• Korean Journal of Materials Research
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• v.28 no.7
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• pp.391-397
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• 2018

This study deals with the microstructure and tensile properties of 700 MPa-grade high-strength and seismic reinforced steel bars. The high-strength reinforced steel bars (600 D13, 600 D16 and 700 D13 specimens) are fabricated by a TempCore process, while the seismic reinforced steel bar (600S D16 specimen) is fabricated by air cooling after hot rolling. For specimens fabricated by the TempCore process, the 600 D13 and 600 D16 specimens have a microstructure of tempered martensite in the surface region and ferrite-pearlite in the center region, while the 700 D13 specimen has a microstructure of tempered martensite in the surface region and bainite in the center region. Therefore, their hardness is the highest in the surface region and shows a tendency to decrease from the surface region to the center region because tempered martensite has a higher hardness than ferrite-pearlite or bainite. However, the hardness of the 600S D16 specimen, which is composed of fully ferrite-pearlite, increases from the surface region to the center region because the pearlite volume fraction increases from the surface region to the center region. On the other hand, the tensile test results indicate that only the 700 D13 specimen with a higher carbon content exhibits continuous yielding behavior due to the formation of bainite in the center region. The 600S D16 specimen has the highest tensile-to-yield ratio because the presence of ferrite-pearlite and precipitates caused by vanadium addition largely enhances work hardening.

• The Journal of Information Technology
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• v.6 no.1
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• pp.71-84
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• 2003

In this paper, it is presented the efficient system for data recognition using the proposed binary code images. The proposed algorithm finds the position of binary image. Through the process of the block region classification, it is classified each block with the edge region using the value of gray level only. Each block region is divided horizontal and vertical edge region. If horizontal edge region blocks are classified over six blocks in any region, the proposed algorithm should search the vertical edge region in the start point of the horizontal edge region. If vertical edge region blocks were found over ten blocks in vertical region, the code image would found. Practical code region is acquired from the rate of the total edge region that is computed from the binary image that is processed with the average value. In case of the wrong rate, it is restarted the code search in the point after start point and the total process is followed. It has a short time than the before process time because it had classified block information. The block processing is faster thant the total process. The proposed system acquires the image from the digital camera and makes binary image from the acquired image. Finally, the proposed system extracts various characters from the binary image.

• Journal of Korean Neurosurgical Society
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• v.58 no.5
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• pp.479-482
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• 2015

We report a case of a 31-year-old woman with glioblastoma multiforme (GBM) in the pineal region with associated leptomeningeal dissemination and lumbar metastasis. The patient presented with severe headache and vomiting. Magnetic resonance imaging (MRI) of the brain showed a heterogeneously enhanced tumor in the pineal region with obstructive hydrocephalus. After an urgent ventricular-peritoneal shunt, she was treated by subtotal resection and chemotherapy concomitant with radiotherapy. Two months after surgery, MRI showed no changes in the residual tumor but leptomeningeal dissemination surrounding the brainstem. One month later, she exhibited severe lumbago and bilateral leg pain. Thoracico-lumbar MRI showed drop like metastasis in the lumbar region. Finally she died five months after the initial diagnosis. Neurosurgeons should pay attention to GBM in the pineal region, not only as an important differential diagnosis among the pineal tumors, but due to the aggressive features of leptomeningeal dissemination and spinal metastasis.

• Nuclear Engineering and Technology
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• v.47 no.4
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• pp.407-415
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• 2015

A developing post-dryout region is characterized by significant heat transfer enhancements compared with the fully developed post-dryout region. The heat transfer enhancements are mainly due to upstream disturbance and entrained droplets in the region immediately downstream of the critical heat flux location. In this paper, an improved heat transfer correlation is developed for the developing post-dryout regions in vertical tubes over a wide range of flow conditions. The correlation represents a correction factor for the fully developed film-boiling look-up table to be applied to the developing post-dryout region. The new correlation significantly improves the heat transfer prediction in the developing post-dryout regions and provides very good agreement with the experimental data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.3
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• pp.472-478
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• 2012

The inspection machine in PCB (printed circuit board) assembly line checks assembly errors by inspecting the images inside of the component inspection region. The component inspection region consists of region of component package and region of soldering. It is necessary to extract the regions automatically for auto-teaching system of the inspection machine. We propose an image segmentation method to extract the component inspection regions automatically from images of PCB. The acquired image is transformed to HSI color model, and then segmented by several regions by clustering method. We develop a modified K-means algorithm to increase the accuracy of extraction. The heuristics generating the initial clusters and merging the final clusters are newly proposed. The vertical and horizontal projection is also developed to distinguish the region of component package and region of soldering. The experimental results are presented to verify the usefulness of the proposed method.

• Journal of Astronomy and Space Sciences
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• v.19 no.3
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• pp.173-180
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• 2002

Images taken with the Chandra X-ray telescope have for the the first time revealed the central, wind-driven, hot bubble (Chu et al. 2001), while Hubble Space Telescope (HST) WFPC2 images of the Cat's Eye nebula, NGC 6543, show that the temperature of the halo region of angular radius ~ 20", is much higher than that of the inner bright H II region. With the coupling of a photoionization calculation to a hydrodynamic simulation, we predict the observed 〔O III〕 line intensities of the halo region with the same O abundance as in the core H II region: oxygen abundance gradient does not appear to exist in the NGC 6543 inner halo. An interaction between a (leaky) fast stellar wind and halo gas may cause the higher excitation temperatures in the halo region and the inner hot bubble region observed with the Chandra X-ray telescope.

• The Journal of Korea Robotics Society
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• v.8 no.2
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• pp.82-91
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• 2013

This paper proposes a building recognition algorithm using watershed image segmentation algorithm and integrated region matching (IRM). To recognize a building, a preprocessing algorithm which is using Gaussian filter to remove noise and using canny edge extraction algorithm to extract edges is applied to input building image. First, images are segmented by watershed algorithm. Next, a region adjacency graph (RAG) based on the information of segmented regions is created. And then similar and small regions are merged. Second, a color distribution feature of each region is extracted. Finally, similar building images are obtained and ranked. The building recognition algorithm was evaluated by experiment. It is verified that the result from the proposed method is superior to color histogram matching based results.

• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.641-649
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• 2017

Multivariate confidence regions were defined using a chi-square distribution function under a normal assumption and were represented with ellipse and ellipsoid types of bivariate and trivariate normal distribution functions. In this work, an alternative confidence region using the multivariate quantile vectors is proposed to define the normal distribution as well as any other distributions. These lower and upper bounds could be obtained using quantile vectors, and then the appropriate region between two bounds is referred to as the quantile confidence region. It notes that the upper and lower bounds of the bivariate and trivariate quantile confidence regions are represented as a curve and surface shapes, respectively. The quantile confidence region is obtained for various types of distribution functions that are both symmetric and asymmetric distribution functions. Then, its coverage rate is also calculated and compared. Therefore, we conclude that the quantile confidence region will be useful for the analysis of multivariate data, since it is found to have better coverage rates, even for asymmetric distributions.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.7-10
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• 2000

Fast block motion estimation technique is proposed to reduce the computational complexity in video coding. In the conventional methods the size of search region is fixed. For small motion regions like background the small size of sea of search region is enough to find a block motion. But for active motion regions the large size of search region is preferred to figure out the accurate motion vector. Therefore, it is reasonable that a block motion is estimated in the variable search region (both the size and the position of it). That is to say, the search region varies according to the predicted motion characteristics of a block. The block motion in video frames has temporal continuity and then the search region of a current block is predicted using the block motion of previous blocks. The computational complexity of the proposed technique is significantly reduced with a good picture quality compared to the conventional methods.

• ETRI Journal
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• v.37 no.5
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• pp.1023-1031
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• 2015

In infrared (IR) images, near targets have a transient distribution at the boundary region, as opposed to a steady one at the inner region. Based on this fact, this paper proposes a novel IR target extraction method that uses both a weighted information entropy (WIE) and an adaptive opening filter to extract near finely shaped targets in IR images. Firstly, the boundary region of a target is detected using a local variance WIE of an original image. Next, a coarse target region is estimated via a labeling process used on the boundary region of the target. From the estimated coarse target region, a fine target shape is extracted by means of an opening filter having an adaptive structure element. The size of the structure element is decided in accordance with the width information of the target boundary and mean WIE values of windows of varying size. Our experimental results show that the proposed method obtains a better extraction performance than existing algorithms.