• Progress in Medical Physics
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• v.18 no.2
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• pp.65-72
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• 2007

An echo planar imaging (EPI)-based spin-echo sequence Is often used to obtain diffusion tensor imaging (DTI) data on most of the clinical MRI systems, However, this sequence is confounded with the susceptibility artifacts, especially on the temporal lobe in the human brain. Therefore, the objective of this study was to design a pulse sequence that relatively immunizes the susceptibility artifacts, but can map diffusion tensor components in a single-shot mode. A multi-slice multi-echo pulsed-gradient spin-echo (MePGSE) sequence with eight echoes wasdeveloped with selective refocusing pulses for all slices to map the full tensor. The first seven echoes in the train were diffusion-weighted allowing for the observation of diffusion in several different directions in a single experiment and the last echo was for crusher of the residual magnetization. All components of diffusion tensor were measured by a single shot experiment. The sequence was applied in diffusive phantoms. The preliminary experimental verification of the sequence was illustrated by measuring the apparent diffusion coefficient (ADC) for tap water and by measuring diffusion tensor components for watermelon. The ADC values in the series of the water phantom were reliable. The MePGSE sequence, therefore, may be useful in human brain studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.631-653
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• 2020

Sign language is a natural, visually oriented and non-verbal communication channel between people that facilitates communication through facial/bodily expressions, postures and a set of gestures. It is basically used for communication with people who are deaf or hard of hearing. In order to understand such communication quickly and accurately, the design of a successful sign language translation system is considered in this paper. The proposed system includes object detection and classification stages. Firstly, Single Shot Multi Box Detection (SSD) architecture is utilized for hand detection, then a deep learning structure based on the Inception v3 plus Support Vector Machine (SVM) that combines feature extraction and classification stages is proposed to constructively translate the detected hand gestures. A sign language fingerspelling dataset is used for the design of the proposed model. The obtained results and comparative analysis demonstrate the efficiency of using the proposed hybrid structure in sign language translation.

• Economic and Environmental Geology
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• v.29 no.6
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• pp.739-744
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• 1996

'Kite' is a newly developed single-channel seismic imaging system capable of producing high resolution three dimensional images of subbottom geology in one traverse of a survey region. The system consists of a horizontally towed hydrophone array and active source. The hydrophone array is towed axis perpendicular to ship direction and the airgun source at the end of the hydrophone array is excited at timed intervals during the progression. The construction of the three dimensional subbottom image was made simply by using conventional multichannel seismic reflection data processing techniques. Common source shot (CSS) gathers of the hydrophone traces are evaluated using Dix's equation for average interval velocity of each subbottom layer. From the interval velocity profile and the normal consolidation stress condition, values of shear modulus, porosity, and shear velocity are deduced from the chosen values of physical constants. The system has been successfully tested at several locations on the North Atlantic continental shelf.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.735-739
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• 2003

This paper presents a direct seek control algorithm for swing-arm type dual stage servo system that consists of a coarse actuator and a fine actuator. The proposed scheme is to design a control system that attenuates the effect of dynamic coupling between the two actuators so that the seek operation can be performed in a single-shot with stability. In an optical drive system with dual stage servo mechanism, the effect of dynamic coupling between the two actuators needs to be handled during the coarse seek operation due to its inherent structure. In an extreme case, the two actuators can collide each other, which leads to critical degradation of the seek performance. To handle this problem, our proposed control scheme is to generate the drive signals such that the two actuators behave as if they are a single fixed body. To this end, a feedforward controller and two feedback controllers are designed that enable the current drive system perform wide range of track seek. Simulation results are provided to show the validity and feasibility of our proposed algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1166-1186
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• 2022

Artistic font design has become an integral part of visual media. However, without prior knowledge of the font domain, it is difficult to create distinct font styles. When the number of characters is limited, this task becomes easier (e.g., only Latin characters). However, designing CJK (Chinese, Japanese, and Korean) characters presents a challenge due to the large number of character sets and complexity of the glyph components in these languages. Numerous studies have been conducted on automating the font design process using generative adversarial networks (GANs). Existing methods rely heavily on reference fonts and perform font style conversions between different fonts. Additionally, rather than capturing style information for a target font via multiple style images, most methods do so via a single font image. In this paper, we propose a network architecture for generating multilingual font sets that makes use of geometric structures as content. Additionally, to acquire sufficient style information, we employ multiple style images belonging to a single font style simultaneously to extract global font style-specific information. By utilizing the geometric structural information of content and a few stylized images, our model can generate an entire font set while maintaining the style. Extensive experiments were conducted to demonstrate the proposed model's superiority over several baseline methods. Additionally, we conducted ablation studies to validate our proposed network architecture.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06b
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• pp.92-97
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• 1998

For the efficient searching and browsing of digital video, it is essential to extract the internal structure of the video contents. As an example, a news video consists of several sections such as politics, economics, sports and others, and also each section consists of individual topics. With this information in hand, users can ore easily access the required video frames. This paper addresses the problem of automatic shot boundary detection and selection of representative frames (R-frames), which are the essential step in recognizing the internal structure of video contents. In the shot boundary detection, a new algorithm that have dual detectors which are designed specifically for the abrupt boundaries (cuts) and gradually changing bounaries respectively is proposed. Compared to the existing 미algorithms that mostly have tried to detect both types by a single mechanism, the proposed algorithm is proved to be more robust and accurate. Also in the problem of R-frame selection, simple mechanical approaches such as selecting one frame every other second have been adopted. However this approach often selects too many R-frames in static short, while drops important frames in dynamic shots. To improve the selection mechanism, a new R-frame selection algorithm that uses motion information extracted from pixel difference is proposed.

• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1621-1626
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• 2017

This study conducts data visualization of common climax patterns of Korean blockbuster films to analyze shots and evaluate scene (subplot unit) arrangement. For this purpose, a model of editing patterns is used to analyze how many climax patterns a film contains. Moreover, a system, which automatically collects shot images and classifies shot sizes of collected data, is designed to demonstrate that a single scene is composed based on a climax pattern. As a scene is a subplot and thus its arrangement cannot fully be analyzed only by climax patterns, dialogues of starring actors are also used to identify scenes, and the result is compared with data visualization results. It detects dialogues between particular actors and visualizes dialogue formation in a network form. Such network visualization enables the arrangement of main subplots to be analyzed, and the box office performance of a film can be explained by the density of subplots. The study of two types comparison analysis is expected to contribute to planning, plotting, and producing films.

• Journal of Astronomy and Space Sciences
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• v.36 no.1
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• pp.35-43
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• 2019

The Optical Wide-field patroL-Network (OWL-Net) is a global optical network for Space Situational Awareness in Korea. The primary operational goal of the OWL-Net is to track Low Earth Orbit (LEO) satellites operated by Korea and to monitor the Geostationary Earth Orbit (GEO) region near the Korean peninsula. To obtain dense measurements on LEO tracking, the chopper system was adopted in the OWL-Net's back-end system. Dozens of angle-only measurements can be obtained for a single shot with the observation mode for LEO tracking. In previous work, the reduction process of the LEO tracking data was presented, along with the mechanical specification of the back-end system of the OWL-Net. In this research, we describe an integrity assessment method of time-position matching and verification of results from real observations of LEO satellites. The change rate of the angle of each streak in the shot was checked to assess the results of the matching process. The time error due to the chopper rotation motion was corrected after re-matching of time and position. The corrected measurements were compared with the simulated observation data, which were taken from the Consolidated Prediction File from the International Laser Ranging Service. The comparison results are presented in the In-track and Cross-track frame.

• The Korean Journal of Pain
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• v.35 no.1
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• pp.14-21
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• 2022

The coronavirus disease 2019 (COVID-19) pandemic, which has been rampant since the end of 2019, has evidently affected pain management in clinical practice. Fortunately, a COVID-19 vaccination program is currently in progress worldwide. There is an ongoing discussion that pain management using steroid injections can decrease COVID-19 vaccine efficacy, although currently there is no direct evidence to support this statement. As such, the feeling of pain in patients is doubled in addition to the co-existing ill-effects of social isolation associated with the pandemic. Thus, in the COVID-19 era, it has become necessary that physicians be able to provide high quality pain management without negatively impacting COVID-19 vaccine efficacy. Steroids can alter the entire process involved in the generation of adaptive immunity after vaccination. The period of hypophysis-pituitary-adrenal axis suppression is known to be 1 to 4 weeks after steroid injection, and although the exact timing for peak efficacy of COVID-19 vaccines is slightly different for each vaccine, the average is approximately 2 weeks. It is suggested to avoid steroid injections for a total of 4 weeks (1 week before and after the two vaccine doses) for the double-shot vaccines, and for 2 weeks in total (1 week before and after vaccination) for a single-shot vaccine. This review focuses on the basic concepts of the various COVID-19 vaccines, the effect of steroid injections on vaccine efficacy, and suggestions regarding an appropriate interval between the administration of steroid injections and the COVID-19 vaccine.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.3
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• pp.193-199
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• 2017

Instead of using only spatial features from a single frame for person re-identification, a combination of spatial and temporal factors boosts the performance of the system. A recurrent neural network (RNN) shows its effectiveness in generating highly discriminative sequence-level human representations. In this work, we implement RNN, three Long Short Term Memory (LSTM) network variants, and Gated Recurrent Unit (GRU) on Caffe deep learning framework, and we then conduct experiments to compare performance in terms of size and accuracy for person re-identification. We propose using GRU for the optimized choice as the experimental results show that the GRU achieves the highest accuracy despite having fewer parameters than the others.