• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.216-219
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• 2004

In this study, the concept and techniques to generate the level lA, lB and 2A image products have been reviewed. In particular, radiometric and geometric corrections and bands registration used to generate level lA, lB and 2A products have been focused in this study. Radiometric correction is performed to take into account radiometric gain and offset calculated by compensating the detector response non-uniformity. And, in order to compensate satellite altitude, attitude, skew effects, earth rotation and earth curvature, some geometric parameters for geometric corrections are computed and applied. Bands registration process using the matching function between a geometry, which is called 'reference geometry', and another one which is corresponds to the image to be registered is applied to images in case of multi-spectral imaging mode. In order to generate level-lA image products, a simple radiometric processing is applied to a level-0 image. Level-lB image has the same radiometry correction as a level-lA image, but is also issued from some geometric corrections in order to compensate skew effects, Earth rotation effects and spectral misregistration. Level-2A image is generated using some geo-referencing parameters computed by ephemeris data, orbit attitudes and sensor angles. Level lA image is tested by visual analysis. The difference between distances calculated level 1 B image and distances of real coordinate is tested. Level 2A image is tested Using checking points.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.368-371
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• 2006

A coastline is the boundary between land and ocean masses. Knowledge of coastline is essential for autonomous navigation, geographical exploration, coastal erosion monitoring and modelling, water line change, etc. Many methods have been researched to extract coastlines from the synthetic aperture radar (SAR) and optic images. Most methods were based on the intensity contrast between land and sea regions. However, in these methods, a coastline detection task was very difficult because of insufficient intensity contrast and the ambiguity in distinguishing coastline from other object line. In this paper, we propose an efficient method for the delineation of coastline using interferometric coherence values estimated from ERS tandem pair. The proposed method uses the facts that a tandem pair of ERS is acquired from a time interval of an accurate day and that the coherent and incoherent values in coherence map are land and water, respectively. The coherence map was generated from ERS tandem pair, filtered by MAP filter, and divided into land and water by the determination of threshold value that is based on the bimodality of the histogram. Finally, a coastline was detected by delineating the boundary pixels. There was a good visual match between the detected coastline and the manually contoured line. The interferometric coherence map will be helpful to identify land and water regions easily, and can be used to many applications that are related with a coastline.

• 지구물리와물리탐사
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• 제6권1호
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• pp.7-12
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• 2003

위성영상에 나타나는 선구조는 화소의 명암차에 바탕을 두어 육안판독이나 자동 추출 알고리즘을 이용하여 추출한다. 보통 육안판독은 다양한 보조자료와 연구자의 경험적 지식을 필요로 하기 때문에 더욱 객관적이고 신속한 자동선구조 추출 알고리즘들이 필요하다. 산악지형 외에도 충적층 요소까지 고려하여 개발된 DSTA(dynamic segment tracing algorithm) 알고리즘은 충적층 지역이 산악지형에 대해 비교적 넓은 부분을 차지하고 있는 경우에 충적층 지역에서 노이즈가 나타난다. 본 연구에서는 이러한 노이즈를 감소시킬 수 있는 알고리즘인 AERA(alluvial effect reducing algorithm)를 개발하여 산악지형과 농경지와 도심지역이 넓게 분포되어 있는 지역에 이를 적용하였으며, 기존의 알고리즘만 이용하여 선구조를 추출한 결과와 비교하여 이의 적용 가능성을 알아보았다.

• Ocean and Polar Research
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• 제35권4호
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• pp.395-405
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• 2013

The aim of this study is to suggest an optimal survey method for coastal habitat monitoring around Weno Island in Chuuk Atoll, Federated States of Micronesia (FSM). This study was carried out to compare and analyze differences between in situ survey (PHOTS) and high spatial satellite imagery (Worldview-2) with regard to the coastal habitat distribution patterns of Weno Island. The in situ field data showed the following coverage of habitat types: sand 42.4%, seagrass 26.1%, algae 14.9%, rubble 8.9%, hard coral 3.5%, soft coral 2.6%, dead coral 1.5%, others 0.1%. The satellite imagery showed the following coverage of habitat types: sand 26.5%, seagrass 23.3%, sand + seagrass 12.3%, coral 18.1%, rubble 19.0%, rock 0.8% (Accuracy 65.2%). According to the visual interpretation of the habitat map by in situ survey, seagrass, sand, coral and rubble distribution were misaligned compared with the satellite imagery. While, the satellite imagery appear to be a plausible results to identify habitat types, it could not classify habitat types under one pixel in images, which in turn overestimated coral and rubble coverage, underestimated algae and sand. The differences appear to arise primarily because of habitat classification scheme, sampling scale and remote sensing reflectance. The implication of these results is that satellite imagery analysis needs to incorporate in situ survey data to accurately identify habitat. We suggest that satellite imagery must correspond with in situ survey in habitat classification and sampling scale. Subsequently habitat sub-segmentation based on the in situ survey data should be applied to satellite imagery.

• 문화기술의 융합
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• 제6권3호
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• pp.341-346
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• 2020

가상현실 콘텐츠에서 시각과 다른 감각의 불일치로 인한 멀미나 어지럼증이 큰 문제로 논란이 되고 있다. 해당 문제를 해결하기 위해 오감을 만족시키는 기기들이 연구가 활발히 진행 중이다. 그 중 촉감에 관한 연구가 가장 많은데 경도나 질감에 의한 연구는 많지만 온도의 관한 연구가 상대적으로 적다. 따라서 본 논문에서는 재질의 온도가 아닌 고온에서 저온으로 이동하는 열에너지의 양을 기반으로 온도를 느낄 수 있는 계산모델을 제시하고자 한다. 열에너지는 물질의 고유특성인 열전도도와 온도, 접촉면적 등에 의해 결정되기 때문에 재질의 종류, 재질과 손의 온도, 물체와 닿는 부위에 따라 사람이 느끼는 열의 정도가 달라진다. 열전도법칙과 비열공식을 이용해 재질의 단위시간당 열에너지 이동양을 계산하고 열전소자를 이용하여 재질의 단위시간당 변하는 열에너지 재현방법을 연구하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.382-387
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• 1992

In order to obtain desired arc welding performance, we already developed an arc welding robot system that enabled coordinated motions of dual arm robots. In this system one robot arm holds a welding target as a positioning device, and the other robot moves the welding torch. Concerning to such a dual arm robot system, the positioning accuracy of robots is one important problem, since nowadays conventional industrial robots unfortunately don't have enough absolute accuracy in position. In order to cope with this problem, our robot system employed teaching playback method, where absolute error are compensated by the operator's visual feedback. Due to this system, an ideal arc welding considering the posture of the welding target and the directions of the gravity has become possible. Another problem still remains, while we developed an original teaching method of the dual arm robots with coordinated motions. The problem is that manual teaching tasks are still tedious since they need fine movements with intensive attentions. Therefore, we developed a 3-dimensional vision guided robot control method for our welding robot system with coordinated motions. In this paper we show our 3-dimensional vision sensor to guide our arc welding robot system with coordinated motions. A sensing device is compactly designed and is mounted on the tip of the arc welding robot. The sensor detects the 3-dimensional shape of groove on the target work which needs to be weld. And the welding robot is controlled to trace the grooves with accuracy. The principle of the 3-dimensional measurement is depend on the slit-ray projection method. In order to realize a slit-ray projection method, two laser slit-ray projectors and one CCD TV camera are compactly mounted. Tactful image processing enabled 3-dimensional data processing without suffering from disturbance lights. The 3-dimensional information of the target groove is combined with the rough teaching data they are given by the operator in advance. Therefore, the teaching tasks are simplified

• Journal of Forest and Environmental Science
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• 제32권2호
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• pp.120-128
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• 2016

Forest sequesters large terrestrial carbon which is stored in the biomass of tree and plays a key role in reducing atmospheric carbon. Thus, the objectives of the present study were to assess the growing stock, above ground biomass and carbon in trees of Ponda watershed of Rajouri district (J&K). IRS-P6 LISS-III satellite data of October 2010 was used for preparation of land use/land cover map and forest density map of the study area by visual interpretation. The growing stock estimation was done for the study area as well as for the sample plots laid in forest and agriculture fields. The growing stock and biomass of trees were estimated using species specific volume equations and using specific gravity of wood, respectively. The total growing stock in the study area was estimated to be $0.25million\;m^3$ which varied between $85.94m^3/ha$ in open pine to $11.58m^3/ha$ in degraded pine forest. However in agriculture area, growing stock volume density of $14.85m^3/ha$ was recorded. Similarly, out of the total biomass (0.012 million tons) and carbon (0.056 million tons) in the study area, open pine forest accounted for the highest values of 43.74 t/ha and 19.68 t/ha and lowest values of 5.68 t/ha and 2.55 t/ha, respectively for the degraded pine forest. The biomass and carbon density in agriculture area obtained was 5.49 t/ha and 2.47 t/ha, respectively. In all the three forest classes Pinus roxburghii showed highest average values of growing stock volume density, biomass and carbon.

• 공학교육연구
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• 제14권4호
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• pp.11-19
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• 2011

본 연구의 목적은 공과대학생을 위한 교수학습 전략에 대한 시사점을 도출하기 위해 공과대학 신입생의 학습양식과 학습전략 수준을 검토하고, 학습양식에 따른 학습전략 활용 정도를 분석하였다. 서울에 소재한 K대학교와 H대학교의 1학년생 273명을 대상으로 분석한 결과, 감각형, 시각형, 숙고형, 순차형이 더 높은 비율을 차지하였다. 학습전략은 3.28점(SD=0.38)으로서 평균 수준이었으며, 상대적으로 초인지 영역의 수준이 가장 높고, 내적관리 영역 수준이 가장 낮은 것으로 나타났다. 한편, 학습양식에 따른 학습전략 수준의 차이를 분석한 결과, 정보처리 차원에서 학습전략의 유의한 차이가 나타났는데 적극적 학습자의 학습전략 수준이 높은 것으로 나타났다. 세부 전략에서는 인지 영역과 내적/외적관리 영역에서 학습양식별 유의한 차이를 보였다. 본 연구 결과에 기초하여 공과대학의 교수전략 및 학습전략에 대한 시사점을 도출하였다.

• Journal of Biosystems Engineering
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• 제17권1호
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• pp.65-78
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• 1992

When using a computer vision system for a measurement, the geometrically distorted input image usually restricts the site and size of the measuring window. A geometrically distorted image caused by the image sensing and processing hardware degrades the accuracy of the visual measurement and prohibits the arbitrary selection of the measuring scope. Therefore, an image calibration is inevitable to improve the measuring accuracy. A calibration process is usually done via four steps such as measurement, modeling, parameter estimation, and compensation. In this paper, the efficient error calibration technique of a geometrically distorted input image was developed using a neural network. After calibrating a unit pixel, the distorted image was compensated by training CMLAN(Cerebellar Model Linear Associator Network) without modeling the behavior of any system element. The input/output training pairs for the network was obtained by processing the image of the devised sampled pattern. The generalization property of the network successfully compensates the distortion errors of the untrained arbitrary pixel points on the image space. The error convergence of the trained network with respect to the network control parameters were also presented. The compensated image through the network was then post processed using a simple DDA(Digital Differential Analyzer) to avoid the pixel disconnectivity. The compensation effect was verified using known sized geometric primitives. A way to extract directly a real scaled geometric quantity of the object from the 8-directional chain coding was also devised and coded. Since the developed calibration algorithm does not require any knowledge of modeling system elements and estimating parameters, it can be applied simply to any image processing system. Furthermore, it efficiently enhances the measurement accuracy and allows the arbitrary sizing and locating of the measuring window. The applied and developed algorithms were coded as a menu driven way using MS-C language Ver. 6.0, PC VISION PLUS library functions, and VGA graphic functions.

• Smart Structures and Systems
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• 제6권3호
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• pp.197-209
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• 2010

The structural state of a bridge is currently examined by visual inspection or by wired sensor techniques, which are relatively expensive, vulnerable to inclement conditions, and time consuming to undertake. In contrast, wireless sensor networks are easy to deploy and flexible in application so that the network can adjust to the individual structure. Different sensing techniques have been used with such networks, but the acoustic emission technique has rarely been utilized. With the use of acoustic emission (AE) techniques it is possible to detect internal structural damage, from cracks propagating during the routine use of a structure, e.g. breakage of prestressing wires. To date, AE data analysis techniques are not appropriate for the requirements of a wireless network due to the very exact time synchronization needed between multiple sensors, and power consumption issues. To unleash the power of the acoustic emission technique on large, extended structures, recording and local analysis techniques need better algorithms to handle and reduce the immense amount of data generated. Preliminary results from utilizing a new concept called Acoustic Emission Array Processing to locally reduce data to information are presented. Results show that the azimuthal location of a seismic source can be successfully identified, using an array of six to eight poor-quality AE sensors arranged in a circular array approximately 200 mm in diameter. AE beamforming only requires very fine time synchronization of the sensors within a single array, relative timing between sensors of $1{\mu}s$ can easily be performed by a single Mote servicing the array. The method concentrates the essence of six to eight extended waveforms into a single value to be sent through the wireless network, resulting in power savings by avoiding extended radio transmission.