• Journal of Korean Society for Atmospheric Environment
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• v.31 no.2
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• pp.157-163
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• 2015

To establish National Reference Method (NRM) for $PM_{2.5}$, operational performance of 5 different commercial gravimetric-based $PM_{2.5}$ measuring instruments was assessed at Bulkwang monitoring station from January 23, 2014 to February 28, 2014. First, physical properties, design, and functional performance of the instruments were assessed. Evaluation was carried out to determine whether operating method for the instruments and levels of QA/QC activities meet the data quality objectives (DQOs). To verify whether DQOs were satisfied, reproducibility of QA/QC procedures, accuracy, relative sensitivity, limit of detection, margin of error, and coefficient of determination of the instruments were also evaluated. Results of flow rate measurement of 15 candidate instruments indicated that all the instruments met performance criteria with accuracy deviation of 4.0% and reproducibility of 0.6%. Comparison of final $PM_{2.5}$ mass concentrations showed that the coefficient of determination ($R^2$) values were greater than or equal to 0.9995, and concentration gradient ranged from 0.97 to 1.03. All the instruments satisfied criteria for NRM with the estimated precision of 1.47~2.60%, accuracy of -1.90~3.00%, and absolute accuracy of 1.02~3.12%. This study found that one particular type of measuring instrument was proved to be excellent, with overall evaluation criteria satisfied.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.2
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• pp.92-98
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• 2017

Generally, image contains geometrical and radiometric errors. Census transform can solve the stereo mismatching problem caused by the radiometric distortion. Since the general census transform compares center of window pixel value with neighbor pixel value, it is hard to obtain an accurate matching result when the difference of pixel value is not large. To solve that problem, we propose a census transform method that applies different 4-step weight for each pixel value difference by applying an assistance window inside the window kernel. If the current pixel value is larger than the average of assistance window pixel value, a high weight value is given. Otherwise, a low weight value is assigned to perform a differential census transform. After generating an initial disparity map using a weighted census transform and input images, the gradient information is additionally used to model a cost function for generating a final disparity map. In order to find an optimal cost value, we use guided filtering. Since the filtering is performed using the input image and the disparity image, the object boundary region can be preserved. From the experimental results, we confirm that the performance of the proposed stereo matching method is improved compare to the conventional method.

• Korean Journal of Ecology and Environment
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• v.46 no.4
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• pp.561-569
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• 2013

This study examines locational environment factors that may affect the vegetation structure in the forests of Naejang National Park. To that end, we selected LAI (Leaf Area Index), diameter at breast height, and tree height as structural variables as well as altitude above sea level, gradient, slope direction, soil moisture, topographic location, and amount of solar radiation as locational environment factors, using the method of canonical correlation analysis in order to find out correlation between them. As to the simple correlation between the locational environment factors and structural variables, the correlation coefficient was relatively low (0.6). The values of LAI, measured along the ridge with higher altitudes, decreased as the soil moisture and solar radiation increased. However, LAI increased as the gradient increased and the slope direction faced the north (farther from the east). In respect of the diameter at breast height, the diameter decreased as the altitude and gradient increased. But the diameter increased as the moisture and solar radiation increased. The tree height decreased as the moisture increased and the site was closer to the ridge. These various correlations show a variety of locational environment factors in the national park, implying that the structural variables are affected by complex locational environment factors. This study conducted a canonical correlation analysis on locational environment factors which may affect the vegetation structure, and the result showed that LAI increased and tree height & diameter at breast height decreased as the solar radiation & moisture decreased and altitude increased. Although more factors that may affect vegetation structure (e.g. climate) should be taken into account, this study is significant in that the vegetation structure, which can adapt to more unfavorable conditions in terms of solar radiation, moisture, and higher altitudes, could be inferred in a statistical way. The results of this study, especially the locational environment factors based on DEM, can be used for assessing diversity of vegetation structure in a forest and for monitoring the structure in a national park on a regular basis so as to establish more effective maintenance plans of a park.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.6
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• pp.477-485
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• 2012

Geometric configurations such as hull shape, wall thickness, stiffener layout, and type of construction materials are the key factors influencing the structural performance of pressure hulls. Traditional theoretical approach provides quick and acceptable solutions for the design of pressure hulls within specific geometric configuration and material. In this paper, alternative approaches that can be used to obtain optimal geometric shape, wall thickness, construction material configuration and stiffener layout of a pressure hull are presented. CAE(Computer Aided Engineering) based design optimization tools are utilized in order to obtain the required structural responses and optimal design parameters. Optimal elliptical meridional profile is determined for a cylindrical pressure hull design using metamodel-based optimization technique implemented in a fully-integrated parametric modeler-CAE platform in ANSYS. While the optimal composite laminate layup and the design of ring stiffener for a thin-walled pressure hull are obtained using gradient-based optimization method in OptiStruct. It is noted that the proposed alternative approaches are potentially effective for pressure hull design.

• Journal of Life Science
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• v.27 no.7
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• pp.746-753
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• 2017

In order to rapidly identify four drums species, Larimichthys polyactis, L. crocea, Atrobucca nibe, and Pseudotolithus elongates, a highly efficient and quick method has been developed using multiplex polymerase chain reaction (PCR) with species-specific primers. Around 1.4 kbp of the mitochondrial COI gene sequences from the four drums species were aligned, and species-specific forward primers were designed, based on the single nucleotide polymorphism (SNP). The optimal conditions for PCR amplification were selected through cross-reactivity, using a gradient PCR method. The PCR results demonstrated species-specific amplification for each species at annealing temperatures between 50 and $62^{\circ}C$. Multiplex species-specific PCR (MSS-PCR) amplification reactions with four pairs of primers were performed for sixteen specimens of each species. MSS-PCR lead to a species-specific amplification of a 1,540 bp fragment in L. polyactis, 1,013 bp in A. nibe, 474 bp in L. crocea, and 182 bp in P. elongates, respectively. The four different sizes of each PCR product can be quickly and easily detected by single gel electrophoresis. The sensitivity of the MSS-PCR of the DNA was up to $0.1ng/{\mu}l$ as a starting concentration for the four different species tested. These results suggest that MSS-PCR, with species-specific primers based on SNP, can be a powerful tool in the rapid identification of the four drums species, L. polyactis, L. crocea, A. nibe, and P. elongates.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.7
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• pp.922-930
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• 2020

Marine accidents involving fishing boats, caused by a loss of stability, have been increasing over the last decade. One of the main reasons for these accidents is a sudden wind attacks. In this regard, the wind loads acting on the ship hull need to be estimated accurately for safety assessments of the motion and maneuverability of the ship. Therefore, this study aims to develop a computational model for the inlet boundary condition and to numerically estimate the wind load acting on a fishing boat. In particular, wind loads acting on a fishing boat at the wind speed profile boundary condition were compared with the numerical results obtained under uniform wind speed. The wind loads were estimated at intervals of 15° over the range of 0° to 180°, and i.e., a total of 13 cases. Furthermore, a numerical mesh model was developed based on the results of the mesh dependency test. The numerical analysis was performed using the RANS-based commercial solver STAR-CCM+ (ver. 13.06) with the k-ω turbulent model in the steady state. The wind loads for surge, sway, and heave motions were reduced by 39.5 %, 41.6 %, and 46.1 % and roll, pitch, and yaw motions were 48.2 %, 50.6 %, and 36.5 %, respectively, as compared with the values under uniform wind speed. It was confirmed that the developed inlet boundary condition describing the wind speed gradient with respect to height features higher accuracy than the boundary condition of uniform wind speed. The insights obtained in this study can be useful for the development of a numerical computation method for ships.

• Journal of Life Science
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• v.31 no.1
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• pp.10-16
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• 2021

The consumption of fresh-cut agricultural materials is increasing due to increased public interest in health and the increase of single-person households. Most fresh-cut agricultural materials can be eaten without heating, thus easily exposing the consumer to food-borne pathogens. As a result, food-borne diseases are increasing worldwide. In the analysis of food-borne pathogens, it is important to detect the strains, but this is time consuming and laborious. Alternative detection methods that have been introduced, include polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE), which is performed without prior culturing. Samples of fresh-cut agricultural materials, such as vegetables, were analyzed by the culture-based method. In 129 samples, non-pathogenic Escherichia coli (3.9%), Bacillus cereus (31.8%), Clostridium perfringens (5.4%), Yersinia enterocolitica (0.8%), and enterohemorrhagic E. coli (0.8%) were detected. Eight samples contaminated with bacteria were randomly selected, further analyzed by PCR-DGGE, and compared with the culture-based method. Two cases detected non-pathogenic E. coli by PCR-DGGE only, despite a lack of detection by the culture method. It was supposed there was possibility of sample loss during its 10-fold dilution for appropriate cultivation. In the detection of high-risk food-borne pathogens, it was found that the detection limit was lower in PCR-DGGE than in the culture-based method (10 CFU/g). This suggests that PCR-DGGE can be alternatively used to detect strains. On the other hand, low-risk food-borne pathogens seem to have higher detection limits in PCR-DGGE. Consequentially, this study contributes to the improvement of food-borne pathogen detection and the prevention of its related-diseases in fresh-cut agricultural materials.

• Korean Journal of Food Science and Technology
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• v.42 no.3
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• pp.287-291
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• 2010

In this study, a simple and rapid pre-treatment method based on liquid extraction was applied for the simultaneous determination of three macrolides (spiramycin, tylosin, and tilmicosin) residues. In these studies, the stock farm products was used as a matrix sample. When the liquid extraction method was compared with the solid phase extraction (SPE) method, the former showed higher recovery percentages and simpler steps than the latter. The macrolids were separated using a reverse-phase C18 ($250\;mm{\times}4.6\;mm$, $5\;{\mu}m$) column and a gradient elution with mobile phases consisting of phosphate buffer (pH 2.5) and acetonitrile. Tylosin and tilmicosin were detected at 288 nm and spiramycin was detected at 232 nm. The average recovery percentage ranged between 83.0-90.2% for samples spiked with the three macrolids at 50 and 100 ng/g The validation results showed that the limit of detection (7 (spiramycin), 12 (tilmiconsin), 12 (tylosin) ng/g)) was under the regulatory tolerances and the linearity from calibration curves was satisfactory for determining the multi-residue of three macrolids in farm products. Monitoring samples were collected at the main cities in Korea as Seoul, Busan, Deajeon, Incheon, Deagu, and Gwangju. Microlide antibiotics were not detected in most samples.

• Journal of the Korean Magnetics Society
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• v.18 no.6
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• pp.211-216
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• 2008

The effects of Si impurity on electronic structures and magnetism of bcc Fe are investigated by using a first-principles method by considering spin-orbit coupling. In order to describe the Si impurity, a 27 atomic bcc Fe supercell has been considered. The Kohn-Sham equation was solved in terms of the all-electron full-potential linearized augmented plane wave (FLAPW) method within the generalized gradient approximation (GGA). The effects of spin-orbit coupling were calculated self-consistently by considering spin-diagonal terms based on second variation method. For the ferromagnetic (FM) state without considering SOC, the spin magnetic moment of the Si impurity was calculated to be $-0.143{\mu}B$, while the magnetic moments of Fe atoms were calculated to be $2.214{\mu}B$, $2.327{\mu}B$, and $2.354{\mu}B$ in away from the Si atom, respectively. However, the FM state with considering SOC, the spin magnetic moment of the Si impurity was calculated to be $-0.144{\mu}B$, which is not affected significantly by SOC, but the spin magnetic moments of Fe atoms were calculated $2.189{\mu}B$, $2.310{\mu}B$, and $2.325{\mu}B$, respectively, which are much reduced value compared to those of the FM state without SOC. Comparing the total charge density and spin density, those features are thought to be originated by the screening distortions of the Fe $t_{2g}$ orbital, which can be obtained by considering SOC.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.