• Journal of the Korea institute for structural maintenance and inspection
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• v.14 no.6
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• pp.171-178
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• 2010

Pipeline structure is one of core underground infrastructure which transports primary sources. Since the almost pipeline structures are placed underground and connected each other complexly, it is difficult to monitor their structural health condition continuously. In order to overcome this limitation of recent monitoring technique, recently, a Ubiquitous Sensor Network (USN) system based on on-line and real-time monitoring system is being developed by the authors' research group. In this study, real-time pipeline health monitoring (PHM) methodology is presented based on electromechanical impedance methods using USN. Two types of damages including loosened bolts and notches are artificially inflicted on the pipeline structures, PZT and MFC sensors that have piezoelectric characteristics are employed to detect these damages. For objective evaluation of pipeline conditions, Damage metric such as Root Mean Square Deviation (RMSD) value was computed from the impedance signals to quantify the level of the damage. Optimal threshold levels for decision making are estimated by generalized extreme value(GEV) based statistical method. Throughout a series of experimental studies, it was reviewed the effectiveness and robustness of proposed PHM system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.415-424
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• 2019

In this paper, a minimum rotation radius was designed and fabricated to overcome the threshold so that elderly or disabled people who have difficulty moving can move and transfer safely and conveniently in a narrow room. In the indoor environment, where the sedentary culture develops, this study aimed to provide convenience for passengers with fracture diseases, geriatric diseases, and other knee and waist diseases. First, links, seats, armrests, covers, motors, batteries, chargers, controllers, etc. were attached to the frame so that they could be moved and lifted indoors. The product design and structure were designed considering the user's environment and physical characteristics, and IoT functions were added. A driving experiment was performed to confirm the operating performance of the manufactured indoor moving and lifting wheelchair. The performance tests, such as continuous running time, turning radius, maximum actuator load, maximum lift height, sound pressure level, minimum sensing distance of the driving aid sensor, interworking of server and app programs, device compatibility, and duty cycle error rate, were performed. As a result of the test, the built-in wheelchair could achieve the performance test target of each item and operate successfully.

• Korean Journal of Optics and Photonics
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• v.17 no.5
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• pp.383-390
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• 2006

The SharkHartmann wavefront sensors are the most popular devices to measure wavefront in the field of adaptive optics. The Shack-Hartmann sensors measure the centroids of spot irradiance distribution formed by each corresponding micro-lens. The centroids are linearly proportional to the local mean slopes of the wavefront defined within the corresponding sub-aperture. The wavefront is then reconstructed from the evaluated local mean slopes. The uncertainty of the Shack-Hartmann sensor is caused by various factors including the detector noise, the limited size of the detector, the magnitude and profile of spot irradiance distribution, etc. This paper investigates the noise propagation in two major centroid evaluation algorithms through computer simulation; 1st order moments of the irradiance algorithms i.e. center of gravity algorithm, and correlation algorithm. First, the center of gravity algorithm is shown to have relatively large dependence on the magnitudes of noises and the shape & size of irradiance sidelobes, whose effects are also shown to be minimized by optimal thresholding. Second, the correlation algorithm is shown to be robust over those effects, while its measurement accuracy is vulnerable to the size variation of the reference spot. The investigation is finally confirmed by experimental measurements of defocus wavefront aberrations using a Shack-Hartmann sensor using those two algorithms.

• Journal of Korea Water Resources Association
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• v.51 no.12
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• pp.1181-1194
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• 2018

This study suggests a novel approach of estimating stream flow discharge using the Synthetic Aperture Radar (SAR) images taken from 2015 to 2017 by European Space Agency Sentinel-1 satellite. Fifteen small to medium sized rivers in the Han River basin were selected as study area, and the SAR satellite images and flow data from water level and flow observation system operated by the Korea Institute of Hydrological Survey were used for model construction. First, we apply the histogram matching technique to 12 SAR images that have undergone various preprocessing processes for error correction to make the brightness distribution of the images the same. Then, the flow estimation model was constructed by deriving the relationship between the area of the stream water body extracted using the threshold classification method and the in-situ flow data. As a result, we could construct a power function type flow estimation model at the fourteen study areas except for one station. The minimum, the mean, and the maximum coefficient of determination ($R^2$) of the models of at fourteen study areas were 0.30, 0.80, and 0.99, respectively.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.497-510
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• 2022

Agricultural reservoirs are essential structures for water supplies during dry period in the Korean peninsula, where water resources are temporally unequally distributed. For efficient water management, systematic and effective monitoring of medium-small reservoirs is required. Synthetic Aperture Radar (SAR) provides a way for continuous monitoring of those, with its capability of all-weather observation. This study aims to evaluate the applicability of SAR in monitoring medium-small reservoirs using Sentinel-1 (10 m resolution) and Capella X-SAR (1 m resolution), at Chari (CR), Galjeon (GJ), Dwitgol (DG) reservoirs located in Ulsan, Korea. Water detected results applying Z fuzzy function-based threshold (Z-thresh) and Chan-vese (CV), an object detection-based segmentation algorithm, are quantitatively evaluated using UAV-detected water boundary (UWB). Accuracy metrics from Z-thresh were 0.87, 0.89, 0.77 (at CR, GJ, DG, respectively) using Sentinel-1 and 0.78, 0.72, 0.81 using Capella, and improvements were observed when CV was applied (Sentinel-1: 0.94, 0.89, 0.84, Capella: 0.92, 0.89, 0.93). Boundaries of the waterbody detected from Capella agreed relatively well with UWB; however, false- and un-detections occurred from speckle noises, due to its high resolution. When masked with optical sensor-based supplementary images, improvements up to 13% were observed. More effective water resource management is expected to be possible with continuous monitoring of available water quantity, when more accurate and precise SAR-based water detection technique is developed.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1309-1320
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• 2023

The technological development and popularization of mobile devices have developed so that users can check their location anywhere and use the Internet. However, in the case of indoors, the Internet can be used smoothly, but the global positioning system (GPS) function is difficult to use. There is an increasing need to provide real-time location information in shaded areas where GPS is not received, such as department stores, museums, conference halls, schools, and tunnels, which are indoor public places. Accordingly, research on the recent indoor positioning technology based on light detection and ranging (LiDAR) equipment is increasing to build a landmark database. Focusing on the accessibility of building a landmark database, this study attempted to develop a technique for estimating the user's location by using a single image taken of a landmark based on a mobile device and the landmark database information constructed in advance. First, a landmark database was constructed. In order to estimate the user's location only with the mobile image photographing the landmark, it is essential to detect the landmark from the mobile image, and to acquire the ground coordinates of the points with fixed characteristics from the detected landmark. In the second step, by applying the bag of words (BoW) image search technology, the landmark photographed by the mobile image among the landmark database was searched up to a similar 4th place. In the third step, one of the four candidate landmarks searched through the scale invariant feature transform (SIFT) feature point extraction technique and Homography random sample consensus(RANSAC) was selected, and at this time, filtering was performed once more based on the number of matching points through threshold setting. In the fourth step, the landmark image was projected onto the mobile image through the Homography matrix between the corresponding landmark and the mobile image to detect the area of the landmark and the corner. Finally, the user's location was estimated through the location estimation technique. As a result of analyzing the performance of the technology, the landmark search performance was measured to be about 86%. As a result of comparing the location estimation result with the user's actual ground coordinate, it was confirmed that it had a horizontal location accuracy of about 0.56 m, and it was confirmed that the user's location could be estimated with a mobile image by constructing a landmark database without separate expensive equipment.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.387-396
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• 2024

Most very high-resolution (VHR) satellite images provide rational polynomial coefficients (RPC) data to facilitate the transformation between ground coordinates and image coordinates. However, initial RPC often contains geometric errors, necessitating correction through matching with ground control points (GCPs). A GCP chip is a small image patch extracted from an orthorectified image together with height information of the center point, which can be directly used for geometric correction. Many studies have focused on area-based matching methods to accurately align GCP chips with VHR satellite images. In cases with seasonal differences or changed areas, edge-based algorithms are often used for matching due to the difficulty of relying solely on pixel values. However, traditional edge extraction algorithms,such as canny edge detectors, require appropriate threshold settings tailored to the spectral characteristics of satellite images. Therefore, this study utilizes deep learning-based edge information that is insensitive to the regional characteristics of satellite images for matching. Specifically,we use a pretrained pixel difference network (PiDiNet) to generate the edge maps for both satellite images and GCP chips. These edge maps are then used as input for normalized cross-correlation (NCC) and relative edge cross-correlation (RECC) to identify the peak points with the highest correlation between the two edge maps. To remove mismatched pairs and thus obtain the bias-compensated RPC, we iteratively apply the data snooping. Finally, we compare the results qualitatively and quantitatively with those obtained from traditional NCC and RECC methods. The PiDiNet network approach achieved high matching accuracy with root mean square error (RMSE) values ranging from 0.3 to 0.9 pixels. However, the PiDiNet-generated edges were thicker compared to those from the canny method, leading to slightly lower registration accuracy in some images. Nevertheless, PiDiNet consistently produced characteristic edge information, allowing for successful matching even in challenging regions. This study demonstrates that improving the robustness of edge-based registration methods can facilitate effective registration across diverse regions.