• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.1045-1049
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• 1995

This paper proposes the state-space model of the thermal behavior of the spindle system to establish dynamic mathematical model of thermal characteristics in machine tool spindle system. the model is derived form physical law of heat transfer and thermoelasticity and represents the thermal behavior induced by uneven thermal expansions whitin a bearing. The model, which is sucessfully validated for two typical configurations of high speed spindle assembles, provides a tool for understanding the basis mechanics of induced thermal expansion as a function of initial preload, spindle speed and housing cooling conditions.

• Smart Media Journal
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• v.10 no.3
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• pp.9-22
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• 2021

Basically, machine learning models use input data to produce results. Sometimes, the input data is too complicated for the models to learn useful patterns. Therefore, feature engineering is a crucial data preprocessing step for constructing a proper feature set to improve the performance of such models. One of the most efficient methods for automating feature engineering is the autoencoder, which transforms the data from its original space into a latent space. However certain factors, including the datasets, the machine learning models, and the number of dimensions of the latent space (denoted by k), should be carefully considered when using the autoencoder. In this study, we design a framework to compare two data preprocessing approaches: with and without autoencoder and to observe the impact of these factors on autoencoder. We then conduct experiments using autoencoders with classifiers on popular datasets. The empirical results provide a perspective regarding the best suited autoencoder for these factors.

• Journal of the Korean Society of Industry Convergence
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• v.21 no.6
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• pp.301-307
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• 2018

This paper deals with the development of the high frequency thawing machine. The fishery products caught over the world are kept frozen to maintain freshness. These fishery products require thawing before they are sold to customers as food. However, the thawing process can cause freshness reduction, drip coming out, quality deterioration, discharging polluted water, as well as a lot of space and time. The high frequency thawing machine developed to solve this problem has a narrow space, a short thawing time and a small drip. The developed high frequency thawing machine can be used in many fields such as fish processing plant, livestock processing plant. This paper describes the design of the high frequency thawing machine by developing the high frequency generator, development of the controller, and the design of mechanism, and shows the superiority of the high frequency thawing machine by the performance evaluation.

• Magazine of korean Tunnelling and Underground Space Association
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• v.4 no.4
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• pp.58-70
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• 2002

• Bulletin of the Korean Space Science Society
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• 2003.10a
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• pp.26-26
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• 2003

During fabrication process for the large space optical surfaces, the traditional bound abrasive grinding with bronze bond cupped diamond wheel tools leaves the machine marks and the subsurface damage to be removed by subsequent loose abrasive lapping. We explored a new grinding technique for efficient quantitative control of precision CNC grinding for space optics materials such as Zerodur. The facility used is a NANOFORM-600 diamond turning machine with a custom grinding module and a range of resin bond diamond tools. The machining parameters such as grit number, tool rotation speed, work-piece rotation speed, depth of cut and feed rate were altered while grinding the work-piece surfaces of 20-100 mm in diameter. The input grinding variables and the resulting surface quality data were used to build grinding prediction models using empirical and multi-variable regression analysis methods. The effectiveness of the grinding prediction model was then examined by running a series of precision CNC grinding operation with a set of controlled input variables and predicted output surface quality indicators. The experiment details, the results and implications are presented.

• Journal of The Korean Astronomical Society
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• v.45 no.2
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• pp.31-38
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• 2012

In this study we apply Support Vector Machine (SVM) to the prediction of geo-effective halo coronal mass ejections (CMEs). The SVM, which is one of machine learning algorithms, is used for the purpose of classification and regression analysis. We use halo and partial halo CMEs from January 1996 to April 2010 in the SOHO/LASCO CME Catalog for training and prediction. And we also use their associated X-ray flare classes to identify front-side halo CMEs (stronger than B1 class), and the Dst index to determine geo-effective halo CMEs (stronger than -50 nT). The combinations of the speed and the angular width of CMEs, and their associated X-ray classes are used for input features of the SVM. We make an attempt to find the best model by using cross-validation which is processed by changing kernel functions of the SVM and their parameters. As a result we obtain statistical parameters for the best model by using the speed of CME and its associated X-ray flare class as input features of the SVM: Accuracy=0.66, PODy=0.76, PODn=0.49, FAR=0.72, Bias=1.06, CSI=0.59, TSS=0.25. The performance of the statistical parameters by applying the SVM is much better than those from the simple classifications based on constant classifiers.

• Journal of Korean Institute of Industrial Engineers
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• v.18 no.2
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• pp.23-29
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• 1992

To compute the expected travel time of storage and retrieval (S/R) machine in automated warehousing systems most of the previous studies assumed that equal number of rack openings are required regardless of the nature of storage assignment rules. It is known that randomized storage assignment rule usually needs less storage to space than needed for full turnover-based assignment rule. The objective of this paper is compute the expected travel time of each assignment rule more equitably by taking into account the storage space required for each rule. First, the rack storage space is determined which satisfies a given service level. Then based on the standard Economic Ordering Quantity model, trade-off analysis is carried out which relates the storage space to the expected travel time of the S/R machine. Finally, example problems are solved to compare the performance of each assignment rule under varying conditions of demand pattern and service level.

• Journal of Biosystems Engineering
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• v.33 no.6
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• pp.446-452
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• 2008

Machine vision system was used for analyzing leaf color disorders of tomato plants in a greenhouse. From the day when a few leave of tomato plants had started to wither, a series of images were captured by 4 times during 14 days. Among several color image spaces, Saturation frame in HSI color space was adequate to eliminate a background and Hue frame was good to detect infected disease area and tomato fruits. The processed image ($G{\sqcup}b^*$ image) by OR operation between G frame in RGB color space and $b^*$ frame in $La^*b^*$ color space was useful for image segmentation of a plant canopy area. This study calculated a ratio of the infected area to the plant canopy and manually analyzed leaf color disorders through an image segmentation for Hue frame of a tomato plant image. For automatically analyzing plant leave disease, this study selected twenty-seven color patches on the calibration bars as the corresponding to leaf color disorders. These selected color patches could represent 97% of the infected area analyzed by the manual method. Using only ten color patches among twenty-seven ones could represent over 85% of the infected area. This paper showed a proposed machine vision system may be effective for evaluating various leaf color disorders of plants growing in a greenhouse.

• Journal of the Korean Statistical Society
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• v.36 no.3
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• pp.367-372
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• 2007

Classification as a tool to extract information from data plays an important role in science and engineering. Among various classification methodologies, support vector machine has recently seen significant developments. The central problem this paper addresses is the accuracy of support vector machine. In particular, we are interested in the situations where fast rates of convergence to the Bayes risk can be achieved by support vector machine. Through learning examples, we illustrate that support vector machine may yield fast rates if the space spanned by an adopted kernel is sufficiently large.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.2
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• pp.37-47
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• 1995

In recent years, it has been variously developed for testing the accuracy of circular motion of NC machine tools, for example Telescoping Ball Bar Method by Bryan, Circular test Method by Knapp and $r^{-{\theta} }$ Method by Tsutsumi etc., but these methods are all 2-dimentional measuring methods on plane. These simple methods of circular motion accuracy test of NC machine tools have been studied by many reserchers as above, but it is not yet settled in the code of measuring methods of motion errors of NC machine tools, because of errors of measuring units and sensors, and also especially the difficulties of centering of measuring units and the spindle of machining center. In this paper, in use of 2 rotary encoders and 1 magnetic type linear scale with resolution of 0.5 .mu. m, it has become possible for measuring of 3 dimentional space motion accuracy.