• Database Research
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• v.34 no.3
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• pp.58-68
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• 2018

Distributed computing helps to efficiently store and process large data on a cluster of multiple machines. The performance of distributed computing is greatly influenced depending on the state of the servers constituting the distributed system. In this paper, we propose a self-diagnosis system that collects log data in a distributed system, detects anomalies and visualizes the results in real time. First, we divide the self-diagnosis process into five stages: collecting, delivering, analyzing, storing, and visualizing stages. Next, we design a real-time self-diagnosis system that meets the goals of real-time, scalability, and high availability. The proposed system is based on Apache Flume, Apache Kafka, and Apache Storm, which are representative real-time distributed techniques. In addition, we use simple but effective moving average and 3-sigma based anomaly detection technique to minimize the delay of log data processing during the self-diagnosis process. Through the results of this paper, we can construct a distributed real-time self-diagnosis solution that can diagnose server status in real time in a complicated distributed system.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.3
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• pp.279-292
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• 2022

Unexpected anomalies in the geotechnical investigation at design stage may cause problems during tunnel excavation. Therefore, it is important to predict the ground condition ahead of a tunnel face during tunnel excavation in order to prevent tunnel collapse. Despite the fulfillment of an electrical resistivity survey at the tunnel face, the existing electrical resistivity survey program can produce distorted results by the limitation of tunnel modelling. In this background, this study develops a modelling program for an electrical resistivity survey considering the tunnel shape. Numerical simulation and inverse calculation were performed for the electrical resistivity survey in the tunnel using the developed program. As a result, it was proved that the developed program could predict accurately the anomalous object's location and condition ahead of the tunnel face.

• Journal of the Korean Geotechnical Society
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• v.38 no.11
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• pp.87-95
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• 2022

The geotechnical information constructed in the National Geotechnical Information DB System has been extensively used in design, construction, underground safety management, and disaster assessment. However, it is necessary to refine the geotechnical information because it has nearly 300,000 established cases containing a lot of missing or incorrect information. This research proposes a method for automatic quality control of geotechnical information using a fully connected neural network. Significantly, the anomalies in geotechnical information were detected using a database combining the standard penetration test results and strata information of Seoul. Consequently, the misclassification rate for the verification data is confirmed as 5.4%. Overall, the studied algorithm is expected to detect outliers of geotechnical information effectively.

• Design & Manufacturing
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• v.16 no.4
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• pp.52-59
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• 2022

Currently, Korea is an aging society and is expected to become a super-aged society in about four years. X-ray devices are widely used for early diagnosis in hospitals, and many X-ray technologies are being developed. The development of X-ray device technology is important, but it is also important to increase the reliability of the device through accurate data management. Sensor nodes such as temperature, voltage, and current of the diagnosis device may malfunction or transmit inaccurate data due to various causes such as failure or power outage. Therefore, in this study, the temperature, tube voltage, and tube current data related to each sensor and detection circuit of the diagnostic X-ray imaging device were measured and analyzed. Based on QC data, device failure prediction and diagnosis algorithms were designed and performed. The fault diagnosis algorithm can configure a simulator capable of setting user parameter values, displaying sensor output graphs, and displaying signs of sensor abnormalities, and can check the detection results when each sensor is operating normally and when the sensor is abnormal. It is judged that efficient device management and diagnosis is possible because it monitors abnormal data values (temperature, voltage, current) in real time and automatically diagnoses failures by feeding back the abnormal values detected at each stage. Although this algorithm cannot predict all failures related to temperature, voltage, and current of diagnostic X-ray imaging devices, it can detect temperature rise, bouncing values, device physical limits, input/output values, and radiation-related anomalies. exposure. If a value exceeding the maximum variation value of each data occurs, it is judged that it will be possible to check and respond in preparation for device failure. If a device's sensor fails, unexpected accidents may occur, increasing costs and risks, and regular maintenance cannot cope with all errors or failures. Therefore, since real-time maintenance through continuous data monitoring is possible, reliability improvement, maintenance cost reduction, and efficient management of equipment are expected to be possible.

• Convergence Security Journal
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• v.24 no.2
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• pp.9-17
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• 2024

This paper examines the security threats to enterprise Generative Artificial Intelligence systems and proposes countermeasures. As AI systems handle vast amounts of data to gain a competitive edge, security threats targeting AI systems are rapidly increasing. Since AI security threats have distinct characteristics compared to traditional human-oriented cybersecurity threats, establishing an AI-specific response system is urgent. This study analyzes the importance of AI system security, identifies key threat factors, and suggests technical and managerial countermeasures. Firstly, it proposes strengthening the security of IT infrastructure where AI systems operate and enhancing AI model robustness by utilizing defensive techniques such as adversarial learning and model quantization. Additionally, it presents an AI security system design that detects anomalies in AI query-response processes to identify insider threats. Furthermore, it emphasizes the establishment of change control and audit frameworks to prevent AI model leakage by adopting the cyber kill chain concept. As AI technology evolves rapidly, by focusing on AI model and data security, insider threat detection, and professional workforce development, companies can improve their digital competitiveness through secure and reliable AI utilization.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.1
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• pp.53-61
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• 2024

Global Navigation Satellite Systems are expected to meet system-defined integrity requirements when users utilize the system for safety critical applications. While the guaranteed integrity performance of GPS and Galileo is publicly available, their integrity assurance procedure and related methodology have not been released to the public in an official document format. This paper summarizes the integrity assurance procedures of Global Positioning System (GPS) and Galileo, which were utilized during their system development, through a literature survey of their integrity assurance methodology. GPS Block II assures system integrity using the following methods: continuous performance monitoring and maintenance on Space Segment (SS) and Control Segment (CS), through a cause and effect analysis of anomalies and a failure analysis. In GPS Block III, to achieve more stringent integrity performance, safety requirements are integrated into the system design and development from its starting phase to the final phase. Galileo's integrity performance is provided in the Integrity Support Message (ISM) format, as Galileo utilizes a Dual Frequency Multi Constellation (DFMC) Satellite Based Augmentation System (SBAS) and Advanced Receiver Autonomous Integrity Monitoring (ARAIM) to serve safety critical applications. The integrity performance of Galileo is ensured by using a methodology similar to GPS Block II (i.e. continuous performance monitoring and maintenance on the system). The integrity assurance procedures reviewed in this paper can be utilized for a new satellite navigation system that will be developed in the near future.

• Journal of Advanced Navigation Technology
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• v.28 no.1
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• pp.1-14
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• 2024

Global navigation satellite system (GNSS) providing position, navigation and timing (PNT) services consist of satellite, ground, and user systems. Monitoring stations, a key element of the ground segment, play a crucial role in continuously collecting satellite navigation signals for service provision and fault detection. These stations detect anomalies such as threats to the signal-in-space (SIS) of satellites, receiver issues, and local threats. They deliver received data and detection results to the master station. This paper introduces the main monitoring algorithms and measurement pre-processing processes for quality assessment and fault detection of received satellite signals in current satellite navigation system monitoring stations. Furthermore, it proposes a strategy for the development of components, architecture, and algorithms for the new regional navigation satellite system (RNSS) monitoring stations.

• Journal of Distribution Science
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• v.13 no.9
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• pp.37-45
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• 2015

Purpose - This paper's aim is to investigate whether or not gross profitability explains the cross-sectional variation of the stock returns in the Korean stock market. Gross profitability is an alternative profitability measure proposed by Novy-Marx in 2013 to predict cross-sectional variation of stock returns in the US. He shows that the gross profitability adds explanatory power to the Fama-French 3 factor model. Interestingly, gross profitability is negatively correlated with the book-to-market ratio. By confirming the gross profitability premium in the Korean stock market, we may provide some implications regarding the well-known value premium. In addition, our empirical results may provide opportunities for the fund distribution industry to promote brand new styles of funds. Research design, data, and methodology - For our empirical analysis, we collect monthly market prices of all the companies listed on the Korea Composite Stock Price Index (KOSPI) of the Korea Exchanges (KRX). Our sample period covers July1994 to December2014. The data from the company financial statementsare provided by the financial information company WISEfn. First, using Fama-Macbeth cross-sectional regression, we investigate the relation between gross profitability and stock return performance. For robustness in analyzing the performance of the gross profitability strategy, we consider value weighted portfolio returns as well as equally weighted portfolio returns. Next, using Fama-French 3 factor models, we examine whether or not the gross profitability strategy generates excess returns when firmsize and the book-to-market ratio are controlled. Finally, we analyze the effect of firm size and the book-to-market ratio on the gross profitability strategy. Results - First, through the Fama-MacBeth cross-sectional regression, we show that gross profitability has almost the same explanatory power as the book-to-market ratio in explaining the cross-sectional variation of the Korean stock market. Second, we find evidence that gross profitability is a statistically significant variable for explaining cross-sectional stock returns when the size and the value effect are controlled. Third, we show that gross profitability, which is positively correlated with stock returns and firm size, is negatively correlated with the book-to-market ratio. From the perspective of portfolio management, our results imply that since the gross profitability strategy is a distinctive growth strategy, value strategies can be improved by hedging with the gross profitability strategy. Conclusions - Our empirical results confirm the existence of a gross profitability premium in the Korean stock market. From the perspective of the fund distribution industry, the gross profitability portfolio is worthy of attention. Since the value strategy portfolio returns are negatively correlated with the gross profitability strategy portfolio returns, by mixing both portfolios, investors could be better off without additional risk. However, the profitable firms are dissimilar from the value firms (high book-to-market ratio firms); therefore, an alternative factor model including gross profitability may help us understand the economic implications of the well-known anomalies such as value premium, momentum, and low volatility. We reserve these topics for future research.

• Archives of Plastic Surgery
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• v.37 no.5
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• pp.695-698
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• 2010

Purpose: Supernumerary nipple or polythelia is one of the developmental anomalies occurring at the embryonic stage and this anomaly usually arises from the milk line. While this atypical feature is determined during early developmental stage, it may not come out obviously or become troublesome until puberty or lactation. Moreover, sometimes it is confused with a pigmented nevus. Methods: Case 1, a 18-year-old woman with intramammary supernumerary breast consisted of another nipple with middle sized areola on the right lower breast was admitted for a $2.8{\times}3.1\;cm$-sized mass on the right breast which was starting appeared 1 year earlier. The preliminary cytological examination of the material obtained by needle aspiration biopsy from the mass was revealed by fibroadenoma with no malignant change. The patient had the surgical excision of the mass and accessory breast. Case 2, a 16 year-old woman admitted for intra-areolar polythelia of the left breast, even she doesn't have any family history of polythelia. Since she wanted surgical correction of her atypical nipple for aesthetic and psychological reasons, we reconstructed the areola using transposition flaps in an S-plasty design. Results: Case 1, the excised supernumerary nipple showed following histological features. In the superficial layer, an acanthotic and hyperpigmented epithelium with elongated rete ridges was found. In the dermis, there were follicles with hairs surrounded by hypertrophic sebaceous glands. In the deepest portion, abundant secretory glomerules and excretory ducts of apocrine gland type were observed. Case 2, follow-up visits 3 months after the procedure showed a satisfactory result with good shape and projection of the nipple. Conclusion: We report two cases of aberrant mammary tissue who underwent surgical correction, including complete breast (with nipple, areola, and glandular tissue) and intra-areolar polythelia according to the Kajava's classification, and the results were satisfactory.

• The Journal of Engineering Geology
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• v.25 no.1
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• pp.21-33
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• 2015

This study tests the potential of detecting small-magnitude earthquakes (~M3.0) and their precursors using a long-term groundwater-monitoring database. In groundwater records from April to June 2012, abnormal changes in water level, temperature, and electrical conductivity were identified in the bedrock monitoring wells of the Gimcheon-Jijwa, Gangjin-Seongjeon, and Gongju-Jeongan stations. These anomalies could be attributed to the M3.1 earthquake that occurred in the Youngdeok area on May 30th, although no linear relationship was found between the scale of changes and the distance between each monitoring station and the epicenter, which is attributed in part to the wide screen design of the monitoring wells. Groundwater monitoring networks designed specifically for monitoring earthquake impacts could provide better information on the safety of underground space and on the security of emergency water-resources in earthquake disaster areas.