• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2009년도 춘계학술대회
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• pp.887-890
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• 2009

잡음이 존재하는 환경에서 채널로 정보를 전송하기 위해서는 정보를 부호화하는 기술이 필요하다. 오류 검출과 정정에 사용되는 여러 가지 부호화 기술 중 Shannon의 한계에 가장 근접한 부호화 기술이 LDPC 부호이다. LDPC 부호와 sum-product 알고리듬의 조합에 의해 얻어지는 복호 특성은 터보 부호, RA(Repeat Accumulate) 부호의 성능에 필적하며, 부호장이 매우 긴 경우에는 이들 성능을 추월한다. 본 논문에서는 영상 정보의 LDPC 부호화와 복호화 기술 원리에 관해 설명하고, Sum-product 알고리듬을 사용하는 LDPC 복호기를 FPGA로 구현한다.

• Nuclear Engineering and Technology
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• 제53권4호
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• pp.1199-1209
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• 2021

The detection of unexpected loose parts in the primary coolant system in a nuclear power plant remains an extremely important issue. It is essential to develop a methodology for the localization and mass estimation of loose parts owing to the high prediction error of conventional methods. An effective approach is presented for the localization and mass estimation of a loose part using machine-learning and deep-learning algorithms. First, a methodology was developed to estimate both the impact location and the mass of a loose part at the same times in a real structure in which geometric changes exist. Second, an impact database was constructed through a series of impact finite-element analyses (FEAs). Then, impact parameter prediction modes were generated for localization and mass estimation of a simulated metallic loose part using machine-learning algorithms (artificial neural network, Gaussian process, and support vector machine) and a deep-learning algorithm (convolutional neural network). The usefulness of the methodology was validated through blind tests, and the noise effect of the training data was also investigated. The high performance obtained in this study shows that the proposed methodology using an FEA-based database and deep learning is useful for localization and mass estimation of loose parts on site.

• 융합신호처리학회논문지
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• 제21권1호
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• pp.7-12
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• 2020

수술 중 마취 깊이를 측정하는 방법으로 뇌파를 이용한 다양한 파라미터들이 사용되고 있으며, 실제 임상에서는 선형분석 기법 중 하나인 SEF가 널리 사용되고 있다. 그러나 최근 EEG를 포함한 생체학적 신호는 비선형 성질을 가지고 있다는 연구결과가 발표되면서, 이를 기반으로 한 파라미터 개발이 이뤄지고 있다. 본 연구에서는 보다 정확한 EEG 측정과 분석을 위해 비선형 분석 기법 기반의 파라미터를 개발과 이에 대한 정현파 분석을 통한 데이터와의 비교 분석을 통해 수술 중 전자장비와 EEG 계측 시 혼입될 수 있는 노이즈를 추출하고자 한다.

• 대한원격탐사학회지
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• 제38권1호
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• pp.45-56
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• 2022

The Ieodo Ocean Research Station (IORS) lies between the exclusive economic zone (EEZ) boundaries of Korea, Japan, and China. The geographical positioning of the IORS makes it ideal for monitoring ships in the area. In this study, we introduce ship monitoring results by Automatic Identification System (AIS) and the Broadband 3G^TM radar, which has been developed for use in small ships using the Frequency Modulated Continuous Wave (FMCW) technique. AIS and FMCW radar data were collected at IORS from November 23th to 30th, 2013. The acquired FMCW radar data was converted to 2-D binary image format over pre-processing, including the internal and external noise filtering. The ship positions detected by FMCW radar images were passed into a tracking algorithm. We then compared the detection and tracking results from FMCW radar with AIS information and found that they were relatively well matched. Tracking performance is especially good when ships are across from each other. The results also show good monitoring capability for small fishing ships, even those not equipped with AIS or with a dysfunctional AIS.

• Nuclear Engineering and Technology
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• 제54권1호
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• pp.193-206
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• 2022

Several algorithms for nuclear power plant (NPP) break event detection, isolation, localization, and size estimation are proposed. A break event can be promptly detected and isolated after its occurrence by simultaneously monitoring changes in the sensing readings and by employing an interquartile range-based isolation scheme. By considering the multi-sensor data block of a break to be rank-one, it can be located as the position whose lead field vector is most orthogonal to the noise subspace of that data block using the Multiple Signal Classification (MUSIC) algorithm. Owing to the flexibility of deep neural networks in selecting the best regression model for the available data, we can estimate the break size using multiple-sensor recordings of the break regardless of the sensor types. The efficacy of the proposed algorithms was evaluated using the data generated by Maanshan NPP simulator. The experimental results demonstrated that the MUSIC method could distinguish two near breaks. However, if the two breaks were close and of small sizes, the MUSIC method might wrongly locate them. The break sizes estimated by the proposed deep learning model were close to their actual values, but relative errors of more than 8% were seen while estimating small breaks' sizes.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2020년도 추계학술대회
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• pp.113-117
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• 2020

Images were taken under various weather such as rain, haze, snow often show low visibility, which can dramatically decrease accuracy of some tasks in computer vision: object detection, segmentation. Besides, previous work to enhance image usually downsample the image to receive consistency features but have not yet good upsample algorithm to recover original size. So, in this research, we jointly implement removal streak in heavy rain image and super resolution using a deep network. We put forth a 2-stage network: a multi-model network followed by a refinement network. The first stage using rain formula in the single image and two operation layers (addition, multiplication) removes rain streak and noise to get clean image in low resolution. The second stage uses refinement network to recover damaged background information as well as upsample, and receive high resolution image. Our method improves visual quality image, gains accuracy in human action recognition task in datasets. Extensive experiments show that our network outperforms the state of the art (SoTA) methods.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2020년도 추계학술대회
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• pp.60-63
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• 2020

With the advent of deep learning, a lot of attempts have been made in computer vision to substitute deep learning models for conventional algorithms. Among them, image classification, object detection, and image restoration have received a lot of attention from researchers. However, most of the contributions were refined in one of the fields only. We propose a new paradigm of model structure. End-to-end model which we will introduce classifies noise of an image and restores accordingly. Through this, the model enhances universality and efficiency. Our proposed model is an 'One-For-All' model which classifies weather condition in an image and returns clean image accordingly. By separating weather conditions, restoration model became more compact as well as effective in reducing raindrops, snowflakes, or haze in an image which degrade the quality of the image.

• Transactions on Control, Automation and Systems Engineering
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• 제3권4호
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• pp.283-288
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• 2001

In this paper, we proposed a method for extracting facial characteristics of human being in an image. Given a pair of gray level sample images taken with and without human being, the face of human being is segmented from the image. Noise in the input images is removed with the help of Gaussian filters. Edge maps are found of the two input images. The binary edge differential image is obtained from the difference of the two input edge maps. A mask for face detection is made from the process of erosion followed by dilation on the resulting binary edge differential image. This mask is used to extract the human being from the two input image sequences. Features of face are extracted from the segmented image. An effective recognition system using the discrete wave let transform (DWT) is used for recognition. For extracting the facial features, such as eyebrows, eyes, nose and mouth, edge detector is applied on the segmented face image. The area of eye and the center of face are found from horizontal and vertical components of the edge map of the segmented image. other facial features are obtained from edge information of the image. The characteristic vectors are extrated from DWT of the segmented face image. These characteristic vectors are normalized between +1 and -1, and are used as input vectors for the neural network. Simulation results show recognition rate of 100% on the learned system, and about 92% on the test images.

• 방송공학회논문지
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• 제16권5호
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• pp.873-882
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• 2011

본 논문에서는 시공간 밝기 변화량에 따라 공간적 보간과 시간적 보간 결과를 가중합하여 주사선을 보간하는 효율적인 디인터레이싱 알고리즘을 제안한다. 공간적 보간에서는 에지 방향성 보정 기능을 포함한 새로운 에지기반 공간적 보간 기법을 적용한다. 일반적으로 기존의 에지를 고려한 공간적 디인터레이싱 알고리즘은 에지의 방향을 잘못 추정할 경우 보간된 영상에서 심각한 화질저하가 발생한다. 이를 보완하고 정확한 에지의 방향을 찾아내기 위해 밝기 차만을 이용한 기존 방법들과는 달리 제안된 방법은 에지 방향성을 검출하고 그 정보에 따라 가중최대빈도수 필터를 이용하여 보간될 화소의 에지의 방향성을 보정한다. 또한, 시간적 변화량 검출 오류를 줄이기 위해 미디언 필터를 적용하여 움직임 검출 성능을 향상시키고, 최종 화소 보간과정에서 시공간적인 밝기 변화량에 따라 공간적 보간과 시간적 보간 결과값의 가중합을 사용하여 움직임이 적은 영역에서의 화질을 향상시킨다. 실험결과 제안된 방법은 기존의 디인터레이싱 방법들에 비하여 주관적 화질뿐만 아니라 객관적인 성능도 우수함을 알 수 있다.

• 한국의학물리학회지:의학물리
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• 제24권3호
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• pp.183-190
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• 2013

광 음향 영상 장치는 최근 들어 연구와 개발이 활발히 진행 중이며 암을 조기 진단할 수 있는 장치로서의 가능성을 보이고 있다. 본 연구에서는 유방암 조기 진단을 위하여 광 음향 단층촬영 방식의 영상 장치를 개발하고 팬텀을 이용하여 그 유용성을 평가하고자 한다. 튜브 팬텀과 닭 가슴살 팬텀을 제작하고 이동 평균 필터와 3~6 MHz의 대역폭을 갖는 대역 통과 여파기를 설계하여 잡음을 제거하고 시간 지연 빔 형성(delay-and-sum beamforming) 알고리즘을 이용하여 광음향 영상을 재구성 하였다. 연구 결과 영상의 재구성에 있어서 빔 형성 알고리즘을 적용하기 전에 대역 통과 여파기와 같은 신호 처리가 효과적임을 보였다.