• 한국철도학회:학술대회논문집
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• 한국철도학회 2007년도 춘계학술대회 논문집
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• pp.1729-1734
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• 2007

Recently, as the feasibility study shows that trans-Korea railway and trans-continental railway are advantageous, interest in high-speed railway system is increasing. Because railway vehicle is environment-friendly and safe compared with airplane and ship, its market-sharing increases gradually. We developed a measurement system for on-line test and evaluation of performances of KHST. The measurement system is composed of software part and hardware part. Perfect interface between multi-users is possible. Nowadays, position data inputs to pulse signal from wheel. Perfect position measurement was limited to slip and slide of vehicle. This measurement makes up for the weak points, Position Detection System using GPS develops. By using the system, Korean High Speed Train is capable of accurate fault position detection.

• Nuclear Engineering and Technology
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• 제33권5호
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• pp.483-497
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• 2001

In this work, a neuro-fuzzy inference system combined with the wavelet denoising, PCA (principal component analysis) and SPRT (sequential probability ratio test) methods is developed to detect the relevant sensor failure using other sensor signals. The wavelet denoising technique is applied to remove noise components in input signals into the neuro-fuzzy system The PCA is used to reduce the dimension of an input space without losing a significant amount of information. The PCA makes easy the selection of the input signals into the neuro-fuzzy system. Also, a lower dimensional input space usually reduces the time necessary to train a neuro-fuzzy system. The parameters of the neuro-fuzzy inference system which estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The residuals between the estimated signals and the measured signals are used to detect whether the sensors are failed or not. The SPRT is used in this failure detection algorithm. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level and the hot-leg flowrate sensors in pressurized water reactors.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2004년도 춘계학술대회논문집
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• pp.593-596
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• 2004

Hub bearings not only sustain the body of a car, but permit wheels to rotate freely. Excessive radial or axial load and many other reasons can cause defects to be created and grown in each component. Therefore, vibration and noise from unwanted defects in outer-race, inner-race or ball elements of a Hub bearing are what we want to detect as early as possible. How early we can detect the faults has to do with how the detection algorithm finds the fault information from measured signal. Fortunately, the bearing signal has periodic impulse train. This information allows us to find the faults regardless how much noise contaminates the signal. This paper shows the basic signal processing idea and experimental results that demonstrate how good the method is.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권8호
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• pp.2964-2983
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• 2015

As the Android operating system has become a key target for malware authors, Android protection has become a thriving research area. Beside the proved importance of system permissions for malware analysis, there is a lot of overlapping in permissions between malware apps and goodware apps. The exploitation of them effectively in malware detection is still an open issue. In this paper, to investigate the feasibility of neuro-fuzzy techniques to Android protection based on system permissions, we introduce a self-adaptive neuro-fuzzy inference system to classify the Android apps into malware and goodware. According to the framework introduced, the most significant permissions that characterize optimally malware apps are identified using Information Gain Ratio method and encapsulated into patterns of features. The patterns of features data is used to train and test the system using stratified cross-validation methodologies. The experiments conducted conclude that the proposed classifier can be effective in Android protection. The results also underline that the neuro-fuzzy techniques are feasible to employ in the field.

• ETRI Journal
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• 제31권2호
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• pp.209-214
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• 2009

In this paper, we propose a novel test methodology for the detection of catastrophic and parametric faults present in analog very large scale integration circuits. An automatic test pattern generation algorithm is proposed to generate piece-wise linear (PWL) stimulus using wavelets and a genetic algorithm. The PWL stimulus generated by the test algorithm is used as a test stimulus to the circuit under test. Faults are injected to the circuit under test and the wavelet coefficients obtained from the output response of the circuit. These coefficients are used to train the neural network for fault detection. The proposed method is validated with two IEEE benchmark circuits, namely, an operational amplifier and a state variable filter. This method gives 100% fault coverage for both catastrophic and parametric faults in these circuits.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2004년도 추계학술대회논문집
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• pp.1093-1096
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• 2004

Bearings are elementary machinery component which loads and do rotating motion. Excessive loads or many other reasons can cause incipient faults to be created and grown in each component. Moreover, it happens that incipient faults which were caused by manufacturing or assembling process' errors of the bearings are created. Finding the incipient faults as early as possible is necessary to the bearings in severe condition: high speed or frequently varying load condition, etc. How early we can detect the faults has to do with how the detection algorithm finds the fault information from measured signal. Fortunately, the bearing fault signal makes periodic impulse train. This information allows us to find the faults regardless how much noise contaminates the signal. This paper shows the basic signal processing idea and experimental results that demonstrate how good the method is.

• 한국지반공학회논문집
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• 제36권10호
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• pp.41-56
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• 2020

지진 경보 시스템이 빠르고 정확하게 가동하기 위해서는 충분한 수량의 계측 시스템 확보와 더불어서 적절한 계측 데이터 해석기술 개발이 요구된다. 신규 지진계를 설치시 많은 비용이 소모되기 때문에, 열차 내 가속도계 등을 대체재로 지진 경보 시스템에 활용하는 것이 효율적이다. 그러나 열차에 설치된 가속도계의 경우, 지진계와는 달리 열차 주행시 진동 데이터가 포함되어 있다. 또한, 지진 발생시 성토구간에 의해서 변화된 지진응답을 계측하게 된다. 본 연구에서는 위의 특성들이 포함된 열차 가속도계 데이터에 기반한 지진감지 기술을 제안하고자 한다. 우선, 성토구간의 지진응답 해석기법을 활용하여 열차가 성토구간을 지날 때 지진이 발생하는 것을 구현한 가상의 열차 가속도 데이터를 구축하였다. 구축한 가속도 데이터를 Short time Fourier Transform(STFT)와 Wavelet Transform(WT)을 활용하여 시간-주파수 분석을 수행하였다. 분석 결과, STFT가 장주기 지진 감지에 적합한 반면, WT의 경우 단주기 지진 감지에 유용함을 확인하였다.

• 한국멀티미디어학회논문지
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• 제22권12호
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• pp.1447-1456
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• 2019

As artificial intelligence technology is actively used in image processing, it is possible to generate high-quality fake images based on deep learning. Fake images generated using GAN(Generative Adversarial Network), one of unsupervised learning algorithms, have reached levels that are hard to discriminate from the naked eye. Detecting these fake images is required as they can be abused for crimes such as illegal content production, identity fraud and defamation. In this paper, we develop a deep-learning model based on CNN(Convolutional Neural Network) for the detection of StyleGAN fake images. StyleGAN is one of GAN algorithms and has an excellent performance in generating face images. We experiment with 48 number of experimental scenarios developed by combining parameters of the proposed model. We train and test each scenario with 300,000 number of real and fake face images in order to present a model parameter that improves performance in the detection of fake faces.

• Journal of Computing Science and Engineering
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• 제9권2호
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• pp.98-107
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• 2015

Hierarchical constraints and constraint satisfaction were analyzed in order to solve the problem of conflict detection in collaborative design. The constraints were divided into two sets: one set consisted of known constraints and the other of unknown constraints. The constraints of the two sets were detected with corresponding methods. The set of the known constraints was detected using an interval propagation algorithm, a back propagation (BP) neural network was proposed to detect the set with the unknown constraints. An immune algorithm (IA) was utilized to optimize the weights and the thresholds of the BP neural network, and the steps were designed for the optimization process. The results of the simulation indicated that the BP neural network that was optimized by IA has a better performance in terms of convergent speed and global searching ability than a genetic algorithm. The constraints were described using the eXtensible Markup Language (XML) for computers to be able to automatically recognize and establish the constraint network. The implementation of the conflict detection system was designed based on constraint satisfaction. A wind planetary gear train is taken as an example of collaborative design with a conflict detection system.

• 대한전자공학회논문지SP
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• 제41권6호
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• pp.99-108
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• 2004

본 논문에서는 CCD(charge-coupled device) 영상 기반의 자동 표적 탐지 시스템(ATD System : Automatic Target Detection System)에 적합한 빠른 탐색 방법을 제안한다. 무기체계에서의 활용을 위해서는 빠른 연산이 주요한 변수인 만큼 이 논문에서는 적은 계산량으로 다양한 표적을 탐지할 수 있는 능력에 주안점을 두고 있다. 표적 훈련(train)단계에서는 구간별 수직 방향 프로젝션을 이용하여 1D의 템플릿을 구성하고 K-means clustering과 이진 트리 구조(binary tree structure)를 활용하여 실제 시험 단계에서 템플릿 정합하는 횟수를 최소화한다. 또한 Correlation-based Adaptive Predictive Search(CAPS)를 이용하여 각각의 템플릿에 적응적인 skip-width를 사용하여 탐색 속도를 높이고 클러터 제거 단계에서는 윤곽선으로부터 추출한 Fourier Descriptor계수를 비교함으로써 초기 탐지에서 타겟으로 오인된 클러터를 모양 정보에 기반해서 제거하는 방법을 사용한다.