• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.11-17
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• 2022

In this paper, a model that can increase the efficiency of work in arranging interior furniture by applying augmented reality technology was studied. In the existing system to which augmented reality is currently applied, there is a problem in that information is limitedly provided depending on the size and nature of the company's product when outputting the image of furniture. To solve this problem, this paper presents an AR labeling algorithm. The AR labeling algorithm extracts feature points from the captured images and builds a database including indoor location information. A method of detecting and learning the location data of furniture in an indoor space was adopted using the CNN technique. Through the learned result, it is confirmed that the error between the indoor location and the location shown by learning can be significantly reduced. In addition, a study was conducted to allow users to easily place desired furniture through augmented reality by receiving detailed information about furniture along with accurate image extraction of furniture. As a result of the study, the accuracy and loss rate of the model were found to be 99% and 0.026, indicating the significance of this study by securing reliability. The results of this study are expected to satisfy consumers' satisfaction and purchase desires by accurately arranging desired furniture indoors through the design and implementation of AR labels.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.119-121
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• 2022

본 논문은 실내가구 인테리어를 배치하는데 있어 증강현실 기술을 적용하여 작업의 효율성을 높일 수 있는 모델을 연구하였다. 현재 증강현실을 적용하는 프로세스에서는 가구의 이미지를 출력할 때 기업의 규모나 제품의 성격 등에 따라 정보가 제한적으로 제공되는 문제를 안고 있다. 이러한 문제점을 해결하기 위하여 본 논문에서 제시하는 알고리즘을 이용하여 AR 레이블링을 생성함으로써, 가구의 정확한 이미지 추출과 함께 가구에 대한 상세한 정보를 제공 받아 사용자가 원하는 가구들을 증강현실을 통해 쉽게 배치할 수 있도록 하는 연구를 진행하였다. 본 연구는 AR 레이블링의 설계, 구현과 3D 렌더링을 통해 원하는 가구들을 실내에 정확히 배치할 수 있어 소비자의 만족도와 구매욕구를 충족시킬 수 있을 것으로 기대된다.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.121-125
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• 2020

With miniaturization of semiconductor, the manufacturing process become more complex, and undetected small changes in the state of the equipment have unexpectedly changed the process results. Fault detection classification (FDC) system that conducts more active data analysis is feasible to achieve more precise manufacturing process control with advanced machine learning method. However, applying machine learning, especially in supervised learning criteria, requires an arduous data labeling process for the construction of machine learning data. In this paper, we propose a semi-supervised learning to minimize the data labeling work for the data preprocessing. We employed equipment status variable identification (SVID) data and optical emission spectroscopy data (OES) in silicon etch with SF6/O2/Ar gas mixture, and the result shows as high as 95.2% of labeling accuracy with the suggested semi-supervised learning algorithm.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.43-50
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• 2017

In this paper, we are proposing improved vanishing points detection and segments labeling methods for inside wall frame detection from indoor image of a piece of having a colour RGB. A lot of research related to recognizing the frame of artificial structures from the image is being performed due to increase in demand for AR technology. But detect the inside wall frame in indoor images have many objects that caused the occlusion is still a difficult issue. Inner wall frame detection methods are usually segment labeling methods and detect vanishing point methods are used together. In order to improve the vanishing point detection method we proposed using inner wall orthogonality which forms the cube. Also we proposed labeling method using tree based learning and superpixel based segmentation method for labelingthe segments in indoor images. Finally, in experiments have shown improved results about inside wall frame detection according to our methods.

• The Korean Journal of Zoology
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• v.38 no.4
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• pp.514-522
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• 1995

In order to examine the interaction of albumin with the sperm during capacitation in mouse, proteins of cauda epididymal sperm were extracted under various conditions and analyzed with SDS-PAGE. Sperm surface labeling patterms were also examined using fluorochroin~conjugated wheat germ agglutinin (WGA) and bovine serum albumin (BSA). Albumin was detached from the sperm surface during the incubation and seemed to be constituted the major protein components of the conditioned media in which sperm incubated for 90 mm. Detachment of albumin from the sperm was not affected by the Ca2+ in the medium. WGA-FITC labeling confirmed that Triton X-100 permeabilired plasma membrane overlaying the apical segment of sperm head and detached plasma membrane associated proteins having negatively charged glycoconjugates. BSA-FITC labeling of epididymal sperm occurred on the apical segment of periacrosoinal region and postacrosomal region of the head. BSA-FITC labeling was not observed in periacrosoinal region of the sperm treated with Ca2+-ionophore ~3187 (10 MM)~ whereas the postacrosome region of acrosome-reacted sperm was still labeled after the AR. These results suggest that albumin bound to the surface of epididymal sperm is detached during the capacitation process, and it might be involved In physiological change of sperm plasma membrane accompanying the capacitation.

• The Korean Journal of Nuclear Medicine Technology
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• v.16 no.2
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• pp.131-134
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• 2012

Purpose : The labeling efficiency of radiopharmaceuticals in nuclear medicine is important in terms of accuracy and reliability of the examination. Usually $^{99m}Tc$-HMPAO used for brain SPECT scan is chemically unstable since lots of impurities are existing. Therefore, occurrence of loss of labeling efficiency is easy to appear. In this paper, labeling and use of $^{99m}Tc$-HMPAO should be helpful through experiments on factors affecting the labeling efficiency of $^{99m}Tc$-HMPAO. Materials and Methods : Domestic HMPAO vials (Dong-A) used for brain SPECT scan were tested. Domestic Samyeong Generator 55.5 GBq (1,500 mCi), TLC measurement sets (ITLC-SG, butanone, saline, TLC chamber) and radio-TLC scanner (Advantest, Bioscan) were used. In the first experiment, after eluting generator at 1, 8, 16, 24, 28 hours apart, each eluted $^{99m}Tc$-pertechnetate were labeled with HMPAO and the labeling efficiency was measured. In the second experiment, after eluting $^{99m}Tc$-pertechnetate from a generator, $^{99m}Tc$-pertechnetate was drawn at 0, 1, 3, 6 hours. And each drawn $^{99m}Tc$-pertechnetate were labeled with HMPAO for measuring labeling efficiency. In the third experiment, labeling efficiency was measured at 0, 0.5, 3, 5, 7 hours after labeling $^{99m}Tc$-HMPAO. Results : In the first experiment, measured values were appeared 95.05, 94.64, 94.94, 95.64, 96.76% in passing order of time. In the second experiment, measured values were appeared 94.38, 94.23, 93.26, 91.03% in passing order of time. In the third experiment, measured values were appeared 95.76, 94.17, 88.19, 83.6, 76.86% in passing order of time. Conclusion : In the first experiment of this paper, labeling efficiency of $^{99m}Tc$-HMPAO labeled with $^{99m}Tc$-pertechnetate eluted after 24 hours from first elution. Additional experiments will be needed to discuss for usability. In the second experiment, the labeling efficiency was slightly decreased in chronological order, but it was measured higher than 90%. Also, additional experiments will be needed to discuss for usability. In the third experiment, the labeling efficiency was decreased considerably. Especially, within 3 hours after the labeling is recommended to use $^{99m}Tc$-HMPAO

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1286-1288
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• 2024

The construction industry, known for its inherent risks and multiple hazards, necessitates effective solutions for hazard identification and mitigation [1]. To address this need, the implementation of machine learning models specializing in object detection has become increasingly important because this technological approach plays a crucial role in augmenting worker safety by proactively recognizing potential dangers on construction sites [2], [3]. However, the challenge in training these models lies in obtaining accurately labeled datasets, as conventional methods require labor-intensive labeling or costly measurements [4]. To circumvent these challenges, synthetic data generation (SDG) has emerged as a key method for creating realistic and diverse training scenarios [5], [6]. The paper reviews the evolution of synthetic data generation tools, highlighting the shift from earlier solutions like Synthpop and Data Synthesizer to advanced game engines[7]. Among the various gaming platforms, Unity 3D and Unreal Engine stand out due to their advanced capabilities in replicating realistic construction hazard environments [8], [9]. Comparing Unity 3D and Unreal Engine is crucial for evaluating their effectiveness in SDG, aiding developers in selecting the appropriate platform for their needs. For this purpose, this paper conducts a comparative analysis of both engines assessing their ability to create high-fidelity interactive environments. To thoroughly evaluate the suitability of these engines for generating synthetic data in construction site simulations, the focus relies on graphical realism, developer-friendliness, and user interaction capabilities. This evaluation considers these key aspects as they are essential for replicating realistic construction sites, ensuring both high visual fidelity and ease of use for developers. Firstly, graphical realism is crucial for training ML models to recognize the nuanced nature of construction environments. In this aspect, Unreal Engine stands out with its superior graphics quality compared to Unity 3D which typically considered to have less graphical prowess [10]. Secondly, developer-friendliness is vital for those generating synthetic data. Research indicates that Unity 3D is praised for its user-friendly interface and the use of C# scripting, which is widely used in educational settings, making it a popular choice for those new to game development or synthetic data generation. Whereas Unreal Engine, while offering powerful capabilities in terms of realistic graphics, is often viewed as more complex due to its use of C++ scripting and the blueprint system. While the blueprint system is a visual scripting tool that does not require traditional coding, it can be intricate and may present a steeper learning curve, especially for those without prior experience in game development [11]. Lastly, regarding user interaction capabilities, Unity 3D is known for its intuitive interface and versatility, particularly in VR/AR development for various skill levels. In contrast, Unreal Engine, with its advanced graphics and blueprint scripting, is better suited for creating high-end, immersive experiences [12]. Based on current insights, this comparative analysis underscores the user-friendly interface and adaptability of Unity 3D, featuring a built-in perception package that facilitates automatic labeling for SDG [13]. This functionality enhances accessibility and simplifies the SDG process for users. Conversely, Unreal Engine is distinguished by its advanced graphics and realistic rendering capabilities. It offers plugins like EasySynth (which does not provide automatic labeling) and NDDS for SDG [14], [15]. The development complexity associated with Unreal Engine presents challenges for novice users, whereas the more approachable platform of Unity 3D is advantageous for beginners. This research provides an in-depth review of the latest advancements in SDG, shedding light on potential future research and development directions. The study concludes that the integration of such game engines in ML model training markedly enhances hazard recognition and decision-making skills among construction professionals, thereby significantly advancing data acquisition for machine learning in construction safety monitoring.

• Nuclear Medicine and Molecular Imaging
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• v.40 no.4
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• pp.228-232
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• 2006

Purpose: electrophilic $^{18}F(T_{1/2}=110\;min)$ radionuclide in the form of $[^{18}F]F_2$ gas is of great significance for labeling radiopharmaceuticals for positron omission tomography (PET). However, its production In high yield and with high specific radioactivity is still a challenge to overcome several problems on targetry. The aim of the present study was to develop a method suitable for the routine production of $[^{18}F]F_2$ for the electrophilic substitution reaction. Materials and Methods: The target was designed water-cooled aluminum target chamber system with a conical bore shape. Production of the elemental fluorine was carried out via the $^{18}O(p,n)^{18}F$ reaction using a two-step irradiation protocol. In the first irradiation, the target filled with highly enriched $^{18}O_2$ was irradiated with protons for $^{18}F$ production, which were adsorbed on the inner surface of target body. In the second irradiation, the mixed gas ($1%[^{19}F]F_2/Ar$) was leaded into the target chamber, fellowing a short irradiation of proton for isotopic exchange between the carrier-fluorine and the radiofluorine absorbed in the target chamber. Optimization of production was performed as the function of irradiation time, the beam current and $^{18}O_2$ loading pressure. Results: Production runs was performed under the following optimum conditions: The 1st irradiation for the nuclear reaction (15.0 bar of 97% enriched $^{18}O_2$, 13.2 MeV protons, 30 ${\mu}A$, 60-90 min irradiation), the recovery of enriched oxygen via cryogenic pumping; The 2nd irradiation for the recovery of absorbed radiofluorine (12.0 bar of 1% $[^{19}F]fluorine/argon$ gas, 13.2 MeV protons, 30 ${\mu}A$, 20-30 min irradiation) the recovery of $[^{18}F]fluorine$ for synthesis. The yield of $[^{18}F]fluorine$ at EOB (end of bombardment) was achieved around $34{\pm}6.0$ GBq (n>10). Conclusion: The production of $^{18}F$ electrophilic agent via $^{18}O(p,n)^{18}F$ reaction was much under investigation. Especially, an aluminum gas target was very advantageous for routine production of $[^{18}F]fluorine$. These results suggest the possibility to use $[^{18}F]F_2$ gas as a electrophilic substitution agent.