• Journal of Sensor Science and Technology
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• v.31 no.6
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• pp.455-460
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• 2022

Brush-like ZnO hierarchical nanostructures decorated with MgxZn1-xO (x = 0.1, 0.2, 0.3, 0.4, and 0.5) were fabricated and examined for application to a gas sensor. They were synthesized using vapor phase growth (VPG) on indium tin oxide (ITO) substrates. To generate electronic accumulation at ZnO surface, MgZnO nanoparticles were prepared by sol-gel method, and the ratio of Mg and Zn was adjusted to optimize the device for NO₂ gas detection. As the electrons in the accumulation layer generated by the heterojunction reacted faster and more frequently with the gas, the sensitivity and speed improved. When tested as sensing materials for gas sensors at 100 ppm NO₂ at 300℃, these MgZnO decorated ZnO nanostructures exhibited an improvement from 165 to 514 times compared to pristine ZnO. The response and recovery time of the MgZnO decorated ZnO samples were shorter than those of the pristine ZnO. Various analyzing techniques, including field-emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDS), and X-ray powder diffraction (XRD) were employed to confirm the growth morphology, atomic composition, and crystalline information of the samples, respectively.

• Journal of IKEEE
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• v.26 no.4
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• pp.568-576
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• 2022

Automatic modulation classification(AMC) is a core technique in Software Defined Radio(SDR) platform that enables smart and flexible spectrum sensing and access in a wide frequency band. In this study, we propose a simple yet accurate deep learning-based method that allows AMC for variable-size radio signals. To this end, we design a classification architecture consisting of two Convolutional Neural Network(CNN)-based models, namely main and small models, which were trained on radio signal datasets with two different signal sizes, respectively. Then, for a received signal input with an arbitrary length, modulation classification is performed by augmenting the input samples using a self-replicating padding technique to fit the input layer size of our model. Experiments using the RadioML 2018.01A dataset demonstrated that the proposed method provides higher accuracy than the existing methods in all signal-to-noise ratio(SNR) domains with less computation overhead.

• The Journal of Engineering Geology
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• v.32 no.2
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• pp.271-280
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• 2022

Landfast sea ice forms near coastlines in polar regions. Continuous monitoring of this sea ice is important, as it plays a key role in the marine ecosystem and affects the operation of nearby research stations. This study detected landfast sea ice around Jang Bogo research station in East Antarctica by stacking interferometric coherence images of Sentinel-1 synthetic aperture radar (SAR) data with 6-, 12- and 18-day temporal baselines. A total of 50 landfast sea ice maps were generated covering July 2017 to June 2018. The time series revealed regional differences in the timing of the maximum extent as well as growth rate of landfast sea ice. Overall, detecting landfast sea ice using interferometric SAR coherence seems promisingly feasible; however, limitations remain owing to low backscattering coefficients from new and smooth sea ice surfaces and subtle movements of sea ice in contact with the Campbell Glacier Tongue.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.145-150
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• 2022

An analytical model based on a modal transmission-line theory (MTLT) is developed to investigate the optical transmission through metal gratings. This model gives well physical meanings for the transmission as well as for the dispersion relations of the modes responsible for high transmission. These concepts provide accurate information even for real metals used in the visible~near-infrared wavelength range, where surface plasmon polaritons (SPP's) are excited. Furthermore, the dispersion relations allow the nature of the propagation modes to be assessed. The propagation modes are hybrid between Fabry-Pérot like modes and SPP's. It is important to consider different period and aspect ratio of metal gratings in order to determine the nature of the hybrid modes. In this paper, the sensing characteristics and mode propagation phenomena of high-performance plasma bio-sensors that depend on these variables were clearly analyzed.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.965-970
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• 2023

Wind profiler and wind lidar produce a vertical profile of winds in high spatiotemporal resolution in the atmospheric boundary layer. The wind lidar makes the wind vector using DBS (Doppler Beam Swinging) and VAD (Velocity Azimuth Display) methods. The DBS method has the advantage of obtaining a wind profile with a fast scan time. On the other hand, there is a restriction that requires at least two beams including vertical beam, which causes a decrease in the data acquisition rate. The VAD method was improved to produce more wind vector of the wind profiler as well as the wind lidar, which generally uses 5 beams. Fourier series was estimated with the radial velocity by the DBS method and wind vector was determined by setting the azimuth interval and applying the radial velocity by the Fourier series to the VAD method. The wind vectors were retrieved at the altitude where the wind was not calculated by the DBS method, and the results of the two methods were consistent.

• Korean Journal of Remote Sensing
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• v.34 no.3
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• pp.451-463
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• 2018

The empirical/statistical models to estimate the ground Particulate Matter ($PM_{2.5}$) concentration from Geostationary Ocean Color Imager (GOCI) Aerosol Optical Depth (AOD) product were developed and analyzed for the period of 2015 in Seoul, South Korea. In the model construction of AOD-$PM_{2.5}$, two vertical correction methods using the planetary boundary layer height and the vertical ratio of aerosol, and humidity correction method using the hygroscopic growth factor were applied to respective models. The vertical correction for AOD and humidity correction for $PM_{2.5}$ concentration played an important role in improving accuracy of overall estimation. The multiple linear regression (MLR) models with additional meteorological factors (wind speed, visibility, and air temperature) affecting AOD and $PM_{2.5}$ relationships were constructed for the whole year and each season. As a result, determination coefficients of MLR models were significantly increased, compared to those of empirical models. In this study, we analyzed the seasonal, monthly and diurnal characteristics of AOD-$PM_{2.5}$model. when the MLR model is seasonally constructed, underestimation tendency in high $PM_{2.5}$ cases for the whole year were improved. The monthly and diurnal patterns of observed $PM_{2.5}$ and estimated $PM_{2.5}$ were similar. The results of this study, which estimates surface $PM_{2.5}$ concentration using geostationary satellite AOD, are expected to be applicable to the future GK-2A and GK-2B.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.241-249
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• 2014

The continuous observations for atmospheric aerosol were carried out during 3 years (2009-2011) by using a multi-wavelength Raman lidar at the Gwangju Institute of Science and Technology (GIST), Korea ($35.11^{\circ}N$, $126.54^{\circ}E$). The particle depolarization ratios were retrieved from the observations in order to distinguish the Asian dust layer. The vertical information of Asian dust layers were used as input parameter for the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model for analysis of its backward trajectories. The source regions and transport pathways of the Asian dust layer were identified. The most frequent source region of Asian dust in Korea was Gobi desert during observation period in this study. The statistical analysis on the particle depolarization ratio of Asian dust was conducted according to their transport route in order to retrieve the variation of optical properties of Asian dust during long-range transport. The transport routes were classified into the Asian dust which was transported to observation site directly from the source regions, and the Asian dust which was passed over pollution regions of China. The particle depolarization ratios of Asian dust which were transported via industrial regions of China was ranged 0.07-0.1, whereas, the particle depolarization ratio of Asian dust which was transported directly from the source regions to observation site were comparably higher and ranged 0.11-0.15. It is considered that the pure Asian dust particle from source regions were mixed with pollution particles, which is likely to spherical particle, during transportation so that the values of particle depolarization of Asian dust mixed with pollution was decreased.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.597-605
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• 2014

The continuous observations for atmospheric aerosols were conducted during 3 years (2009 to 2011) by using Gwangju Institute of Science and Technology (GIST) multi-wavelength Raman lidar at Gwangju, Korea ($35.10^{\circ}N$, $126.53^{\circ}E$). The aerosol depolarization ratios calculated from lidar data were used to identify the Asian dust layer. The optical properties of Asian dust layer were different according to its vertical distribution. In order to investigate the difference between the optical properties of each individual dust layers, the transport pathway and the transport altitude of Asian dust were analyzed by Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. We consider that the variation of optical properties were influenced not only their transport pathway but also their transport height when it passed over anthropogenic pollution source regions in China. The lower particle depolarization ratio values of $0.12{\pm}0.01$, higher lidar ratio of $67{\pm}9sr$ and $68{\pm}9sr$ at 355 nm and 532 nm, respectively, and higher ${\AA}ngstr\ddot{o}m$ exponent of $1.05{\pm}0.57$ which are considered as the optical properties of pollution were found. In contrast with this, the higher particle depolarization ratio values of $0.21{\pm}0.09$, lower lidar ratio of $48{\pm}5sr$ and $46{\pm}4sr$ at 355 nm and 532 nm, respectively, and lower ${\AA}ngstr\ddot{o}m$ exponent of $0.57{\pm}0.24$ which are considered as the optical properties of dust were found. We found that the degree of mixing of anthropogenic pollutant aerosols in mixed Asian dust govern the variation of optical properties of Asian dust and it depends on their altitude when it passed over the polluted regions over China.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1605-1613
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• 2023

The physical properties of the ocean interior are determined by temperature and salinity. To observe them, we rely on satellite observations for broad regions of oceans. However, the satellite for salinity measurement, Soil Moisture Active Passive (SMAP), has low temporal and spatial resolutions; thus, more is needed to resolve the fast-changing coastal environment. To overcome these limitations, the algorithm to use the Geostationary Ocean Color Imager-II (GOCI-II) of the Geo-Kompsat-2B (GK-2B) was developed as the inputs for a Multi-layer Perceptron Neural Network (MPNN). The result shows that coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (RRMSE) between GOCI-II based sea surface salinity (SSS) (GOCI-II SSS) and SMAP was 0.94, 0.58 psu, and 1.87%, respectively. Furthermore, the spatial variation of GOCI-II SSS was also very uniform, with over 0.8 of R² and less than 1 psu of RMSE. In addition, GOCI-II SSS was also compared with SSS of Ieodo Ocean Research Station (I-ORS), suggesting that the result was slightly low, which was further analyzed for the following reasons. We further illustrated the valuable information of high spatial and temporal variation of GOCI-II SSS to analyze SSS variation by the 11th typhoon, Hinnamnor, in 2022. We used the mean and standard deviation (STD) of one day of GOCI-II SSS, revealing the high spatial and temporal changes. Thus, this study will shed light on the research for monitoring the highly changing marine environment.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1109-1123
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• 2020

In South Korea with forest as a major land cover class (over 60% of the country), many wildfires occur every year. Wildfires weaken the shear strength of the soil, forming a layer of soil that is vulnerable to landslides. It is important to identify the severity of a wildfire as well as the burned area to sustainably manage the forest. Although satellite remote sensing has been widely used to map wildfire severity, it is often difficult to determine the severity using only the temporal change of satellite-derived indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR). In this study, we proposed an approach for determining wildfire severity based on machine learning through the synergistic use of Sentinel-1A Synthetic Aperture Radar-C data and Sentinel-2A Multi Spectral Instrument data. Three wildfire cases-Samcheok in May 2017, Gangreung·Donghae in April 2019, and Gosung·Sokcho in April 2019-were used for developing wildfire severity mapping models with three machine learning algorithms (i.e., Random Forest, Logistic Regression, and Support Vector Machine). The results showed that the random forest model yielded the best performance, resulting in an overall accuracy of 82.3%. The cross-site validation to examine the spatiotemporal transferability of the machine learning models showed that the models were highly sensitive to temporal differences between the training and validation sites, especially in the early growing season. This implies that a more robust model with high spatiotemporal transferability can be developed when more wildfire cases with different seasons and areas are added in the future.