• Aerospace Engineering and Technology
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• v.10 no.2
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• pp.11-19
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• 2011

A localization development of Composite rotor blade for Smart Unmanned Aerial Vehicle(SUAV) has been conducted. Overall localization development encompassed selection of domestic composite material having similar properties to that of original one at its best, coupon tests for data base of new material properties, re-calculation of blade sectional properties, and validation of structural/dynamic design requirements such as isolation of rotor natural frequency from excitation, static and fatigue strength, aeroelastic stability. The results of all these activities are described. This paper briefly discusses the improved manufacturing process for the localization of SUAV blade.

• 한국연소학회:학술대회논문집
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• 2015.12a
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• pp.23-24
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• 2015

In this talk, we will present what we believe is the state-of-the-art of the numerical modeling and simulation of the combustion processes as they relate to typical scramjet engines. The free-stream Mach number is hypersonic, but the speed is not sufficiently decelerated at the inlet/isolator, as in ramjets, so that combustion takes place under supersonic conditions. This creates some difficulties for most turbulence-combustion models. We delve into the details of these problems, by discussing the software programs that have a long track record for scramjet combustion simulation; with a focus on the accuracy of the baseline numerical methods used, the turbulence modeling/simulation approach, the comparative fidelity of the turbulence-combustion interaction models, ability to simulate premixed/non-premixed/partially-premixed, quenching/re-ignition capabilities, the numerical spark-plug method, Damkholer number regimes supported, and the effects of variable Prandtl, Schmidt, and Lewis numbers. Validation results from high-speed and low-speed combustion applications will also be presented.

• Proceedings of the Korean Society of Toxicology Conference
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• 2003.10b
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• pp.109-110
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• 2003

To validate a novel mouse model, gpt delta, for in vivo mutagenesis, the Mammalian Mutagenesis Society (MMS), a subgroup of the Environmental Mutagen Society of Japan (JEMS) (JEMS/MMS), performed a collaborative study as the third trial for transgenic animal assay. In this mouse model, point mutations and deletions re separately identified by gpt (6-thioguanine-resistant) and Spi- (sensitive to P2 interference) selections, respectively.(omitted)

• Korean Journal of Remote Sensing
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• v.19 no.5
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• pp.363-370
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• 2003

This paper describes a rectification procedure that relies on a polynomial model derived from the imaging geometry without loss of accuracy. By using polynomial model, one can effectively eliminate the iterative process to find an image pixel corresponding to each output grid point. With the imaging geometry and ephemeris data, a geo-location polynomial can be constructed from grid points that are produced by solving three equations simultaneously. And, in order to correct the local distortions induced by the geometry and terrain height, a distortion model has been incorporated in the procedure, which is a function of incidence angle and height at each pixel position. With this function, it is straightforward to calculate the pixel displacement due to distortions and then pixels are assigned to the output grid by re-sampling the displaced pixels. Most of the necessary information for the construction of polynomial model is available in the leader file and some can be derived from others. For validation, sample images of ERS-l PRI and Radarsat-l SGF have been processed by the proposed method and evaluated against ground truth acquired from 1:25,000 topography maps.

• East Asian mathematical journal
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• v.37 no.3
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• pp.333-354
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• 2021

A support vector machine (SVM) is a state-of-the-art machine learning model rooted in structural risk minimization. SVM is underestimated with regards to its application to real world problems because of the difficulties associated with its use. We aim at showing that the performance of SVM highly depends on which kernel function to use. To achieve these, after providing a summary of support vector machines and kernel function, we constructed experiments with various benchmark datasets to compare the performance of various kernel functions. For evaluating the performance of SVM, the F1-score and its Standard Deviation with 10-cross validation was used. Furthermore, we used taylor diagrams to reveal the difference between kernels. Finally, we provided Python codes for all our experiments to enable re-implementation of the experiments.

• Journal of Information Processing Systems
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• v.18 no.2
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• pp.197-207
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• 2022

The applications and user interfaces (UIs) of smart mobile devices are constantly diversifying. For example, deep learning can be an innovative solution to classify widgets in screen images for increasing convenience. To this end, the present research leverages captured images and the ReDraw dataset to write deep learning datasets for image classification purposes. First, as the validation for datasets using ResNet50 and EfficientNet, the experiments show that the dataset composed in this study is helpful for classification according to a widget's functionality. An implementation for widget detection and classification on RetinaNet and EfficientNet is then executed. Finally, the research suggests the Widg-C and Widg-D datasets-a deep learning dataset for identifying the widgets of smart devices-and implementing them for use with representative convolutional neural network models.

• 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.

• Korean Journal of Food Science and Technology
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• v.49 no.3
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• pp.258-264
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• 2017

The stability and accuracy of ultra-performance liquid chromatography (UPLC) used for evaluating the contents of rutin and quercetin in tartary buckwheat (Fagopyrum tataricum Gaertn.) seeds extracted by seven different extraction methods were determined. The seven extraction methods were reflux extraction (RE), ultra-sonification extraction (UE), stirrer extraction (SE), RE after UE (UE+RE), RE after SE (SE+RE), UE after SE (SE+UE), and RE with UE after SE (SE+UE+RE). Among the seven extraction methods used, RE yielded comparatively higher contents of rutin (2,277 mg/ 100 g) and quercetin (158 mg/100 g) than those yielded by other six extraction methods. The intra-day repeatability and inter-day precision of RE was 0.4-3.2% considering relative standard deviation (RSD), while accuracy was 88.8-102.4%. Therefore, RE with UPLC would be a rapid, accurate, and stable method for analyzing rutin and quercetin contents in tartary buckwheat.

• Journal of Microbiology and Biotechnology
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• v.19 no.7
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• pp.718-726
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• 2009

The objective of this study was to develop and validate secondary models that can predict growth parameters of L. monocytogenes Scott A as a function of concentrations (0-3%) of a commercial potassium lactate (PL) and sodium diacetate (SDA) mixture, pH (5.5-7.0), and temperature (4-37DC). A total of 120 growth curves were fitted to the Baranyi primary model that directly estimates lag time (LT) and specific growth rate (SGR). The effects of the variables on L. monocytogenes Scott A growth kinetics were modeled by response surface analysis using quadratic and cubic polynomial models of the natural logarithm transformation of both LT and SGR. Model performance was evaluated with dependent data and independent data using the prediction bias ($B_f$) and accuracy factors ($A_f$) as well as the acceptable prediction zone method [percentage of relative errors (%RE)]. Comparison of predicted versus observed values of SGR indicated that the cubic model fits better than the quadratic model, particularly at 4 and $10^{\circ}C$. The $B_f$and $A_f$for independent SGR were 1.00 and 1.08 for the cubic model and 1.08 and 1.16 for the quadratic model, respectively. For cubic and quadratic models, the %REs for the independent SGR data were 92.6 and 85.7, respectively. Both quadratic and cubic polynomial models for SGR and LT provided acceptable predictions of L. monocytogenes Scott A growth in the matrix of conditions described in the present study. Model performance can be more accurately evaluated with $B_f$and $A_f$and % RE together.

• Korean Journal of Agricultural and Forest Meteorology
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• v.11 no.1
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• pp.19-26
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• 2009

The standardization of eddy-covariance data processing is essential for the analysis and synthesis of vast amount of data being accumulated through continuous observations in various flux measurement networks. End users eventually benefit from the open and transparent standardization protocol by clear understanding of final products such as evapotranspiration and gross primary productivity. In this paper, we briefly introduced KoFlux efforts to standardize data processing methodologies and then estimated uncertainties of surface fluxes due to different processing methods. Based on our scrutiny of the data observed at Gwangneung KoFlux site, net ecosystem exchange and ecosystem respiration were sensitive to the selection of different processing methods. Gross primary production, however, was consistent within errors due to cancellation of the differences in NEE and Re, emphasizing that independent observation of ecosystem respiration is required for accurate estimates of carbon exchange. Nocturnal soil evaporation was small and thus the annually integrated evapotranspiration was not sensitive to the selection of different data processing methods. The implementation of such standardized data processing protocol to AsiaFlux will enable the establishment of consistent database for validation of models of carbon cycle, dynamic vegetation, and land-atmosphere interaction at regional scale.