• Smart Structures and Systems
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• v.8 no.1
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• pp.93-105
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• 2011

This paper presents a flexible low-profile antenna sensor for fatigue crack detection and monitoring. The sensor was inspired by the sense of pain in bio-systems as a protection mechanism. Because the antenna sensor does not need wiring for power supply or data transmission, it is an ideal candidate as sensing elements for the implementation of engineering sensor skins with a dense sensor distribution. Based on the principle of microstrip patch antenna, the antenna sensor is essentially an electromagnetic cavity that radiates at certain resonant frequencies. By implementing a metallic structure as the ground plane of the antenna sensor, crack development in the metallic structure due to fatigue loading can be detected from the resonant frequency shift of the antenna sensor. A monostatic microwave radar system was developed to interrogate the antenna sensor remotely. Fabrication and characterization of the antenna sensor for crack monitoring as well as the implementation of the remote interrogation system are presented.

• Proceedings of the KSRS Conference
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• v.1
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• pp.138-141
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• 2006

In developing algorithms to retrieve the sea surface temperature (SST) and sea surface wind speed from the Advanced Microwave Scanning Radiometer (AMSR) aboard the AQUA and the Advanced Earth Observation Satellite-II (ADEOS-II), data from the SeaWinds aboard ADEOS-II were helpful. Since features of the ocean microwave emission (Tb) related with ocean wind are not well understood, in case of using only AMSR data, combination of AMSR and SeaWinds revealed pronounced features about the ocean Tb. Two results from combinations of the two sensors were shown in this paper. One result was obtained at wind speeds over about 6m/s, in which the ocean Tb varies with the air-sea temperature difference, even though the SeaWinds wind speed is fixed at the same values. The ocean Tb increases as the air-sea temperature difference becomes negative, i.e., the boundary condition becomes unstable. This result indicates that the air temperature should be included in AMSR SST algorithm. The second result was obtained from comparison of two wind speeds between AMSR and SeaWinds. There is a small difference of two wind speeds, which might be related with several mechanisms, such as evaporation and plankton.

• Proceedings of the KSRS Conference
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• v.1
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• pp.146-149
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• 2006

Marine surface winds observed by two microwave sensors, SeaWinds and Advanced Microwave Scanning Radiometer (AMSR), on the Advanced Earth Observing Satellite-II (ADEOS-II) are evaluated by comparison with off-shore moored buoy observations. The wind speed and direction observed by SeaWinds are in good agreement with buoy data with root-mean-squared (rms) differences of approximately 1 m $s^{-1}$ and $20^{\circ}$, respectively. No systematic biases depending on wind speed or cross-track wind vector cell location are discernible. The effects of oceanographic and atmospheric environments on the scatterometry are negligible. The wind speed observed by AMSR also exhibited reasonable agreement with the buoy data in general with rms difference of 1.2 m $s^{-1}$. Systematic bias which was observed in earlier versions of the AMSR winds has been removed by algorithm refinements. Intercomparison of wind speeds globally observed by SeaWinds and AMSR on the same orbits also shows good agreements. Global wind speed histograms of the SeaWinds data and European Centre for Medium-range Weather Forecasts (ECMWF) analyses agree precisely with each other, while that of the AMSR wind shows slight deviation from them.

• Journal of Ocean Engineering and Technology
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• v.34 no.4
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• pp.277-284
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• 2020

Underwater acoustics, which is the study of phenomena related to sound waves in water, has been applied mainly in research on the use of sound navigation and range (SONAR) systems for communication, target detection, investigation of marine resources and environments, and noise measurement and analysis. The main objective of underwater acoustic remote sensing is to obtain information on a target object indirectly by using acoustic data. Presently, various types of machine learning techniques are being widely used to extract information from acoustic data. The machine learning techniques typically used in underwater acoustics and their applications in passive SONAR systems were reviewed in the first two parts of this work (Yang et al., 2020a; Yang et al., 2020b). As a follow-up, this paper reviews machine learning applications in SONAR signal processing with a focus on active target detection and classification.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.828-830
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• 2003

We have constructed a level-1 processor to generate brightness temperatures using the direct-broadcast data from the passive microwave radiometer onboard Aqua satellite. Although 50-minute half-orbit data, called a granule, are being routinely produced, to our knowledge, this is the first attempt to process about 10-minute long direct-broadcast data. We modified the processor designed for a granule to process the direct-broadcast data. After the modification, our brightness temperature product differs from the reference by 0.2K rms. Sea surface temperatures are retrieved to demonstrate the utility of AMSR-E.

• Journal of the Optical Society of Korea
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• v.15 no.1
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• pp.96-102
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• 2011

Simultaneous measurements using a scanning spectrograph system and transmissometer were performed for the first time over an urban site in Gwangju, Korea, to derive the ambient $NO_2$ volume mixing ratio. The differential slant column densities retrieved from the scanning spectrograph system were converted to volume mixing ratios using the light traveling distance along the scanning line of sight derived from the transmissometer light extinction coefficients. To assess the performance of this system, we compared the derived $NO_2$ volume mixing ratios with those measured by an in situ chemiluminescence monitor under various atmospheric conditions. For a cloudless atmosphere, the linear correlation coefficient (R) between the two data sets (i.e., data derived from the scanning spectrograph and from the in situ monitor) was 0.81; the value for a cloudy atmosphere was 0.69. The two sets of $NO_2$ volume mixing ratios were also compared for various wind speeds. We also consider the measurement errors, as estimated from an error propagation analysis.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.954-961
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• 2010

Ground-based remote sensing can be used as one of the non-destructive, fast, and real-time diagnostic tools for quantifying yield, biomass, and nitrogen (N) stress during growing season. This study was conducted to assess biomass and nitrogen (N) status of paddy rice (Oryza sativa L.) plants under N stress using passive and active ground-based remote sensors. Nitrogen application rates were 0, 70, 100, and 130 kg N $ha^{-1}$. At each growth stage, reflectance indices measured with active sensor showed higher correlation with DW, N uptake and N concentration than those with the passive sensor. NIR/Red and NIR/Amber indices measured with Crop Circle active sensors generally had a better correlation with dry weight (DW), N uptake and N content than vegetation indices from Crop Circle passive sensor and NDVIs from active sensors. Especially NIR/Red and NIR/amber ratios at the panicle initiation stage were most closely correlated with DW, N content, and N uptake. Rice grain yield, DW, N content and N uptake at harvest were highly positively correlated with canopy reflectance indices measured with active sensors at all sampling dates. N application rate explains about 91~92% of the variability in the SI calculated from NIR/Red or NIR/Amber indices measured with Crop Circle active sensors on 12 July. Therefore, the in-season sufficiency index (SI) by NIR/Red or NIR/Amber index from Crop Circle active sensors can be used for determination of N application rate.

• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.947-960
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• 2017

Soil moisture plays an important role to affect the Earth's radiative energy balance and water cycle. In general, satellite observations are useful for estimating the soil moisture content. Passive microwave satellites have an advantage of direct sensitivity on surface soil moisture. However, their coarse spatial resolutions (10-36 km) are not suitable for regional-scale hydrological applications. Meanwhile, in-situ ground observations of point-based soil moisture content have the disadvantage of spatially discontinuous information. This paper presents an experimental soil moisture retrieval using Sentinel-1 SAR (Synthetic Aperture Radar) with 10m spatial resolution for cropland in South Korea. We developed a soil moisture retrieval algorithm based on the technique of linear regression and SVR (support vector regression) using the ground observations at five in-situ sites and Sentinel-1 SAR data from April to October in 2015-2017 period. Our results showed the polarization dependency on the different soil sensitivities at backscattered signals, but no polarization dependence on the accuracies. No particular seasonal characteristics of the soil moisture retrieval imply that soil moisture is generally more affected by hydro-meteorology and land surface characteristics than by phenological factors. At the narrower range of incidence angles, the relationship between the backscattered signal and soil moisture content was more distinct because the decreasing surface interference increased the retrieval accuracies under the condition of evenly distributed soil moisture (during the raining period or on the paddy field). We had an overall error estimate of RMSE (root mean square error) of approximately 6.5%. Our soil moisture retrieval algorithm will be improved if the effects of surface roughness, geomorphology, and soil properties would be considered in the future works.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1273-1282
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• 2018

Recently, active research on the Arctic Ocean has been conducted due to the influence of global warming and new Arctic ship route. Although previous studies already calculated quantitative extent of sea ice using passive microwave radiometers, melting at the edge of sea ice and surface roughness were hardly considered due to low spatial resolution. Since Sentienl-1A/B data in Extended Wide (EW) mode are being distributed as free of charge and bulk data for Arctic sea can be generated during a short period, the entire Arctic sea ice data can be covered in high spatial resolution by mosaicking bulk data. However, Sentinel-1A/B data in EW mode, especially in HV polarization, needs significant radiometric correction for further classification. Thus, in this study, we developed algorithms that can correct thermal noise and scalloping effects, and confirmed that Arctic sea ice and open-water were well classified using the corrected dual-polarization SAR data.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.677-686
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• 2014

Sea ice covers wide area in Terra Nova Bay in East Antarctica where the Jangbogo Antarctic Research Station was built in 2014, which affects greatly on the sailing of an icebreaker research vessel. In this study, we analyzed the optimum sea route and sailable period of the icebreaker to visit the Jangbogo Antarctic Research Station by using sea ice concentration data observed by passive microwave sensors such as Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS) for the last decade, and by using sea route of the Araon, an icebreaker of Republic of Korea, from 2010 to 2012. It is found that Araon sailed in the route of sea ice concentration up to 78%. Sailing speed of the Araon decreased due to increasing sea ice concentration. However, Araon maintained the speed close to the average speed for the entire sailing period (~11 kn) in the route of sea ice concentration up to 70%. Therefore, we confirm that the Araon can sail typically in the route which shows sea ice concentration below 70%. We derived annually available sailing period in recent 10 years for the sea route of the Araon in 2010, 2011 and 2012, which is defined as the period showing sea ice concentration below 70% through the route. Maximum sailable period was analyzed to be 61 and 62 days for the route of the Araon in 2010 and 2011, respectively. However, the typical sailing in the routes was unavailable in some years because sea ice concentration was higher than 70% through the routes. Meanwhile, the sailable period for the routes of the Araon in 2012 was observed in every year, which was a minimum of 15 days and is a maximum of 89 days. Therefore, we could suggest that optimum route of icebreaker to visit the Jangbogo Antarctic Research Station is the route of the Araon in 2012. High resolution images from SAR or optical sensors are necessary to investigate sea ice condition near shoreline of Jangbogo research station due to several kilometers of low resolution of sea ice concentration.