• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.11 no.1
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• pp.37-45
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• 1993

We research on the coordinate transformation between Korean geodetic system and WGS 1984. We made the wide area an object of our research. This area covers the range of longitude $1^\circ$ 32'and latitude $1^\circ$ 7'and is four times wider than a first stage research published in 1992. For this research, we performed GPS observations at 10 control point (seven astrogeodetic points, three triangulation points) and eight banch marks nearby control points, and then transformed the coordinates of WGS 84 to Korean geodetic system using the 7 parameter method, Molodensky method and MRE method. From this test, we compared the precision of the coordinate transformation by each method and checked which method is more applicable to Korea. We could find that the precision by 7 parameter method is three times better than the other methods and that the coordinate transformation by 7 parameter method is possible with the precision of 0".017, 0".016, 0.329 m in latitude, longitude and height. We could check and correct the height blunder of the control points by the bench marks nearby control point. We also could find the precision of coordinate transformation is more improved by the correction of height blunder.t blunder.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.3C
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• pp.92-101
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• 2005

It is difficult to detect the accurate detection which leads the camera it moves follows in change of the noise or illumination and Also, it could be recognized with backgound if the object doesn't move during hours. In this paper, the proposed method is updating changed background image as much as N＊M pixel mask as time goes on after get a difference between imput image and first background image. And checking image pixel can efficiently detect moving by computing fixed distance pixel instead of operate all pixel. Also, set up minimum area of object to use boundary point of object abstracted through checking image pixel and motion detect of object. Therefore motion detection is available as is fast and correct without doing checking image pixel every Dame. From experiment, the designed and implemented system showed high precision ratio in performance assessment more than 90 percents.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.909-919
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• 2023

Benthic marine invertebrates, the invertebrates living on the bottom of the ocean, are an essential component of the marine ecosystem, but excessive reproduction of invertebrate grazers or pirate creatures can cause damage to the coastal fishery ecosystem. In this study, we compared and evaluated You Only Look Once Version 7 (YOLOv7), the most widely used deep learning model for real-time object detection, and detection tansformer (DETR), a transformer-based model, using underwater images for benthic marine invertebratesin the coasts of South Korea. YOLOv7 showed a mean average precision at 0.5 (mAP@0.5) of 0.899, and DETR showed an mAP@0.5 of 0.862, which implies that YOLOv7 is more appropriate for object detection of various sizes. This is because YOLOv7 generates the bounding boxes at multiple scales that can help detect small objects. Both models had a processing speed of more than 30 frames persecond (FPS),so it is expected that real-time object detection from the images provided by divers and underwater drones will be possible. The proposed method can be used to prevent and restore damage to coastal fisheries ecosystems, such as rescuing invertebrate grazers and creating sea forests to prevent ocean desertification.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.5 s.311
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• pp.12-21
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• 2006

In this paper, we propose a technique for extracting attentive objects in images using feature maps, regardless of the complexity of images and the position of objects. The proposed method uses feature maps with edge and color information in order to extract attentive objects. We also propose a reference map which is created by integrating feature maps. In order to create a reference map, feature maps which represent visually attentive regions in images are constructed. Three feature maps including edge map, CbCr map and H map are utilized. These maps contain the information about boundary regions by the difference of intensity or colors. Then the combination map which represents the meaningful boundary is created by integrating the reference map and feature maps. Since the combination map simply represents the boundary of objects we extract the candidate object regions including meaningful boundaries from the combination map. In order to extract candidate object regions, we use the convex hull algorithm. By applying a segmentation algorithm to the area of candidate regions to separate object regions and background regions, real object regions are extracted from the candidate object regions. Experiment results show that the proposed method extracts the attentive regions and attentive objects efficiently, with 84.3% Precision rate and 81.3% recall rate.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.742-745
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• 2004

If a personal robot is popularized like a personal computer in the future, many kinds of robots will appear and the number of manufacturers will increase as a matter of course. In such circumstances, it can be inefficient, in case each manufacturer makes a whole platform individually. The solutions for this problem are to modularize a robot component (hardware and software) functionally and to standardize each module. Each module is developed and sold by each special maker and a consumer purchases desired modules and integrates them. The standardization of a module includes the unification of electrical and mechanical interface. In this paper, the standard interfaces of modules are proposed and CMR(Component Modularized Robot)-P2 made with the modules(brain, sensor, mobile, arm) is introduced. In order to simplify and to make the modules light, a frame is used for supporting a robot and communication/power lines. The name of a method and the way to use that are defined dependently on the standard interfaces in order to use a module in other modules. Each module consists of a distributed object and that can be implemented in the random language and platform. The sensor, mobile and arm modules are developed on Pentium or ARM CPU and embedded Linux OS using the C programming language. The brain module is developed on Pentium CPU and Windows OS using the C, C++ and RPL(Robot Programming Language). Also tasks like pass planning, localization, moving, object perception and face perception are developed. In our test, modules got into gear and CMR-P2 executed various scenarios like guidance, errand and guarding completely.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.20-27
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• 2003

In this paper, we implemented a content-based image retrieval system that user can choose a wanted query region of object and retrieve similar object from image database. Query image is induced to wavelet transformation after divided into hue components and gray components that hue features is extracted through color autocorrelogram and dispersion in hue components. Texture feature is extracted through autocorrelogram and GLCM in gray components also. Using features of two components, retrieval is processed to compare each similarity with database image. In here, weight value is applied to each similarity value. We make up for each defect by deriving features from two components beside one that elevations of recall and precision are verified in experiment results. Moreover, retrieval efficiency is improved by weight value. And various features of database images are indexed automatically in feature library that make possible to rapid image retrieval.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.669-672
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• 2001

We propose an efficient algorithm for an interactive content-based image retrieval using relevance feedback. In the proposed algorithm, a new query feature vector first is yielded from the average feature vector of the relevant images that is fed back from the result images of the previous retrieval. Each component weight of a feature vector is computed from an inverse of standard deviation for each component of the relevant images. The updated feature vector of the query and the component weights are used in the iterative retrieval process. In addition, the irrelevant images are excluded from object images in the next iteration to obtain additional performance improvement. In order to evaluate the retrieval performance of the proposed method, we experiment for three image databases, that is, Corel, Vistex, and Ultra databases. We have chosen wavelet moments, BDIP and BVLC, and MFS as features representing the visual content of an image. The experimental results show that the proposed method yields large precision improvement.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.223-228
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• 2006

Monitoring structures is important to maintain the safety and serviceability of the structures. The maximum displacement in the structure should be precisely and frequently monitored because it is a direct assessment index indicating its stiffness. However, no practical method has been developed to monitor such displacement precisely, particularly for high-rise buildings and long span bridges because they cannot be easily accessible. To overcome such difficult accessibility, we propose to use a LIDAR system that remotely samples the surface of an object using laser pulses and generates the coordinates of numerous points on the surface. By analyzing the LIDAR points sampled from the surfaces of a deformed structure, we can precisely determine the displacement of the structure. In this study, we thus develop a novel method based the LIDAR system and perform an indoor experiment to prove its performance. This experimental results strongly supports that the displacement measurement using the LIDAR system are enough accurate to be used for structural analyses.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.20 no.5
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• pp.666-672
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• 2011

In this paper, we describes the development of automatic inspection system for detecting the defects on photovoltaic wafer by using machine vision. Until now, The defect inspection process was manually performed by operators. So these processes caused the produce of poorly-made articles and inaccuracy results. To improve the inspection accuracy, the inspection system is not only configured, but the image processing algorithm is also developed. The inspection system includes dimensional verification and pattern matching which compares a 2-D image of an object to a pattern image the method proves to be computationally efficient and accurate for real time application and we confirmed the applicability of the proposed method though the experience in a complex environment.

• Journal of Power Electronics
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• v.19 no.2
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• pp.443-453
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• 2019

As the core component of transmission systems, converters are very prone to failure. To improve the accuracy of fault diagnosis for wind power converters, a fault feature extraction method combined with a wavelet transform and compressed sensing theory is proposed. In addition, an improved AdaBoost-SVM is used to diagnose wind power converters. The three-phase output current signal is selected as the research object and is processed by the wavelet transform to reduce the signal noise. The wavelet approximation coefficients are dimensionality reduced to obtain measurement signals based on the theory of compressive sensing. A sparse vector is obtained by the orthogonal matching pursuit algorithm, and then the fault feature vector is extracted. The fault feature vectors are input to the improved AdaBoost-SVM classifier to realize fault diagnosis. Simulation results show that this method can effectively realize the fault diagnosis of the power transistors in converters and improve the precision of fault diagnosis.