• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1131-1139
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• 2020

The static analysis of the source code is to find the remaining security weaknesses for a wide range of source codes. The static analysis tool is used to check the result, and the static analysis expert performs spying and false detection analysis on the result. In this process, the amount of analysis is large and the rate of false positives is high, so a lot of time and effort is required, and a method of efficient analysis is required. In addition, it is rare for experts to analyze only the source code of the line where the defect occurred when performing positive/false detection analysis. Depending on the type of defect, the surrounding source code is analyzed together and the final analysis result is delivered. In order to solve the difficulty of experts discriminating positive and false positives using these static analysis tools, this paper proposes a method of determining whether or not the security weakness found by the static analysis tools is a spy detection through artificial intelligence rather than an expert. In addition, the optimal size was confirmed through an experiment to see how the size of the training data (source code around the defects) used for such machine learning affects the performance. This result is expected to help the static analysis expert's job of classifying positive and false positives after static analysis.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.1-8
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• 2023

If there is a defect in the wheel bearing, which is a major part of the car, it can cause problems such as traffic accidents. In order to solve this problem, big data is collected and monitoring is conducted to provide early information on the presence or absence of wheel bearing failure and type of failure through predictive diagnosis and management technology. System development is needed. In this paper, to implement such an intelligent wheel hub bearing maintenance system, we develop an embedded system equipped with sensors for monitoring reliability and soundness and algorithms for predictive diagnosis. The algorithm used acquires vibration signals from acceleration sensors installed in wheel bearings and can predict and diagnose failures through big data technology through signal processing techniques, fault frequency analysis, and health characteristic parameter definition. The implemented algorithm applies a stable signal extraction algorithm that can minimize vibration frequency components and maximize vibration components occurring in wheel bearings. In noise removal using a filter, an artificial intelligence-based soundness extraction algorithm is applied, and FFT is applied. The fault frequency was analyzed and the fault was diagnosed by extracting fault characteristic factors. The performance target of this system was over 12,800 ODR, and the target was met through test results.

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.5
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• pp.474-482
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• 2002

One dimensional Radiometric scanning system was fabricated and tested as a filmless radiographic inspection system, which could be applied to the evaluation of the corrosion and deposits in the pipeline. This system is composed of the single radioactive source of the collimated focusing beam, and single scintillation detector of BGO, and the mechanical scanning system to transport and align the source and detector, and the operating software to automatically control the mechanical scan system. The performance of the system was simulated using GEANT4 software. This system is applied to one specimen having an artificial falw(flat bottom hole) in the pipe and the other specimen with thickness variation. For the inspection by using the radioactive source in the pipeline, it is possible to evaluate the corrosion and deposits in real time and without film.

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.1
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• pp.38-44
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• 2002

MFL method is a qualitative inspection tool and is a reliable, fast and economical NDT method. The application of MFL method to the inspection of storage tank floor plates has been shown to be a viable means. Examination of tank floors previously depended primarily upon ultrasonic test methods that required slow and painstaking application. Therefor most ultrasonic inspection of storage tank has been limited to spot testing only. Our NDE group have developed magnetic flux leakage system to overcome limitation of ultrasonic test. The developed system consists of magnetic yoke, array sensor, crawler and software. It is proved that the system is able to detect artificial flaw like 3.2mm diameter, 1.2mm depth in 6mm thick steel plate.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.10
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• pp.799-807
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• 2002

The vibration signal can give an indication of the condition of rotating machinery, highlighting potential faults such as unbalance, misalignment and bearing defects. The features in the vibration signal provide an important source of information for the faults diagnosis of rotating machinery. When additional training data become available after the initial training is completed, the conventional neural networks (NNs) must be retrained by applying total data including additional training data. This paper proposes the fault diagnostics algorithm using the ART-Kohonen network which does not destroy the initial training and can adapt additional training data that is suitable for the classification of machine condition. The results of the experiments confirm that the proposed algorithm performs better than other NNs as the self-organizing feature maps (SOFM) , learning vector quantization (LYQ) and radial basis function (RBF) NNs with respect to classification quality. The classification success rate for the ART-Kohonen network was 94 o/o and for the SOFM, LYQ and RBF network were 93 %, 93 % and 89 % respectively.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2013.10a
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• pp.366-369
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• 2013

위치, 항법 및 시각정보가 육상, 해상, 항공 등 지구상 거의 모든 공간에서 다양한 목적으로 활용되고 있는 위성항법시스템(GNSS, Global Navigation Satelite System)은 그 중요성이 더욱 높아지고 있으며 미국이 제공해 주는 GPS의 고의잡음(SA)제거 및 성능 향상을 통하여 GNSS를 이용한 응용분야의 발전은 더욱 확대되고 있는 실정이다. 이러한 위성항법시스템의 P.N.T(Position, Navigation and Timing) 서비스 기능을 사회의 전박적인 주요 인프라 시설에 기초적인 원리의 정확성을 더하고 지속성과 무결함성을 제공할 수 있기 때문에 신뢰성을 가지고 여러분야에 활용되고 있으나 지상에서 약20,200km 고도에 위치한 인공위성에서 미약한 신호를 송신함으로써 GNSS 수신기가 의도적이든 비의도적이든 동일한 주파수 밴드의 신호에 영향을 받을 경우 정상적인 역할을 할 수 없다. 우리나라의 경우 P.N.T 서비스 기반 자체가 GPS에 전적으로 의존하고 있으며 유사시 이를 대체할 항법 시스템을 별도로 갖추고 있지 않고 있어 미국의 고의적인 GPS 서비스의 중단이나 주변국가의 방해전파 송신으로 인한 GPS 신호 중단사태가 발생할 경우에는 우리나라 국가 주요 인프라에 치명적인 피해를 입힐 수 있기 때문에 대체항법을 시스템을 구축하여 독자적인 P.N.T 서비스를 제공할 수 있는 시스템 구축이 국가적인 차원에서 절실하게 필요하다.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.1
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• pp.60-68
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• 2007

It has been conducted by laser vision sensor for weldability estimation of front-bead after doing high speed butt laser welding of any condition. It has been developed a real time GUI(Graphic User Interface) system for weldability application in the basis of texts and field qualify levels. In the reference of bead imperfections, defects absolute position and defects intensity index of front-bead in the basis of formability reference, it has been produced a weldability estimation and defects intensity index of back-bead by back propagation neural network. In the result of by comparing measuring data by laser vision sensor of back-bead and data by back propagation neural network of one, it has been shown the similar results. Finally, under knowledge of welding condition in production line, it has been conducted a weldability estimation of back-bead only in knowledge of informations of front-bead data without using laser vision sensor or welding inspection experts and furthermore it can be used data for final inspection results of back-bead.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.2
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• pp.170-177
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• 2009

The objective of this study is to evaluate an acoustic emission (AE) source characterization and fracture behavior of the SM45C steel by using back-propagation neural network (BPN). In previous research Ref. [8] about k-nearest neighbor classifier (k-NNC) continuity, we used K-means clustering method as an unsupervised learning method for obtaining multi-variate AE main data sets, such as AE counts, energy, amplitude, risetime, duration and counts to peak. Similarly, we applied k-NNC and BPN as a supervised learning method for obtaining multi-variate AE working data sets. According to the error of convergence for determinant criterion Wilk's ${\lambda}$, heuristic criteria D&B(Rij) and Tou values are discussed. As a result, in k-NNC before fracture signal is detected or when fracture signal is detected, showed that produce some empty classes in BPN. And we confirmed that could save trouble in AE signal processing if suitable error of convergence or acceptable encoding error give to BPN.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.394-399
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• 2012

In recent years, MEMS sensors show huge attraction in machine condition monitoring, which have advantages in power, size, cost, mobility and flexibility. They can integrate with smart sensors and MEMS sensors are batch product. So the prices are cheap. And the suitability of it for condition monitoring is researched by experimental study. This paper presents a comparative study and performance test of classification of MEMS sensors in target machine fault classification by 3 intelligent classifiers. We attempt to signal validation of MEMS sensor accuracy and reliability and performance comparisons of classifiers are conducted. MEMS accelerometer and MEMS current sensors are employed for experiment test. In addition, a simple feature extraction and cross validation methods were applied to make sure MEMS sensors availabilities. The result of application is good for using fault classification.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.623-625
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• 2018

As the issue of particulate matter spreads, services for providing particulate matter information in real time are increasing. However, when a sensor node for collecting particulate matter is defective, a corresponding service may not be provided. To solve these problems, it is necessary to predict and deduce particulate matter. In this paper, a particulate matter prediction model is designed using artificial neural network algorithm based on past particulate matter and meteorological data to predict particulate matter. Also, the prediction results are compared by learning the input data of the model in the design stage.