• Transactions of the Korean Society of Mechanical Engineers B
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• v.24 no.1
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• pp.21-28
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• 2000

Although commercial PIV systems have been widely used for the non-intrusive velocity field measurement of fluid flows, they are still under development and have considerable room for improvement. In this study, a single-frame double-exposure PIV system using a high-resolution CCD camera was developed. A pulsed Nd:Yag laser and high-resolution CCD camera were synchronized by a home-made control circuit. In order to resolve the directional ambiguity problem encountered in the single-frame PIV technique, the second particle image was genuinely shifted in the CCD sensor array during the time interval dt. The velocity vector field was determined by calculating the displacement vector at each interrogation window using cross-correlation with 50% overlapping. In order to check the effect of spatial resolution of CCD camera on the accuracy of PIV velocity field measurement, the developed PIV system with three different resolution modes of the CCD camera (512 ${\times}$ 512, lK ${\times}$ IK, 2K ${\times}$ 2K) was applied to a turbulent flow which simulate the Zn plating process of a steel strip. The experimental model consists of a snout and a moving belt. Aluminum flakes about $1{\mu}m$ diameter were used as scattering particles for the liquid flow in the zinc pot and the gas flow above the zinc surface was seeded with atomized olive oil with an average diameter of 1-$3{\mu}m$. Velocity field measurements were carried out at the strip speed $V_s$=1.0 m/s. The 2K ${\times}$ 2K high-resolution PIV technique was significantly superior compared to the smaller pixel resolution PIV system. For the cases of 512 ${\times}$ 512 and 1K ${\times}$ 1K pixel resolution PIV system, it was difficult to get accurate flow structure of viscous flow near the wall and small vortex structure in the region of large velocity gradient.

• Journal of the Korean Solar Energy Society
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• v.33 no.5
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• pp.51-59
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• 2013

It is usual to evaluate the performance of the cool roof by measuring in-site rooftop temperature using thermal infra-red camera. The principal advantage of rooftop thermal infrared image acquired in oblique vantage point of super high-rise building as a remote sensor is to provide, in a cost-effective manner, area-wide information required for a scattered rooftop target with different colors, utilizing wide view angle and multi-temporal data coverage. This research idea was formulated by incorporating the concept of traditional remote sensing into rooftop temperature monitoring. Correlations between infrared image of super high-rise building and in-situ data were investigated to compare rooftop surface temperature for a total of four different rooftop locations. The results of the correlations analyses indicate that the rooftop surface temperature by the infrared images of super high-rise building alone could be explained yielding $R^2$ values of 0.951. The visible permanent record of the oblique thermal infra-red image was quite useful in better understanding the nature and extent of rooftop color that occurs in sampling points. This thermal infrared image acquired in oblique vantage point of super high-rise made it possible to identify area wide patterns of rooftop temperature change subject to many different colors, which cannot be acquired by traditional in-site field sampling. The infrared image of super high-rise building breaks down the usual concept of field sampling established as a conventional cool roof performance evaluation technique.

• Korean Journal of Food Science and Technology
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• v.34 no.6
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• pp.947-953
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• 2002

Two types of electronic nose were used for investigating the quality of dried lavers stored at 5, 15, and $30^{\circ}C$ RH of 32, 43, and 75%. The electronic nose is composed of metal oxide sensors, and GC is based on SAW sensor. Quality change in dried lavers was described in terms of the sensitivities $(R_{gas}/R_{air})$ of the sensors. Principal component analysis (PCA) was carried out using data obtained from six metal oxide sensors. The first principal component scores were correlated with quality changes of dried lavers. As storage time increased, the stored laver cluster separated from that of fresh lavers. A chromatogram was obtained from GC based on SAW sensor. Olfactory image, A $VaporPrint^{TM}$ image for pattern recognition, showed a significant difference between the stored and the fresh samples. Dried lavers during storage at $30^{\circ}C$ and 75% had bacterial counts of $5.7{\times}10^6\;CFU/g$ after 8 day. Increase of microbial count correlated with the response of electronic nose $(r^2=0.87)$. Whereas, color values showed no correlation.

• The KIPS Transactions:PartC
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• v.15C no.4
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• pp.281-288
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• 2008

Recently, the advance of multimedia hardware has fostered the development of wireless multimedia sensor network which is able to ubiquitously obtain multimedia content such as image or audio from the environment. The multimedia data which has several characteristics such as large size and correlation between the data requires reliability in transmission. However, the existing solution which take the focus on the efficiency of network mainly, is not appropriate to transmit the multimedia data. In the paper, we proposes a reliable asynchronous image transfer protocol, RAIT. RAIT applies double sliding window method in node-to-node image tansfer to prevent the packet loss caused by network congestion. The double sliding window consists of one sliding window for the receiving queue, which is used for prevention of packet loss caused by communication failure between nodes and the other sliding window for the sending queue which prevents the packet loss caused by network congestion. the routing node prevents the packet loss and guarantees the fairness between the nodes by scheduling the packets based on the image non-preemptively. The RAIT implements the double sliding window method by cross layer design between RAIT layer, routing layer, and queue layer. The experiment shows that RAIT guarantees the reliability of image transmission compared with the existing protocol.

• Smart Structures and Systems
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• v.19 no.2
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• pp.225-236
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• 2017

Railroad bridges in North America are an integral but aging part of the railroad network and are typically only monitored using visual inspections. When quantitative information is required for assessment, railroads often monitor bridges using accelerometers. However without a sensor to directly measure displacements, it is difficult to interpret these results as they relate to bridge performance. Digital Image Correlation (DIC) is a non-contact sensor technology capable of directly measuring the displacement of any visible bridge component. In this research, a railroad bridge was monitored under load using DIC and accelerometers. DIC measurements are directly compared to serviceability limits and it is observed that the bridge is compliant. The accelerometer data is also used to calculate displacements which are compared to the DIC measurements to assess the accuracy of the accelerometer measurements. These measurements compared well for zero-mean lateral data, providing measurement redundancy and validation. The lateral displacements from both the accelerometers and DIC at the supports were then used to determine the source of lateral displacements within the support system.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.8
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• pp.667-675
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• 2013

This paper presents vision-based techniques for underwater landmark detection, map-based localization, and SLAM (Simultaneous Localization and Mapping) in structured underwater environments. A variety of underwater tasks require an underwater robot to be able to successfully perform autonomous navigation, but the available sensors for accurate localization are limited. A vision sensor among the available sensors is very useful for performing short range tasks, in spite of harsh underwater conditions including low visibility, noise, and large areas of featureless topography. To overcome these problems and to a utilize vision sensor for underwater localization, we propose a novel vision-based object detection technique to be applied to MCL (Monte Carlo Localization) and EKF (Extended Kalman Filter)-based SLAM algorithms. In the image processing step, a weighted correlation coefficient-based template matching and color-based image segmentation method are proposed to improve the conventional approach. In the localization step, in order to apply the landmark detection results to MCL and EKF-SLAM, dead-reckoning information and landmark detection results are used for prediction and update phases, respectively. The performance of the proposed technique is evaluated by experiments with an underwater robot platform in an indoor water tank and the results are discussed.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.597-602
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• 2014

We propose hybrid-sensing system for human tracking. This system uses laser scanners and image sensors and is applicable to wide and crowded area such as hallway of university. Concretely, human tracking using laser scanners is at base and image sensors are used for human identification when laser scanners lose persons by occlusion, entering room or going up stairs. We developed the method of human identification for this system. Our method is following: 1. Best-shot images (human images which show human feature clearly) are obtained by the help of human position and direction data obtained by laser scanners. 2. Human identification is conducted by calculating the correlation between the color histograms of best-shot images. It becomes possible to conduct human identification even in crowded scenes by estimating best-shot images. In the experiment in the station, some effectiveness of this method became clear.

• 한국지형공간정보학회:학술대회논문집
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• 2002.11a
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• pp.73-80
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• 2002

RFM is believed to be universally applicable to any type of the sensor. Most of researches carried out lately are concentrated on terrain-independent method, but the researches about approvement of accuracy by way of terrain-dependent method are required to increase a practical use of satellite imagery in nonprofessional groups. This research focused on a means to improve RFM solution, a matching technique, and a generation of DEM through a correlation analysis, with terrain-dependent solution. The result shows that accuracy problem which is caused by over-parameterization on RFCs was removed through correlation analysis, and it was possible to generate a accurate DEM with terrain-dependent solution. And also, the application of RFM with different satellite images show sensor independent characteristics of RFM

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.6B
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• pp.1191-1202
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• 1999

We propose an efficient vehicle detection and classification algorithm for an electronic toll collection using the feature which is robust to abrupt intensity change between consecutive frames. The local correlation coefficient between wavelet transformed input and reference images is used as such a feature, which takes advantage of textural similarity. The usefulness of the proposed feature is analyzed qualitatively by comparing the feature with the local variance of a difference image, and is verified by measuring the improvements in the separability of vehicle from shadowy or shadowless road for a real test image. Experimental results from field tests show that the proposed vehicle detection and classification algorithm performs well even under abrupt intensity change due to the characteristics of sensor and occurrence of shadow.

• Korean Journal of Remote Sensing
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• v.26 no.3
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• pp.305-316
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• 2010

The MODerate Resolution Imaging Spectroradiometer (MODIS) sensor has 36 bands at 250m, 500m, 1km spatial resolution. However, 500m or 1km MODIS data exhibits a few limitations when low resolution data is applied at small areas that possess complex land cover types. In this study, we produce seven 250m spectral bands by fusing two MODIS 250m bands into five 500m bands. In order to recommend the best fusion method by which one acquires MODIS data, we compare seven fusion methods including the Brovey transform, principle components algorithm (PCA) fusion method, the Gram-Schmidt fusion method, the least mean and variance matching method, the least square fusion method, the discrete wavelet fusion method, and the wavelet-PCA fusion method. Results of the above fusion methods are compared using various evaluation indicators such as correlation, relative difference of mean, relative variation, deviation index, peak signal-to-noise ratio index and universal image quality index, as well as visual interpretation method. Among various fusion methods, the local mean and variance matching method provides the best fusion result for the visual interpretation and the evaluation indicators. The fusion algorithm of 250m MODIS data may be used to effectively improve the accuracy of various MODIS land products.