• Journal of Welding and Joining
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• v.13 no.4
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• pp.132-146
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• 1995

Flaw classification(determination of the flaw type) and flaw sizing (prediction of the flaw shape, orientation and sizing parameters) are very important issues in ultrasonic nondestructive evaluation of weldments. In this work, new techniques for both classification and sizing of flaws in weldments are described together with extensive review of previous works on both topics. In the area of flaw classification, a methodology is developed which can solve classification problems using probabilistic neural networks, and in the area of flaw sizing, a time-of-flight equivalent(TOFE) sizing method is presented.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.760-762
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• 2003

This study confirmed the usefulness of short-wavelength infrared (SWIR) in the discrimination and classification of evergreen forest types. A forested area near Hisayama and Sasaguri in Fukuoka Prefecture, Japan, served as the study area. Warm-temperate forest vegetation dominates the study site vegetation. Coniferous plantation forest, natural broad-leaved forest, and bamboo forest were analyzed using LANDSAT5/TM and SPOT4/HRVIR remote sensing data. Samples were extracted for the three forest types, and reflectance factors were compared for each band. Kappa coefficients of various band combinations were also compared by classification accuracy. For the LANDSAT5/TM data observed in April, October, and November, Bands 5 and 7 showed significant differences between bamboo, broad-leaved, and coniferous forests. The same significant difference was not recognized in the visible or near-infrared regions. Classification accuracy, determined by supervised classification, indicated distinct improvements in band combinations with SWIR, as compared to those without SWIR. Similar results were found for both LANDSAT5/TM and SPOT4/HRVIR data. This study identified obvious advantages in using SWIR data in forest-type discrimination and classification.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.27 no.11
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• pp.574-580
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• 2015

Climatic zone in building code is an administrative district classification reflecting regional climatic characteristics. Use of Degree-Days is a fundamental method that can be used in various building design codes, analysis of building energy performance, and establishment of minimum thermal transmittance of building envelopes. Many foreign countries, such as the USA, the EU, Australia, Italy, India, China, etc., have already adapted climatic zone classification with degree-days, precipitation or amount of water vapor based on the characteristics of their own country's climate. In Korea, however, the minimum requirements for regional thermal transmittance are classified separately for the Jungbu area, Nambu area and Jeju Island with no definite criterion. In this study, degree-days of 255 Korean cities were used for climatic zone classification. Outdoor dry-bulb temperature data from the Korea Meteorological Administration for 1981~2010 was used to calculate degree-days. ArcGIS and the calculated degree-days were utilized to analyze and visualize climatic zone classification. As a result, depending on the distribution and distinctive differences in degree-days, four climatic zones were derived : 1) Central area, 2) Mountain area of Gyeonggi and Gangwon provinces, 3) Southern area, and 4) Jeju Island. The climatic zones were suggested per administrative district for easy public understanding and utilization.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.10-13
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• 2004

Hyperspectral data has a great advantage to classify various surface materials that are spectrally similar. In this study, we attempted to classify man-made materials in urban area using Hyperion data. Hyperion imagery of Seoul was initially processed to minimize radiometric distortions caused by sensor and atmosphere. Using color aerial photographs. we defined seven man-made surfaces (concrete, asphalt road. railroad, buildings, roof, soil, shadow) for the classification in Seoul. The hyperspectral data showed the potential to identify those manmade materials that were difficult to be classified by multispectral data. However. the classification of road and buildings was not quite satisfactory due to the relatively low spatial resolution of Hyperion image. Further, the low radiometric quality of Hyperion sensor was another limitation for the application in urban area.

• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.141-150
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• 2018

Crop type classification is essential for supporting agricultural decisions and resource monitoring. Remote sensing techniques, especially using hyperspectral imagery, have been effective in agricultural applications. Hyperspectral imagery acquires contiguous and narrow spectral bands in a wide range. However, large dimensionality results in unreliable estimates of classifiers and high computational burdens. Therefore, reducing the dimensionality of hyperspectral imagery is necessary. In this study, the Random Forest (RF) classifier was utilized for dimensionality reduction as well as classification purpose. RF is an ensemble-learning algorithm created based on the Classification and Regression Tree (CART), which has gained attention due to its high classification accuracy and fast processing speed. The RF performance for crop classification with airborne hyperspectral imagery was assessed. The study area was the cultivated area in Chogye-myeon, Habcheon-gun, Gyeongsangnam-do, South Korea, where the main crops are garlic, onion, and wheat. Parameter optimization was conducted to maximize the classification accuracy. Then, the dimensionality reduction was conducted based on RF variable importance. The result shows that using the selected bands presents an excellent classification accuracy without using whole datasets. Moreover, a majority of selected bands are concentrated on visible (VIS) region, especially region related to chlorophyll content. Therefore, it can be inferred that the phenological status after the mature stage influences red-edge spectral reflectance.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.807-810
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• 2003

This research uses high-resolution satellite images as a source of collecting farmland information. For effectively extract the paddy area, we use texture information and different classify methods to assist the satellite image classification. First, using maximum likelihood classifier to extract paddy information from images. The results show that User Accuracy and Procedure Accuracy of the paddy area can increase from 80.60% to 95.45% and 84.38% to 95.45%. Second, establishing a paddy Knowledge Base and using Knowledge Base Classifier to extract paddy area, and result shows the User Accuracy and Producer Accuracy to be 92.16% and 90.06%. Finally, The result shows we can effectively contribute to the paddy field information extraction from high-resolution satellite images.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.1
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• pp.40.2-40.2
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• 2011

We investigated solar flare occurrence probability depending on sunspot group classification and its area change. For this study, we used the McIntosh sunspot group classification and then selected most flare-productive six sunspot groups : DKI, DKC, EKI, EKC, FKI and FKC. For each group, we classified it into three sub-groups according to the sunspot group area change : increase, steady and decrease. For sunspot data, we used the NOAA's active region information for 19 years (from 1992.01 to 2010.12). As a result, we found that the probabilities of the all "increase" sub-groups is noticeably higher than those of other sub-groups. In case of FKC McIntosh sunspot group, for example, the M-class flare occurrence probability of the "increase" sub-group is 65% while the "decrease" and "steady" sub-groups are 50% and 44%, respectively. In summary, when sunspot group area increases, the probability of solar flares noticeably increases. This is statistical evidence that magnetic flux emergence is an very important mechanism for triggering solar flares.

• Journal of the Korean Society of Safety
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• v.26 no.5
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• pp.33-40
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• 2011

In the hazardous areas where explosive liquids, vapors and gases exist, electrical apparatus/equipment should have explosion-proof construction. The consuming of liquefied natural gas(LNG) has markedly increased in the industrial field, especially in aspect of some thermoprocessing equipment, boiler, dryer, furnace, annealer, kiln, regenerative thermal oxidizer(RTO) and so on. Because it has many merits, clean fuel, safety, no transportation/storage facility and so on. It is strongly recommend that the classification of hazards has to be decided to prevent and protect explosion which may occur in thermoprocessing equipment. In this paper, the operated thermoprocessing equipments in industrial area investigated and explosion risk assessment about LNG leakage from its facilities was performed through numerical calculation and computer simulation. Finally, we suggest the systemic/technical approach for safety assessments of thermoprocessing equipments consumed LNG fuel which are specially subjected to classification of hazardous area.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.3
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• pp.203-209
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• 2015

This paper discusses the effectiveness of using Landsat 8 TIR (Thermal Infrared) band images to improve the accuracy of landuse/landcover classification of urban areas. According to classification results for the study area using diverse band combinations, the classification accuracy using an image fusion process in which the TIR band is added to the visible and near infrared band was improved by 4.0%, compared to that using a band combination that does not consider the TIR band. For urban area landuse/landcover classification in particular, the producer’s accuracy and user’s accuracy values were improved by 10.2% and 3.8%, respectively. When MLC (Maximum Likelihood Classification), which is commonly applied to remote sensing images, was used, the TIR band images helped obtain a higher discriminant analysis in landuse/landcover classification.

• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.105-118
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• 2016

The purpose of this study is to improve the classification system of sub-divided land cover map among the land cover maps provided by the Ministry of Environment. To accomplish the purpose, first, the overseas country land cover map classification items were examined in priority. Second, the area ratio of each item established by applying the previous sub-divided classification system was analyzed. Third, the survey on the improvement of classification system targeting the users (experts and general public) who actually used the sub-divided land cover map was carried out. Fourth, a new classification system which improved the previous system by reclassifying 41 classification items into 33 items was finally established. Fifth, the established land cover classification items were applied on study area, and the land cover classification result according to the improvement method was compared with the previous classification system. Ilsan area in Goyang city where there are diverse geographic features with various land surface characteristics such as the urbanization area and agricultural land were distributed evenly were selected as the study area. The basic images used in this study were 0.25 m aerial ortho-photographs captured by the National Geographic Information Institute (NGII), and digital topographic map, detailed stock map plan, land registration map and administrative area map were used as the relevant reference data. As a result of applying the improved classification system into the study area, the area of culture-sports, leisure facilities was $1.84km^2$ which was approximately more than twice larger in comparison to the previous classification system. Other areas such as transportation and communication system and educational administration facilities were not classified. The result of this study has meaningful significance that it reflects the efficiency for the establishment and renewal of sub-divided land cover map in the future and actual users' needs.