• Proceedings of the KIEE Conference
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• 2006.07c
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• pp.1723-1724
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• 2006

자기누설탐상법은 비파피검사 방법의 하나로 대상물체를 외부에서 착자시켜 함이 발생할 경우에 결함부위에서 자기누설이 발생하도록 하여, 누설된 자기장을 측정하여 결함의 유무와 크기 등을 판정하는 시스템이다. 본 논문에서는 MFL 방식의 범용 NDT 검사기의 개발을 위해 영구자석을 이용하여 소형 비파괴 검사기를 설계하고, 3차원 유한요소법을 이용하여 해석하고 실제 데이터를 측정하여 그 결과를 비교 분석하였다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.930-933
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• 2005

Non-destructive inspection techniques using laser have been breading their application areas as well as growing their measurement skills together with the rapid development of circumferential technology like fiber optics. computer and image processing The ESPI technique is already on the stage of on-line testing with commercial products in developed country nations. Especially, this technique is expected to be applied to the nuclear industry, automobile and aerospace because it is proper for the vibration measurement and it can be applied to objects of a high temperature. This paper describes the use of the ESPI system for measuring vibration patterns on the reflecting objects. Using this system, high-quality Jo fringes for identifying mode shapes are displayed. A bias vibration is introduced into the reference beam to shift the Jo fringes so that fringe shift algorithms can be used to determine vibration amplitude. Using this method. amplitude fields for vibrating objects were obtained directly from the time-average interferometer recorded by the ESPI system.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1350-1353
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• 2015

In this paper, we have researched about how to detect the fish robot objects in aquarium. We had used designed fish robots DOMI ver1.0, which had researched and developed for aquarium underwater robot. The model of the robot fish is analysis to maximize the momentum of the robot fish and the body of the robot is designed through the analysis of the biological fish swimming. We are planned to non-external equipment to find the position and manipulated the position using creating boundary to fish robot to detect the fish robot objects. Also, we focused the detecting fish robot in aquarium by using boundary algorithm. In order to the find the object boundary, it is filtering the video frame to picture frames and changing the RGB to gray. Then, applied the boundary algorithm stand of equations which operates the boundary for objects. We called these procedures is kind of image processing that can distinguish the objects and background in the captured video frames. It was confirmed that excellent performance in the field test such as filtering image, object detecting and boundary algorithm.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.70-70
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• 2017

영상처리 기법을 이용한 유속 측정 방법인 표면영상유속계는 비접촉식으로 간편하게 유속을 측정할 수 있다는 장점이 있지만 영상 내 추적자의 움직임을 식별하기 어려운 야간의 경우와 새벽의 안개가 발생하는 경우에 대한 유속 측정의 어려움이 있었다. 표면영상유속계를 이용한 야간 유속 측정은 조명과 적외선 카메라를 이용하여 수표면을 가시화하는 방법을 통해 현장 적용성을 검증하였으나, 안개 발생 상황에서는 적용하기 어렵다는 한계가 있었다. 야간과 안개 등의 한계를 동시에 극복하기 위한 방법으로 원적외선 카메라를 이용한 연구들이 이루어지고 있지만 아직 시작단계이고, 원적외선의 경우 주변 환경 변화에 따라 물체의 표면온도가 검출되는 파장이 달라져 영상의 품질에 차이가 발생하기 때문에 이에 대한 다양한 실험적 연구가 필요하다. 이에 본 연구에서는 야외 개수로에서 드라이아이스를 이용하여 안개 조건을 재현하고, 다양한 흐름 조건에서 원적외선 영상을 이용한 표면유속 측정 적용성을 검토하였다. 안개가 발생하는 경우 원적외선 표면영상 유속계를 적용한 결과 안개가 없을 때의 유속 측정 결과와 거의 일치하는 것을 확인하였다. 따라서 원적외선 카메라를 이용한 표면유속 측정 방법은 야간과 안개가 발생하는 상황에 모두 사용하기에 적합한 것을 나타났다. 향후 하천 유량조사에 원적외선 카메라를 활용한다면 기존의 표면영상유속계의 비가시 환경에 대한 한계들을 많은 부분 극복할 수 있을 것으로 기대한다.

• The Journal of Korea Robotics Society
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• v.14 no.4
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• pp.357-362
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• 2019

This paper proposes a model and train method that can real-time detect objects and distances estimation based on a monocular camera by applying deep learning. It used YOLOv2 model which is applied to autonomous or robot due to the fast image processing speed. We have changed and learned the loss function so that the YOLOv2 model can detect objects and distances at the same time. The YOLOv2 loss function added a term for learning bounding box values x, y, w, h, and distance values z as 클래스ification losses. In addition, the learning was carried out by multiplying the distance term with parameters for the balance of learning. we trained the model location, recognition by camera and distance data measured by lidar so that we enable the model to estimate distance and objects from a monocular camera, even when the vehicle is going up or down hill. To evaluate the performance of object detection and distance estimation, MAP (Mean Average Precision) and Adjust R square were used and performance was compared with previous research papers. In addition, we compared the original YOLOv2 model FPS (Frame Per Second) for speed measurement with FPS of our model.

• Journal of Broadcast Engineering
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• v.26 no.1
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• pp.88-98
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• 2021

Recently, the crime prevention and culprit identification even in a low illuminated environment by CCTV is becoming ever more important. In a low lighting situation, CCTV applications capture images under infrared lighting since it is unobtrusive to human eye. Although the infrared lighting leads to advantage of capturing an image with abundant fine texture information, it is hard to capture the color information which is very essential in identifying certain objects or persons in CCTV images. In this paper, we propose a method to acquire color information through DCGAN from an image captured by CCTV in a low lighting environment with infrared lighting and a method to remove color noise in the acquired color image.

• Journal of Korea Multimedia Society
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• v.25 no.4
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• pp.600-609
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• 2022

In supervised learning, labeling of all data is essential, and in particular, in the case of object detection, all objects belonging to the image and to be learned have to be labeled. Due to this problem, continual learning has recently attracted attention, which is a way to accumulate previous learned knowledge and minimize catastrophic forgetting. In this study, a continaul learning model is proposed that accumulates previously learned knowledge and enables learning about new objects. The proposed method is applied to CenterNet, which is a object detection model of anchor-free manner. In our study, the model is applied the knowledge distillation algorithm to be enabled continual learning. In particular, it is assumed that all output layers of the model have to be distilled in order to be most effective. Compared to LWF, the proposed method is increased by 23.3%p mAP in 19+1 scenarios, and also rised by 28.8%p in 15+5 scenarios.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.117-126
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• 2020

A maritime object detection system is an intelligent assistance system to maritime autonomous surface ship(MASS). It detects automatically floating debris, which has a clash risk with objects in the surrounding water and used to be checked by a captain with a naked eye, at a similar level of accuracy to the human check method. It is used to detect objects around a ship. In the past, they were detected with information gathered from radars or sonar devices. With the development of artificial intelligence technology, intelligent CCTV installed in a ship are used to detect various types of floating debris on the course of sailing. If the speed of processing video data slows down due to the various requirements and complexity of MASS, however, there is no guarantee for safety as well as smooth service support. Trying to solve this issue, this study conducted research on the minimization of computation volumes for video data and the increased speed of data processing to detect maritime objects. Unlike previous studies that used the Hough transform algorithm to find the horizon and secure the areas of interest for the concerned objects, the present study proposed a new method of optimizing a binarization algorithm and finding areas whose locations were similar to actual objects in order to improve the speed. A maritime object detection system was materialized based on deep learning CNN to demonstrate the usefulness of the proposed method and assess the performance of the algorithm. The proposed algorithm performed at a speed that was 4 times faster than the old method while keeping the detection accuracy of the old method.

• Journal of KIISE:Databases
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• v.36 no.4
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• pp.263-279
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• 2009

Shape features in a content-based image retrieval (CBIR) system are divided into two classes: contour-based and region-based. Contour-based shape features are simple but they are not as efficient as region-based shape features. Most systems using the region-based shape feature have to extract the region firs t. The prior works on region-based systems still have shortcomings. They are complex to implement, particularly with respect to region extraction, and do not sufficiently use the spatial relationship between regions in the distance model In this paper, a region extraction method that is the combination of an edge-based method and a region growing method is proposed to accurately extract regions inside an object. Edges inside an object are accurately detected based on the Canny edge detector and the Hough transform. And the modified Integrated Region Matching (IRM) scheme which includes the adjacency relationship of regions is also proposed. It is used to compute the distance between images for the similarity search using shape features. The experimental results show the effectiveness of our region extraction method as well as the modified IRM. In comparison with other works, it is shown that the new region extraction method outperforms others.

• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.663-673
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• 2010

As a tool used for video maintenance, Video segmentation divides videos in hierarchical and structural manner. This technique can be considered as a core technique that can be applied commonly for various applications such as indexing, abstraction or retrieval. Conventional video segmentation used adaptive threshold to split video by calculating difference between consecutive frames and threshold value in window with fixed size. In this case, if the time difference between occurrences of cuts is less than the size of a window or there is much difference in neighbor feature, accurate detection is impossible. In this paper, Improved Adaptive threshold algorithm which enables determination of window size according to video format and reacts sensitively on change in neighbor feature is proposed to solve the problems above. Post-Processing method for decrement in error caused by camera flash and fast movement of large objects is applied. Evaluation result showed that there is 3.7% improvement in performance of detection compared to conventional method. In case of application of this method on modified video, the result showed 95.5% of reproducibility. Therefore, the proposed method is more accurated compared to conventional method and having reproducibility even in case of various modification of videos, it is applicable in various area as a video maintenance tool.