• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.537-538
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• 2021

코딩 교육에 대한 중요성이 높아짐에 따라 국가 주도적 교육이 강화되고 있다. 컴퓨터 프로그래밍이 낯선 교육생을 위해 교육 커리큘럼에 블록 코딩을 도입하고 있으나 낮은 흥미도로 인해 여전히 교육 성취도가 낮게 나타난다. 본 논문에서는 컴퓨터 프로그래밍에 대한 관심을 유발하고 학습을 보조하는 드론 기반의 체감형 교육 프로그램모델을 제시한다. 제시하는 교육 모델은 사용자가 코딩한 블록 코드를 파이썬 코드로 변환하여 보여주고, 블록 코드로 첨부된 드론의 동작을 제어하도록 코딩할 수 있다. 사용자의 심화학습을 위해 추가적으로 제공하는 웨어러블 장갑 컨트롤러를 통해 드론과 연관하여 동작 제어가 가능하게 하여 흥미 유발과 더불어 학습 효과 향상을 기대할 수 있다.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.4
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• pp.1-9
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• 2023

In this paper, we propose a method to recognize the braille blocks for embedded devices in real time through deep learning. First, a deep learning model for braille block recognition is trained on a high-performance computer, and the learning model is applied to a lightweight tool to apply to an embedded device. To recognize the walking information of the braille block, an algorithm is used to determine the path using the distance from the braille block in the image. After detecting braille blocks, bollards, and crosswalks through the YOLOv8 model in the video captured by the embedded device, the walking information is recognized through the braille block path discrimination algorithm. We apply the model lightweight tool to YOLOv8 to detect braille blocks in real time. The precision of YOLOv8 model weights is lowered from the existing 32 bits to 8 bits, and the model is optimized by applying the TensorRT optimization engine. As the result of comparing the lightweight model through the proposed method with the existing model, the path recognition accuracy is 99.05%, which is almost the same as the existing model, but the recognition speed is reduced by 59% compared to the existing model, processing about 15 frames per second.

• Journal of Broadcast Engineering
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• v.16 no.1
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• pp.123-132
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• 2011

Recently, DVC (Distributed Video Coding) is receiving a lot of attention as one of the low complexity video encoding techniques suitable for various applications with computation-limited and/or power-limited environment, and is being actively studied for improving the coding efficiency. This paper proposes a rate-distortion based selective block encoding scheme. First, the motion information is obtained in the process of generating side information at decoder and received through the feedback channel, and then, based on this information, the proposed method performs a selective block encoding based on rate-distortion optimization. Experimental results show that the performance of the proposed scheme reaches up to 2.2 dB PSNR gain over the existing scheme. Moreover, it is shown that the complexity can be reduced by encoding parts of region considering rate-distortion cost.

• Journal of Broadcast Engineering
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• v.15 no.5
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• pp.642-652
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• 2010

Recently, DVC (Distributed Video Coding) techniques are drawing a lot of interests as one of the future research works to achieve low complexity encoding in various applications. But, due to the limited computational complexity, the performances of DVC algorithms are inferior to those of conventional international standard video coders, which use zig-zag scan, run length code, entropy code and skipped macroblock. In this paper, in order to overcome the performance limit of the DVC system, the distortion for every block is estimated when side information is found at the decoder and then we propose a new selective block encoding scheme which provides the encoder side with the motion information for the highly distorted blocks and then allows the sender to encode the motion compensated frame difference signal. Through computer simulations, it is shown that the coding efficiency of the proposed scheme reaches almost that of the conventional inter-frame coding scheme.

• Journal of the Society of Naval Architects of Korea
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• v.43 no.1 s.145
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• pp.87-95
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• 2006

There are hundreds of ship blocks which are under the block assembly, painting, and outfitting assembly works in the pre-erection shops of shipyard. Generally, each block is planned to be processed in a pre-erection shop according to the block type by the long-term production-scheduling before six months. However, many blocks can't be processed in the planned time and the planned shop since the before and after block-processing changes or delays the planned sequential works in pre-erection shops. Therefore, it is essential to monitor the current location of each block and work in process to cope with the changed situation of pre-erection shops. Present study integrates PDA, GPS, and CDMA not only to chase the location of each block but also to exchange the pre-erection work order and the work report between the production-scheduling server and the production managers in the pre-erection shops. This study shows a prototype for the block tracing and process monitoring in the pre-erection shops.

• Journal of Hospice and Palliative Care
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• v.21 no.4
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• pp.158-162
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• 2018

Breast cancer related lymphedema (BCRL) is one of the most intractable complications after surgery. Patients suffer from physical impairment, as well as psychological depression. Moreover, a recent study revealed that cellulitis significantly increased the risk of BCRL, and cellulitis has been suggested as a risk factor of BCRL development. We describe a patient treated with stellate ganglion blocks (SGBs) without steroid for relief of symptoms and reduction of the arm circumference of breast cancer-related infectious lymphedema in a month. We measured the arm circumference at four locations; 10 cm and 5 cm above and below the elbow crease, numeric rating scale (NRS) score, lymphedema and breast cancer questionnaire (LBCQ) score on every visit to the pain clinic. A serial decrease of the arm circumference and pain score were observed after second injection. In the middle of the process, cellulitis recurred, we performed successive SGBs to treat infectious lymphedema. The patient was satisfied with the relieved pain and swelling, especially with improved shoulder range of motion as it contributes to better quality of life. This case describes the effects of SGB for infectious BCRL patients. SGB could be an alternative or ancillary treatment for infectious BCRL patients.

• Journal of the Korean Society of Radiology
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• v.17 no.1
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• pp.25-30
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• 2023

As 3D printing technology is used in the medical field, interest in metal materials is increasing. The Department of Radiation Oncology uses a shielding block to shield the patient's normal tissue from unnecessary exposure during electron beam therapy. However, problems such as handling of heavy metal materials such as lead and cadmium, reproducibility according to skill level and uncertainty of arrangement have been reported. In this study, candidate materials that can be used for metal 3D printing are selected, and the physical properties and radiation dose of each material are analyzed to develop a customized shielding block that can be used in electron beam therapy. As candidate materials, aluminum alloy (d = 2.68 g/cm³), titanium alloy (d = 4.42 g/cm³), and cobalt chromium alloy (d = 8.3 g/cm³) were selected. The thickness of the 95% shielding rate point was derived using the Monte Carlo Simulation with the irradiation surface and 6, 9, 12, and 16 energies. As a result of the simulation, among the metal 3D printing materials, cobalt chromium alloy (d = 8.3 g/cm³) was similar to the existing shielding block (d = 9.4 g/cm³) in shielding thickness for each energy. In a follow-on study, it is necessary to evaluate the usefulness in clinical practice using customized shielding blocks made by metal 3D printing and to verify experiments through various radiation treatment plan conditions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37C no.12
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• pp.1271-1280
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• 2012

In the recent parking assistant systems, finding out the distance to the object behind a car is often performed by the range sensors such as ultrasonic sensors, radars. However, the installation of additional sensors on the used vehicle could be difficult and require extra cost. On the other hand, the motion stereo technique that extracts distance information using only an image sensor was also proposed. However, In the stereo rectification step, the motion stereo requires good features and exacts matching result. In this paper, we propose a fast algorithm that extracts the accurate distance information for the parallel parking situation using the consecutive images that is acquired by a rear-view camera. The proposed algorithm uses the quadrangle transform of the image, the horizontal line integral projection, and the blocking-based correlation measurement. In the experiment with the magna parallel test sequence, the result shows that the line-accurate distance measurement with the image sequence from the rear-view camera is possible.

• Annual Conference on Human and Language Technology
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• 1994.11a
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• pp.207-210
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• 1994

한글 전자사전은 많은 양의 데이타를 저장할 수 있어야 하며, 빠른 검색 속도를 제공해야 한다. 기존의 트라이는 공통접두사만을 압축하기 때문에 사전의 크기가 방대하다는 단점이 있다. 본 논문에서는 DAWG(Directed Acyclic Word Graph)를 이용하여 공통접미사까지 압축하였고, 검색과 기억장소의 효율을 위하여, 링크드리스트 구조의 DAWG를 유형별 배열 구조로 바꾸었다. 전국의 각 학교 이름들을 대상으로 실험한 결과, 본 논문에서 제안한 DAWG를 이용한 배열 구조의 사전은 트라이와 비교하여 볼 때, 검색 연산의 성능은 동일하게 유지하면서 기억 장소의 효율과 압축율에서 효과적이었다. 또한, 트라이보다 주기억장치와 보조기억장치와의 블록 입출력횟수를 줄임으로써 전체 검색 시간을 낮출 수 있었다.

• 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.