• Journal of Aerospace System Engineering
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• v.13 no.3
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• pp.23-31
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• 2019

The design requirements of modern satellites vary depending on the purpose of operation. Like conventional medium and large-scale satellites, small satellites which operate on low orbit may also serve military purposes. As a result, there is increased demand for high-resolution photos and videos and multi-target observation becomes important. The most important design parameter for multi-target observation is the satellites' maneuverability. For increased maneuverability, the miniaturization is required to increase the stiffness of the satellite as this decreases the mass moment of inertia of the satellite. In the case of a solar panel having relatively low stiffness compared to the satellites' body, vibrations are generated when the attitude maneuver is performed, which greatly influences the image acquisition. For verification of such vibrational characteristics, the satellites is modeled as a reduced model, and experimental zig for simulating attitude maneuver is introduced. A rigidity simulator for simulating the stiffness of the satellite is also proposed. Additionally, the objective of the experimental method is to simulate the maneuvering angle of the satellite based on the winding length of the wire using a step motor, and to experimentally verify the vibration characteristics of the satellite body and the solar panel generated during the maneuvering test.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.369-377
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• 2021

Augmented reality smart-glass enables healthcare providers to use patient and their care related information without interference of workflow. In addition, augmented reality smart-glass simulation had advantages in improving competency via remote collaboration and real-time information sharing. This study investigated perception and attitude regarding augmented reality smart glass based healthcare simulation on three different groups of healthcare major students, computer major students, developers and faculties. Using convenience sampling method, data were obtained from 95 participants and statistical analysis were performed using SPSS 25.0. Developer and faculty group showed the highest scores, followed by healthcare major students. There was the high expectation on augmented reality smart-glass for skill acquisition and the high performance and big screen were essential features of device. The findings of this study revealed that differences between healthcare and computer major students exist and strategies to reduce those gaps are required to adopt augmented reality smart glass in healthcare settings.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.3
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• pp.59-70
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• 2021

With the recent advancements in the Internet of Things, context-aware system that provides customized services become important to consider. The existing context-aware systems analyze data generated around the user and abstract the context information that expresses the state of situations. However, these datasets is mostly unstructured and have difficulty in processing with simple approaches. Therefore, providing context-aware services using the datasets should be managed in simplified method. One of examples that should be considered as the unstructured datasets is a deep learning application. Processes in deep learning applications have a strong coupling in a way of abstracting dataset from the acquisition to analysis phases, it has less flexible when the target analysis model or applications are modified in functional scalability. Therefore, an abstraction model that separates the phases and process the unstructured dataset for analysis is proposed. The proposed abstraction utilizes a description name Analysis Model Description Language(AMDL) to deploy the analysis phases by each fat client is a specifically designed instance for resource-oriented tasks in edge computing environments how to handle different analysis applications and its factors using the AMDL and Fat client profiles. The experiment shows functional scalability through examples of AMDL and Fat client profiles targeting a vehicle image recognition model for vehicle access control notification service, and conducts process-by-process monitoring for collection-preprocessing-analysis of unstructured data.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.637-643
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• 2022

Beauty in the modern society is a professional occupation in which art and science are fusion-integrated, and in order to enter as a beautician, obtaining a national technical certificate is a routine customs clearance procedure. As it is time to present objective data on the national technology license and employment fields preferred by prospective beauticians, it is a time to analyze the perceptions and trends of the national technical license of the beautician preferred by beauty academy students who design success in the future beauty industry. Did. As a result of the analysis, the preferred national skill certificate and the desired employment field showed a very high correlation, and the personality and interests of the male, younger, unmarried, and student groups were selected as the priority, while the 30s or older, married or divorced, self-employed, and office workers, Housewives had a much higher rate of employment prospects. Through this study, it is possible to seek the essential tendency and development direction of beauty talents, and it is thought that it will set a desirable direction for R&D for education of national technical qualifications in the future and greatly contribute to the activation of the beauty academy market.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.251-263
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• 2022

This paper presents an idea of ScanSAR image formation. For image formation of ScanSAR that utilizes the burst mode for raw signal acquisition, most conventional single burst methods essentially require a step of azimuth stitching which contributes to radiometric and phase distortions to some extent. Time-domain cross correlation could replace SPECAN which is most popularly used for ScanSAR processing. The core idea of the proposed method is that it is possible to relieve the necessity of azimuth stitching by an extension of Doppler bandwidth of the reference function to the burst cycle period. Performance of the proposed method was evaluated by applying it to the raw signals acquired by a spaceborne SAR system, and results satisfied all image quality requirements including 3 dB width, peak-to-sidelobe ratio (PSLR), compression ratio,speckle noise, etc. Image quality of ScanSAR is inferior to that of Stripmap in all aspects. However, it is also possible to improve the quality of ScanSAR image competitive to that of Stripmap if focused on a certain parameter while reduced qualities of other parameters. Thus, it is necessary for a ScanSAR processor to offer a great degree of flexibility complying with different requirements for different applications and techniques.

• Korean Chemical Engineering Research
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• v.60 no.2
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• pp.300-307
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• 2022

Time-resolved serial femtosecond crystallography (TR-SFX) is a powerful technique for determining temporal variations in the structural properties of biomacromolecules on ultra-short time scales without causing structure damage by employing femtosecond X-ray laser pulses generated by an X-ray free electron laser (XFEL). The mixing rate of reactants and biomolecule samples, as well as the hit rate between crystal samples and x-ray pulses, are critical factors determining TR-SFX performance, such as accurate image acquisition and efficient sample consumption. We here develop two distinct sample delivery systems that enable ultra-fast mixing and on-demand droplet injecting via pneumatic application with a square pulse signal. The first strategy relies on inertial mixing, which is caused by the high-speed collision and subsequent coalescence of droplets ejected through a double nozzle, while the second relies on on-demand pneumatic jetting embedded with a 3D-printed micromixer. First, the colliding behaviors of the droplets ejected through the double nozzle, as well as the inertial mixing within the coalesced droplets, are investigated experimentally and numerically. The mixing performance of the pneumatic jetting system with an integrated micromixer is then evaluated by using similar approaches. The sample delivery system devised in this work is very valuable for three-dimensional biomolecular structure analysis, which is critical for elucidating the mechanisms by which certain proteins cause disease, as well as searching for antibody drugs and new drug candidates.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.2
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• pp.133-155
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• 2022

The purpose of this study was to develop the indicatives for the analysis of the effects of Gyeonggi-do Book Sharing Project on pre-schoolers of supporter' reading care and thereby, suggest some data useful to the establishment of a reading culture promotion policy in Gyeonggi-do. Preceding studies and cases were reviewed to analyze the effects of the book-sharing project on pre-schoolers of supporter' reading care and thereby, develop some measurement indicatives, and thus, the indicatives were verified by professionals using the Delphi technique. Then, supporter of 3~5 year-old pre-schoolers were sampled from 7 cities and counties in Gyeonggi-do (Pocheon-si, Yangpyeong-gun, Yeoju-si, Dongducheon-si, Gapyeong-gun, Yeoncheon-gun and Yangju-si) to be divided into control and test groups and thereby, their reading care effect indicatives were compared before and after the test. The theoretical background is theory of family literacy, emergent literacy and parenting efficacy. As a result of developing the indicatives for analysis of pre-schoolers of supporter's reading care effects and comparing them for the sample pre-schoolers of supporter, before and after the test, the book-sharing project was found effective in improving reading care. The most difficult problem in pre-schoolers' earlier reading education involves acquisition of reading habit. So, it is deemed necessary to operate a regular book sharing project involving public organization and homes. As a result of developing the indicatives and analyzing the effects of the book-sharing project, it was confirmed that the project would serve to improve pre-schoolers of support's reading care and therefore, this study seems to provide some ground for the operation of a sustainable book-sharing project to narrow the education divide and promote a book reading culture in Gyeonggi-do.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.166-172
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• 2022

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.

• Journal of the Korean Society of Radiology
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• v.17 no.7
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• pp.1139-1147
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• 2023

The purpose of this paper is to present and evaluate the performance of a method for controlling the dose for optimal image acquisition while minimizing patient exposure by applying a small-sized Photomultiplier(SiPM) sensor inside a portable detector. Portable detectors have the advantage of being able to quickly access the patient's location for rapid diagnosis, but this mobility comes with the challenge of dose control. This paper presents a method to identify the dose that can have the DQE and optimal image quality of the detector through image evaluation based on IEC62220-1-1, an international standard for X-ray imaging devices, and to identify the optimal dose by matching the ADU of the image and the output of the SiPM Sensor. The Skull AP image was acquired by implementing the detector manufacturer's reference dose. The optimal dose was 342.8 µGy, and the optimal controlled dose was 148.3 µGy, which is 57 % of the manufacturer's reference dose. The Chest AP image was 81.9 µGy and the optimal controlled dose was 27.9 µGy, which is a high dose reduction effect of 66 %. In addition, the two images were analyzed by five radiologists and found to have no clinically significant difference in anatomical delineation.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.119-125
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• 2023

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.