• 자원환경지질
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• 제30권5호
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• pp.459-467
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• 1997

In order to examine the primary trends and characteristics of geological lineaments along the southwestern boundary of Okcheon zone, we carried out the analysis of geological lineament trends over six selected sub-areas using Landsat-5 TM images and digital elevation model. The trends of lineaments is determined by a minimum variance method, and the resulting geological lineament map can be obtained through generalized Hough transform. We have corrected look direction biases reduces the interpretability of remotely sensed image. An approach of histogram modification is also adopted to extract drainage pattern specifically in alluvial plains. The lineament extracting method adopted in this study is very effective to analyze geological lineaments, and that helps estimate geological trends associated various with the tectonic events. In six sub-areas, the general trends of lineaments are characterized NW, NNW, NS-NNE, and NE directions. NW trends in Cretaceous volcanic rocks and Jurassic granite areas may represent tension joints that developed by rejuvenated end of the Early Cretaceous left-lateral strike-slip motion along the Honam Shear Zone, while NE and NS-NNE trends correspond to fault directions which are parallel to the above Shear Zone. NE and NW trends in Granitic Gneiss are parallel to the direction of schitosity, and NS-NNE and NE trends are interpreted the lineation by compressive force which acted by right-lateral strike-slip fault from late Triassic to Jurassic. And in foliated Granite, NE and NNE trends are coincided with directions of ductile foliation and Honam Shear Zone, and NW-NNW trends may be interpreted direction of another compressional foliation (Triassic to Early Jurassic) or end of the Early Cretaceous tensional joints. We interpreted NS-NNE direction lineation is related with the rejuvenated Chugaryung Fault System.

• Smart Structures and Systems
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• 제13권3호
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• pp.453-471
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• 2014

The fault diagnosis of rolling element bearings has drawn considerable research attention in recent years because these fundamental elements frequently suffer failures that could result in unexpected machine breakdowns. Artificial intelligence algorithms such as artificial neural networks (ANNs) and support vector machines (SVMs) have been widely investigated to identify various faults. However, as the useful life of a bearing deteriorates, identifying early bearing faults and evaluating their sizes of development are necessary for timely maintenance actions to prevent accidents. This study proposes a new two-layer structure consisting of support vector regression machines (SVRMs) to recognize bearing fault patterns and track the fault sizes. The statistical parameters used to track the fault evolutions are first extracted to condense original vibration signals into a few compact features. The extracted features are then used to train the proposed two-layer SVRMs structure. Once these parameters of the proposed two-layer SVRMs structure are determined, the features extracted from other vibration signals can be used to predict the unknown bearing health conditions. The effectiveness of the proposed method is validated by experimental datasets collected from a test rig. The results demonstrate that the proposed method is highly accurate in differentiating between fault patterns and determining their fault severities. Further, comparisons are performed to show that the proposed method is better than some existing methods.

• 한국통신학회논문지
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• 제28권8A호
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• pp.569-580
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• 2003

다중 채널 스위치 구조의 중요한 이점 중의 하나는 스위치 내부의 장애에 대한 내성 (tolerance)을 스위치 구조에 결합시킬 수 있다는 것이다. 예를 들어 하나의 다중 채널 그룹에 속하는 경로에 장애가 있을 경우, 장애 경로로 통과했어야 하는 트래픽을 나머지 경로가 책임 질 수 있다. 또한 스위치 소자에 발생하는 장애는 ATM 셀(cell)의 잘못된 라우팅을 야기하거나 순서를 뒤바꾸게 할 수 있다. 본 논문에서는 다중 채널 크로스바(crossbar) ATM 스위치에서의 장애 위치 알고리즘을 제안하였다. 최적의 알고리즘은 시간적으로 최상의 성능을 보여주지만, 계산상으로는 복잡하여 결과적으로 실제 구현을 어렵게 만든다. 이러한 단점을 극복하기 위해 최적의 알고리즘보다 계산상으로 효율적인 온라인 알고리즘을 제안하였다. 성능은 시뮬레이션을 통해 검증하였으며 그 결과로서 온라인 알고리즘의 성능은 랜덤 (random) 트래픽 및 버스트한 (bursty) 트래픽에 대해 거의 최적에 가까운 성능을 보여 준다. 끝으로 장애 위치 확인 알고리즘에 의해 제공되는 정보를 이용한 장애 복구 알고리즘을 제안하였다.

• 한국소음진동공학회논문집
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• 제21권3호
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• pp.199-205
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• 2011

This paper proposes a framework to predict the mechanical fault of hot rolling mill system (HRMS). The optimum process of HRMS is usually identified by the rotating velocity of working roll. Therefore, observing the velocity of working roll is relevant to early know the HRMS condition. In this paper, we propose the framework which consists of two methods namely spectrum matrix which related to case-based fast Fourier transform(FFT) analysis, and three dimensional condition monitoring based on novel visualization. Validation of the proposed method has been conducted using vibration data acquired from HRMS by accelerometer sensors. The acquired data was also tested by developed software referred as hot rolling mill facility analysis module. The result is plausible and promising, and the developed software will be enhanced to be capable in prediction of remaining useful life of HRMS.

• 한국기계가공학회지
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• 제11권4호
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• pp.154-159
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• 2012

Condition monitoring(CM) is a method based on Non-destructive test(NDT). Therefore, recently many kind of NDT were applied for CM. Acoustic emission(AE) is widely used for the early detection of faults in rotating machinery in these days also. Because its sensitivity is higher than normal accelerometers and it can detect low energy vibration signals. A machine tool consist of many parts such as the bearings, gears, process tools, shaft, hydro-system, and so on. Condition of Every part is connected with product quality finally. To increase the quality of products, condition monitoring of the components of machine tool is done completely. Therefore, in this paper, acoustic emission method is used to detect a machine fault seeded in a gearbox. The AE signals is saved, and power spectrums and feature values, peak value, mean value, RMS, skewness, kurtosis and shape factor, were determined through Matlab.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 추계학술대회논문집
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• pp.549-555
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• 2003

The technology of machine condition monitoring is used effectively to detect the machine faults at an early stage using different machine quantities, such as current, voltage, temperature and vibration. Induction motors are most widely used to drive pumps, compressors and fans in industrial drives. This paper presents approach to data fusion using Dempster-Shafer theory because only one technique has uncertainty. So we can obtain advanced accuracy of the machine fault diagnosis. Vibration and current quantities are applied to diagnose three-phase induction motor.

• Journal of Information Processing Systems
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• 제8권2호
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• pp.241-262
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• 2012

An understanding of quality attributes is relevant for the software organization to deliver high software reliability. An empirical assessment of metrics to predict the quality attributes is essential in order to gain insight about the quality of software in the early phases of software development and to ensure corrective actions. In this paper, we predict a model to estimate fault proneness using Object Oriented CK metrics and QMOOD metrics. We apply one statistical method and six machine learning methods to predict the models. The proposed models are validated using dataset collected from Open Source software. The results are analyzed using Area Under the Curve (AUC) obtained from Receiver Operating Characteristics (ROC) analysis. The results show that the model predicted using the random forest and bagging methods outperformed all the other models. Hence, based on these results it is reasonable to claim that quality models have a significant relevance with Object Oriented metrics and that machine learning methods have a comparable performance with statistical methods.

• 대한자원환경지질학회:학술대회논문집
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• 대한자원환경지질학회 2003년도 춘계 학술발표회 논문집
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• pp.330-330
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• 2003

Several volcanics are found within the Tertiary sedimentary basins, southeastern part of Korea. The sedimentary basins have been interpreted to have formed in the framework of separation of the East Sea. The volcanics are Eocene or Early and Middle Miocene in ages, showing a distincetve chronological gap, and show mafic and silicic (bimodal) in composotion. The Miocene volcanics were regionally and stratigraphically grouped into two varieties along the Hyeongsan fault; younger volcanics (13.6-15.2 Ma, K) from the north of the fault, erupted after the opening of the East Sea, and older volcanics (16.2-21.1 Ma) from the south of the fault. (omitted)

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2013년도 추계학술대회 논문집
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• pp.331-337
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• 2013

Acoustic Emission technique is widely applied to develop the early fault detection system, and the problem about a signal processing method for AE signal is mainly focused on. In the signal processing method, envelope analysis is a useful method to evaluate the bearing problems and Wavelet transform is a powerful method to detect faults occurred on rotating machinery. However, exact method for AE signal is not developed yet. Therefore, in this paper two methods which are Hilbert transform and DET for feature extraction. In addition, we evaluate the classification performance with varying the parameter from 2 to 15 for feature selection DET, 0.01 to 1.0 for the RBF kernel function of SVR, and the proposed algorithm achieved 94% classification accuracy with the parameter of the RBF 0.08, 12 feature selection.

• 한국광물학회지
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• 제29권1호
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• pp.11-22
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• 2016

경북 포항시 남구 동해면 금광리 일대의 전기 마이오세 현무암층 내에 단층파쇄대의 중심에 약 5-10 cm의 폭을 가진 흑색의 단층점토가 길게 연장되어 나타났다. 이 단층점토에 대해 XRD, FTIR, DTA/TGA, SEM, TEM, XRF, EPMA 등으로 자세히 분석한 결과, Fe 성분을 다량 함유하는 Fe-세피올라이트인 것으로 확인되었다. 이 외에 단층파쇄대의 변질광물은 주로 스멕타이트인 것으로 나타났으며, 신선한 모암인 현무암에도 스멕타이트가 상당량 포함되어 나타났다. 이러한 구성광물의 산상으로 보아, Fe-세피올라이트는 현무암의 고화 형성 후에 다소 깊은 곳에서 단층작용과 함께 열수변질작용이 관여하여 형성된 것으로 사료된다.