• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.464-469
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• 2019

Tapered roller bearings are used widely in vans, trucks, and trains because they can support the vehicle in a stable manner even under a heavy load. The cage of a tapered roller bearing maintains the gap between the rollers, which prevents friction wear and suppresses heating. If the cage is severely deformed due to resonance, the roller may not be able to roll smoothly and even leave the cage. Consequently, it is very important to analyze the dynamic characteristics of the cage for reliable performance of a bearing. The cage essentially has geometrical tolerance in the manufacturing process. In this paper, the effects of those geometrical imperfections on the dynamic characteristics of the cage were investigated. As a result, natural frequency separation occurred near the natural frequency of the ideal cage due to geometrical imperfections. In addition, the interval was proportional to the magnitude of the geometric error, and the interval increased with increasing mode number.

• 한국전산유체공학회:학술대회논문집
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• 2009.04a
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• pp.245-251
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• 2009

Numerical algorithms for solving the incompressible Navier-Stokes equations using P2P1 finite element are compared regarding the eigenvalues of matrices. P2P1 element allocates pressure at vertex nodes and velocity at both vertex and mid nodes. Therefore, compared to the P1P1 element, the number of pressure variables in the P2P1 element decreases to 1/4 in the case of two-dimensional problems and to 1/8 in the three-dimensional problems. Fully-implicit-integrated, semi-implicit- integrated and semi-segregated finite element formulations using P2P1 element are compared in terms of elapsed time, accuracy and eigenvlue distribution (condition number). For the comparison,they have been applied to the well-known benchmark problems. That is, the two-dimensional unsteady flows around a fixed circular cylinder and decaying vortex flow are adopted to check spatial accuracy.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.10
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• pp.48-58
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• 1998

We present a method that can recognize face images using independent component analysis that is used mainly for blind sources separation in signal processing. We assumed that a face image can be expressed as the sum of a set of statistically independent feature images, which was obtained by using independent component analysis. Face recognition was peformed by projecting the input image to the feature image space and then by comparing its projection components with those of stored reference images. We carried out face recognition experiments with a database that consists of various varied face images (total 400 varied facial images collected from 10 per person) and compared the performance of our method with that of the eigenface method based on principal component analysis. The presented method gave better results of recognition rate than the eigenface method did, and showed robustness to the random noise added in the input facial images.

• Smart Media Journal
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• v.8 no.2
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• pp.74-79
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• 2019

Traditional compound noun decomposition algorithms often face challenges of decomposing compound nouns into separated nouns when unregistered unit noun is included. It is very difficult for those traditional approach to handle such issues because it is impossible to register all existing unit nouns into the dictionary such as proper nouns, coined words, and foreign words in advance. In this paper, in order to solve this problem, compound noun decomposition problem is defined as tag sequence labeling problem and compound noun decomposition method to use syllable unit embedding and deep learning technique is proposed. To recognize unregistered unit nouns without constructing unit noun dictionary, compound nouns are decomposed into unit nouns by using LSTM and linear-chain CRF expressing each syllable that constitutes a compound noun in the continuous vector space.

• 한국신재생에너지학회:학술대회논문집
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• 2010.11a
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• pp.129.1-129.1
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• 2010

석탄가스화로부터 얻어진 합성가스는 CO, $H_2$가 주성분으로, 그 자체를 연료로 사용하여 발전을 하거나 또는 적절한 정제, 분리 및 합성을 통해 다양한 원료물질을 생산할 수 있다. 이러한 석탄의 청정 사용 기술은 최근의 에너지 분야에서 많은 관심을 불러일으키고 있는 고유가 현상 및 석유자원 고갈에 대비할 수 있는 현실적인 방법의 하나로 여겨지고 있다. 석유를 대체할 에너지원으로서 석탄을 이용하는 다양한 응용 방법 중의 하나로 가스화 반응을 통해 발생하는 합성가스를 이용한 SNG 제조 공정을 들 수 있는데, 이는 석탄 등의 고체 시료를 이용하여 메탄이 주성분인 연료가스를 생산하는 것이다. SNG(Synthesis Natural Gas 또는Substitute Natural Gas)는 합성천연가스 또는 대체천연가스로 불리어지는데 주로 석탄의 가스화를 통해 얻어진 합성가스(syngas 또는 synthesis gas)인 CO, $H_2$를 촉매에 의한 합성반응을 통해 얻을 수 있다. SNG 합성 반응(메탄화 반응)은 보통 수성가스 전환 공정과 가스 정제 공정을 거친 합성가스를 $CH_4$로 전환하는 것으로 석탄을 이용한 SNG 제조 공정에서 가장 핵심 공정인 메탄화 반응은 높은 발열반응으로 주로 니켈 촉매를 사용하며 $250{\sim}400^{\circ}C$에서 반응이 이루어진다. SNG 합성 반응은 공급되는 합성가스의 조성($H_2$/CO 비), 공급되는 합성가스의 유량과 반응기에 충진된 촉매의 부피와의 관계를 나타낸 공간속도, 반응온도 등의 조건에 따라 반응 특성이 달라질 수 있다. 가스화 반응을 통해 생성되는 합성가스를 이용한 SNG 합성반응(메탄화 반응)의 특성을 파악하기 위하여 Lab-scale 규모의 고정층 반응기를 이용하여 Ni 함량이 다른 2종류의 촉매를 대상으로 반응온도 및 압력에 따른 CO 전환율, $CH_4$ 선택도, $CH_4$ 생산성 변화를 파악하였다. 실험 결과 반응기의 온도가 350도 이상의 조건에서 CO 전환율은 99.8%이상, $CH_4$ 선택도는 90.7%이상으로 나타났으며, 공간속도가 2,000 1/h 이상의 조건에서는 $CH_4$ 생산성이 500 ml/g-cat, h을 만족하였다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.4
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• pp.455-461
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• 2002

This paper presents a new parametric log line equation of polar form for Hough transform in log-polar plane, in which it can remove the well-known unboundedness problem of Hough parameters. Bolduc's method is used to generate a log-polar image dividing the fovea and periphery from a Cartesian image. Edges of the fovea and periphery are detected by using the Sobel mask and the proposed space-variant gradient mask, and are combined in the log-polar plane. The sampled points that might constitute a log line are quite sparse in a deep peripheral region due to severe under-sampling, which is an inherent property of LPM. To cope with such under-sampling, we determine the values of cumulative cells in Hough space by using the space-variant weighting. In our experiments, the proposed method demonstrates its validity of detecting not only the lines passing through both the fovea and periphery but also the lines in a deep periphery.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.3 no.3
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• pp.9-18
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• 1983

In many geological investigation, a data sequence may be created which consisted of a succession of mutually exclusive state. However, now our interest lie in the nature of transition from one state to another, rather than in the position of states in the sequence. In this paper, authors studied for the deformation of HAEUNDAE beach by transition matrices and empirical eigenfunction method. In this paper, 5-yr. set of profile data from HAEUNDAE beach measured at several monthly or yearly intervals are analyzed by using the transition matrices and empirical eigen-function. Results of this study indicate that the transition matrices and empirical eigenfunctions are useful in the analysis of beach profile data and provide objective insight in the nature of profile configuration.

• Journal of KIISE:Software and Applications
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• v.35 no.10
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• pp.616-626
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• 2008

The face shape extracted by the depth values has different appearance as the most important facial feature information and the face images decomposed into frequency subband are signified personal features in detail. In this paper, we develop a method for recognizing the range face images by combining the multiple frequency domains for each depth image and depth fusion using fuzzy integral. For the proposed approach, the first step tries to find the nose tip that has a protrusion shape on the face from the extracted face area. It is used as the reference point to normalize for orientated facial pose and extract multiple areas by the depth threshold values. In the second step, we adopt as features for the authentication problem the wavelet coefficient extracted from some wavelet subband to use feature information. The third step of approach concerns the application of eigenface and Linear Discriminant Analysis (LDA) method to reduce the dimension and classify. In the last step, the aggregation of the individual classifiers using the fuzzy integral is explained for extracted coefficient at each resolution level. In the experimental results, using the depth threshold value 60 (DT60) show the highest recognition rate among the regions, and the depth fusion method achieves 98.6% recognition rate, incase of fuzzy integral.

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1501-1514
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• 2008

The face shape extracted by the depth values has different appearance as the most important facial information. The face images decomposed into frequency subband are signified personal features in detail. In this paper, we develop a method for recognizing the range face images by multiple frequency domains for each depth image using the modified fuzzy c-mean algorithm. For the proposed approach, the first step tries to find the nose tip that has a protrusion shape on the face from the extracted face area. And the second step takes into consideration of the orientated frontal posture to normalize. Multiple contour line areas which have a different shape for each person are extracted by the depth threshold values from the reference point, nose tip. And then, the frequency component extracted from the wavelet subband can be adopted as feature information for the authentication problems. The third step of approach concerns the application of eigenface to reduce the dimension. And the linear discriminant analysis (LDA) method to improve the classification ability between the similar features is adapted. In the last step, the individual classifiers using the modified fuzzy c-mean method based on the K-NN to initialize the membership degree is explained for extracted coefficient at each resolution level. In the experimental results, using the depth threshold value 60 (DT60) showed the highest recognition rate among the extracted regions, and the proposed classification method achieved 98.3% recognition rate, incase of fuzzy cluster.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.6
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• pp.48-59
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• 2000

Eigendecomposition based direction-of-arrival(DOA) estimation algorithm such as MUSIC(multiple signal classification) is known to perform well and provide high resolution in white noise environment. However, its performance degrades severely when the noise process is not white. In this paper we consider the DOA estimation problem in a colored noise environment as a problem of extracting periodic signals from noise, and we take the problem to the wavelet domain. Covariance matrix of multiscale components which are obtained by taking wavelet decomposition on the noise has a special structure which can be approximated with a banded sparse matrix. Compared with noise the correlation between multiscale components of narrowband signal decays slowly, hence the covariance matrix does not have a banded structure. Based on this fact we propose a DOA estimation algorithm that transforms the covariance matrix into wavelet domain and removes noise components located in specific bands. Simulations have been carried out to analyze the proposed algorithm in colored noise processes with various correlation properties.