• 한국진공학회지
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• 제6권S1호
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• pp.43-46
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• 1997

High resolution x-ray diffraction and two dimensional triple axis mapping were used to characterize a group of GaN layers of about 1.1$\mu$m grown by direct current plasma molecular beam epitaxy technique on 6H-SiC(0001). A FWHM of 11.9 arcmins for an $\omega$ scan and 1.2 arcmins for an $\omega$/2$\theta$ scan were observed. A careful study of the rocking curves showed there were some large mosaics in the GaN layer and a tilt of $0.029^{\circ}$ between the GaN layer and the SIC substrate was detected. The two dimensional triple axis mapping showed that the GaN mosaica were disoriented in the (0001) plane but rather uniformed in direction perpendicular to the plane. A mosaics were disoriented in the (0001) plane but rather uniformed in direction perpendicular to the plane. A mosaic model was deduced to explain the phenomenon and the dislocation density was estimated to be about~$10^9\;\textrm{cm}^{-2}$ acc ding to the model.

• Genomics & Informatics
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• 제17권1호
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• pp.10.1-10.3
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• 2019

To identify miRNA-mRNA interaction pairs associated with binary phenotypes, we propose a hierarchical structural component model for miRNA-mRNA integration (HisCoM-mimi). Information on known mRNA targets provided by TargetScan is used to perform HisCoM-mimi. However, multiple databases can be used to find miRNA-mRNA signatures with known biological information through different algorithms. To take these additional databases into account, we present our advanced application software for HisCoM-mimi for binary phenotypes. The proposed HisCoM-mimi supports both TargetScan and miRTarBase, which provides manually-verified information initially gathered by text-mining the literature. By integrating information from miRTarBase into HisCoM-mimi, a broad range of target information derived from the research literature can be analyzed. Another improvement of the new HisCoM-mimi approach is the inclusion of updated algorithms to provide the lasso and elastic-net penalties for users who want to fit a model with a smaller number of selected miRNAs and mRNAs. We expect that our HisCoM-mimi software will make advanced methods accessible to researchers who want to identify miRNA-mRNA interaction pairs related with binary phenotypes.

• The Journal of Advanced Prosthodontics
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• 제10권5호
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• pp.335-339
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• 2018

PURPOSE. The purpose of the present study was to compare scanning trueness and precision between an abutment impression and a stone model according to dental computer-aided design/computer-aided manufacturing (CAD/CAM) evaluation standards. MATERIALS AND METHODS. To evaluate trueness, the abutment impression and stone model were scanned to obtain the first 3-dimensional (3-D) stereolithography (STL) file. Next, the abutment impression or stone model was removed from the scanner and re-fixed on the table; scanning was then repeated so that 11 files were obtained for each scan type. To evaluate precision, the abutment impression or stone model was scanned to obtain the first 3-D STL file. Without moving it, scanning was performed 10 more times, so that 11 files were obtained for each scan type. By superimposing the first scanned STL file onto the other STL files one by one, 10 color-difference maps and reports were obtained; i.e., 10 experimental scans per type. The independent t-test was used to compare root mean square (RMS) data between the groups (${\alpha}=.05$). RESULTS. The $RMS{\pm}SD$ values of scanning trueness of the abutment impression and stone model were $22.4{\pm}4.4$ and $17.4{\pm}3.5{\mu}m$, respectively (P<.012). The $RMS{\pm}SD$ values of scanning precision of the abutment impression and stone model were $16.4{\pm}2.9$ and $14.6{\pm}1.6{\mu}m$, respectively (P=.108). CONCLUSION. There was a significant difference in scanning trueness between the abutment impression and stone model, as evaluated according to dental CAD/CAM standards. However, all scans showed high trueness and precision.

• 한국정밀공학회지
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• 제21권2호
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• pp.186-194
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• 2004

Micro-stereolithography is a newly proposed technology as a means that can fabricate a 3D micro-structure of free form. It makes a 3D micro-structure by dividing the shape into many slices of relevant thickness along horizontal surfaces, hardening each layer of slice with a focused laser beam, and stacking them up to a desired shape. In this technology, differently from the conventional stereolithography, scale effect is dominant. To realize micro-stereolithography technology, we developed the micro-stereolithography apparatus which is composed of an Ar+ laser, x-y-z stages. controllers. optical devices and scan path generation software. Related processes were developed, too. Using the system, a number of micro-structures were successfully fabricated. Some of these samples are shown for prove this system. Laser scan path generation algorithm and software considering photopolymer solidification phenomena as well as given 3D model were developed. Sample fabrication of developed software shows relatively high dimensional accuracy compared to the uncompensated result.

• 대한핵의학회지
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• 제27권2호
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• pp.248-255
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• 1993

To Assessment of the quantified renal uptake rates in every $^{99m}Tc$-DMSA scan tests of patients is actually difficult because of time consumption and complicated calculations required to measure the correct dose of the infused radionulide and radiation decay, the adjustment for the depth of kidney and the subtraction of background count. We've formulated two regression models for the quantified renal uptake rates[I] from the simple renal uptake rates[H] with a square shaped ROI (Region-Of-Interest) in 25 cases (Group 1) and with a kidney shaped ROI in 37 cases (Group 2), respectively. The regression model for the Group 1 was $[I]_1$=0.885 $[H]_1$-4.575 (P<0.005), and for the Group 2 was $[I]_2$=0.591 $[H]_2$-2.105 (p<0.005). The formal charts were clinically convenient to evaluate the individual renal functions in patients with $^{99m}Tc$-DMSA renal scan.

• 복식문화연구
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• 제21권4호
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• pp.585-595
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• 2013

The most researches developing pattern of compression style sportswear were targeted at the live model that has muscular body build. The purpose of this study was developing a method for classifying men's lower body types in terms of muscular body build. The 3D human body scan data and body measurements of 30s of Size Korea were analyzed. The subjects (n=203) were men between the ages of 30 and 39 years. Men's muscular body build was classified with two key dimensions, thigh girth and calf girth. The subjects were divided into four groups. From each group, average subjects (n=42) whose height and weight were close to the mean value ($mean{\pm}1/2$ S.D.) were selected. 42 subjects were divided up as four groups. Group I (n=7) was thigh and calf developed body type. Group II (n=9) was thigh developed body type. Group III (n=11) was calf developed body type. Group IV (n=15) was thigh and calf undeveloped body type. Four groups had distinct different at widths (n=4), depths (n=4), and girths (n=9) dimensions. The results showed that the muscular men in their 30s could be defined by thigh and calf girths. The thigh developed muscular men had thigh girth over 60cm and the calf developed muscular men had calf girth over 38cm. From each group one representative was selected by 3D body scan figure.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.361-370
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• 2020

Automatic building material recognition has been a popular research interest over the past decade because it is useful for construction management and facility management. Currently, the extensively used methods for automatic material recognition are mainly based on 2D images. A terrestrial laser scanner (TLS) with a built-in camera can generate a set of coloured laser scan data that contains not only the visual features of building materials but also other attributes such as material reflectance and surface roughness. With more characteristics provided, laser scan data have the potential to improve the accuracy of building material recognition. Therefore, this research aims to develop a TLS-based building material recognition method by combining machine learning techniques. The developed method uses material reflectance, HSV colour values, and surface roughness as the features for material recognition. A database containing the laser scan data of common building materials was created and used for model training and validation with machine learning techniques. Different machine learning algorithms were compared, and the best algorithm showed an average recognition accuracy of 96.5%, which demonstrated the feasibility of the developed method.

• Imaging Science in Dentistry
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• 제32권1호
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• pp.35-39
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• 2002

Purpose: The altered gantry angle during scanning for some multiplanar reconstruction CT program (CT/MPR) may cause distortion of the image. The aim of this study was to ascertain whether there is a image distortion in a reformatted image when the gantry and the object are equally inclined using ToothPix and DentaScan program. Materials and Methods: A resin block model with four cylindrical holes and a human dry mandible were used. Two MPR software packages, ToothPix and DentaScan program, were used for reformatted panoramic images. The block and the gantry were equally inclined at 0°, 15°, and 30°. Results: With ToothPix program, a resin block model with empty holes and a dry mandible showed inclined images in the reformatted panoramic image. Increasing the gantry angle, the depth and inclination of the holes were increased in the reformatted central panoramic images. However, a resin block model with gutta perch a in its holes and a dry mandible with a wire in its mandibular canal didn't show image distortion. With DentaScan program, image distortion was not seen in any situation. Conclusion: ToothPix program may distort the reformatted image when the gantry angle is not at zero degrees. However, with DentaScan program, the patient may be positioned comfortably and the gantry can be adjusted to the patient positioning.

• 방송공학회논문지
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• 제28권3호
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• pp.293-301
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• 2023

표준화되지 않은 의료 데이터 수집 및 관리는 여전히 수동으로 진행되고 있어, 이 문제를 해결하기 위해 딥 러닝을 사용해 CT 데이터를 분류하는 연구들이 진행되고 있다. 하지만 대부분 연구에서는 기본적인 CT slice인 axial 평면만을 기반으로 모델을 개발하고 있다. CT 영상은 일반 이미지와 다르게 인체 구조만 묘사하기 때문에 CT scan을 재구성하는 것만으로도 더 풍부한 신체적 특징을 나타낼 수 있다. 이 연구는 axial 평면뿐만 아니라 CT 데이터를 2D로 변환하는 여러가지 방법들을 통해 보다 높은 성능을 달성할 수 있는 방법을 찾고자 한다. 훈련은 5가지 부위의 CT 스캔 1042개를 사용했고, 모델 평가를 위해 테스트셋 179개, 외부 데이터셋으로 448개를 수집했다. 딥러닝 모델 개발을 위해 ImageNet으로 사전 학습된 InceptionResNetV2를 백본으로 사용하였으며, 모델의 전체 레이어를 재 학습했다. 실험결과 신체 부위 분류에서는 재구성 데이터 모델이 99.33%를 달성하며 axial 모델보다 1.12% 더 높았고, 조영제 분류에서는 brain과 neck에서만 axial모델이 높았다. 결론적으로 axial slice로만 훈련했을 때 보다 해부학적 특징이 잘 나타나는 데이터로 학습했을 때 더 정확한 성능 달성이 가능했다.

• 대기
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• 제23권4호
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• pp.453-470
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• 2013

As a part of the KIAPS Observation Processing System (KOPS), we have developed the modules of satellite radiance data pre-processing and quality control, which include observation operators to interpolate model state variables into radiances in observation space. AMSU-A (Advanced Microwave Sounding Unit-A) level-1d radiance data have been extracted using the BUFR (Binary Universal Form for the Representation of meteorological data) decoder and a first guess has been calculated with RTTOV (Radiative Transfer for TIROS Operational Vertical Sounder) version 10.2. For initial quality checks, the pixels contaminated by large amounts of cloud liquid water, heavy precipitation, and sea ice have been removed. Channels for assimilation, rejection, or monitoring have been respectively selected for different surface types since the errors from the skin temperature are caused by inaccurate surface emissivity. Correcting the bias caused by errors in the instruments and radiative transfer model is crucial in radiance data pre-processing. We have developed bias correction modules in two steps based on 30-day innovation statistics (observed radiance minus background; O-B). The scan bias correction has been calculated individually for each channel, satellite, and scan position. Then a multiple linear regression of the scan-bias-corrected innovations with several predictors has been employed to correct the airmass bias.