• Atmosphere
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• v.30 no.2
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• pp.169-181
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• 2020

We conducted a study on the impact of observation station density; this was done in order to enable the accurate estimation of spatial meteorological variables. The purpose of this study is to help operate an efficient observation network by examining distributions of temperature, relative humidity, and wind speed in a test area of a three-dimensional meteorological observation project in the Yeongdong region in 2019. For our analysis, we grouped the observation stations as follows: 41 stations (for Step 4), 34 stations (for Step 3), 17 stations (for Step 2), and 10 stations (for Step 1). Grid values were interpolated using the kriging method. We compared the spatial accuracy of the estimated meteorological grid by using station density. The effect of increased observation network density varied and was dependent on meteorological variables and weather conditions. The temperature is sufficient for the current weather observation network (featuring an average distance about 9.30 km between stations), and the relative humidity is sufficient when the average distance between stations is about 5.04 km. However, it is recommended that all observation networks, with an average distance of approximately 4.59 km between stations, be utilized for monitoring wind speed. In addition, this also enables the operation of an effective observation network through the classification of outliers.

• Journal of Science Education
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• v.36 no.1
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• pp.153-165
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• 2012

This study investigated the frequent mistakes and the causes of the alternative conceptions in the animal classification by using the questionnaire and interview with the middle school students (N=300). As results, some students have difficulties classifying suggested animals into vertebrates or invertebrates : snakes (31.7%), shrimps (28.3%), turtles (25.6%), frogs (24.7%), and starfish (10.7%) in order of precedence. These errors seemed to be caused by intuitive thinking over characteristics of physical motions and appearance of suggested animals, wrong inference from comparing to features of familiar animals and the lack of observation experience of the vertebrate backbone. Furthermore, the results showed that relatively many students made a mistake classifying subgroup members of vertebrates such as classifying salamanders into the class Reptilia (45.3%) and turtles into Amphibia (40.3%). It is likely that those errors are affected by ambiguousness of classification terminology (e.g. the term of Amphibia) and weak ability in relating the physiological and ecological feature to standard of classification feature. In addition, sociocultural factors could influence animal classification as 'bat in birds', 'whale in fish, and 'penguin in mammals'. The present study implied that teaching and learning animal classification may require an appropriate guide focused on activities to explore major characteristics used for the animal classification standard through providing more chances of animal observation rather than the cramming method of learning induced by technical memorizing.

• Atmosphere
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• v.29 no.5
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• pp.639-658
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• 2019

The classification of fog type and the characteristics of fog based on fog events over South Korea were investigated using a 3-year (2015~2017) visibility meter data. One-minute visibility meter data were used to identify fog with present weather codes and surface observation data. The concept of fog events was adopted for the better definition of fog properties and more objective classification through the detailed investigation of life cycle of fog. Decision tree method was used to classify the fog types and the final fog types were radiation fog, advection fog, precipitation fog, cloud base lowering fog and morning evaporation fog. We enhanced objectivity in classifying the types of fog by adding the satellite and the buoy observations to the conventional usage of AWS and ceilometer data. Radiation fog, the most common type in South Korea, frequently occurs in inland during autumn. A considerable number of advection fogs occur in island area in summer, especially in July. Precipitation fog accounts for more than a quarter of the total fog events and frequently occurs in islands and coastal areas. Cloud base lowering fog, classified using ceilometer, occurs occasionally for all areas but the occurrence rate is relatively high in east and west coastal area. Morning evaporation fog type is rarely observed in inland. The occurrence rate of thick fog with visibility less than 100 meters is amount to 21% of total fog events. Although advection fog develops into thick fog frequently, radiation fog shows the minimum visibility, in some cases.

• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.3
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• pp.55-64
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• 2014

While utilizing high resolution satellite image for land use classification has been popularized, object-oriented classification has been adapted as an affordable classification method rather than conventional statistical classification. The aim of this study is to extract the paddy field area using object-oriented classification with time series NDVI from high-resolution satellite images, and the RapidEye satellite images of Jaeryung-gun in North Korea were used. For the implementation of object-oriented classification, creating objects by setting of scale and color factors was conducted, then 3 different land use categories including paddy field, forest and water bodies were extracted from the objects applying the variation of time-series NDVI. The unclassified objects which were not involved into the previous extraction classified into 6 categories using unsupervised classification by clustering analysis. Finally, the unsuitable paddy field area were assorted from the topographic factors such as elevation and slope. As the results, about 33.6 % of the total area (32313.1 ha) were classified to the paddy field (10847.9 ha) and 851.0 ha was classified to the unsuitable paddy field based on the topographic factors. The user accuracy of paddy field classification was calculated to 83.3 %, and among those, about 60.0 % of total paddy fields were classified from the time-series NDVI before the unsupervised classification. Other land covers were classified as to upland(5255.2 ha), forest (10961.0 ha), residential area and bare land (3309.6 ha), and lake and river (1784.4 ha) from this object-oriented classification.

• Journal of the Korean association of regional geographers
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• v.7 no.1
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• pp.35-50
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• 2001

Since remotely sensed images of coastal wetlands are very sensitive to spatial resolution, it is very important to select an optimum resolution for particular geographic phenomena needed to be represented. Scale is one of the most important factors in spatial analysis techniques, which is defined as a spatial and temporal interval for a measurement or observation and is determined by the spatial extent of study area or the measurement unit. In order to acquire the optimum scale for a particular subject (i.e., coastal wetlands), measuring and representing the characteristics of attribute information extracted from the remotely sensed images are required. This study aims to explore and analyze the scale effects of attribute information extracted from remotely sensed coastal wetlands images. Specifically, it is focused on identifying the effects of scale in response to spatial resolution changes and suggesting a methodology for exploring the optimum spatial resolution. The LANDSAT TM image of Sunchon Bay was classified by a supervised classification method, Six land cover types were classified and the Kappa index for this classification was 84.6%. In order to explore the effects of scale in the classification procedure, a set of images that have different spatial resolutions were created by a aggregation method. Coarser images were created with the original image by averaging the DN values of neighboring pixels. Sixteen images whose resolution range from 30 m to 480 m were generated and classified to obtain land cover information using the same training set applied to the initial classification. The values of Kappa index show a distinctive pattern according to the spatial resolution change. Up to 120m, the values of Kappa index changed little, but Kappa index decreased dramatically at the 150m. However, at the resolution of 240 m and 270m, the classification accuracy was increased. From this observation, the optimum resolution for the study area would be either at 240m or 270m with respect to the classification accuracy and the best quality of attribute information can be obtained from these resolutions. Procedures and methodologies developed from this study would be applied to similar kinds and be used as a methodology of identifying and defining an optimum spatial resolution for a given problem.

• Journal of Korean Academy of Nursing
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• v.17 no.1
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• pp.9-23
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• 1987

Nursing service, as the largest user of labor resources, has become concerned about appropriate allocation of staffing resources. Therefore, this project was designed to measure quantitatively the direct nursing care provided to patients and to develop a new patient classification system based on the direct nursing care activities. The initial step in the development of the classification instrument was to identify the content of direct nursing activities. The frequency with which these activities were carried out, the total time spent in carrying them out and the average time for one performance of each of the nursing activities was calculated. The next step was to select the items for the classification instrument taking into account these direct nursing activities. A list of 40 items was prepared. These items were then classified into 8 major categories: personal hygiene, moving ＆ exercise, nutrition & elimination, observation, medication, treatment, collecting specimens and other care activities for severity ill patients. Each item was assigned a value unit based on the average time required by the nursing staff to complete the specific item. The third step was to determine the practicality of the items and value units, so an attempt was made to establish content validity for these items and units by obtaing a consensus from 8 head nurses, representing eight different departments. The 4th step was to conducted a pilot study to establish the score range for the classification boundaries. For this purpose an instrument was designed using the list of items and value units and a prepared classification criteria as a guideline to validate the patient classification. A judgment group consisting of 52 supervisory nurses and head nurses were asked to select the proper patient to fit each classification criteria and to fill out the instrument for each patient. The total value unit and the frequency for each classification group was calculated. According to the frequency distribution, the score range for the classification group was determined as follows : 0~15 for groupI, 16~30 for group II, 31~50 for group III, and above 51 for group IV. Finally a patient classification form was developed.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1149-1154
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• 2022

The purpose of this research is to select representative observation stations for winter observation equipment performance tests and to present indoor and outdoor conditions for performance tests by considering snowfall, snowfall days, latitude, and altitude distribution for observation stations operated by the Korea Meteorological Administration. Using the snowfall data observed during the winter for 30 years (1981-2010), ten representative observation stations are selected to consider the classification of snowfall days by class, latitude, and altitude distribution of observation stations. As a result of analysis, the suitable point for outdoor experiments was selected as Daegwallyeong, the average number of snowfall days and snowfall days of 5cm or more were 57.5 and 13.2 days, respectively. The indoor experimental conditions are considered to be suitable under temperatures of -15 to 5℃ and humidity of 50% or higher. Results of this research can be used as basic information for conditions and test beds for performance tests of equipment that can respond to heavy snow disasters in winter.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.281-285
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• 2002

The observation fuzzy data in random effect and balanced designs for one way classification by using a the matrix formulation, we can estimate the fuzzy variance components for the ozone depletion example and test by the agreement index.

• Earthquakes and Structures
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• v.15 no.1
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• pp.1-8
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• 2018

A new site classification system and site coefficients based on local site conditions in Korea were developed and implemented as a part of minimum design load requirements for general seismic design. The new site classification system adopted bedrock depth and average shear wave velocity of soil above the bedrock as parameters for site classification. These code provisions were passed through a public hearing process before it was enacted. The public hearing process recommended to modify the naming of site classes and adjust the amplification factors so that the level of short-period amplification is suitable for economical seismic design. In this paper, the new code provisions were assessed using dynamic centrifuge tests and by comparing the design response spectra (DRS) with records from 2016 Gyeongju earthquake, the largest earthquake in history of instrumental seismic observation in Korea. The dynamic centrifuge tests were performed to simulate the representative Korean site conditions, such as shallow depth to bedrock and short-period amplification characteristics, and the results corroborated with the new DRS. The Gyeongju earthquake records also showed good agreement with the DRS. In summary, the new code provisions are reliable for representing the site amplification characteristic of shallow bedrock condition in Korea.

• Journal of information and communication convergence engineering
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• v.21 no.3
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• pp.208-215
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• 2023

Deep learning techniques provide powerful solutions to several pattern-recognition problems, including Raman spectral classification. However, these networks require large amounts of labeled data to perform well. Labeled data, which are typically obtained in a laboratory, can potentially be alleviated by data augmentation. This study investigated various data augmentation techniques and applied multiple deep learning methods to Raman spectral classification. Raman spectra yield fingerprint-like information about chemical compositions, but are prone to noise when the particles of the material are small. Five augmentation models were investigated to build robust deep learning classifiers: weighted sums of spectral signals, imitated chemical backgrounds, extended multiplicative signal augmentation, and generated Gaussian and Poisson-distributed noise. We compared the performance of nine state-of-the-art convolutional neural networks with all the augmentation techniques. The LeNet5 models with background noise augmentation yielded the highest accuracy when tested on real-world Raman spectral classification at 88.33% accuracy. A class activation map of the model was generated to provide a qualitative observation of the results.