• Journal of the Korean Society for Precision Engineering
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• v.30 no.1
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• pp.67-76
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• 2013

We present an innovative real-time laser welding monitoring technique employing the correlation analysis of the plasma plume optical emission generated during the process. The plasma optical radiation emitted during Nd:YAG laser welding of S45C steel samples has detected with a Photodiode and analyzed under different process conditions. The discrete DC voltage difference, filter methods and wavelet transform has been used to decompose the optical signal into various discrete series of sequences over different frequency bands. Considering that wavelet analysis can decompose the optical signals, extract the characteristic information of the signals and define the defects location accurately, it can be used to implement process-control of laser welding.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.270-272
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• 2021

최근 영상신호처리에 대한 딥러닝 기술이 비약적으로 발전함에 따라 다양한 방면으로 시도되고 있다. 그 중 machine level vision 에서 인지 기능을 하는 optical flow 를 end-to-end 학습 방식으로 제시하여 고성능 결과물을 도출하는 RAFT(Recurrent All-pairs Field Transform for Optical flow, 2020)에 대해 분석하고자 한다. RAFT 는 입력된 두 이미지에 대한 4D correlation volume 을 구축하여 모든 픽셀에 대한 정보를 사용한다. 또한, recurrent neural network 에서 차용한 반복적인 연산 학습 구조를 통하여 결과물인 flow field 의 정확도를 높인다. 해당 모델은 stereo dataset 을 사용하는 다른 모델에 비해 학습 시간이 짧고 용량이 작으면서 error rate 은 낮은 모습을 보인다. 현재 많은 연구에서 optical flow 를 접목하려는 움직임을 보이고 있고 다양하게 활용될 가능성이 다분하다는 점에서 주목할 가치가 있다.

• Journal of the Optical Society of Korea
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• v.18 no.5
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• pp.523-530
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• 2014

Machine material and structural strain are critical factors for appraising mechanical properties and safety. Particularly in three and four-point bending tests, which appraise the deflection and flexural strain of an object due to external force, measurements are made by the crosshead movement or deflection meter of a universal testing machine. The Digital Image Correlation (DIC) method is one of the non-contact measurement methods. It uses the image analyzing method that compares the reference image with the deformed image for measuring the displacement and strain of the objects caused by external force. Accordingly, the advantage of this method is that the object's surface roughness, shape, and temperature have little influence. However, its disadvantage is that it requires extensive time to compare the reference image with the deformed image for measuring the displacement and strain. In this study, an algorithm is developed for DIC that can improve the speed of image analysis for measuring the deflection and strain of an object caused by a three-point bending load. To implement this algorithm for improving the speed of image analysis, LabVIEW 2010 was used. Furthermore, to evaluate the accuracy of the developed fast correlation algorithm, the deflection of an aluminum specimen under a three-point bending load was measured by using the universal test machine and DIC measurement system.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1911-1923
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• 2022

The availability of high-resolution satellite images provides precise information without direct observation of the research target. Korea Multi-Purpose Satellite (KOMPSAT), also known as the Arirang satellite, has been developed and utilized for earth observation. The machine learning model was continuously proven as a good classifier in classifying remotely sensed images. This study aimed to compare the performance of the support vector machine (SVM) model in classifying the land cover of the Delaware River port area on high and medium-resolution images. Three optical images, which are KOMPSAT-2, KOMPSAT-3A, and Sentinel-2B, were classified into six land cover classes, including water, road, vegetation, building, vacant, and shadow. The KOMPSAT images are provided by Korea Aerospace Research Institute (KARI), and the Sentinel-2B image was provided by the European Space Agency (ESA). The training samples were manually digitized for each land cover class and considered the reference image. The predicted images were compared to the actual data to obtain the accuracy assessment using a confusion matrix analysis. In addition, the time-consuming training and classifying were recorded to evaluate the model performance. The results showed that the KOMPSAT-3A image has the highest overall accuracy and followed by KOMPSAT-2 and Sentinel-2B results. On the contrary, the model took a long time to classify the higher-resolution image compared to the lower resolution. For that reason, we can conclude that the SVM model performed better in the higher resolution image with the consequence of the longer time-consuming training and classifying data. Thus, this finding might provide consideration for related researchers when selecting satellite imagery for effective and accurate image classification.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.1
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• pp.8-16
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• 2006

The communication through optical fiber is taking an important role of the expansion of communication network with excellent transmitting rate and quality. As the optical communication is introduced to the backbone network at first and becomes a general communication method of network, the demand of kernel parts of optical communication such as PLC(Planar Light Circuit), Coupler, and WDM(Wavelength Division Multiplexing) element increases. The alignment and the attachment technology are very important in the fabrication of optical elements. In this paper, the driving mechanism of ultra precision stage is studied with the aim of optimal design of stage. The travel and the resolution of stage are investigated. The hysteresis of the stage is generated because of PZT actuator. The hysteresis and the inverse hysteresis are modeled in X, Y, and Z-axis motion. The input data of desired displacement of the stage according to input voltage is obtained from the inverse hysteresis equation. In the result of experiments with the input data, the errors due to hysteresis are well compensated.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.414-417
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• 2005

Single point diamond turning technique for optical crystals is studied in this paper. The main factors which are influential the machined surface quality are discovered and regularities of machining process are drawn. Optical crystals have found more and more important applications in the field of modern optics. Optical crystals are mostly brittle materials of poor machinability. The traditional machining method is polishing which has many shortcomings such as low production efficiency, poor ability to be automatically controlled and edge effect of the workpiece. The purpose of our research is to find the optimal machining conditions for ductile cutting of optical crystals and to apply the SPDT technique to the manufacturing of ultra precision optical components of brittle material(Ge). Many technical challenges are being tried for the large space infrared telescope, which is one of the major objectives of the National Strategic Technology Road Map (NSTRM).

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.6
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• pp.118-123
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• 2008

Weakness process can be called ferrule array and epoxy filling process at connector manufacturing process, and a lot of problems happen as think as general manufacturing process at early investment. Wished to improve this and working environment mend of worker on childhood(planning phase) and problem that is happened at done ferrule array and epoxy injection by emphasis target. By ferrule sorting and Improvement of epoxy filling process, bring authoritativeness elevation of product by fraction defective decrease of product by sized work along with productivity elevation. On the other hand, working jigs are various in characteristics of optical connector manufacturing line. There have been lots of restriction in practice because the applicability of this system is only for single type model though the network should be smooth between lines. Thus, it is not only needed the recognition of necessity in industrial line but also the development of automation system arraying ferrule and filling epoxy in the manufacturing process. It is found that the present system development enhances productivity fairly and prevents industrial disaster in the optical connector manufacturing system.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.232-237
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• 1998

This paper describes a new method for estimating time-to-collision which exhibits high tolerance to noise contained in camera images. Time to collision (TTC) is one of the most important parameters available from a camera attached to a mobile machine. TTC indirectly stands far the translation speed of the camera and is usually calculated either from successive images or optical flow by using intimate relationship between TTC and flow divergence. In most cases, however, it is not easy to get accurate optical flow, which makes it difficult to calculate TTC. In this study it is proved that if the target has a smooth surface, the average of divergence over any point-symmetric region on the image is equal to the divergence of the center of the region. It means that required divergence can be calculated by integrating optical flow vectors over a symmetric region. It is expected that in the process of the integration, accidental noise is canceled if they are independent of optical flow and the motion of the camera. Experimental results show that TTC can be estimated regardless of the surface condition. It is also shown that influence of noise is eliminated as the area of integration increases.

• Journal of Biosystems Engineering
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• v.22 no.3
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• pp.295-302
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• 1997

An ultimate purpose of this study was to develop an automatic system for brown rice quality inspection using image processing technique. In this study emphasis was put on developing an algorithm for discriminating the brown rice kernels depending on their external quality with a color image processing system equipped with an adaptor magnifying the input image and optical fiber for oblique lightening. Primarily, geometical and optical features of images were analyzed with paddy and the various brown rice kernel samples such as a sound, cracked, peen-transparent, green-opaque, colored, white-opaque and brokens. Secondary, geometrical and optical parameters significant for identifying each rice kernels were screened by a statistical analysis(STEPWISE and DISCRIM procedure, SAS wer. 6) and an algorithm fur on- line discrimination of the rice kernels in static state were developed, and finally its performance was evaluated. The results are summarized as follows. 1) It was ascertained that the cracked kernels can be detected when e incident angle of the oblique light is less than 2$0^{\circ}C$ but detectivity was significantly affected by the angle between the direction of the oblique light and the longitudinal axis of the rice kernel and also by the location of the embryo with respect to the oblique light. 2) The most significant Parameters which can discriminate brown rice kernels are area, length and R, B and r values among the several geometrical and optical parameters. 3) Discrimination accuracies of the algorithm were ranged from 90% to 96% for a sound, cracked, colored, broken and unhulled, about 81 % for green-transparent and white-opaque and 75 % for green-opaque, respectively.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.5
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• pp.621-625
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• 2010

This paper presents a hand motion tracking method for hand gesture interface using a camera in mobile devices such as a smart phone and PDA. When a camera moves according to the hand gesture of the user, global optical flows are generated. Therefore, robust hand movement estimation is possible by considering dominant optical flow based on histogram analysis of the motion direction. A continuous hand gesture is segmented into unit gestures by motion state estimation using motion phase, which is determined by velocity and acceleration of the estimated hand motion. Feature vectors are extracted during movement states and hand gestures are recognized at the end state of each gesture. Support vector machine (SVM), k-nearest neighborhood classifier, and normal Bayes classifier are used for classification. SVM shows 82% recognition rate for 14 hand gestures.