• Journal of the Society of Naval Architects of Korea
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• v.45 no.6
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• pp.726-734
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• 2008

While the structural safety assessment of Cargo Containment System(CCS) in membrane type LNG carriers has to be carried out in consideration of sloshing impact pressure, it is very difficult to figure out its dynamic response behaviors due to its very complex structural arrangements/materials and complicated phenomena of sloshing impact loading. For the development of its original technique, it is necessary to understand the characteristics of dynamic response behavior of CCS structure under sloshing impact pressure. In this study, for the exact understanding of dynamic response behavior of CCS structure in membrane Mark III type LNG carriers under sloshing impact pressure, its wet drop impact response analyses were carried out by using Fluid-Structure Interaction(FSI) analysis technique of LS-DYNA code, and were also validated through a series of wet drop experiments for the enhancement of more accurate shock response analysis technique. It might be thought that the structural response behaviors of impact response analysis, such as impact pressure impulses and resulted strain time histories, generally showed very good agreement with experimental ones with very appropriate use of FSI analysis technique of LS-DYNA code, finite element modeling and material properties of CCS structure, finite element modeling and equation of state(EOS) of fluid domain.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.4
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• pp.447-452
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• 2006

The remote sensing technique was applied to enhance the water qualify model validation in a large water body. Since the satellite image usually covers the wide surface area of a large water body, it can compensate for the lark of measured data points required for model calibration and verification. This paper describes the analysis of Landsat FTM+images collected on April 29th and September 4th in year 2000 to evaluate surface water temperature of Lake Paldang. The water temperature data obtained from the satellite image were compared with model results by estimating three different methods of error criteria. The residual ratios on April 29th and September 4th were 0.13 and 0.04 respectively. This showed that the model result accords with the data obtained from the process of satellite image. Without considering atmospheric interference, however, transformation process of satellite image causes relatively large residual ratio in the surface water temperature distribution pattern on April 29th. In the future study, therefore, the atmospheric properties of image acquisition point needs to be considered for the application of radiance transformation model.

• Journal of Korea Water Resources Association
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• v.49 no.3
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• pp.263-273
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• 2016

This study applied conditional merging (CM) spatial interpolation technique to obtain the satellite and in-situ composite soil moisture data. For the analysis, 24 gages of hourly in-situ data sets from the Rural Development Administration (RDA) of Korea and the satellite soil moisture data retrieved from Advanced Microwave Scanning Radiometer-Earth observing system (AMSR-E) were used. In order to verify the performance of the CM method, leave-one-out cross validation was used. The cross validation result was spatially interpolated to figure out spatial correlation of the CM method. The results derived from this study are as follow: (1) The CM method produced better soil moisture map over Korean Peninsula than AMSR-E did for the over 100 days out of total 113 days considered for the analysis. (2) The method of CM showed high correlation with gage density and better performance on the western side of Korean peninsula due to high spatial gauge density. (3) The performance of CM is not affected by the non-rainy season unlike to AMSR-E data is. Overall, the result of this study indicates that the CM method can be applied for predicting soil moisture at ungaged locations.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.6
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• pp.125-138
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• 2019

This study aims to highlight the possibility in identifying species composition of fish communities using the environmental DNA (eDNA) metabarcoding technique, from both of the technical introduction and the pilot test at urban ecological streams. This new emerging survey technique using eDNA is getting popular in the world as a compensating way for the conventional field survey. However, the application to the domestic cases has yet to be studied. We attempted to use this technique for identifying fish species observed at four survey points in Hwangguji-chon, Suwon City. As a result, the detected number of species by eDNA sampled once in May was significantly matched with the total number of observed species in annual field surveys. Additionally eDNA results indicated the presence possibility of the unobserved species in field last year, even though the validation may be required. This survey technique seems to be more efficient and applicable to diverse situations of the fields and species, thereby needs to be studied further. We discussed the pros and cons of the application and summarized the research directions in future.

• Archives of Plastic Surgery
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• v.43 no.5
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• pp.430-437
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• 2016

Background Accurate breast volume assessment is a prerequisite to preoperative planning, as well as intraoperative decision making in breast reconstruction surgery. The use of three-dimensional surface imaging (3D scanning) to assess breast volume has many advantages. However, before employing 3D scanning in the field, the tool's validity should be demonstrated. The purpose of this study was to confirm the validity of 3D-scanning technology for evaluating breast volume. Methods We reviewed the charts of 25 patients who underwent breast reconstruction surgery immediately after total mastectomy. Breast volumes using the Axis Three 3D scanner, water-displacement technique, and magnetic resonance imaging (MRI) were obtained bilaterally in the preoperative period. During the operation, the tissue removed during total mastectomy was weighed and the specimen volume was calculated from the weight. Then, we compared the volume obtained from 3D scanning with those obtained using the water-displacement technique, MRI, and the calculated volume of the tissue removed. Results The intraclass correlation coefficient (ICC) of breast volumes obtained from 3D scanning, as compared to the volumes obtained using the water-displacement technique and specimen weight, demonstrated excellent reliability. The ICC of breast volumes obtained using 3D scanning, as compared to those obtained by MRI, demonstrated substantial reliability. Passing-Bablok regression showed agreement between 3D scanning and the water-displacement technique, and showed a linear association of 3D scanning with MRI and specimen volume, respectively. Conclusions When compared with the classical water-displacement technique and MRI-based volumetry, 3D scanning showed significant reliability and a linear association with the other two methods.

• Journal of Biomedical Engineering Research
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• v.29 no.3
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• pp.198-204
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• 2008

Uroflowmetry is a non-invasive clinical test useful for screening benign prostatic hyperplasia(BPH) common in the aged men. The current standard way to obtain the urinary flow rate is to continuously acquire the urine weight signal proportional to volume over time. The present study proposed an alternative technique measuring pressure to overcome noise problems present in the standard weight measuring technique. Experiments were performed to simultaneously acquire both weight and pressure changes during urination of 9 normal men. Noise components were separated from volume signals converted from both weight and pressure signals based on the polynomial signal model. Signal-to-noise ratio was defined as the ratio of the energies between signal and noise components of the measured volume changes, which was 8.5 times larger in the pressure measuring technique, implying that cleaner signal could be obtained, more immune to noisy environments. When four important diagnostic parameters were estimated, excellent correlation coefficients higher than 0.99 were resulted with mean relative errors less than 5%. Therefore, the present pressure measurement seemed valid as an alternative technique for uroflowmetry.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.1
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• pp.15-20
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• 2008

Near infrared reflectance spectroscopy (NIRS) is a rapid and accurate analytical method for determining the composition of agricultural products and feeds. This study was conducted to measure anthocyanin contents in black colored soybean by using NIRS system. Total 300 seed coat of black colored soybean samples previously analyzed by HPLC were scanned by NIRS and over 250 samples were selected for calibration and validation equation. A calibration equation calculated by MPLS(modified partial least squares) regression technique was developed in which the coefficient of determination for anthocyanin pigment C3G, D3G and Pt3G content was 0.952, 0.936, and 0.833, respectively. Each calibration equation was applied to validation set that was performed with the remaining samples not included in the calibration set, which showed high positive correlation both in C3G and D3G content file. In case Pt3G, the prediction model was needed more accuracy because of low $R^2$ value in validation set. This results demonstrate that the developed NIRS equation can be practically used as a rapid screening method for quantification of C3G and D3G contents in black colored soybean.

• Journal of Food Hygiene and Safety
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• v.29 no.4
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• pp.312-315
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• 2014

The present study demonstrated the development and validation of the method for the quantification of phenol in food using gas chromatography coupled with mass spectrometry (GC-MS). After spiking of internal standard (Phenol-$d_5$) to food, those samples were extracted with organic solvent mixture (acetone : dichloromethane = 1 : 1, v/v) using ultra sonic extractor and cleaned by gel permeation chromatography (GPC) technique. The amount of phenol was determined by GC/MS. To validate the developed method, we evaluated parameters were the selectivity, linearity, accuracy, precision, and recovery. To demonstrate the selectivity of the method, blank samples of rice, corn, and fish(mackerel) were prepared and subjected to GC-MS analysis. To verify the linearity of the method, six different standard concentrations of phenol at 0.01, 0.05, 0.1, 0.5, 1 and 2.5 mg/kg were evaluated. The correlation coefficient ($r^2$) of calibration curve was 0.9999. The recovery rate for phenol standard calculated by internal standard method were 82.2~101.5% for samples fortified with 0.25, 0.50, and 1.0 mg/kg, respectively. Also the repeatability and reproducibility for validation of precision were 0.2~5.5%. According to the result of the validation, this established method was suitable for AOAC guideline. The limit of detection (LOD) for phenol analysis were 0.03~0.1 mg/kg, and the limit of quantification (LOQ) were 0.1~0.3 mg/kg. Therefore, we established the optimal analysis method for determination of phenol in food using GPC and GC/MS.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.9
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• pp.375-384
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• 2021

Deep learning techniques have been proven to have high performance in image processing and are applied in various fields. The most widely used methods for validating a deep learning model include a holdout verification method, a k-fold cross verification method, and a bootstrap method. These legacy methods consider the balance of the ratio between classes in the process of dividing the data set, but do not consider the ratio of various features that exist within the same class. If these features are not considered, verification results may be biased toward some features. Therefore, we propose a deep learning model validation method based on data feature coverage for image classification by improving the legacy methods. The proposed technique proposes a data feature coverage that can be measured numerically how much the training data set for training and validation of the deep learning model and the evaluation data set reflects the features of the entire data set. In this method, the data set can be divided by ensuring coverage to include all features of the entire data set, and the evaluation result of the model can be analyzed in units of feature clusters. As a result, by providing feature cluster information for the evaluation result of the trained model, feature information of data that affects the trained model can be provided.

• Journal of The Korean Astronomical Society
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• v.56 no.2
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• pp.213-224
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• 2023

Type II solar radio bursts show frequency drifts from high to low over time. They have been known as a signature of coronal shock associated with Coronal Mass Ejections (CMEs) and/or flares, which cause an abrupt change in the space environment near the Earth (space weather). Therefore, early detection of type II bursts is important for forecasting of space weather. In this study, we develop a deep-learning (DL) model for the automatic detection of type II bursts. For this purpose, we adopted a 1-D Convolution Neutral Network (CNN) as it is well-suited for processing spatiotemporal information within the applied data set. We utilized a total of 286 radio burst spectrum images obtained by Hiraiso Radio Spectrograph (HiRAS) from 1991 and 2012, along with 231 spectrum images without the bursts from 2009 to 2015, to recognizes type II bursts. The burst types were labeled manually according to their spectra features in an answer table. Subsequently, we applied the 1-D CNN technique to the spectrum images using two filter windows with different size along time axis. To develop the DL model, we randomly selected 412 spectrum images (80%) for training and validation. The train history shows that both train and validation losses drop rapidly, while train and validation accuracies increased within approximately 100 epoches. For evaluation of the model's performance, we used 105 test images (20%) and employed a contingence table. It is found that false alarm ratio (FAR) and critical success index (CSI) were 0.14 and 0.83, respectively. Furthermore, we confirmed above result by adopting five-fold cross-validation method, in which we re-sampled five groups randomly. The estimated mean FAR and CSI of the five groups were 0.05 and 0.87, respectively. For experimental purposes, we applied our proposed model to 85 HiRAS type II radio bursts listed in the NGDC catalogue from 2009 to 2016 and 184 quiet (no bursts) spectrum images before and after the type II bursts. As a result, our model successfully detected 79 events (93%) of type II events. This results demonstrates, for the first time, that the 1-D CNN algorithm is useful for detecting type II bursts.