• 한국전산유체공학회:학술대회논문집
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• 2008.10a
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• pp.92-96
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• 2008

For the Mach reflection of symmetric shock waves, only the wave configuration of an oMR(DiMR+DiMR) is theoretically admissible. For asymmetric shock waves, an oMR(DiMR+InMR) will be possible if the two slip layers assemble a convergent-divergent stream tube while an oMR(InMR+InMR) is absolutely impossible. In this paper, an overall Mach reflection configuration with double inverse MR patterns is confirmed using the CFD technique. Classical two- and three-shock theories are also applied for the theoretical analysis. In addition, oscillations of shock wave patterns are computed for the interaction of a hypersonic flow and double-wedge-like geometries.

• Transactions of the Korean Society of Automotive Engineers
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• v.3 no.6
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• pp.262-273
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• 1995

For rotating machinery with high speed and high efficiency, large labor and high expenses are required to conduct machine health monitoring. Therefore, it becomes necessary to develop new diagnosis technique which can detect abnormalities of the rotating machinery effectively. In this paper, it is identified that bispectrum analysis technique can be successfully applied to dectect the abnormalities of the roating machinery through computer simulation, and results of the bispectrum analysis are patterned in griding form. Further, pattern recognition technique using back propagation algorithm, which is one of neural network algorithm, being consisted of patterned input layer and output layer for abnormal status, is applied to detect the abnormalities of simulator which is able to make up various kinds of abnorml conditions(misalignment, unbalance, rubbing etc.) of the rotating machinery.

• Physical Therapy Korea
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• v.10 no.2
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• pp.99-110
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• 2003

The purpose of this study was to analyze the effects of three different pelvic tilts on sit-to-stand ativities and to suggest a new therapeutic approach for movement reeducation in patients who have difficulty with sit-to-stand activities. The three different pelvic tilts were: (1) comfortable pelvic tilt sit-to-stand (CPT STS), (2) posterior pelvic tilt sit-to-stand (PPT STS) and (3) anterior pelvic tilt sit-to-stand (APT STS). To analyze the kinematic component of STS, a motion analysis system (Zebris) was applied to the ankle, knee, hip joint, and thigh-off area. Also, to determine the onset time of muscle contraction, surface electrodes were placed to the rectus femoris muscle (RF), the vastus lateralis muscle (VL), the biceps femoris muscle (BF), the tibialis anterior muscle (TA), the gastrocnemius muscle (GCM), and the soleus muscle (SOL). One-way repeated ANOVA was used for the statistical analysis. First, significant differences were found in kinematic variables for the hip, knee, ankle joint, and thigh-off among the three activities. Second, there was significant difference in muscle activation pattern in TA. VL. and BF among three activities. In conclusion, the findings of this study suggest the following evaluative and therapeutic approach for STS activity: (1) Changes in knee and ankle joints should be prioritized and recruitment order differences in VL and RF can be generated to accomplish abnormal STS activity. (2) APT STS can be introduced for movement efficiency and functional advantage when abnormal STS is treated.

• BMB Reports
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• v.40 no.1
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• pp.119-124
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• 2007

Adenoviral vectors are among the most promising vectors available for human gene therapy. However, the use of recombinant adenoviral vectors, including replicationcompetent adenovirus (RCA), raises a variety of safety concerns in relation to the development of new therapies based on gene therapy. To examine how organic compounds change in rat plasma following the injection of adenovirus, $\beta$-galactosidase expressing recombinant adenovirus (designated rAdLacZ) or RCA, we investigated the content of fatty acids (FAs), which are important biochemical indicators in pathological conditions. Pattern recognition analysis on the level of FAs in rat plasma is described for the visual discrimination of adenovirus infection groups from normal controls. Plasma FAs from four control rats (normal group), and from four rats with rAdLacZ infection and six rats with RCA infection (the two abnormal groups), were examined by gas chromatography-mass spectrometry in selected ion monitoring modes as their tert-butyldimethylsilyl derivatives. In total, 20 FAs were positively detected and quantified. The results of the Student's t-test on the normal mean of two abnormal groups, the levels of three FAs (p<0.05) from rAdLacZ group and eleven FAs (p<0.05) from RCA group were significantly different. When star symbol plotting was applied to the group mean values of 20 FAs after normalization to the corresponding normal mean values, the resulting eicosagonal star patterns of the two infected groups were distorted into similar shapes, but were distinguishable from each other. Thus, these approaches will be useful for screening and monitoring of diagnostic markers for the effects of infection following the use of adenoviral vectors in gene therapy.

• Korean Journal of Veterinary Pathology
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• v.2 no.2
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• pp.73-84
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• 1998

This study was carried out to investigate on the serum chemistry and the DNA ploidy changes in carcinogenesis of the rat liver and kidney. Sixty male Sprague-Dawley rats were divided into two groups. Group I was non-treated control. Group II was given initiators (2,2'-dihydroxy- di-N-propylnitrosamine, 0.1% in drinking water(d.w.) for 1 week and N-ethyl-N-hydroxy-ethylnitrosamine; 0.15% in d.w. for 1 week) and promoters (3'methyl-cholanthrene; 3'MC, l0mg/kg, intraperitoneally(i.p.) twice a week and DL-serine; 0.05% in d.w. for 5 weeks, from 3 to 8 weeks). All examinations were performed at 12 and 20 weeks RBC, HGBCp＜0.05) and PCVCp＜0.01) significantly decreased in Group II at 20 weeks. Activities of ALT, AST(p＜0.05) and GGT(p＜0.01) were significantly increased in Group II at 20 weeks. Flow cytometric analysis showed hepatocyte nuclei from normal livers were predominantly tetraploid(66~67%) and then diploid(28~30%). Most of hepatocyte nuclei from carcinogen-treated rats were diploid (52~68%) and less were tetraploid(28~42%). Neoplastic liver nodules and hepatocellular carcinoma contained almost exclusively diploid nuclei. Renal cell nuclei from normal kidney were predominantly diploid(88~93%), those from carcinogen-treated rats had an abnormal DNA-content peak(aneuploidy, 6-7%), near the tetraploidy area. These results suggest that diploidy may be an effective screening marker of the liver carcinogenesis. Aneuploidy may be an useful marker in assessment of the experimental renal carcinogenesis.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.520-537
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• 2019

This paper proposes a system that can detect the data leakage pattern using a convolutional neural network based on defining the behaviors of leaking data. In this case, the leakage detection scenario of data leakage is composed of the patterns of occurrence of security logs by administration and related patterns between the security logs that are analyzed by association relationship analysis. This proposed system then detects whether the data is leaked through the convolutional neural network using an insider malicious behavior graph. Since each graph is drawn according to the leakage detection scenario of a data leakage, the system can identify the criminal insider along with the source of malicious behavior according to the results of the convolutional neural network. The results of the performance experiment using a virtual scenario show that even if a new malicious pattern that has not been previously defined is inputted into the data leakage detection system, it is possible to determine whether the data has been leaked. In addition, as compared with other data leakage detection systems, it can be seen that the proposed system is able to detect data leakage more flexibly.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.6
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• pp.36-45
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• 2005

This paper addresses the application of motor current spectral analysis for the detection of rolling-element bearing damage in induction machines. We set the experimental test bed. They is composed of the normal condition bearing system, the abnormal rolling-element bearing system of 2 type induction motors with shaft deflection system by external force and a hole drilled through the outer race of the shaft end bearing of the four pole test motor. We have developed the embedded distributed fault tolerant and fault diagnosis system for industrial motor. These mechanisms are based on two 32-bit DSPs and each TMS320F2407 DSP module is checking stator current The effects on the stator current spectrum are described and related frequencies are also determined. This is an important result in the formulation of a fault detection scheme that monitors the stator currents. We utilized the FFT(Fast Fourier Transform), Wavelet analysis and averaging signal pattern by inner product tool to analyze stator current components. Especially, the analyzed results by inner product clearly illustrate that the stator signature analysis can be used to identify the presence of a bearing fault.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.78-83
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• 2019

Sounds based machine fault diagnosis recovers all the studies that aim to detect automatically abnormal sound on machines using the acoustic emission by these machines. Conventional methods that use mathematical models have been found inaccurate because of the complexity of the industry machinery systems and the obvious existence of nonlinear factors such as noises. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose here an automatic fault diagnosis method of hand drills using discrete wavelet transform(DWT) and pattern recognition techniques such as artificial neural networks(ANN). We first conduct a filtering analysis based on DWT. The power spectral density(PSD) is performed on the wavelet subband except for the highest and lowest low frequency subband. The PSD of the wavelet coefficients are extracted as our features for classifier based on ANN the pattern recognition part. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.2 s.245
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• pp.194-201
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• 2006

Gait analysis is essential to identify accurate cause and knee condition from patients who display abnormal walking. Traditional linear tools can, however, mask the true structure of motor variability, since biomechanical data from a few strides during the gait have limitation to understanding the system. Therefore, it is necessary to propose a more precise dynamic method. The chaos analysis, a nonlinear technique, focuses on understand how variations in the gait pattern change over time. Eight healthy eight subjects walked on a treadmill for 100 seconds at 60 Hz. Three dimensional walking kinematic data were obtained using two cameras and KWON3D motion analyzer. The largest Lyapunov exponent from the measured knee angular displacement time series was calculated to quantify local stability. This study quantified the variability present in time series generated from gait parameter via chaos analysis. Knee flexion-extension patterns were found to be chaotic. The proposed Lyapunov exponent can be used in rehabilitation and diagnosis of recoverable patients.

• Journal of Applied Reliability
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• v.18 no.1
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• pp.20-32
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• 2018

Purpose: The purpose of this study is to set up an anomaly detection criteria for sensor data coming from a motorcycle. Methods: Five sensor values for accelerator pedal, engine rpm, transmission rpm, gear and speed are obtained every 0.02 second from a motorcycle. Exploratory data analysis is used to find any pattern in the data. Traditional process control methods such as X control chart and time series models are fitted to find any anomaly behavior in the data. Finally unsupervised learning algorithm such as k-means clustering is used to find any anomaly spot in the sensor data. Results: According to exploratory data analysis, the distribution of accelerator pedal sensor values is very much skewed to the left. The motorcycle seemed to have been driven in a city at speed less than 45 kilometers per hour. Traditional process control charts such as X control chart fail due to severe autocorrelation in each sensor data. However, ARIMA model found three abnormal points where they are beyond 2 sigma limits in the control chart. We applied a copula based Markov chain to perform statistical process control for correlated observations. Copula based Markov model found anomaly behavior in the similar places as ARIMA model. In an unsupervised learning algorithm, large sensor values get subdivided into two, three, and four disjoint regions. So extreme sensor values are the ones that need to be tracked down for any sign of anomaly behavior in the sensor values. Conclusion: Exploratory data analysis is useful to find any pattern in the sensor data. Process control chart using ARIMA and Joe's copula based Markov model also give warnings near similar places in the data. Unsupervised learning algorithm shows us that the extreme sensor values are the ones that need to be tracked down for any sign of anomaly behavior.