• Journal of Genetic Medicine
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• v.7 no.1
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• pp.37-44
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• 2010

Purpose : Kabuki syndrome is a multiple congenital malformation syndrome with mental retardation. It was named after its characteristic appearance, a face resembling that of an actor in a Kabuki play. To date, six Korean cases of Kabuki syndrome have ever been reported. Here, we present the phenotypic and genetic characteristics of six patients with Kabuki syndrome. Materials and Methods : Between 2003 and 2009, six Korean girls have been diagnosed and followed up as Kabuki syndrome at Center for Genetic Diseases of Ajou University Hospital. Their clinical and laboratory data were collected and analyzed by the retrospective review of medical records. Results : All six patients showed the characteristic facial dysmorphism and developmental delay. Persistent fingertip pads were also found in all patients. Most patients showed postnatal growth retardation (83.3%) and hypotonia (83.3%). Opthalmologic problems were common, particularly for strabismus (83.3%). Congenital heart defects were present in three patients (50%). Skeletal abnormalities including 5th finger shortening (83.3%), clinodactyly (50%), joint hypermobility (50%) and hip dislocation (16.7%) were also observed. There was no patient who had positive family history for Kabuki syndrome. Cytogenetic and molecular cytogenetic analyses including karyotyping and array CGH could not reveal any underlying genetic cause of Kabuki syndrome. Conclusion : Korean patients with Kabuki syndrome showed a broad spectrum of clinical features affecting multiple organ systems. Although clinical manifestations of Kabuki syndrome have been well established, our results failed to detect recurrent chromosome aberrations which could cause Kabuki syndrome. Its natural history and genetic background remains to be further studied for providing appropriate management and genetic counseling.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.248-258
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• 2021

The study analyzed the financial determinants of corporate R&D intensity that require more attention from academics and practitioners in the Korean capital market. Domestic small and medium enterprises (SMEs) may face with developing substitutes by making more R&D investments in scale and scope, given the unprecedented economic conditions such as the limitation of importing core components and materials from other nation(s). KOSDAQ-listed SMEs were selected as sample data, whose R&D expenditures may be less than those of large firms during the post-global financial turmoil period (2010~2018). Static panel data model was applied, along with Tobit and stepwise regression models, for examining the validity of results. Logit, probit, and complementary log-log regressions were also employed for a relative analysis. R&D expenditures in the prior year, the interaction effect between the previous R&D intensity and high-tech sector, firm size, and growth rate were significant to determine R&D intensity. Moreover, a majority of explanatory variables were found to change between the years 2011 and 2018, while time-lagged effects between the R&D intensity and growth rate exist. Results of the study are expected to be used for future research to detect optimal levels of R&D expenditures for the value maximization of SMEs.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.175-182
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• 2020

The non-contact eye tracking is a nonintrusive human-computer interface providing hands-free communications for people with severe disabilities. Recently. it is expected to do an important role in non-contact systems due to the recent coronavirus COVID-19, etc. This paper proposes a novel approach for an eye mouse using an eye tracking method based on a context-aware based AdaBoost multi-region classifier and ASSL algorithm. The conventional AdaBoost algorithm, however, cannot provide sufficiently reliable performance in face tracking for eye cursor pointing estimation, because it cannot take advantage of the spatial context relations among facial features. Therefore, we propose the eye-region context based AdaBoost multiple classifier for the efficient non-contact gaze tracking and mouse implementation. The proposed method detects, tracks, and aggregates various eye features to evaluate the gaze and adjusts active and semi-supervised learning based on the on-screen cursor. The proposed system has been successfully employed in eye location, and it can also be used to detect and track eye features. This system controls the computer cursor along the user's gaze and it was postprocessing by applying Gaussian modeling to prevent shaking during the real-time tracking using Kalman filter. In this system, target objects were randomly generated and the eye tracking performance was analyzed according to the Fits law in real time. It is expected that the utilization of non-contact interfaces.

• Science of Emotion and Sensibility
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• v.25 no.1
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• pp.91-102
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• 2022

As virtual space activities become more common, human-virtual agents such as avatars are more frequently used instead of people, but the uncanny valley effect, in which people feel uncomfortable when they see artifacts that look similar to humans, is an obstacle. In this study, we explored the uncanny valley effect for celebrity avatars. We manipulated the degree of atypicality by adjusting the eye size in photos of celebrities, ordinary people, and their avatars and measured the intensity of the uncanny valley effect. As a result, the uncanny valley effect for celebrities and celebrity avatars appeared to be stronger than the effect for ordinary people. This result is consistent with previous findings that more robust facial representations are formed for familiar faces, making it easier to detect facial changes. However, with real faces of celebrities and ordinary people, as in previous studies, the higher the degree of atypicality, the greater the uncanny valley effect, but this result was not found for the avatar stimulus. This high degree of tolerance for atypicality in avatars seems to be caused by cartoon characters' tendency to have exaggerated facial features such as eyes, nose, and mouth. These results suggest that efforts to reduce the uncanny valley in the virtual space service using celebrity avatars are necessary.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.363-372
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• 2022

Since the recent COVID-19 Pandemic, the ransomware fandom has intensified along with the expansion of remote work. Currently, anti-virus vaccine companies are trying to respond to ransomware, but traditional file signature-based static analysis can be neutralized in the face of diversification, obfuscation, variants, or the emergence of new ransomware. Various studies are being conducted for such ransomware detection, and detection studies using signature-based static analysis and behavior-based dynamic analysis can be seen as the main research type at present. In this paper, the frequency of ".text Section" Opcode and the Native API used in practice was extracted, and the association between feature information selected using K-means Clustering algorithm, Cosine Similarity, and Pearson correlation coefficient was analyzed. In addition, Through experiments to classify and detect worms among other malware types and Cerber-type ransomware, it was verified that the selected feature information was specialized in detecting specific ransomware (Cerber). As a result of combining the finally selected feature information through the above verification and applying it to machine learning and performing hyper parameter optimization, the detection rate was up to 93.3%.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.10
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• pp.63-70
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• 2018

Biomedical signal measurement technology using images has been developed, and researches on respiration signal measurement technology for maintaining life have been continuously carried out. The existing technology measured respiratory signals through a thermal imaging camera that measures heat emitted from a person's body. In addition, research was conducted to measure respiration rate by analyzing human chest movement in real time. However, the image processing using the infrared thermal image may be difficult to detect the respiratory organ due to the external environmental factors (temperature change, noise, etc.), and thus the accuracy of the measurement of the respiration rate is low.In this study, the images were acquired using visible light and infrared thermal camera to enhance the area of the respiratory tract. Then, based on the two images, features of the respiratory tract region are extracted through processes such as face recognition and image matching. The pattern of the respiratory signal is classified through the k-nearest neighbor classifier, which is one of the statistical classification methods. The respiration rate was calculated according to the characteristics of the classified patterns and the possibility of breathing rate measurement was verified by analyzing the measured respiration rate with the actual respiration rate.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.6 no.2
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• pp.103-111
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• 2003

Purpose: This study was undertaken to evaluate the gastroduodenal pathology and Helicobacter pylori infection in children with upper gastrointestinal symptoms. Methods: One hundred and seven pediatric patients with upper gastrointestinal symptoms were undergone endoscopy at the Gyeongsang National University Hospital from June 1990 to April 1991. Histopathologic examination was done by H & E staining of gastric antral biopsy specimen and gastritis was defined according to the Sydney System. Tissue H. pylori status was evaluated with the urease test using Christensen's urea broth and H & E or Warthin-Starry silver staining of gastric antral biopsy specimen. IgG Immunoblotting were also performed to detect specific anti-H. pylori antibody in these patients. Results: The reasons for endoscopy were recurrent abdominal pain, acute abdominal pain, sallow face, hunger pain, and frequent nausea. Variable degrees of gastric mucosal hyperemia were found in most of the patients. Gastric hemorrhagic spots, gastric ulcer, duodenal ulcer, duodenal erosion, and hemorrhagic duodenitis were rare endoscopic findings. Histologic chronic gastritis was found in 88% of 107 patients. Histologic chronic duodenitis was observed in all 99 patients whose tissue were available. Gastric tissue H. pylori was positive in 57% of 107 patients by one of the ureasetest, H & E staining and Warthin-Starry silver staining. However, gastric tissue H. pylori detection rate was lower in the younger age groups. Anti-H. pylori IgG antibodies were detectable in 96% of 107 patients. Conclusion: Chronic gastroduodenitis and anti-H. pylori IgG antibody were ubiquitous in children with upper gastrointestinal symptoms.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1173-1183
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• 2023

Over the past 20 years, Korea's overall import and export cargo volume has increased at an average annual rate of approximately 5.3%. About 99% of the cargo is still being transported by sea. Due to recent increases in maritime cargo volume, congestion in maritime logistics has become challenging due to factors such as the COVID-19 pandemic and conflicts. Continuous monitoring of ports has become crucial. Various ground observation systems and Automatic Identification System (AIS) data have been utilized for monitoring ports and conducting numerous preliminary studies for the efficient operation of container terminals and cargo volume prediction. However, small and developing countries' ports face difficulties in monitoring due to environmental issues and aging infrastructure compared to large ports. Recently, with the increasing utility of artificial satellites, preliminary studies have been conducted using satellite imagery for continuous maritime cargo data collection and establishing ocean monitoring systems in vast and hard-to-reach areas. This study aims to visually detect ships docked at berths in the Busan New Port using high-resolution satellite imagery and quantitatively evaluate berth utilization rates. By utilizing high-resolution satellite imagery from Compact Advanced Satellite 500-1 (CAS500-1), Korea Multi-Purpose satellite-3 (KOMPSAT-3), PlanetScope, and Sentinel-2A, ships docked within the port berths were visually detected. The berth utilization rate was calculated using the total number of ships that could be docked at the berths. The results showed variations in berth utilization rates on June 2, 2022, with values of 0.67, 0.7, and 0.59, indicating fluctuations based on the time of satellite image capture. On June 3, 2022, the value remained at 0.7, signifying a consistent berth utilization rate despite changes in ship types. A higher berth utilization rate indicates active operations at the berth. This information can assist in basic planning for new ship operation schedules, as congested berths can lead to longer waiting times for ships in anchorages, potentially resulting in increased freight rates. The duration of operations at berths can vary from several hours to several days. The results of calculating changes in ships at berths based on differences in satellite image capture times, even with a time difference of 4 minutes and 49 seconds, demonstrated variations in ship presence. With short observation intervals and the utilization of high-resolution satellite imagery, continuous monitoring within ports can be achieved. Additionally, utilizing satellite imagery to monitor changes in ships at berths in minute increments could prove useful for small and developing country ports where harbor management is not well-established, offering valuable insights and solutions.