• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.283-284
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• 2010

• Journal of the Korean Society for Precision Engineering
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• v.22 no.5 s.170
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• pp.7-15
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• 2005

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1129-1131
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• 2008

We injection molded a plate with micro surface features including micro prizms & micro channels patterns on its surface and investigated the replication of the micro features depending on the mold temperature which is one of typical process parameters. The size of the patterns were 8um, 10um, 15um of prizm features & 15um, 30um, 45um of channel features. The size of the plate is about $400mm{\times}400mm$ and the thickness is 1mm of plate. the repliction of the mucro features turned out to depend on the mold temperature and also the location on the plate. The pressure and the feature of the melt in the cavity were also measured in real-time for the investigation on the micro feature replication.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.41.5-41
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• 2002

This paper presents a feature extraction algorithm for vision-based micromanipulation. In order to guarantee of the accurate micromanipulation, most of micromanipulation systems use vision sensor. Vision data from an optical microscope or high magnification lens have vast information, however, characteristics of micro image such as emphasized contour, texture, and noise are make it difficult to apply macro image processing algorithms to micro image. Grasping points extraction is very important task in micromanipulation because inaccurate grasping points can cause breakdown of micro gripper or miss of micro objects. To solve those problems and extract grasping points for micromanipulation...

• Journal of the Korean Society for Precision Engineering
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• v.28 no.5
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• pp.578-584
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• 2011

In mechanical cutting process, burr was generated at workpiece by cutting tool generally. It is working disturbance during manufacturing process. Besides burr was taken shape relatively large size more micro scale machining than macro scale machining. Many researches have been studied to remove micro burr(de-burring), because it was negative effect for accuracy of machining shape. However, micro de-burring was constrained by burr height, micro feature and so on. In this paper, experimental research was carried out to compare de-burring characteristics of $Al_2O_3$ abrasive and polymer.

• Convergence Security Journal
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• v.14 no.5
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• pp.17-24
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• 2014

This study proposed LDP (Local Directional Pattern) as a new local micro pattern for facial expression recognition to solve noise sensitive problem of LBP (Local Binary Pattern). The proposed method extracts 8-directional components using $m{\times}m$ mask to solve LBP's problem and choose biggest k components, each chosen component marked with 1 as a bit, otherwise 0. Finally, generates a pattern code with bit sequence as 8-directional components. The result shows better performance of rotation and noise adaptation. Also, a new local facial feature can be developed to present both PFF (permanent Facial Feature) and TFF (Transient Facial Feature) based on the proposed method.

• The Journal of the Acoustical Society of Korea
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• v.38 no.6
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• pp.687-696
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• 2019

Conventional feature of seismic classification focuses on strong seismic classification, while it is not suitable for classifying micro-seismic waves. We propose a feature extraction method based on histogram and Principal Component Analysis (PCA) in frequency-time space suitable for classifying seismic waves including strong, micro, and artificial seismic waves, as well as noise classification. The proposed method essentially employs histogram and PCA based features by concatenating the frequency and time information for binary classification which consist strong-micro-artificial/noise and micro/noise and micro/artificial seismic waves. Based on the recent earthquake data from 2017 to 2018, effectiveness of the proposed feature extraction method is demonstrated by comparing it with existing methods.

• Journal of the Korean Society for Precision Engineering
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• v.33 no.9
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• pp.715-721
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• 2016

In this paper, the classification rate of micro-cracks in silicon wafers was improved using a SVM. In case I, we investigated how feature data of micro-cracks and SVM parameters affect a classification rate. As a result, weighting vector and bias did not affect the classification rate, which was improved in case of high cost and sigmoid kernel function. Case II was performed using a more high quality image than that in case I. It was identified that learning data and input data had a large effect on the classification rate. Finally, images from cases I and II and another illumination system were used in case III. In spite of different condition images, good classification rates was achieved. Critical points for micro-crack classification improvement are SVM parameters, kernel function, clustered feature data, and experimental conditions. In the future, excellent results could be obtained through SVM parameter tuning and clustered feature data.

• Transactions of Materials Processing
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• v.19 no.3
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• pp.191-196
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• 2010

Tungsten-carbide as typical difficult-to-cut material has excellent mechanical properties such as high thermal resistivity, mechanical strength and chemical durability. However, it is next to impossible for tungsten-carbide to be fabricated the needed parts by cutting process. In this study, for establishing the micro fabrication method of tungsten-carbide for micro injection or compression molding core, the investigation on micro cutting characteristics of tungsten-carbide green part which is made by powder injection molding process and easy to cut relatively was performed. For this, micro endmilling experiments of tungsten-carbide green part were performed according to various cutting conditions. Finally, the wear trend of micro endmill and the appearance of micro rib according to feed-rate and cutting depth per step were analyzed through SEM images of micro cutting feature and microscope images of micro tools.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2007.05a
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• pp.75-78
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• 2007

Recently, the need for micro mold or micro mechanical parts has been rapidly increased. As feature size decreases, conventional machining processes show their limitation. Micro electrical discharging machining (EDM) and electrochemical machining (ECM) have many advantages in micro machining. They can be used to make structures of micro scale, or even nano scale size. In this paper, the application of micro EDM and ECM has been investigated.