• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.221.2-221.2
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• 2012

우주물체 전자광학 감시체계(OWL: Optical Wide-field Patrol)는 관측소들의 자동운영을 통한 인공위성의 궤도정보추출이 목적이다. 이를 위해 각각의 관측소에서 매일 밤 운영되어야 하는 관측명령을 자동으로 생성하는 스케줄러를 개발하였다. 스케줄러는 OWL 본부가 설치될 한국천문연구원의 NOS(Network Operating System) 서버에 설치 운영된다. 스케줄러는 사용자가 정한 관측대상 인공위성의 관측우선순위와 OC(Orbit Calculation) 서브시스템이 제공한 위성궤도정보를 바탕으로, 시간에 따른 관측수행내용을 기록한 관측명령서(OCF: Observation Command File)를 작성한다. 작성된 OCF는 각 관측소가 관측을 시작하기 전까지 해당 관측소로 전달되며, 관측소는 OCF를 바탕으로 관측을 수행하게 된다. 스케줄러는 "제한조건반영" 및 "OCF작성" 등 2부분으로 구성된다. "제한조건반영"은 관측시스템의 특징을 스케줄러에 반영하는 단계로써 시야각 등 광학계의 특징, 필터 등 주변 장비의 특징, CCD 카메라의 노출대기시간 등 검출기의 특징 등이 이에 포함된다. 사용자는 장비의 교체 및 개선 등 관측시스템 변경이 발생하는 경우 "제한조건반영"에 이를 적용함으로써 스케줄러가 새로운 시스템에 쉽게 적용할 수 있다. "OCF작성"은 "제한조건반영"의 내용을 바탕으로 관측대상위성을 선정하는 , 위성 관측 구간 중 최대한 많은 노출 횟수를 산출하는 , 한 장의 영상에서 최대한 많은 궤도 정보를 획득하기 위한 등 3개의 알고리즘에 의해 OCF를 작성한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.11A
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• pp.1946-1956
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• 2001

In this paper, we propose a novel 2-D partial matching algorithm based on model-based stochastic analysis of feature correspondences in a relation vector space, which is quite robust to shape variations as well as invariant to geometric transformations. We represent an object using the ARG (Attributed Relational Graph) model with features of a set of relation vectors. In addition, we statistically model the partial occlusion or noise as the distortion of the relation vector distribution in the relation vector space. Our partial matching algorithm consists of two-phases. First, a finite number of candidate sets areselected by using logical constraint embedding local and structural consistency Second, the feature loss detection is done iteratively by error detection and voting scheme thorough the error analysis of relation vector space. Experimental results on real images demonstrate that the proposed algorithm is quite robust to noise and localize target objects correctly even inseverely noisy and occluded scenes.

• 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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.3
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• pp.249-256
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• 2016

Recently, a number of techniques to ensure the free walking for the visually impaired and transportation vulnerable have been studied. As a device for free walking, there are such as a smart cane and smart glasses to use the computer vision, ultrasonic sensor, acceleration sensor technology. In a typical technique, such as techniques for finds object and detect obstacles and walking area and recognizes the symbol information for notice environment information. In this paper, we studied recognization algorithm of the selected symbols that are required to visually impaired, with the deep learning algorithm. As a results, Use CNN(Convolutional Nueral Network) technique used in the field of deep-learning image processing, and analyzed by comparing through experimentation with various deep learning architectures.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.252-252
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• 2010

적외선 센서는 빛의 유무에 관계없이 주 야간 전방의 물체에서 발산하는 미약한 적외선(열선)을 감지하여 영상으로 재현하는 열상시스템은 자동차 야간 운전자 보조용 나이트 비젼, 핵심 시설의 감시 관리, 군수 등의 분야에 적용되어지고 있는 최첨단, 고부가가치를 지니고 있는 기술이다. 양자형은 센서 특성은 좋으나 냉각기(작동온도: $-196^{\circ}C$) 및 고진공 패키지인 dewar를 사용하는 반면에, 열형은 대부분 상온에서 동작되는 온도안정화를 위한 전자냉각모듈만을 구비하면 되므로 저가형으로 제작이 가능한 비냉각형 적외선 센서이다. 본 연구에서는 적외선 센서용 진공패키지 조립공정 및 패키지된 센서의 측정기술을 개발하였다. 금속 메탈패키지를 제작하였으며, 금속 진공패키지는 소자냉각용 TE Cooler와 장수명 진공유지를 위한 getter, 그리고 센서칩, 온도센서를. 장착하여 칩을 조립하였다. Cap ass'y와 base envelop의 솔더링 공정을 수행하였으며, 진공패키지의 진공유지를 위해 TMP를 이용하여 진공을 유지하고, 약 5일동안 패키지 bake-out을 수행하였다. 진공압력은 $10^{-7}\;torr$ 이하를 유지하였으며, getter를 활성화시키고, pinch-off 공정으로 조립 ass'y를 완성하였다. 진공 패키지의 기밀성은 He leak tester를 이용하여 측정하였으며, ${\sim}10^{-9}\;std.cm^3/sec$로 기밀성을 유지하였다. TE cooler를 작동한 온도안정성은 0.05 K 이하였다. 볼로미터 센서의 반응도는 $10^2\;V/W$ 이상을 나타내었으며, 탐지도는 $2{\times}10^8cm-Hz^{1/2}/W$를 나타내었다. 본 연구를 통하여 얻어진 결과는 향후 2차원 열영상용 어레이 검출기 및 웨이퍼수준의 패키징 공정에 유용하게 응용될 것으로 판단된다.

• Korean Journal of Plant Tissue Culture
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• v.25 no.3
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• pp.201-206
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• 1998

To develop the fruits of cucumber (Cucumis sativus L.) producing high yields of superoxide dismutase (SOD), the MnSOD cDNA from pea (Pisum sativum) under the control of the cauliflower mosaic virus 35S promoter was introduced into cucumber using Agrobacterium tumefaciens (strain LBA 4404)-mediated transformation. The kanamycin-resistant shoots were selected on the selection medium containing MS basal salt, 1.0 mg/L zeatin, 0.1 mg/L IAA, 300 mg/L claforan, and 100 mg/L kanamycin. After 6 weeks of culture on the selection medium, the shoots were transferred to MS medium containing 0.2 mg/L NAA to induce roots. PCR analysis using the primers for neomycin phosphotransferase (NPTII) gene revealed that three plantlets were transformed. The fruits of one transgenic plant had approximately 3.2-fold higher SOD activity than those of non-transgenic plants. MnSOD isoenzyme band was strongly detected on native gel in fruits of transgenic plants.

• Journal of Korea Multimedia Society
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• v.22 no.1
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• pp.44-54
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• 2019

Recently, image-based object detection has made great progress with the introduction of Convolutional Neural Network (CNN). Many trials such as Region-based CNN, Fast R-CNN, and Faster R-CNN, have been proposed for achieving better performance in object detection. YOLO has showed the best performance under consideration of both accuracy and computational complexity. However, these data-driven detection methods including YOLO have the fundamental problem is that they can not guarantee the good performance without a large number of training database. In this paper, we propose a data sampling method using CycleGAN to solve this problem, which can convert styles while retaining the characteristics of a given input image. We will generate the insufficient data samples for training more robust object detection without efforts of collecting more database. We make extensive experimental results using the day-time and night-time road images and we validate the proposed method can improve the object detection accuracy of the night-time without training night-time object databases, because we converts the day-time training images into the synthesized night-time images and we train the detection model with the real day-time images and the synthesized night-time images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1310-1316
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• 2021

Recently, with the development of artificial intelligence and IoT technology, the importance of video processing such as object tracking, medical imaging, and object recognition is increasing. In particular, the noise reduction technology used in the preprocessing process demands the ability to effectively remove noise and maintain detailed features as the importance of system images increases. In this paper, we provide a modified weight filter based on pixel matching in an AWGN environment. The proposed algorithm uses a pixel matching method to maintain high-frequency components in which the pixel value of the image changes significantly, detects areas with highly relevant patterns in the peripheral area, and matches pixels required for output calculation. Classify the values. The final output is obtained by calculating the weight according to the similarity and spatial distance between the matching pixels with the center pixel in order to consider the edge component in the filtering process.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.187-190
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• 2022

본 논문은 메타버스 등 인공지능 연계 증강/가상현실 부동 중계 플랫폼에서 부동산 영상 기반 매물 소개 시스템 구축에서 사생활 및 개인정보가 영상에 담기게 될 수 있는 위험이 존재하기에 부동산 영상 내의 개인정보 및 민감 정보를 인공지능 기술을 기반으로 검출하여 삭제해주고 복원해주는 인공지능 기술 연구개발을 목표로 하였다. 한국형 부동산 내 민감 object 를 정의하고, 최신 인공지능 딥러닝 기술 기반 민감 object detection 알고리즘을 연구 개발하며, 영상에서 삭제된 부분은 인공지능 기술을 기반으로 물체가 없는 실제 공간영상으로 복원해주는 영상복원 기술도 연구 개발하였다. 한국형 부동산 환경 (영상 촬영 조도, 디스플레이 스타일, 주변 가구 배치 등)에 맞는 인공지능 모델 구축을 위하여, 자체적으로 한국 영상 database 구축 및 Transfer learning for target domain adaptation 을 진행하였다. 제안된 알고리즘은 일반적인 환경에서 98%의 정확도와 challenge 환경에서 (occlusion 빛 반사, 저조도 등) 81%의 정확도를 보였다. 본 기술은 Proptech 분야에서 주목받고 있는 메타버스 기반 온라인 중계 서비스 기술을 활성화하기 위하여 기획되었으며, 특히 메타버스 부동산 중계 플랫폼의 활성화를 위하여 사생활 보호 측면에서 필요한 중요 기술을 인공지능 기술을 활용하여 연구 개발하였다.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.124-132
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• 2022

Recently, due to COVID-19, online remote classes and non-face-to-face exams have made it difficult to manage class attitudes and exam cheating. Therefore, there is a need for a system that automatically recognizes and detects the behavior of students online. Action recognition, which recognizes human action, is one of the most studied technologies in computer vision. In order to develop such a technology, data including human arm movement information and information about surrounding objects, which can be key information in online classes and exams, are needed. It is difficult to apply the existing dataset to this system because it is classified into various fields or consists of daily life action. In this paper, we propose a dataset that can classify attitudes in real-time online tests and classes. In addition, it shows whether the proposed dataset is correctly constructed through comparison with the existing action recognition dataset.