellent classification with an accuracy of 92.22% in the prediction set was achieved. Using the distribution information of THz reflection signal intensity from sample surface and absorption coefficient of the sample, an attempt was made to use the TPI system to identify the boundaries of the different tissues involved (prostate tumors, normal and smooth muscles). The location of three identified regions in the terahertz images (frequency domain slice absorption coefficient imaging, 1.2 THz) were compared with those obtained from the histopathologic examination. The tissue tumor region had a distinctively visible color and could well be distinguished from other tissue regions in terahertz images. Results indicate that a THz spectroscopy imaging system can be efficiently used in conjunction with the proposed advanced computer-based mathematical analysis method to identify tumor regions in the paraffin tissue mass of prostate cancer.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.5
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• pp.619-628
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• 2009

LiDAR has capability of rapid data acquisition and provides useful information for reconstructing surface of the Earth. However, Extracting information from LiDAR data is not easy task because LiDAR data consist of irregularly distributed point clouds of 3D coordinates and lack of semantic and visual information. This thesis proposed methods for automatic extraction of buildings and 3D detail modeling using airborne LiDAR data. As for preprocessing, noise and unnecessary data were removed by iterative surface fitting and then classification of ground and non-ground data was performed by analyzing histogram. Footprints of the buildings were extracted by tracing points on the building boundaries. The refined footprints were obtained by regularization based on the building hypothesis. The accuracy of building footprints were evaluated by comparing with 1:1,000 digital vector maps. The horizontal RMSE was 0.56m for test areas. Finally, a method of 3D modeling of roof superstructure was developed. Statistical and geometric information of the LiDAR data on building roof were analyzed to segment data and to determine roof shape. The superstructures on the roof were modeled by 3D analytical functions that were derived by least square method. The accuracy of the 3D modeling was estimated using simulation data. The RMSEs were 0.91m, 1.43m, 1.85m and 1.97m for flat, sloped, arch and dome shapes, respectively. The methods developed in study show that the automation of 3D building modeling process was effectively performed.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.20 no.4
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• pp.77-87
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• 2017

In this study, we investigated the physical form of planting foundation of the parks and green spaces in the waterside of Korea and classified them into groups showing common features. It was clssified into 7 kinds of parks and green spaces of 27 waterside parks in Korea including landscape, ecology, art, shields, site boundaries, windbreaks, and soundproofing. As a result, the study was carried out on the detailed type and size estimation through the sampling survey of planting foundation of landscape and ecological type mounding which can be statistically analyzed. Landscape and ecological mounding have the characteristics of securing the ecological stability of the waterside planting areas and the diversity of planting landscape. It is possible to create a green landscape through various terrain changes such as enclosing, focusing, and panoramic view. The physical characteristics of ecological and landscape type mounding can be expressed as height, width, and length And physical data can appear in various forms and sizes depending on the purpose and function of the buffer effect of the land use in the waterside planting areas, the landscape creation, the ecological buffer. In this study, the range of the physical scale for landscape and ecological mounding of waterside parks and green spaces was calculated. The range of the mounding height was analyzed to be less than 1.25m and more than 1.25m and the average height was 0.74~1.08m and 1.75~2.75m respectively. In addition, the range of width of mounding was less than 6.13m, 6.13~17.5m, and more than 17.5m, and the average width of each was 3.45~4.95m, 7.05~10.85m and 31.54~51.54m respectively. The range for the length of mounding was less than 50m, 50~500m, and more than 500m. The mean length of each mounding was 34.0m, 116.3m and 955.8m. It is difficult to distinguish the difference between the waterside planting areas and the urban greenery in the purpose and function of landscape and ecological mounding. However, considering the average distance of 60m from the waterside and the average height of 1.26m, we can conclud that opened planting foundation is prefered to high mounding designs in waterside planting areas. It is expected that the results presented for the improvement of the logical and spatial value of the waterside parks and green areas planting foundation design can be served as the basic data helpful for practical application in landscape architecture planning and design.

• Journal of Biomedical Engineering Research
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• v.40 no.5
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• pp.215-221
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• 2019

The respiratory medical device is a medical device that delivers optimal oxygen or a certain amount of humidification to a patient by delivering artificial respiration to a patient through a machine when the patient has lost the ability to breathe spontaneously. These include respirators for use in chronic obstructive pulmonary disease and anesthesia or emergency situations, and positive airway pressure devices for treating sleep apnea, and as the population of COPD (chronic obstructive pulmonary disease) and elderly people worldwide surge, the market for the respiratory medical devices it is getting bigger. As the demand for both airway pressure devices, there is a problem that the ventilator standard is applied because the reference standard has not been established. Therefore, the boundaries between the items are blurred due to the purpose, intended use, and method of use overlapping similar items in a respiratory medical device. In addition, for both airway pressure devices, there is a problem that the ventilator standard is applied because the reference standard has not been established. Therefore, in this study, we propose clear classification criteria for the respiratory medical devices according to the purpose, intended use, and method of use and provide safety and performance evaluation guidelines for those items to help quality control of the medical devices. And to contribute to the rapid regulating and improvement of public health. This study investigated the safety and performance test methods through the principles of the respiratory medical device, national and international standards, domestic and international licensing status, and related literature surveys. The results of this study are derived from the safety and performance test items in the individual ventilator(ISO 80601-2-72), the International Standard for positive airway pressure device (ISO 80601-2-70), The safety and performance of humidifiers (ISO 80601-2-74) and the safety evaluation items related to home healthcare environment (IEC 60601-1-11), In addition, after reviewing the guidelines drawn up through expert consultation bodies including manufacturers and importers, certified test inspection institutions, academia, etc., the final guidelines were established through revision and supplementation. Therefore, in this study, we propose guidelines for evaluating the safety and performance of the respiratory medical device in accordance with growing technology development.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.967-977
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• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.