• Nuclear Engineering and Technology
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• v.37 no.4
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• pp.395-400
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• 2005

An environmental radon monitoring system, comprising a radon-cup, an etching system, and a track counting system, was constructed. The radon cup is a cylindrical chamber with a radius of 2.2 cm and a height of 3.2 cm in combination with a CR-39 detector. Carbon is impregnated in the bodies of the detector chamber to avoid problem of an electrostatic charge. The optimized etching condition for the CR-39 exposed to a radon environment turned out to be a 6 N NaOH solution at 70^{\circ}$ over a 7hour period. The bulk etch rate under the optimized condition was $1.14{\pm}0.03\;{\mu}m\;h^{-1}$. The diameter of the tracks caused by radon and its progeny were found to be in the range of $10\~25\;{\mu}m$ under the optimized condition. The track images were observed with a track counting system, which consisted of an optical microscope, a color charged couple device (CCD) camera, and an image processor. The calibration factor of this system is obtained to be $0.105{\pm}0.006$ tracks $cm^2$ per Bq $m^{-3}$ d.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.5
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• pp.163-169
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• 2021

In this paper, we implemented a deep learning-based automatic object tracking and handy motion control drone system and analyzed the performance of the proposed system. The drone system automatically detects and tracks targets by analyzing images obtained from the drone's camera using deep learning algorithms, consisting of the YOLO, the MobileNet, and the deepSORT. Such deep learning-based detection and tracking algorithms have both higher target detection accuracy and processing speed than the conventional color-based algorithm, the CAMShift. In addition, in order to facilitate the drone control by hand from the ground control station, we classified handy motions and generated flight control commands through motion recognition using the YOLO algorithm. It was confirmed that such a deep learning-based target tracking and drone handy motion control system stably track the target and can easily control the drone.

• Current Optics and Photonics
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• v.3 no.1
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• pp.27-32
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• 2019

We present an autofocus tracking system implemented by the digital refocusing of digital holographic microscopy (DHM) and the tunability of an electrically tunable lens (ETL). Once the defocusing distance of an image is calculated with the DHM, then the focal plane of the imaging system is optically tuned so that it always gives a well-focused image regardless of the object location. The accuracy of the focus is evaluated by calculating the contrast of refocused images. The DHM is performed in an off-axis holographic configuration, and the ETL performs the focal plane tuning. With this proposed system, we can easily track down the object drifting along the depth direction without using any physical scanning. In addition, the proposed system can simultaneously obtain the digital hologram and the optical image by using the RGB channels of a color camera. In our experiment, the digital hologram is obtained by using the red channel and the optical image is obtained by the blue channel of the same camera at the same time. This technique is expected to find a good application in the long-term imaging of various floating cells.

• Smart Structures and Systems
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• v.23 no.4
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• pp.359-371
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• 2019

As part of a structural health monitoring system, the relative geometric relationship between a ship and bridge has been recognized as important for bridge authorities and ship owners to avoid ship-bridge collision. This study proposes a novel computer vision method for the real-time geometric parameter identification of moving ships based on a single shot multibox detector (SSD) by using transfer learning techniques and monocular vision. The identification framework consists of ship detection (coarse scale) and geometric parameter calculation (fine scale) modules. For the ship detection, the SSD, which is a deep learning algorithm, was employed and fine-tuned by ship image samples downloaded from the Internet to obtain the rectangle regions of interest in the coarse scale. Subsequently, for the geometric parameter calculation, an accurate ship contour is created using morphological operations within the saturation channel in hue, saturation, and value color space. Furthermore, a local coordinate system was constructed using projective geometry transformation to calculate the geometric parameters of ships, such as width, length, height, localization, and velocity. The application of the proposed method to in situ video images, obtained from cameras set on the girder of the Wuhan Yangtze River Bridge above the shipping channel, confirmed the efficiency, accuracy, and effectiveness of the proposed method.

• Journal of Fashion Business
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• v.23 no.4
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• pp.1-12
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• 2019

The study investigated trends in wall-covering displays in interior design stores. Although studies reported design trends at well-known exhibitions overseas such as Heimtextil and Maison objet, many different cases present actual realistic design flows. This study analyzes the actual market flow rather than design as an exhibition concept, and presents the interior CMF trends in 2019. The CMF design of wall-covering displayed in New York D&D Building in 2019 can be summarized as follows: W was the most frequently seen show-window, but like R, which is a strong color, it is also used to convey surrealistic images. The store entrance was designed to attract consumers' attention inside, and was constructed to reflect the actual trend. In the 2019 New York market, the wall-covering of Gray and YR were displayed through the shop entrance to suggest substantial sales. In addition, the demand for gold metallic wall-covering is significant as gold was strong in many forms. This study represents a valuable resource to identify trends in wall-covering from 2017 to 2019 compared with previous studies. This study represents a valuable foundation for a wide range of topics related to the use of wall-covering for interior decoration.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.2
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• pp.59.4-60
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• 2018

Ultra-diffuse galaxies (UDGs) are an unusual galaxy population. They are ghostlike galaxies with fainter surface brightness than normal dwarf galaxies, but they are as large as MW-like galaxies. The key question on UDGs is whether they are 'failed' giant galaxies or 'extended' dwarf galaxies. To answer this question, we study UDGs in massive galaxy clusters. We find an amount of UDGs in deep HST images of three Hubble Frontier Fields clusters, Abell 2744 (z=0.308), Abell S1063 (z=0.347), and Abell 370 (z=0.374). These clusters are the farthest and most massive galaxy clusters in which UDGs have been discovered until now. The color-magnitude relations show that most UDGs have old stellar population with red colors, while a few of them show bluer colors implying the existence of young stars. The stellar masses of UDGs show that they have less massive stellar components than the bright red sequence galaxies. The radial number density profiles of UDGs exhibit a drop in the central region of clusters, suggesting some of them were disrupted by strong gravitational potential. Their spatial distributions are not homogeneous, which implies UDGs are not virialized enough in the clusters. With virial masses of UDGs estimated from the fundamental manifold, most UDGs have M_200 = 10^10 - 10^11 M_Sun indicating that they are dwarf galaxies. However, a few of UDGs more massive than 10^11 M_Sun indicate that they are close to failed giant galaxies.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.2
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• pp.33.3-34
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• 2018

Galaxy clusters contain a diffuse component of stars outside galaxies, that is observed as intracluster light (ICL). Since the ICL abundance increases during various dynamical exchanges of galaxies, the amount of ICL can act as a measurement tool for the dynamical stage of galaxy clusters. There are two prominent ICL formation scenarios; one is related to the brightest cluster galaxy (BCG) major mergers, and the other to the tidal stripping of galaxies. However, it is still under debate as to which is the main ICL formation mechanism. In this study we improve on earlier observational constraints of the ICL origin, by investigating it in a massive fossil cluster at z~0.47. Fossil clusters are believed to be dynamically matured galaxy clusters which have dominant BCGs. Recent simulation studies imply that, BCGs have assembled 85~90% of their mass by z~0.4 (e.g., Contini et al. 2014). Thus our target is an optimal test bed to examine the BCG-related scenario. Our deep images and Multi-Object Spectroscopic observations of the target fossil cluster (Gemini North 2018A) allow us to extract the ICL distribution, ICL color map and ICL fraction to cluster light. We will present a possible constraint of the ICL origin and discuss its connection to the BCG and the host galaxy cluster.

• Food Science of Animal Resources
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• v.42 no.1
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• pp.98-110
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• 2022

Meat jerky is a type of meat snack with a long shelf life, light weight, and unique sensory properties. However, meat jerky requires a long manufacturing time, resulting in high energy consumption. In this study, beef jerky was prepared by injecting different concentrations of brine at different hot-air drying times (0-800 min). When the brine injection levels were increased to 30%, the drying characteristics of beef jerky, such as drying time and effective moisture diffusivity, were significantly improved owing to the relatively high water content and the formation of porous structures. The physicochemical properties (e.g. meat color, porosity, shear force, and volatile basic nitrogen) of the beef jerky injected with 30% brine were improved owing to the shortened drying time. Scanning electron microscopy images showed that the beef jerky structure became porous and irregular during the brine injection process. Our novel processing technique for manufacturing beef jerky leads to improved quality characteristics and shortened drying times.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2345-2358
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• 2022

This paper presents a method to generate stained-glass animation from video inputs. The method initially segments an input video volume into several regions considered as fragments of glass by mean-shift segmentation. However, the segmentation predominantly results in over-segmentation, causing several tiny segments in a highly textured area. In practice, assembling significantly tiny or large glass fragments is avoided to ensure architectural stability in stained glass manufacturing. Therefore, we use low-frequency components in the segmentation to prevent over-segmentation and subdivide segmented regions that are oversized. The subdividing must be coherent between adjacent frames to prevent temporal artefacts, such as flickering and the shower door effect. To temporally subdivide regions coherently, we obtain a panoramic image from the segmented regions in input frames, subdivide it using a weighted Voronoi diagram, and thereafter project the subdivided regions onto the input frames. To render stained glass fragment for each coherent region, we determine the optimal match glass fragment for the region from a dataset consisting of real stained-glass fragment images and transfer its color and texture to the region. Finally, applying lead came at the boundary of the regions in each frame yields temporally coherent stained-glass animation.

• Journal of Biomedical Engineering Research
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• v.43 no.5
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• pp.353-360
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• 2022

Laryngeal disease harms quality of life, and laryngoscopy is critical in identifying causative lesions. This study extracts and analyzes using radiomics quantitative features from the lesion in laryngoscopy images and will fit and validate a classifier for finding meaningful features. Searching the region of interest for lesions not classified by the YOLOv5 model, features are extracted with radionics. Selected the extracted features are through a combination of three feature selectors, and three estimator models. Through the selected features, trained and verified two classification models, Random Forest and Gradient Boosting, and found meaningful features. The combination of SFS, LASSO, and RF shows the highest performance with an accuracy of 0.90 and AUROC 0.96. Model using features to select by SFM, or RIDGE was low lower performance than other things. Classification of larynx lesions through radiomics looks effective. But it should use various feature selection methods and minimize data loss as losing color data.