• Tribology and Lubricants
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• v.39 no.2
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• pp.35-42
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• 2023

Bearing is a mechanical component that supports loads and transmits rotation. As the application of high-value-added products such as semiconductors, aviation, and robots have recently become diverse and more precise, an accurate bearing performance prediction and evaluation technology is required. Bearing performance evaluation can be divided into evaluations based on bearing theory and on numerical analysis. An evaluation based on numerical analysis is a technique that has been highlighted because the problems that remained unsolved owing to time problems can be solved through recent developments in computers. However, current studies have the disadvantage of not considering the essential changes over time and bearing rotation. In this study, bearing performance evaluation based on rigid body dynamic analysis considering rotation and load over time is performed. Rigid body dynamic analysis is performed for deep groove ball bearing to calculate the load applied by the ball. The reliability of the analysis is verified by comparing it with the results calculated using bearing theory. In addition, rigid body dynamic analysis is performed for automotive wheel bearings to calculate the contact angle and load applied by the ball for cases where axial load and radial load are applied, respectively. The effect of rotation and load over time is evaluated from these results.

• Journal of Microbiology and Biotechnology
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• v.33 no.12
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• pp.1543-1551
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• 2023

The recently published high-resolution R388 T4SS structure provides exciting new details about the complete complex of T4SS, including the components making up the stalk and arches, numerous symmetry mismatches between regions of the complex, and an intriguing interpretation of the closed stalk and radial symmetry of the inner membrane complex, which is related to pilus biogenesis assembly. However, there are a few unidentified densities in the electron microscopy map and portions of the identified component sequences for which the structure is not yet known. It is also unclear how well this minimized DNA-transporting T4SS predicts the structure of other T4SSs, such as expanded systems and those that transport proteins rather than DNA. In this review, we evaluate what can be inferred from the recent high-resolution structure of the R388 T4SS with respect to the Cag and Dot/Icm systems. These systems were selected because, given what is currently known about these systems, we expect them to present most structural differences compared to the R388 T4SS structure. Furthermore, we discuss bacterial physiology and diversity, the T4SS structures and their variations between different bacterial species. These insights may prove beneficial for researchers who elucidate the structure and functions of T4SS in different bacterial species.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.477-484
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• 2016

Small industrial appliances such as fan, audio, electric rice cooker mostly consist of ABS, PP, PS materials. In colored plastics, it is possible to classify by near infrared(NIR) spectroscopy, while in black plastics, it is very difficult to classify black plastic because of the characteristic of black material that absorbs the light. So the RBFNNs pattern classifier is introduced for sorting electrical and electronic waste plastics through LIBS(Laser Induced Breakdown Spectroscopy) spectrometer. At the preprocessing part, PCA(Principle Component Analysis), as a kind of dimension reduction algorithms, is used to improve processing speed as well as to extract the effective data characteristics. In the condition part, FCM(Fuzzy C-Means) clustering is exploited. In the conclusion part, the coefficients of linear function of being polynomial type are used as connection weights. PSO and 5-fold cross validation are used to improve the reliability of performance as well as to enhance classification rate. The performance of the proposed classifier is described based on both optimization and no optimization.

• Journal of the Korean Society of Groundwater Environment
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• v.7 no.2
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• pp.89-96
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• 2000

Slug tests are the most widely used field method for quantification of hydraulic conductivity of porous media. Well recovery is affected by well casing, borehole radii, screened length, hydraulic conductivity, and specific storage of porous media. In this study, a new slug tests model was developed through finite element approximation and the validity and usefulness of the model were tested in various ways. Water level fluctuation in a well under slug test and cons-equent groundwater flow in the surrounding porous medium were appropriately coupled through estimation of well-flux using an iteration technique. Numerical accuracy of the model was verified using the Cooper et al. (1967) solution. The model has advantages in simulations for monitored slug tests, partial penetration, and inclusion of storage factor. Volume coverage of slug tests is significantly affected by storage factor. Magnitude and speed of propagation of head changes from a well increases as storage factor becomes low. It will be beneficial to use type curves of monitored head transients in the surrounding porous formation for estimation of specific storage. As the vertical component of groundwater flow is enhanced, the influence of storage factor on well recovery decreases. For a radial-vertical flow around a partially penetrated well, deviations between hydraulic estimates by various methods and data selection of recovery curve are negligible on practical purposes, whereas the deviations are somewhat significant for a radial flow.

• Journal of Astronomy and Space Sciences
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• v.16 no.2
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• pp.227-240
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• 1999

We performed CCD photometric observations of W UMa type contact binary BV Dra during eight nights from May 1996 to June 1999 using 61cm telescope at Sobaeksan Optical Astronomy Observatory, and completed BV R light curves of the system. From our observations, we derived nine new times of minimum lights (five timings for primary eclipse, four for secondary) and determined new light elements with the times of minima observed since 1999. Our BV R light curves and Batten & Lu(1986)'s radial-velocity ones were simultaneously analyzed with contact mode (Mode 3) of Wilson-Devinney's binary model, and the photometric and spectroscopic solutions for BV Dra were solved. In the analysis, we derived the solutions of 1999 light curves with and without spots, respectively. As the results, asymmetry of light curves may be interpreted as produced by the existence of two spots; hot spot on the secondary and cool on the primary. Combining solutions of light curves and radial-velocity ones, absolute dimensions of BV Dra are $M_1=0.40M_{odot}$, $M_2=1.01M_{odot}$, $R_1=0.72R_{odot}$, $R_2=0.40R_{odot}$. In mass-radius diagram, the less massive and hotter primary component of BV Dra is near TAMS and the secondary is near ZAMS, which is very similar to the other W-type W UMa binaries.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1150-1158
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• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.722-729
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• 2010

Prediction problems are widely used in medical domains. For example, computer aided diagnosis or prognosis is a key component in a CDSS (Clinical Decision Support System). SVMs with nonlinear kernels like RBF kernels, have shown superior accuracy in prediction problems. However, they are not preferred by physicians for medical prediction problems because nonlinear SVMs are difficult to visualize, thus it is hard to provide intuitive interpretation of prediction results to physicians. Nomogram was proposed to visualize SVM classification models. However, it cannot visualize nonlinear SVM models. Localized Radial Basis Function (LRBF) was proposed which shows comparable accuracy as the RBF kernel while the LRBF kernel is easier to interpret since it can be linearly decomposed. This paper presents a new tool named VRIFA, which integrates the nomogram and LRBF kernel to provide users with an interactive visualization of nonlinear SVM models, VRIFA visualizes the internal structure of nonlinear SVM models showing the effect of each feature, the magnitude of the effect, and the change at the prediction output. VRIFA also performs nomogram-based feature selection while training a model in order to remove noise or redundant features and improve the prediction accuracy. The area under the ROC curve (AUC) can be used to evaluate the prediction result when the data set is highly imbalanced. The tool can be used by biomedical researchers for computer-aided diagnosis and risk factor analysis for diseases.

• Resources Recycling
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• v.26 no.2
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• pp.46-55
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• 2017

In this study, the design methodology of Radial Basis Function Neural Networks is developed with the aid of Laser Induced Breakdown Spectroscopy and also applied to the practical plastics sorting system. To identify black plastics such as ABS, PP, and PS, RBFNNs classifier as a kind of intelligent algorithms is designed. The dimensionality of the obtained input variables are reduced by using PCA and divided into several groups by using K-means clustering which is a kind of clustering techniques. The entire data is split into training data and test data according to the ratio of 4:1. The 5-fold cross validation method is used to evaluate the performance as well as reliability of the proposed classifier. In case of input variables and clusters equal to 5 respectively, the classification performance of the proposed classifier is obtained as 96.78%. Also, the proposed classifier showed superiority in the viewpoint of classification performance where compared to other classifiers.

• Journal of The Korean Astronomical Society
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• v.38 no.2
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• pp.219-222
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• 2005

We present the high-resolution (2"-4") images of the molecular envelopes surrounding the evolved stars, V Hya, VY CMa, and ${\pi}^1$ Gru observed with the Submillimeter Array. The CO J=2-1 and 3-2 images of the carbon star V Hya show that the circumstellar structure of this star consists of three kinematic components; there is a flattened disk-like envelope that is expanding with a velocity of ${\~}16 km\;s^{-1}$, the second component is the medium-velocity wind having a deprojected velocity of 40-120 km $s^{-l}$ moving along the disk plane, and the third one is the bipolar molecular jet having an extreme velocity of 70-185 km $s^{-l}$. The axis of this high velocity jet is perpendicular to the plane of the disk-like envelope. We found that the circumstellar structure of the S-star ${\pi}^1$ Gru traced by the CO J =2-1 resembles that of V Hya quite closely; the star is surrounded by the expanding disk-like envelope and is driving the medium-velocity wind along the disk plane. We also obtained the excellent images of VY CMa with the CO and $^{13}CO$ J=2-1 and $SO\;6_5-5_4$ lines. The maps of three molecular lines show that the envelope has a significant velocity gradient in the east-west direction, suggesting that the envelope surrounding VY CMa is also flattened and expanding along its radial direction. The high-resolution images obtained with the SMA show that some AGB stars are associated with the asymmetric mass loss including the equatorial wind and bipolar jet.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.2
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• pp.35.1-35.1
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• 2015

We present the results of extinction measurements toward the main ejecta shell of the Cassiopeia A supernova (SN) remnant using the flux ratios between the two near-infrared (NIR) [Fe II] lines at 1.26 and $1.64{\mu}m$. We find a clear correlation between the NIR extinction (E(J-H)) and the radial velocity of ejecta knots, showing that redshifted knots are systematically more obscured than blueshifted ones. This internal "self-extinction" strongly indicates that a large amount of SN dust resides inside and around the main ejecta shell. At one location in the southern part of the shell, we measure E(J-H) by the SN dust of $0.23{\pm}0.05mag$. By analyzing the spectral energy distribution of thermal dust emission at that location, we show that there are warm (~100K) and cool (~40K) SN dust components and that the latter is responsible for the observed E(J-H). We investigate the possible grain species and size of each component and find that the warm SN dust needs to be silicate grains such as $MgSiO_3$, $Mg_2SiO_4$, and $SiO_2$, whereas the cool dust could be either small (${\leq}0.01{\mu}m$) Fe or large (${\geq}0.01{\mu}m$) Si grains. We suggest that the warm and cool dust components in Cassiopeia A represent grain species produced in diffuse SN ejecta and in dense ejecta clumps, respectively.