• Proceedings of the Korean Information Science Society Conference
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• 2002.10e
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• pp.103-105
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• 2002

802.11과 같은 무선 네트워크에서는 전송오류에 의한 패킷손실이 많이 발생한다. 802.11 MAC 프로토콜에서는 에러 복구를 위해 ARQ방식을 통한 재전송을 통하여 에러를 정정하나 채널 에러 율이 증가하면 재전송 방식의 효율은 급격히 저하된다. 또한 재전송을하는데 있어서 다시 RTS와 CTS를 전송하여 데이터를 보낼 수 있는 채널을 확보해야 하므로 상당한 전송부하가 발생한다. 이에 재전송 없이 효율적인 에러 복구를 위해서는 FEC방식이 필요하다. 그러나 정적인 FEC방식은 연속적으로 변화하는 무선 채널의 전송 오류율에알맞은 정정 코드를 채택하지 못해 과도한 대역폭 낭비로 인하여 효율이 떨어지는 문제가 있다. 이러한 문제를 개선하기 위해서는 채널의 상태에 따라 정정 코드를 동적으로 변경하는 것이 필요하다. 본 논문은 FEC방식을 802.11 MAC 프로토콜에 적용할 수 있는 방안에 대해서 기술하고 채널 에러 변화에 따라 능동적으로 정정 코드 양을 조절하여 재 전송하는적응적 FEC 알고리즘을 제안한다. 본 논문에서 제안한 적응적 FEC 알고리즘을 802.11 MAC 프로토콜에 적용하여 성능을 측정한 결과 최대 80%정도 성능이 향상된 것을 확인할 수 있었다.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.25-26
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• 2007

전력계통의 복잡화와 대도시의 지중선로 및 전력전자 설비 사용 확대로 써지성 고주파 성분이 포함된 특이 고장현상이 증가하고 있으며, 이와 관련 있는 것으로 추정되는 보호계전기의 원인 불명 동작 사례도 최근에 발생하고 있다. 보호계전기의 동작은 부동작, 정동작 및 오동작으로 구분할 수 있는데 보호계전기가 부동작 또는 오동작할 경우 광역정전으로 진전될 가능성이 크진다. 따라서 보호계전기의 정상 동작은 광역정전을 예방하는 시발점이 된다. 보호계전기의 비정상 동작사례를 살펴보면 CT비 선정의 부적합으로 인한 설비소손 및 오동작과 CT 포화 또는 원인불명의 차전류로 인한 오동작 사례가 다수 발생하고 있다. 본 논문은 보호계전기의 비정상적 동작을 방지하고 동작 신뢰도를 제고하기 위한 비율차동계전기의 알고리즘 개선 방안과 바람직한 정정 기법에 대해 고찰하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.73-74
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• 2021

코로나로 인해 X-ray, CT, MRI와 같은 의료영상 분야에서 딥러닝을 많이 접목시키고 있다. 간단히 접할 수 있는 X-ray 영상으로 코로나 진단을 위해 CNN, R-CNN 등과 같은 영상 딥러닝 분야에서 많은 연구가 진행되고 있다. 의료영상 기반 딥러닝 학습은 바이오마커를 정확히 찾아내고, 최소한의 손실률과 높은 정확도를 필요로한다, 따라서 본 논문에서는 높은 정확도를 위한 학습 모델을 선정하고 실험을 진행하였다.

• Journal of the Korean Society for Nondestructive Testing
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• v.18 no.3
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• pp.181-190
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• 1998

A computerized tomography system was developed using the X-ray source that has diameter of 5 micrometer. The system is used for the nondestructive testing of specimens with diameter below 20 mm. The convolution back projection algorithm was adopted for the reconstruction of cross sectional image, and the shape of the X-ray beam was let parallel beam or fan beam to compare each resultant image. Our CT system was constructed to operate based on the personal computer. The sectional images of the fabricated specimens were reconstructed and analyzed. The reconstructed images well coincided with real images taken with optical microscope and gave us enough reports on the defects in the ceramic specimen. The resolution of the system regarded as about $20{\sim}30$ micrometers.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.11
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• pp.2077-2082
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• 2008

Recent the advanced technologies in medical imaging such as magnetic resonance imaging (MRI) and computed tomography (CT) make doctors improve the diagnostic skill with detailed anatomical information. In general, it is necessary to get a number of MRI images in order to obtain more detail information. However, the performance of MRI machines of privately run hospitals is not good and thus we may obtain only a few of MRI images. If 3D surface reconstruction is accomplished with a few slices, then it generates 3D surface of poor qualify. This paper propose a way to Set a 3D surface of high quality from a few of number of slices. First of all, our algorithm detects the boundary of tissues which we want to reconstruct as a 3D object and find out the set of vortices on the boundary. And then we generate a 3D implicit surface to interpolate the boundary points by using radial basis function. Lastly, we render the 3D implicit surface by using Marching cube algorithms.

• Journal of Creative Information Culture
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• v.6 no.2
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• pp.65-75
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• 2020

The necessaries of Flowchart learning for software education have been discussed but most studies were conducted on learning methods. In this study, Unplugged Flowchart Learning programs for fifth grade students were developed and taught, and their effectiveness were analyzed. The programs were made of 8 themes(16 periods) based on the learner's levels. The effectiveness of the programs were qualitatively analyzed based on classwork sheets, as well as observation and interview. Computational Thinking tests were pre-tested and post-tested for qualitative analyses. This study found that all sub-areas of CT of the students who took the Unplugged flowchart learning program were significantly improved as well as the overall scores of CT. In particular, students' improvements in the area of abstraction and automation was notable. Various interactions between teacher-learners and learners-learners were observed during class, and were found to have positive effects on changes in learners' attitudes and perceptions.

• Tunnel and Underground Space
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• v.31 no.5
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• pp.374-384
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• 2021

In this study, we propose a new approach for automatic fracture detection in CT scan images of rock specimens. This approach is built on top of two-stage object detection deep learning algorithm called Faster R-CNN with a major modification of using rotated bounding box. The use of rotated bounding box plays a key role in the future work to overcome several inherent difficulties of fracture segmentation relating to the heterogeneity of uninterested background (i.e., minerals) and the variation in size and shape of fracture. Comparing to the commonly used bounding box (i.e., axis-align bounding box), rotated bounding box shows a greater adaptability to fit with the elongated shape of fracture, such that minimizing the ratio of background within the bounding box. Besides, an additional benefit of rotated bounding box is that it can provide relative information on the orientation and length of fracture without the further segmentation and measurement step. To validate the applicability of the proposed approach, we train and test our approach with a number of CT image sets of fractured granite specimens with highly heterogeneous background and other rocks such as sandstone and shale. The result demonstrates that our approach can lead to the encouraging results on fracture detection with the mean average precision (mAP) up to 0.89 and also outperform the conventional approach in terms of background-to-object ratio within the bounding box.

• Journal of KIISE:Software and Applications
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• v.32 no.5
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• pp.357-365
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• 2005

We present a B-spline fitting method based on genetic algorithm for the extraction of object contours from the complex image sequence, where objects with similar shape and intensity are adjacent each other. The proposed algorithm solves common malfitting problem of the existing B-spline fitting methods including snakes. Classical snake algorithms have not been successful in such an image sequence due to the difficulty in initialization and existence of multiple extrema. We propose a B-spline fitting method using a genetic algorithm with a new initial population generation and fitting function, that are designed to take advantage of the contour of the previous slice. The test results show that the proposed method extracts contour of individual object successfully from the complex image sequence. We validate the algorithm by false-positive/negative errors and relative amounts of agreements.

• Journal of the Korean Society of Radiology
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• v.7 no.1
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• pp.9-15
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• 2013

In this study, texture feature analysis (TFA) algorithm to automatic recognition of liver disease suggests by utilizing computed tomography (CT), by applying the algorithm computer-aided diagnosis (CAD) of hepatocellular carcinoma (HCC) design. Proposed the performance of each algorithm was to comparison and evaluation. In the HCC image, set up region of analysis (ROA, window size was $40{\times}40$ pixels) and by calculating the figures for TFA algorithm of the six parameters (average gray level, average contrast, measure of smoothness, skewness, measure of uniformity, entropy) HCC recognition rate were calculated. As a result, TFA was found to be significant as a measure of HCC recognition rate. Measure of uniformity was the most recognition. Average contrast, measure of smoothness, and skewness were relatively high, and average gray level, entropy showed a relatively low recognition rate of the parameters. In this regard, showed high recognition algorithms (a maximum of 97.14%, a minimum of 82.86%) use the determining HCC imaging lesions and assist early diagnosis of clinic. If this use to therapy, the diagnostic efficiency of clinical early diagnosis better than before. Later, after add the effective and quantitative analysis, criteria research for generalized of disease recognition is needed to be considered.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.541-546
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• 2006

3차원 관상동맥을 분석하기 위해서는 혈관의 분기점, 극단점, 혈관의 계층적 구조 관계를 함축적으로 표현하는 것이 매우 중요하다. 본 논문에서는3차원 CT 혈관 조영 영상으로부터 관상동맥의 3차원 골격을 자동으로 추출하는 방법을 개발하였다. 먼저, CT혈관 조영술에 의해 획득된 슬라이스 이미지로부터 3차원 조작 및 수술 시뮬레이션 등을 위하여 혈관의 3차원 표면에 대한 메쉬 모델을 생성한다. 생성된 메쉬 모델이 임의로 변형된 후에도 자동으로 골격을 쉽게 추출할 수 있도록 메쉬 모델을 복셀화하는 단계를 거친다. 이렇게 얻어진 복셀 모델로부터 표면복셀을 결정하고 표면 복셀로부터 객체 복셀까지의 유클리드 거리값를 계산하여 유클리드 거리맵(EDM)을 계산한다. 계산된 EDM 으로부터 객체 복셀이 가지게 되는 최대 내접 구를 계산하여 Discrete Medial Surface을 생성하게 되는데 이것은 골격의 후보가 된다. 골격의 후보집합 복셀에 대하여 Dijkstra 최단 경로 결정 알고리즘을 적용하여 골격을 자동으로 추출하게 된다. 이렇게 추출된 3차원 골격은 관상동맥 수술 시뮬레이션 등의 다양한 형상 분석에 유용하게 사용될 수 있다.