• Archives of Plastic Surgery
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• v.39 no.4
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• pp.329-332
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• 2012

Background Ankyloglossia or tongue-tie is a congenital anomaly characterized by an abnormally short lingual frenum. Its prevalence in the newborn population is approximately 4%. Its mode of inheritance has been studied in some articles, but no conclusion has been established. Also, no relevant report has been published in Korea. This study was conducted to elucidate the genetic inheritance of ankyloglossia via pedigree analysis. Methods In this study, 149 patients with no other congenital anomaly who underwent frenuloplasty between March 2001 and March 2010 were studied. Pedigrees were made via pre- or post-operative history taking, and patients with uncertain histories were excluded. In the patient group that showed a hereditary nature, the male-to-female ratio, inheritance rate, and pattern of inheritance were investigated. Results One hundred (67.11%) of the patients were male and 49 (32.89%) were female (male-female ratio=2.04:1). Ninety-one (61.07%) patients reported no other relative with ankyloglossia, and 58 (38.93%) patients had a relative with this disease. The inheritance rate was 20.69% in the 58 cases with a hereditary nature. In the group with no family history of ankyloglossia, the male-female ratio was 3.79:1, which significantly differed from that of the group with a family history of ankyloglossia. X-chromosome mediated inheritance and variation in the gene expression was revealed in the pedigree drawn for the groups with hereditary ankyloglossia. Conclusions Ankyloglossia has a significant hereditary nature. Our data suggest X-linked inheritance. This study with 149 patients, the first in Korea, showed X-linked inheritance in patients with a sole anomaly.

• Convergence Security Journal
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• v.18 no.5_1
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• pp.45-51
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• 2018

As data utilization and importance becomes important, data-related accidents and damages are gradually increasing. Especially, insider threats are the most harmful threats. And these insider threats are difficult to detect by traditional security systems, so rule-based abnormal behavior detection method has been widely used. However, it has a lack of adapting flexibly to changes in new attacks and new environments. Therefore, in this paper, we propose an adaptive anomaly movement detection framework based on a statistical Markov model to detect insider threats in advance. This is designed to minimize false positive rate and false negative rate by adopting environment factors that directly influence the behavior, and learning data based on statistical Markov model. In the experimentation, the framework shows good performance with a high F2-score of 0.92 and suspicious behavior detection, which seen as a normal behavior usually. It is also extendable to detect various types of suspicious activities by applying multiple modeling algorithms based on statistical learning and environment factors.

• Journal of Internet Computing and Services
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• v.8 no.2
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• pp.55-65
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• 2007

BcN is a high-quality broadband network for multimedia services integrating telecommunication, broadcasting, and Internet seamlessly at anywhere, anytime, and using any device. BcN is Particularly vulnerable to intrusion because it merges various traditional networks, wired, wireless and data networks. Because of this, one of the most important aspects in BcN is security in terms of reliability. So, in this paper, we suggest the sharing mechanism of security data among various service networks on the BcN. This distributed, hierarchical architecture enables BcN to be robust of attacks and failures, controls data traffic going in and out the backbone core through IP edge routers integrated with IDRS. Our proposed anomaly detection scheme on IDRS for BcN service also improves detection rate compared to the previous conventional approaches.

• Journal of Korea Port Economic Association
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• v.37 no.1
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• pp.179-196
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• 2021

Port congestion rate at Busan Port has increased for three years. Port congestion causes container reconditioning, which increases the dockyard labor's work intensity and ship owner's waiting time. If congestion is prolonged, it can cause a drop in port service levels. Therefore, this study proposed an anomaly detection method using ARIMA(Autoregressive Integrated Moving Average) model with the daily volume data from 2013 to 2020. Most of the research that predicts port volume is mainly focusing on long-term forecasting. Furthermore, studies suggesting methods to utilize demand forecasting in terms of port operations are hard to find. Therefore, this study proposes a way to use daily demand forecasting for port anomaly detection to solve the congestion problem at Busan port.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.2
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• pp.115-125
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• 2003

As Web services are generally open for external uses and not filtered by Firewall, these result in attacker's target. Web attacks which exploit vulnerable web-applications and malicious users' requests cause economical and social problems. In this paper, we are modelling general web service usages based on user-web-session and detect anomal usages with Bayesian estimation method. Finally we propose SAD(Session Anomaly Detection) for detection unknown web attacks. To evaluate SAD, we made an experiment on attack simulation with web vulnerability scanner, whisker. The results show that the detection rate of SAD is over 90%, which is influenced by several features such as size of window or training set, detection filter method and web topology.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.10 no.4
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• pp.59-71
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• 2000

Due to the advance of computer and communication technology, intrusions or crimes using a computer have been increased rapidly while various information has been provided to users conveniently. As a results, many studies are necessary to detect the activities of intruders effectively. In this paper, a new association algorithm for the anomaly detection model is proposed in the process of generating user\`s normal patterns. It is that more recently observed behavior get more affection on the process of data mining. In addition, by clustering generated normal patterns for each use or a group of similar users, it is possible to identify the usual frequency of programs or command usage for each user or a group of uses. The performance of the proposed anomaly detection system has been tested on various system Parameters in order to identify their practical ranges for maximizing its detection rate.

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.1-6
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• 2021

In the modern society, traffic problems are occurring as vehicle ownership increases. In particular, the incidence of highway traffic accidents is low, but the fatality rate is high. Therefore, a technology for detecting an abnormality in a vehicle is being studied. Among them, there is a vehicle anomaly detection technology using deep learning. This detects vehicle abnormalities such as a stopped vehicle due to an accident or engine failure. However, if an abnormality occurs on the road, it is possible to quickly respond to the driver's location. In this study, we propose a deep learning-based vehicle anomaly detection using road CCTV data. The proposed method preprocesses the road CCTV data. The pre-processing uses the background extraction algorithm MOG2 to separate the background and the foreground. The foreground refers to a vehicle with displacement, and a vehicle with an abnormality on the road is judged as a background because there is no displacement. The image that the background is extracted detects an object using YOLOv4. It is determined that the vehicle is abnormal.

• Smart Structures and Systems
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• v.29 no.1
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• pp.117-127
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• 2022

Data anomalies seriously threaten the reliability of the bridge structural health monitoring system and may trigger system misjudgment. To overcome the above problem, an efficient and accurate data anomaly detection method is desiderated. Traditional anomaly detection methods extract various abnormal features as the key indicators to identify data anomalies. Then set thresholds artificially for various features to identify specific anomalies, which is the artificial experience method. However, limited by the poor generalization ability among sensors, this method often leads to high labor costs. Another approach to anomaly detection is a data-driven approach based on machine learning methods. Among these, the bidirectional long-short memory neural network (BiLSTM), as an effective classification method, excels at finding complex relationships in multivariate time series data. However, training unprocessed original signals often leads to low computation efficiency and poor convergence, for lacking appropriate feature selection. Therefore, this article combines the advantages of the two methods by proposing a deep learning method with manual experience statistical features fed into it. Experimental comparative studies illustrate that the BiLSTM model with appropriate feature input has an accuracy rate of over 87-94%. Meanwhile, this paper provides basic principles of data cleaning and discusses the typical features of various anomalies. Furthermore, the optimization strategies of the feature space selection based on artificial experience are also highlighted.

• The korean journal of orthodontics
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• v.30 no.4 s.81
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• pp.467-473
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• 2000

As a rectangular wire Is inserted into edgewise brackets the wire exerts a force system three-dimensionally. The force system may include bending force in first and second orders and a torsional force in third order Analytical and experimental studies on bending force have been Introduced, but information about torsion is still lack. The purpose of this study was to estimate the torsional moment in the force system of rectangular arch wires through theoretical and experimental studies. Wires most frequently used for third order control were selected as study materials. Cross sections of 0.016x0.022, 0.017x0.025, 0.019x0.025 inch rectangular wires in foot different materials such as stainless steel(Ormco), TMA(Ormco), NiTi(Ormco), and braided stainless steel (DentaFlex, Dentaurum) were used. The torque/twist rate of each test material was calculated using the torsion formula. Torque/twist rate, yield torsional moment, and ultimate torsional moment were measured with a torque gauge. The torsion formula assesses that the torque/twist rate (T/$\theta$) is proportional to the characteristics of material (G) and cross section (J), and is inversely proportional to the length of wire (L). Most experimental results corresponded with the formula. The relative stiffness was calculated for reference to a logical sequence of wire changes.

• Journal of Chest Surgery
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• v.17 no.2
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• pp.305-314
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• 1984

From May 1977 to April 1984, 204 cases of open heart surgery were performed under cardiopulmonary bypass. There were 99 male and 105 female patients ranging in age from 19 months to 58 years. 136 cases [66.7%] were congenital heart disease, and 68 cases [33.3%] were acquired heart disease, which were 66 valvular disease [97.1%], 1 IVC obstruction, and 1 myxoma. There were 136 congenital heart anomaly with 16 operative deaths [11.8%], consisting of 94 acyanotic cases with 7 death [7.4%] and 42 cases of cyanotic cases with 9 deaths [21.4%]. In 66 patients of acquired valvular disease, 52 valves were implanted; 47 mitral valve replacement with 4 death [8.5%] and 5 double valve replacement [MVR+AVR] with 1 death [20%]. Postoperative, warfarin sodium was medicated with checking prothrombin time. Finally, the operative mortality was 11.8% in congenital anomaly, and 11.8% in acquired heart disease, overall mortality rate was 8.5%.