• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.10 no.1
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• pp.15-19
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• 2014

A Failure or degradation of reactor upper head penetration is a recurring problem due to long term operation at nuclear power plants. And a flaw in the reactor upper head penetration has caused unplanned plant shutdown for repair as well as high economic impact on the plants. Consequently, a detection of flaws is of the utmost importance. Prior to the replacement of reactor upper head penetration, some utilities have repaired the flaws of reactor upper head penetration generated by overlay weld. Until now, only the base metal in reactor upper head penetration has been inspected according to 10 CFR 50.55a and ASME code case N-729-1. Accordingly, it is difficult to detect manufacturing defects and repair defects in overlay weld. This paper presents a case study on the application of Time of Flight Diffraction technique for reactor head penetration mockup with artificial flaws in overlay weld. This study offers a way to understand the flaws detected in reactor upper head penetration overlay weld.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.11 no.2
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• pp.1-5
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• 2015

A failure or degradation of reactor upper head penetration is a troublesome problem at Nuclear Power Plants. A flaw in the reactor upper head penetration can result in unplanned plant shutdown for repair, and cause serious economic losses on the plants. Consequently, a detection of flaws is a matter of more importance. Until now, only the base metal, not including J-groove weld, in reactor upper head penetration has been inspected in accordance with 10 CFR 50.55a and ASME code case N-729-1 requirements. Accordingly, it is rather difficult to detect manufacturing defects and repair defects in J-groove weld. This paper presents a case study on the application of Time of Flight Diffraction UT technique to examine the J-groove weld in reactor head penetration using reactor head penetration mockup with artificial flaws. We expect that this study result will offer a way to understand the non-destructive examination technology for J-groove weld in reactor upper head penetration.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.5
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• pp.519-532
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• 2024

Anomaly detection for the penetration rate of tunnel boring machines (TBMs) is crucial for effective risk management in TBM tunnel projects. However, previous machine learning models for predicting the penetration rate have struggled with imbalanced data between normal and abnormal penetration rates. This study aims to enhance the performance of machine learning-based anomaly detection for the penetration rate by utilizing a data augmentation technique to address this data imbalance. Initially, six input features were selected through correlation analysis. The lowest and highest 10% of the penetration rates were designated as abnormal classes, while the remaining penetration rates were categorized as a normal class. Two prediction models were developed, each trained on an original training set and an oversampled training set constructed using SMOTE (synthetic minority oversampling technique): an XGB (extreme gradient boosting) model and an XGB-SMOTE model. The prediction results showed that the XGB model performed poorly for the abnormal classes, despite performing well for the normal class. In contrast, the XGB-SMOTE model consistently exhibited superior performance across all classes. These findings can be attributed to the data augmentation for the abnormal penetration rates using SMOTE, which enhances the model's ability to learn patterns between geological and operational factors that contribute to abnormal penetration rates. Consequently, this study demonstrates the effectiveness of employing data augmentation to manage imbalanced data in anomaly detection for TBM penetration rates.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.203-207
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• 1999

In this paper we propose the automated generation algorithm of penetration scenario using association mining technique. Until now known intrusion detections are classified into anomaly detection and misuse detection. The former uses statistical method, features selection, neural network method in order to decide intrusion, the latter uses conditional probability, expert system, state transition analysis, pattern matching for deciding intrusion. In proposed many intrusion detection algorithms unknown penetrations are created and updated by security experts. Our algorithm automatically generates penetration scenarios applying association mining technique to state transition technique. Association mining technique discovers efficient and useful unknown information in existing data. In this paper the algorithm we propose can automatically generate penetration scenarios to have been produced by security experts and is easy to cope with intrusions when it is compared to existing intrusion algorithms. Also It has advantage that maintenance cost is not high.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.10
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• pp.521-528
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• 2007

This paper proposes a new method for the high- speed penetration detection between the 3D human body model and the 3D cloth model using a virtual cylinder, and for the correction of the 3D cloth model. Penetration sometimes occurs locally, when the cloth model is adopted geometrically to the body. This method establishes the virtual cylinder surrounding the body model and the cloth model, and selects at a time the candidates of the penetrated points using the virtual cylinder. Finally, the penetrated points are detected among the candidates. Shift of the vertices or division of the edges in the penetrated points can correct the cloth model geometrically. This method works faster than the physical-based method. The latter requires the repeated detection of the penetrated points using bounding volume and the repeated corrections of the cloth model using dynamics.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.38-50
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• 2023

Recent advances in artificial intelligence (AI) technology have enabled the integration of AI technology into image sensors, such as Closed-Circuit Television (CCTV), to detect specific traffic incidents. However, most incident detection methods have been carried out using fixed equipment. Therefore, there have been limitations to incident detection for all roadways. Nevertheless, the development of mobile image collection and analysis technology, such as image sensors and edge-computing, is spreading. The purpose of this study is to estimate the reducing effect of the incident detection time according to the introduction level of mobile image collection and analysis equipment (or connected vehicles). To carry out this purpose, we utilized data on the number of incidents collected by the Suwon branch of the Gyeongbu expressway in 2021. The analysis results showed that if the market penetration rate (MPR) of connected vehicles is 4% or higher for two-lane expressway and 3% or higher for three-lane expressways, the incident detection time was less than one minute. Furthermore, if the MPR is 0.4% or higher for two-lane expressways and 0.2% or higher for three-lane expressways, the incident detection time decreased compared to the average incident detection time announced by the Korea Expressway Corporation for both two-lane and three-lane expressways.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.2
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• pp.371-384
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• 2001

In these days, it is continuously increased to the intrusion of system in internet environment. The methods of intrusion detection can be largely classified into anomaly detection and misuse detection. The former uses statistical methods, features selection method in order to detect intrusion, the latter uses conditional probability, expert system, state transition analysis, pattern matching. The existing studies for IDS(intrusion detection system) use combined methods. In this paper, we propose a new intrusion detection algorithm combined both state transition analysis and association mining techniques. For the intrusion detection, the first step is generated state table for transmitted commands through the network. This method is similar to the existing state transition analysis. The next step is decided yes or no for intrusion using the association mining technique. According to this processing steps, we present the automated generation algorithm of the penetration scenarios.

• Geomechanics and Engineering
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• v.24 no.1
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• pp.1-14
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• 2021

In this study, a new approach using electrical resistivity measurement was proposed to detect grout penetration and to evaluate the grouting performance for such as waterproof efficiency in single rock fracture. For this purpose, an electrical resistivity monitoring system was designed to collect multi-channel data in real time. This was applied to a system for grout injection/penetration using a transparent fracture replica with various aperture sizes and water-cement mix ratio. The electrical resistivity was measured under various grout penetration conditions in real time, which results were directly compared to the visual observation images of grout penetration/distribution. Moreover, the grouting success status after the curing process was evaluated by measuring the electrical resistivity in relation to changes in frequency in fracture cells where grout injection and penetration were completed. Consequently, it was determined that the electrical resistivity monitoring system could be applied effectively to the detection of successful penetration of grouting into a target area and to actual field evaluation of the grouting performance and long-term stability of underground rock structures.

• Proceedings of the KWS Conference
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• 1996.05a
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• pp.104-107
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• 1996

• Journal of the Korean Physical Society
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• v.73 no.11
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• pp.1764-1773
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• 2018

A-PET is a quad-head PET scanner developed for use in small-animal imaging. The dimensions of its volumetric field of view (FOV) are $46.1{\times}46.1{\times}46.1mm^3$ and the gap between the detector modules has been minimized in order to provide a highly sensitive system. However, such a small FOV together with the quad-head geometry causes image quality degradation. The main factor related to image degradation for the quad-head PET is the mispositioning of events caused by the penetration effect in the detector. In this paper, we propose a precise method for modelling the system at the high spatial resolution of the A-PET using a LOR (line of response) based ML-EM (maximum likelihood expectation maximization) that allows for penetration effects. The proposed system model provides the detection probability of every possible ray-path via crystal sampling methods. For the ray-path sampling, the sub-LORs are defined by connecting the sampling points of the crystal pair. We incorporate the detection probability of each sub-LOR into the model by calculating the penetration effect. For comparison, we used a standard LOR-based model and a Monte Carlo-based modeling approach, and evaluated the reconstructed images using both the National Electrical Manufacturers Association NU 4-2008 standards and the Geant4 Application for Tomographic Emission simulation toolkit (GATE). An average full width at half maximum (FWHM) at different locations of 1.77 mm and 1.79 mm are obtained using the proposed system model and standard LOR system model, which does not include penetration effects, respectively. The standard deviation of the uniform region in the NEMA image quality phantom is 2.14% for the proposed method and 14.3% for the LOR system model, indicating that the proposed model out-performs the standard LOR-based model.