• Proceedings of the Korean Radioactive Waste Society Conference
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• 2005.11a
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• pp.134-142
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• 2005

The experimental setups for flow visualization and processes identification in laboratory scale (so cal led Flow Lab.) has developed to get ideas and answer fundamental questions of flow and migration in geologic media. The setup was made of a granite block of $50{\times}50cm$ scale and a transparent acrylate plate. The tracers used in this experiments were tritiated water, anions, and sorbing cations as well as an organic dye, eosine, to visualize migration paths. The migration plumes were taken with a digital camera as a function of time and stored as digital images. A migration model was also developed to describe and identify the transport processes. Computer simulation was carried out not only for the hydraulic behavior such as distributions of pressure and flow vectors in the fracture but also for the migration plume and the elution curves.

• Journal of Astronomy and Space Sciences
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• v.17 no.1
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• pp.19-32
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• 2000

A total of 1,018 observations (509 in B, 509 in V) of the eclipsing binary VW Cep was made during 7 nights from April through May in 1999 at Sobaeksan Optical Astronomy Observatory, using the CCD camera attached to the 61cm telescope. A time of minimum light of HJD2451327.2282 was determined from our data, and we constructed BV light curves with the data. Using, Wilson-Devinney’s binary model, we analized the light curves. The absolute dimension of $M_1=0.95M_\odot,M_2=0.33M_\odot,R_1=1.02R_\odot,R_2=0.66R_\odot$ of the VW Cep system were derived from our light curve solution and Kaszas et al. (1998) spectroscopic results.

• Journal of Astronomy and Space Sciences
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• v.23 no.3
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• pp.289-302
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• 2006

For a reconstruct draft of the Gyupyo (Gnomon) in King Sejong Era, we collected a lot of documents of the Chosen Dynasty. With the result we made a reduced model by 1/10 and drawing set for the Gnomon in Sejong Era. It is composed of the Gyu, Yongpyo, Hoengryang (a beam), Yeongbu and granitic prop. You can read the scale where the shadow of vertically-standing stick touches the horizontal Gyu. A Shadow-Define. (Yeongbu) was used to focus the shadow cast by the sun's center, measuring the (gnomon) shadow length precisely. A Yeongbu is made of a leaf of copper in the middle of which is pierced a pin-hole for the tiny optical image of the sun which tilted to face the incident sunlight, used the principle of the a pin-hole camera and Scheimplug principle.

• Journal of Astronomy and Space Sciences
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• v.23 no.3
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• pp.177-188
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• 2006

A total of 920 observations (230 in ${\Delta}B$, 230 in ${\Delta}V$, 230 in ${\Delta}R$, 230 in ${\Delta}I$) for V523 Cas were made on 5 nights from January 6 to 24 in 2003 using the 61cm telescope with 2K CCD camera of the Sobaeksan Optical Astronomy Observatory of KASI. From our observations 9 times of minimum light were newly determined. Combined analysis of our new BVRI light curves with the double-lined radial velocity curves of the Rucinski et al.'s (2003) were made with the 2004 Wilson-Devinney (WD) binary model to yield new physical parameters of the V523 system. Small asymmetries in light curves were explained with the adoption of a cool spot on the hot primary and a hot spot on the cool secondary.

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1491-1500
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• 2008

This paper proposes an intelligent surveillance system to recognize suspicious patterns of the human behavior by using the Hidden Markov Model. First, the method finds foot area of the human by motion detection algorithm from image sequence of the surveillance camera. Then, these foot locus form observation series of features to learn the HMM. The feature that is position of the human foot is changed to each code that corresponds to a specific label among 16 local partitions of image region. Therefore, specific moving patterns formed by the foot locus are the series of the label numbers. The Baum-Welch algorithm of the HMM learns each suspicious and specific pattern to classify the human behaviors. To recognize the inputted human behavior pattern in a test image, the probabilistic comparison between the learned pattern of the HMM and foot series to be tested decides the categorization of the test pattern. The experimental results show that the method can be applied to detect a suspicious person prowling in corridor.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.851-859
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• 2002

This paper discusses the design of a working system that visually recognizes hand gestures for the control of a window based user interface. We present a method for tracking the fingertip of the index finger using a single camera. Our method is based on CAMSHIFT algorithm and performs better than the CAMSHIFT algorithm in that it tracks well particular hand poses used in the system in complex backgrounds. We describe how the location of the fingertip is mapped to a location on the monitor, and how it Is both necessary and possible to smooth the path of the fingertip location using a physical model of a mouse pointer. Our method is able to track in real time, yet not absorb a major share of computational resources. The performance of our system shows a great promise that we will be able to use this methodology to control computers in near future.

• Journal of the Korean Wood Science and Technology
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• v.45 no.6
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• pp.797-808
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• 2017

Automatic wood species identification systems have enabled fast and accurate identification of wood species outside of specialized laboratories with well-trained experts on wood species identification. Conventional automatic wood species identification systems consist of two major parts: a feature extractor and a classifier. Feature extractors require hand-engineering to obtain optimal features to quantify the content of an image. A Convolutional Neural Network (CNN), which is one of the Deep Learning methods, trained for wood species can extract intrinsic feature representations and classify them correctly. It usually outperforms classifiers built on top of extracted features with a hand-tuning process. We developed an automatic wood species identification system utilizing CNN models such as LeNet, MiniVGGNet, and their variants. A smartphone camera was used for obtaining macroscopic images of rough sawn surfaces from cross sections of woods. Five Korean softwood species (cedar, cypress, Korean pine, Korean red pine, and larch) were under classification by the CNN models. The highest and most stable CNN model was LeNet3 that is two additional layers added to the original LeNet architecture. The accuracy of species identification by LeNet3 architecture for the five Korean softwood species was 99.3%. The result showed the automatic wood species identification system is sufficiently fast and accurate as well as small to be deployed to a mobile device such as a smartphone.

• Korean Journal of Optics and Photonics
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• v.14 no.5
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• pp.504-508
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• 2003

We present a new point-diffraction interferometer, which has been devised for the three-dimensional profile measurement of light scattering rough surfaces. The interferometer system has multiple sources of two-point-diffraction and a CCD camera composed of an array of two-dimensional photodetectors. Each diffraction source is an independent two-point-diffraction interferometer made of a pair of single-mode optical fibers, which are housed in a ceramic ferrule to emit two spherical wave fronts by means of diffraction at their free ends. The two spherical wave fronts then interfere with each other and subsequently generate a unique fringe pattern on the test surface. A He-Ne source provides coherent light to the two fibers through a 2${\times}$l optical coupler, and one of the fibers is elongated by use of a piezoelectric tube to produce phase shifting. The xyz coordinates of the target surface are determined by fitting the measured phase data into a global model of multilateration. Measurement has been performed for the warpage inspection of chip scale packages (CSPs) that are tape-mounted on ball grid arrays (BGAs) and backside profile of a silicon wafer in the middle of integrated-circuit fabrication process. When a diagonal profile is measured across the wafer, the maximum discrepancy turns out to be 5.6 ${\mu}{\textrm}{m}$ with a standard deviation of 1.5 ${\mu}{\textrm}{m}$.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.114-123
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• 2014

Vehicle Black Box (Event Data Recorder EDR) only recognizes the general surrounding environments of load. In addition, general EDR is difficult to recognize the images of a sudden illumination change. It appears that the lens is being a severe distortion. Therefore, general EDR does not provide the clues of the circumstances of the accident. To solve this problem, we estimate the value of Normalized Luminance Descriptor(NLD) and Normalized Contrast Descriptor(NCD). Illumination change is corrected using Normalized Image Quality(NIQ). Second, we are corrected lens distortion using model of Field Of View(FOV) based on designed method of fisheye lens. As a result, we propose integration algorithm of two methods that correct distortions of images using each Gamma Correction and Lens Correction in parallel.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.5
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• pp.55-64
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• 2018

In this paper, we propose fully convolutional neural networks based automatic detection of a pothole that directly causes driver's safety accidents and the vehicle damage. First, the training DB is collected through the camera installed in the vehicle while driving on the road, and the model is trained in the form of a semantic segmentation using the fully convolutional neural networks. In order to generate robust performance in a dark environment, we augmented the training DB according to brightness, and finally generated a total of 30,000 training images. In addition, a total of 450 evaluation DB was created to verify the performance of the proposed automatic pothole detection, and a total of four experts evaluated each image. As a result, the proposed pothole detection showed robust performance for missing.