• Journal of Sensor Science and Technology
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• v.10 no.6
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• pp.295-303
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• 2001

White emission thin film electroluminecent device was fabricated with ZnS for phosphor layers and BST ferroelectric thin film for insulating layers. The ZnS:Mn and $ZnS:SmF_3$ layers were used for emission of red color. Also the $ZnS:TbF_3$ and $ZnS:AgF_3$ layers were used to emission of green and blue color, respectively. And the fabrication conditions of the BST insulating layers were followings, that is, the composition ratio of target, substrate temperature, working pressure and operating gas ratio were $Ba_{0.5}Sr_{0.5}Ti_{0.3}$, $400^{\circ}C$, 30 mTorr and 9:1, respectively. The thickness of phosphor were 150 nm for each layers and the insulating layers of upper and bottom were 400 nm and 200 nm, respectively. The luminesence threshold voltage was $75\;V_{rms}$ and the maximum brightness of the thin film electroluminecent device was $3200\;cd/m^2$ at $100\;V_{rms}$.

• Journal of Sensor Science and Technology
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• v.6 no.5
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• pp.423-429
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• 1997

We fabricated PLT thin films on ITO substrate for flat pannel display and investigated the characteristics, then we applicated to electroluminescent device and investigated application possibility. When we fabricated PLT thin films with substrate temperature of $500^{\circ}C$, and pressure of 30 mTorr, the relative deielectric constant and breakdown electricfield of PLT thin films were 120 and 3.2MV/cm. The electric resistivity was $2.0{\times}10^{12}{\Omega}{\cdot}cm$. PLT thin films had polycrystal structure of perovskite and pyrochlore at the higher substrate temperature than $450^{\circ}C$, and had good crystallinity at higher pressure. To use PLT insulator film and ZnS:Mn phosphor, we fabricated thin film electroluminescent device of ITO/PLT/ZnS:Mn/PLT/Al structure. At the result, threshold voltage was $35.2V_{rms}$ and brightness was $2400cd/m^{2}$ at $50V_{rms}$ and 1kHz. Maximum luminescence efficiency was 0.811m/W.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.320-323
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• 2014

Today is going with the advancement of intelligent transportation systems and traffic environment and helping to provide safe and convenient service through a mobile device work with the popularization of the vehicle black box. The traffic flow by a variety of causes is constantly changing, it is often unable to prepare the driver, depending on external factors can not be controlled by the power of the public, leading to a major accident. The system needs to pass the real-time weather data in the inter-operator to prevent this. The proposed detection algorithm weather information delivered real-time weather information for this paper. The weather condition is detected by using the contrast between the histogram of the motion of the wiper and the clear day algorithm. In general, the wiper is worked in extreme weather conditions that will have a value different contrast due to rain or snow. Situation was considered clear, snowy conditions, such as using it on a rainy situation. First, designated as ROI (Region Of Interest) of the minimum area that can be detected in order to reduce the amount of calculation for the wiper, the wiper, which was detected through the operation of the threshold Thresholding the brightness of the vehicle wiper. In addition, we distinguish the value of each meteorological situation by using contrast. Results was obtained to 80% for the snow conditions, a rainy situation.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.979-995
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• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.363-368
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• 2003

We have successfully developed a more effective algorithm to extract the lineament in the area covered by wide alluvial deposits characterized by a relatively narrow range of brightness in the Landsat TM image, while the currently used algorithm is limited to the mountainous areas. In the new algorithm, flat areas mainly consisting of alluvial deposits were selected using the Local Enhancement from the Digital Elevation Model (DEM). The aspect values were obtained by 3${\times}$3 moving windowing of Zevenbergen & Thorno's Method, and then the slopes of the study area were determined using the aspect values. After the lineament factors in the alluvial deposits were revealed by comparing the threshold values, the first rank lineament under the alluvial deposits were extracted using the Hough transform In order to extract the final lineament, the lowest points under the alluvial deposits in a given topographic section perpendicular to the first rank lineament were determined through the spline interpolation, and then the final lineament were chosen through Hough transform using the lowest points. The algorithm developed in this study enables us to observe a clearer lineament in the areas covered by much larger alluvial deposits compared with the results extracted using the conventional existing algorithm. There exists, however, some differences between the first rank lineament, obtained using the aspect and the slope, and the final lineament. This study shows that the new algorithm more effectively extracts the lineament in the area covered with wide alluvlal deposits than in the areas of converging slope, areas with narrow alluvial deposits or valleys.