• Journal of Radiation Industry
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• v.7 no.1
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• pp.29-35
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• 2013

Microbiological populations and the sterility of commercial powdered products treated with gamma irradiation at 0~10 kGy were investigated before using them as ingredients for a non-cooked Saengsik product. We evaluated a total of 14 powdered products: 8 powdered cereals, 3 powdered tubers, and 3 powdered leafy vegetables. The total numbers of bacterial populations in non-irradiated powdered cereals, tubers, and leafy vegetables were 2.7~6.9, 5.6~6.0, and $5.3{\sim}6.8\;log\; CFU{\cdot}g^{-1}$, respectively. Moreover, coliform bacteria were not indicated in adlay, millet, germinated brown rice, soybean, and mulberry leaves powder within detection limit ($2.0\;log\; CFU{\cdot}g^{-1}$). The number of Bacillus cereus exceeded $3.0\;log\; CFU{\cdot}g^{-1}$ (the maximum limit for Saengsik products) in all samples, excluding perilla seeds, buckwheat, barley, oat, potato, and Jerusalem artichoke powder. However, a dose of 6 kGy of gamma irradiation reduced the microbiological populations in all samples, and all the powdered products met the microbial requirements for Saengsik products. Futhermore, it was confirmed that all microorganisms in the 9 powdered products, except fermented brown rice, sweet potatoes, and 3 leafy vegetables, were sterilized by 10 kGy of gamma irradiation.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.123-131
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• 2010

In this paper we applied Ground-Based Synthetic Aperture Radar(GB-SAR) interferometry to detect artificial displacement of a reflector and performed an atmospheric humidity correction to improve the accuracy. A series of GB-SAR images were obtained using a center frequency of 5.3 GHz with a range resolution of 25 cm and a azimuth resolution of $0.324^{\circ}$, all in full-polarization (HH, VV, VH, HV) modes. A triangular trihedral corner reflector was located 160 m away from the system, and the artificial displacements of 0-40 mm was implemented during the GB-SAR image acquisition. The result showed that the RMS error between the actual and measured displacements, averaged in all polarization data, was 1.22 mm, while the maximum error in case of the 40 mm displacement was 2.72 mm at HH-polarization. After the atmospheric correction with respect to the humidity, the RMS error was reduced to 0.52 mm. We conclude that a GB-SAR system can be used to monitor the possible displacement of artificial/natural scatterers and the stability assessment with sub-millimeter accuracy.

• Journal of the Korean Magnetics Society
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• v.25 no.5
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• pp.162-168
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• 2015

The ${\mu}$-turn coil having a width of ${\mu}m$ on the GMR-SV (giant magnetoresistance-spin valve) device based on the antiferromagnetic IrMn layer was fabricated by using the optical lithography process. In the case of GMR-SV film and GMR-SV device, the magnetoresistance ratios and the magnetic sensitivities are 4.4%, 2.0%/Oe and 1.6 %, 0.1%/Oe, respectively. In the y-z plane the distribution of magnetic field of GMR-SV device and $10{\mu}$-turns coil which put under the several magnetic bead(MB)s with a diameter of $1{\mu}m$ attached to RBC (red blood cell) was analyzed by the computer simulation using the finite element method. When the AC currents of 20 kHz from 0.1 mA to 10.0 mA flow to the 10 turns ${\mu}$-coil, the magnetic field at the position of $z=0{\mu}m$ at the center of coil was calculated from $30.1{\mu}T$ to $3060{\mu}T$ in proportion to the current. The magnetic field at the position of $z=10{\mu}m$ was decreased to one-sixth of that of $z=0{\mu}m$. It was confirmed that the $10{\mu}$-turn coil having enough magnitude of magnetic field for the capture of RBC is possible to use as a biosensor for the detection of magnetic beads attached to RBC.

• Nuclear Engineering and Technology
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• v.45 no.4
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• pp.513-522
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• 2013

The ODSCC detected in the TSP position of Ulchin 3&4 SGs are typical ODSCC of Alloy 600MA tubes. The causative chemical environment is formed by concentration of impurities inside the occluded region formed by the tube surface, egg crate strips, and sludge deposit there. Most cracks are detected at or near the line contacts between the tube surface and the egg crate strips. The region of dense crack population, as defined as between $4^{th}$ and $9^{th}$ TSPs, and near the center of hot leg hemisphere plane, coincided well with the region of preferential sludge deposition as defined by thermal hydraulics calculation using SGAP computer code. The cracks developed homogeneously in a wide range of SGs, so that the number of cracks detected each outage increased very rapidly since the first detection in the $8^{th}$ refueling outage. The root cause assessment focused on investigation of the difference in microstructure and manufacturing residual stress in order to reveal the cause of different susceptibilities to ODSCC among identical six units. The manufacturing residual stress as measured by XRD on OD surface and by split tube method indicated that the high residual stress of Alloy 600MA tube played a critical role in developing ODSCC. The level of residual stress showed substantial variations among the six units depending on details of straightening and OD grinding processes. Youngwang 3&4 tubes are less susceptible to ODSCC than U3 and U4 tubes because semi-continuous coarse chromium carbides are formed along the grain boundary of Y3&4 tubes, while there are finer less continuous chromium carbides in U3 and U4. The different carbide morphology is caused by the difference in cooling rate after mill anneal. There is a possibility that high chromium content in the Y3&4 tubes, still within the allowable range of Alloy 600, has made some contribution to the improved resistance to ODSCC. It is anticipated that ODSCC in Y5&6 SGs will be retarded more considerably than U3 SGs since the manufacturing residual stress in Y5&6 tubes is substantially lower than in U3 tubes, while the microstructure is similar with each other.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.871-884
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• 2021

The purpose of this study was to determine ways to increase efficiency in constructing and verifying artificial intelligence learning data on land cover using aerial and satellite images, and in applying the data to AI learning algorithms. To this end, multi-resolution datasets of 0.51 m and 10 m each for 8 categories of land cover were constructed using high-resolution aerial images and satellite images obtained from Sentinel-2 satellites. Furthermore, fine data (a total of 17,000 pieces) and coarse data (a total of 33,000 pieces) were simultaneously constructed to achieve the following two goals: precise detection of land cover changes and the establishment of large-scale learning datasets. To secure the accuracy of the learning data, the verification was performed in three steps, which included data refining, annotation, and sampling. The learning data that wasfinally verified was applied to the semantic segmentation algorithms U-Net and DeeplabV3+, and the results were analyzed. Based on the analysis, the average accuracy for land cover based on aerial imagery was 77.8% for U-Net and 76.3% for Deeplab V3+, while for land cover based on satellite imagery it was 91.4% for U-Net and 85.8% for Deeplab V3+. The artificial intelligence learning datasets on land cover constructed using high-resolution aerial and satellite images in this study can be used as reference data to help classify land cover and identify relevant changes. Therefore, it is expected that this study's findings can be used in the future in various fields of artificial intelligence studying land cover in constructing an artificial intelligence learning dataset on land cover of the whole of Korea.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1235-1243
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• 2021

Geostationary Ocean Color Imager 2 (GOCI-II) on Geo-KOMPSAT-2 (GK2B)satellite was developed as a mission successor of GOCI on COMS which had been operated for around 10 years since launch in 2010 to observe and monitor ocean color around Korean peninsula. GOCI-II on GK2B was successfully launched in February of 2020 to continue for detection, monitoring, quantification, and prediction of short/long term changes of coastal ocean environment for marine science research and application purpose. GOCI-II had already finished IAC and IOT including early in-orbit calibration and had been handed over to NOSC (National Ocean Satellite Center) in KHOA (Korea Hydrographic and Oceanographic Agency). Radiometric calibration was periodically conducted using on-board solar calibration system in GOCI-II. The final calibrated gain and offset were applied and validated during IOT. And three video parameter sets for one day and 12 video parameter sets for a year was selected and transferred to NOSC for normal operation. Star measurement-based INR (Image Navigation and Registration) navigation filtering and landmark measurement-based image geometric correction were applied to meet the all INR requirements. The GOCI2 INR software was validated through INR IOT. In this paper, status and results of IOT, radiometric calibration and INR of GOCI-II are analysed and described.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1233-1242
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• 2021

In this study, a model was developed to determine whether flooding occurred using image data, which is unstructured data. CNN-based VGG16 and VGG19 were used to develop the flood classification model. In order to develop a model, images of flooded and non-flooded images were collected using web crawling method. Since the data collected using the web crawling method contains noise data, data irrelevant to this study was primarily deleted, and secondly, the image size was changed to 224×224 for model application. In addition, image augmentation was performed by changing the angle of the image for diversity of image. Finally, learning was performed using 2,500 images of flooding and 2,500 images of non-flooding. As a result of model evaluation, the average classification performance of the model was found to be 97%. In the future, if the model developed through the results of this study is mounted on the CCTV control center system, it is judged that the respons against flood damage can be done quickly.

• Journal of Bio-Environment Control
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• v.28 no.3
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• pp.212-217
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• 2019

Bacterial wilt caused by Ralstonia solanacearum is a major disease that affects tomato plants widely. R. solanacearum is a soil born pathogen which limits the disease control measures. Therefore, breeding of resistant tomato variety to this disease is important. To identify the susceptible variety, degree of disease resistance has to be determined. In this study, micro sap flow sensor is used for accurate prediction of resistant degree. The sensor is designed to measure sap flow and water use in stems of plants. Using this sensor, the susceptibility to bacterial wilt disease can be identified two to three days prior to the onsite of symptoms after innoculation of R. solanacearum. Thus, this find of diagnosis approach can be utilized for the early detection of bacterial wilt disease.

• 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.34 no.6_3
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• pp.1383-1398
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• 2018

Sea surface wind is one of the most fundamental variables for understanding diverse marine phenomena. Although scatterometers have produced global wind field data since the early 1990's, the data has been used limitedly in oceanic applications due to it slow spatial resolution, especially at coastal regions. Synthetic Aperture Radar (SAR) is capable to produce high resolution wind field data. KOMPSAT-5 is the first Korean satellite equipped with X-band SAR instrument and is able to retrieve the sea surface wind. This study presents the validation results of sea surface wind derived from the KOMPSAT-5 backscattering coefficient data for the first time. We collected 18 KOMPSAT-5 ES mode data to produce a matchup database collocated with buoy stations. In order to calculate the accurate wind speed, we preprocessed the SAR data, including land masking, speckle noise reduction, and ship detection, and converted the in-situ wind to 10-m neutral wind as reference wind data using Liu-Katsaros-Businger (LKB) model. The sea surface winds based on XMOD2 show root-mean-square errors of about $2.41-2.74m\;s^{-1}$ depending on backscattering coefficient conversion equations. In-depth analyses on the wind speed errors derived from KOMPSAT-5 backscattering coefficient data reveal the existence of diverse potential error factors such as image quality related to range ambiguity, discrete and discontinuous distribution of incidence angle, change in marine atmospheric environment, impacts on atmospheric gravity waves, ocean wave spectrum, and internal wave.