• Journal of Preventive Medicine and Public Health
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• v.26 no.2 s.42
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• pp.293-309
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• 1993

With expanded and extended coverage of the national medical insurance and fast growing health care expenditures, appropriateness of health service utilization and quality of care are concerns of both health care providers and insurers as well as patients. An accurate patient classification system is a basic tool for effective health care policies and efficient health services management. A classification system applicable to Korean medical information-Korean Diagnosis Related Groups (K-DRGs)-was developed based on the U.S. Refined DRGs, and the performance of the developed system was assessed in this study. In the process of the development, first the Korean coding systems for diagnoses and procedures were converted to the systems used in the definition of the U.S. Refined DRGs using the mapping tables formulated by physician panels. Then physician panels reviewed the group definition, and identified medical practice patterns different in two countries. The definition was modified for the differences in K-DRGs. The process resulted in 1,199 groups in the system. Several groups in Refined DRGs could not be differentiated in K-DRGs due to insufficient medical information, and several groups could not be defined due to procedures which were not practiced in Korea. However, the classification structure of Refined DRGs was retained in K-DRGs. The developed system was evaluated fur its performance in explaining variations in resource use as measured by charges and length of stay(LOS), for both all and non-extreme discharges. The data base used in this evaluation included 373,322 discharges which was a random sample of discharges reviewed and payed by the medical insurance during the five-month period from September 1990. The proportion of variance in resource use which was reduced by classifying patients into K-DRGs-r-square-was comparable to the performance of the U.S. Refined DRGs: .39 for charges and .25 for LOS for all discharges, and .53 for charges and .31 for LOS for non-extreme discharges. Another measure analyzed to assess the performance was the coefficient of variation of charges within individual K-DRGs. A total of 966 K-DRGs (87.7%) showed a coefficient below 100%, and the highest coefficient among K-DRGs with more than 30 discharges was 159%.

• Sleep Medicine and Psychophysiology
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• v.25 no.2
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• pp.82-91
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• 2018

Objectives: Synchronous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) has been used to explore sleep stage dependent functional brain networks. Despite a growing number of sleep studies using EEG-fMRI, few studies have conducted network analysis on whole night sleep due to difficulty in data acquisition, artifacts, and sleep management within the MRI scanner. Methods: In order to perform network analysis for whole night sleep, we proposed experimental procedures and data processing techniques for EEG-fMRI. We acquired 6-7 hours of EEG-fMRI data per participant and conducted signal processing to reduce artifacts in both EEG and fMRI. We then generated a functional brain atlas with 68 brain regions using independent component analysis of sleep fMRI data. Using this functional atlas, we constructed sleep level dependent functional brain networks. Results: When we evaluated functional connectivity distribution, sleep showed significantly reduced functional connectivity for the whole brain compared to that during wakefulness. REM sleep showed statistically different connectivity patterns compared to non-REM sleep in sleep-related subcortical brain circuits. Conclusion: This study suggests the feasibility of exploring functional brain networks using sleep EEG-fMRI for whole night sleep via appropriate experimental procedures and signal processing techniques for fMRI and EEG.

• Journal of Cadastre & Land InformatiX
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• v.49 no.1
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• pp.85-101
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• 2019

Recently, thank to the popularization of light-weight drone through the significant developments in computer technologies as well as the advanced automated procedures in photogrammetry, Unmanned Aircraft Systems have led to a growing interest in industry as a whole. Documentation, maintenance, and restoration projects of large scaled cultural property would required accurate 3D phenomenon modeling and efficient visual inspection methods. The object of this study verify on the accuracies achieved of 3D phenomenon reconstruction as well as on the validity of the preservation, maintenance and restoration of large scaled cultural property by UAS photogrammetry. The test object is cltural heritage(treasure 1324) that is the rock-carved standing Bodhisattva in Soraesan Mountain, Siheung, documented in Goryeo Period(918-1392). This standing Bodhisattva has of particular interests since it's size is largest stone Buddha carved in a rock wall and is wearing a lotus shaped crown that is decorated with arabesque patterns. The positioning accuracy of UAS photogrammetry were compared with non-target total station survey results on the check points after creating 3D phenomenal models in real world coordinates system from photos, and also the quantified informations documented by Culture Heritage Administration were compared with UAS on the bodhisattva image of thin lines. Especially, tests the validity of UAS photogrammetry as a alternative method of visual inspection methods. In particular, we examined the effectiveness of the two techniques as well as the relative fluctuation of rock surface for about 2 years through superposition analysis of 3D points cloud models produced by both UAS image analysis and ground laser scanning techniques. Comparison studies and experimental results prove the accuracy and efficient of UAS photogrammetry in 3D phenomenon modeling, maintenance and restoration for various large-sized Cultural Heritage.

• Korean Journal of Environment and Ecology
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• v.33 no.1
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• pp.86-106
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• 2019

We investigated the environmental conditions of natural habitats of T. nuda. The species was found on rocky northern hills ($60{\sim}90^{\circ}$) near the stream where the sea level ranges 95~145m. The average annual temperature of the habitats was lower than other places of South Korea. The differences of the lowest and the highest of the year was significantly huge than any other places. Plants were growing at the edge of stream that water reached but not submerged. Most of plants were found in North, Northeast or Northwest. It is suggested that these species require moist and low sunlight for growth. The common vegetation along with the T. nuda includes Mukdenia rossii, Selaginella rossii, Calamagrostis epigeios, and Rhododendron yedoense f. poukhanense. The dominance values and sociability of T. nuda were below 3 in all studied habitats and the variance of the number of individuals among the habitats was very high. As the optimum habitats for the T. nuda are decreasing due to the extreme precipitation patterns. It is also expected that the number of T. nuda will be decreased in the future. Therefore restoration activity in situ or ex situ must be conducted to conserve this valuable plant species.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.722-732
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• 2021

A recent rapid growth in the number of Chinese international students in Korea has generated interest in the key factors to affect their acculturation in domestic culture. In accordance with the growing interest, the present study aims to empirically test and analyze the effects of demographics (gender, age, the length of stay, the length of Korean language education and the level of Korean language skills), SNS use patterns (personal network sizes and hours of use for Korean and Chinese SNS, and motivations of Korean or Chinese SNS uses), and social support through Korean and Chinese SNS on acculturation for Chinese international students in Korea. A total of 322 Chinese international students in Korea participated in the online survey. Results showed that use of Chinese SNS for entertainment had an negative impact on the acculturation, while the use of Korean SNS had an overall positive impact. Finally, this study suggests that the practitioners regarding the issue of acculturation for international students in Korea should focus more on specific guidelines to help their appropriate SNS uses rather than on to prevent their SNS addiction problems.

• Korean Journal of Environment and Ecology
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• v.36 no.2
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• pp.202-216
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• 2022

Animals exhibit certain behaviors and movement patterns as they react to their internal needs, external stimuli, and surrounding environments. They have a bounded range in which they mostly spend their time, and it is referred to as a home range. Based on the fact that the home range is a critical area for the survival and preservation of species, there has been a growing body of research on developing more precise home range estimation methods to use the estimated ranges as a ground for establishing an effective conservation policy since the early 1940s. Recent rapid advancements in telemetry technology that resulted in the presence of autocorrelation between locations with short time intervals revealed the limitations of the existing estimators. Many novel estimators have been developed to compensate for it by incorporating autocorrelation in calculating home ranges. However, studies on the animal home range are still in their early stage in Korea, and newly developed methodologies have not yet been adopted. Therefore, this study aims to introduce the foreign home range estimation methods and foster domestic research activities on home ranges. Firstly, we compared and contemplated seven estimators by categorizing them into geometrical and statistical methodologies and then divided them into estimators that assume independent observations and those that consider autocorrelation in each category. After that, the home ranges of black-tailed gulls (Larus crassirostris) were calculated using GPS tracking data for the month of June and derived home range estimators by applying the methodology introduced in this study. We analyzed and compared the results to discuss the strengths and weaknesses of each method. Lastly, we proposed a guideline that can help researchers choose an appropriate estimator for home range calculation based on the animal location data characteristics and analysis purpose.

• Journal of the korean academy of Pediatric Dentistry
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• v.48 no.4
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• pp.414-424
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• 2021

The purpose of this retrospective study was to evaluate the skeletal and dental maturity according to the vertical facial type and sex in Korean children in the developmental stage. In total, 184 participants aged 8 - 14 years were selected and divided into three groups based on the mandibular plane angle. For the comparison between the sexes, the three groups were each divided into male and female subgroups. The skeletal and dental maturity were assessed using lateral cephalograms, hand-wrist radiographs and panoramic radiographs. The vertical growth group showed significantly greater cervical vertebral and hand-wrist maturity than that in the horizontal growth group. Dental maturity was the highest in the vertical growth group. Girls showed greater skeletal maturity than boys, and no distinct difference was observed between the dental maturity of the sexes. Analysis of the vertical facial type in children can provide ancillary indicators that may help determine the optimal timing for orthodontic treatment initiation. Earlier initiation of orthodontic treatment may be considered for patients with vertical facial growth patterns.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.449-456
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• 2021

An intrusion detection system is a technology that detects abnormal behaviors that violate security, and detects abnormal operations and prevents system attacks. Existing intrusion detection systems have been designed using statistical analysis or anomaly detection techniques for traffic patterns, but modern systems generate a variety of traffic different from existing systems due to rapidly growing technologies, so the existing methods have limitations. In order to overcome this limitation, study on intrusion detection methods applying various machine learning techniques is being actively conducted. In this study, a comparative study was conducted on data preprocessing techniques that can improve the accuracy of anomaly detection using NGIDS-DS (Next Generation IDS Database) generated by simulation equipment for traffic in various network environments. Padding and sliding window were used as data preprocessing, and an oversampling technique with Adversarial Auto-Encoder (AAE) was applied to solve the problem of imbalance between the normal data rate and the abnormal data rate. In addition, the performance improvement of detection accuracy was confirmed by using Skip-gram among the Word2Vec techniques that can extract feature vectors of preprocessed sequence data. PCA-SVM and GRU were used as models for comparative experiments, and the experimental results showed better performance when sliding window, skip-gram, AAE, and GRU were applied.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.401-418
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• 2023

Recently, the socio-economic interest in Large Language Models (LLMs) has been growing due to the emergence of ChatGPT. As a type of generative AI, LLMs have reached the level of script creation. In this regard, it is important to address the issue of discrimination (sexism, racism, religious discrimination, ageism, etc.) in the performing arts in general or in specific performing arts works or organizations in a large language model that will be widely used by the general public and professionals. However, there has not yet been a full-scale investigation and discussion on the issue of discrimination in the performing arts in large-scale language models. Therefore, the purpose of this study is to textually analyze the perceptions of discrimination issues in the performing arts from LMMs and to derive implications for the performing arts field and the development of LMMs. First, BBQ (Bias Benchmark for QA) questions and measures for nine discrimination issues were used to measure the sensitivity to discrimination of the giant language models, and the answers derived from the representative giant language models were verified by performing arts experts to see if there were any parts of the giant language models' misperceptions, and then the giant language models' perceptions of the ethics of discriminatory views in the performing arts field were analyzed through the content analysis method. As a result of the analysis, implications for the performing arts field and points to be noted in the development of large-scale linguistic models were derived and discussed.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1243-1244
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• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.