• Korean Journal of Environmental Biology
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• v.35 no.4
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• pp.526-532
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• 2017

Development, survival, and reproduction of brown planthopper (BPH) Nilaparvata lugens $St{\aa}l$ (Hemiptera: Delphacidae), were studied in laboratory at $25{\pm}2^{\circ}C$, $65{\pm}5%$ RH and a 16L : 8D hours photoperiodism on five rice cultivars of: Dongjin 1ho, Chungchungbyeo, Jangseongbyeo, Chinnongbyeo and Jungmo 1045. BPH nymphs successfully survived on all rice cultivars, although survival rate was lowest on Jangseongbyeo (36.0%). Developmental time of immature stages ranged from $11.7{\pm}0.59d$ on Jungmo 1045 to $12.8{\pm}0.59d$ on Chinnongbyeo. Reproductive period and female longevity were longest on Dongjin 1ho, Chinnongbyeo and Jungmo 1045 while highest fecundity of N. lugens being observed on these three rice cultivars. Highest and lowest net reproductive rates were calculated on rice cultivars, Jungmo 1045 and Jangseongbyeo, respectively. Mean generation time was the longest on rice cultivar Dongjin 1ho. Respective descending order of intrinsic rates of population increase were on Jungmo 1045, Chinnongbyeo, Dongjin 1ho, Chungchungbyeo and Jangseongbyeo. These population parameters showed that N. lugens can successfully survive and reproduce on Chinnongbyeo and Jungmo 1045.

• Journal of the Korean Society of Food Science and Nutrition
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• v.44 no.1
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• pp.14-23
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• 2015

This study examined the glycemic indices (GIs) and glycemic loads of carbohydrate-rich snacks in Korea according to variety and cooking method. The most popular carbohydrate snacks (corn, potatoes, sweet potatoes, chestnuts, and red beans) from the Korean National Health and Nutrition Examination Survey nutrient database were cooked using a variety of conventional cooking methods (steaming, baking, porridge, puffing, and frying). The GIs of foods were measured in 60 healthy males after receiving permission from the University Hospital institutional review board (KMC IRB 1306-01). Blood glucose and insulin levels were then measured at 0, 15, 30, 60, 90, and 120 min after consuming glucose, and each test food contained 50 g of carbohydrates (corn: 170.0 g, potatoes: 359.7 g, sweet potatoes: 160.3 g, chestnuts: 134.8 g, red beans: 73.1 g). GI values for test foods were calculated based on the increase in the area under the blood glucose response curve for each subject. Steamed potatoes ($93.6{\pm}11.6$), corn porridge ($91.8{\pm}19.5$), baked sweet potatoes ($90.9{\pm}9.6$), baked potatoes ($78.2{\pm}14.5$), steamed corn ($73.4{\pm}9.9$), and steamed sweet potatoes ($70.8{\pm}6.1$) were shown to be considered high GI foods, whereas baked chestnuts ($54.3{\pm}6.3$), red bean porridge ($33.1{\pm}5.5$), steamed red beans ($22.1{\pm}3.2$), fried potatoes ($41.5{\pm}7.8$), and ground and pan-fried potatoes ($28.0{\pm}5.1$) were considered as low GI foods. The results suggest that the cooking method of carbohydrate-rich snacks is an important determinant of GI values.

• Korean Journal of Dental Materials
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• v.44 no.3
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• pp.273-280
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• 2017

The aim of the present study was to investigate the effects of specimen dimension on the flexural properties and testing reliability of dental composite resin. The composite resin was prepared experimentally by mixing a resin matrix with silanated micrometer glass filler at 50 vol%. Flexural specimens with various dimension in specimen's width were fabricated by light curing using a split metal mold; $25{\times}2{\times}2mm$, $25{\times}2{\times}4mm$, $25{\times}2{\times}6mm$ in length ${\times}$ height ${\times}$ width. The flexural strength and modulus were determined according to ISO 4049 test protocol at a span length of 20 mm (normal-flexural strength; NFS). Another flexural test was conducted using mini-sized specimens ($12{\times}2{\times}2mm$, $12{\times}2{\times}4mm$, $12{\times}2{\times}6mm$) from the broken specimens at a span length of 10 mm (mini-flexural strength; MFS). Data were analyzed with ANOVA and Duncan's post-hoc test and the test reliability was evaluated by Weibull analysis. Results showed that there are generally no significant difference in flexural strength with the increase in the specimen width in NFS and MFS tests. However, the test reliability of flexural strength based on Weibull analysis was largely changed with the variables in the dimension of width and span length. The flexural modulus of NFS was increased as the dimension of specimens width increased while there was no trend in flexural modulus of MFS test. Overall results recommend that the evaluation of flexural properties and the reliability of dental composite resins should be performed with more than one test method.

• Korean Journal of Remote Sensing
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• v.17 no.1
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• pp.33-44
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• 2001

The paper presents several satellite models and satellite image decomposition methods for inaccessible area where ground control points can hardly acquired in conventional ways. First, 10 different satellite sensor models, which were extended from collinearity condition equations, were developed and then behavior of each sensor model was investigated. Secondly, satellite images were decomposed and also pseudo images were generated. The satellite sensor model extended from collinearity equations was represented by the six exterior orientation parameters in $1^{st}$, $2^{nd}$ and $3^{rd}$ order function of satellite image row. Among them, the rotational angle parameters such as $\omega$(omega) and $\Phi$(phi) correlated highly with positional parameters could be assigned to constant values. For inaccessible area, satellite images were decomposed, which means that two consecutive images were combined as one image, The combined image consists of one satellite image with ground control points and the other without ground control points. In addition, a pseudo image which is an imaginary image, was prepared from one satellite image with ground control points and the other without ground control points. In other words, the pseudo image is an arbitrary image bridging two consecutive images. For the experiments, SPOT satellite images exposed to the similar area in different pass were used. Conclusively, it was found that 10 different satellite sensor models and 5 different decomposed methods delivered different levels of accuracy. Among them, the satellite camera model with 1st order function of image row for positional orientation parameters and rotational angle parameter of kappa, and constant rotational angle parameter omega and phi provided the best 60m maximum error at check point with pseudo images arrangement.

• Journal of Life Science
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• v.31 no.9
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• pp.787-795
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• 2021

Eupatorium chinensis var. simplicifolium (EUC) has anti-inflammatory and antioxidant effects. Young sprouts of EUC have been used as food for a long time, and the whole EUC plant has been used as an herbal remedy in oriental medicine. Arteriosclerosis, or chronic inflammation in arterial vessels, is a cardiovascular disease and is involved in various disorders. Cardiovascular diseases such as restenosis and neuropathic hyperplasia are mainly caused by abnormal growth and movement due to multiple growth factors in vascular smooth muscle cells (VSMCs). Platelet-derived growth factor (PDGF) is a mitogen released from damaged vessel walls and is involved in the proliferation and migration of VSMCs. To determine the effects of EUC on the abnormal proliferation and migration of VSMCs, the present study investigated intracellular signaling pathways in PDGF-BB-induced VSMCs treated with and without EUC. Pretreating PDGF-BB-induced VSMCs with EUC tended to effectively decrease cell proliferation and migration. Subsequently, the intracellular growth-related signaling pathways of AKT, phospholipase C gamma (PLC-γ), and mitogen-activated protein kinase (MAPK) were investigated using western blotting to confirm inhibited phosphorylation. Furthermore, flow cytometry data showed that EUC blocked the cell cycle of VSMCs. These results suggest that EUC can inhibit the proliferation and migration of VSMCs by controlling the cell cycle and growth factor receptors. Furthermore, this indicates that EUC can be used as a preventative against cardiovascular disease resulting from abnormal proliferation and migration of VSMCs.

• Journal of IKEEE
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• v.25 no.3
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• pp.493-500
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• 2021

In this paper, we propose the development of deep learning structure to improve quality of polygonal containers. The deep learning structure consists of a convolution layer, a bottleneck layer, a fully connect layer, and a softmax layer. The convolution layer is a layer that obtains a feature image by performing a convolution 3x3 operation on the input image or the feature image of the previous layer with several feature filters. The bottleneck layer selects only the optimal features among the features on the feature image extracted through the convolution layer, reduces the channel to a convolution 1x1 ReLU, and performs a convolution 3x3 ReLU. The global average pooling operation performed after going through the bottleneck layer reduces the size of the feature image by selecting only the optimal features among the features of the feature image extracted through the convolution layer. The fully connect layer outputs the output data through 6 fully connect layers. The softmax layer multiplies and multiplies the value between the value of the input layer node and the target node to be calculated, and converts it into a value between 0 and 1 through an activation function. After the learning is completed, the recognition process classifies non-circular glass bottles by performing image acquisition using a camera, measuring position detection, and non-circular glass bottle classification using deep learning as in the learning process. In order to evaluate the performance of the deep learning structure to improve quality of polygonal containers, as a result of an experiment at an authorized testing institute, it was calculated to be at the same level as the world's highest level with 99% good/defective discrimination accuracy. Inspection time averaged 1.7 seconds, which was calculated within the operating time standards of production processes using non-circular machine vision systems. Therefore, the effectiveness of the performance of the deep learning structure to improve quality of polygonal containers proposed in this paper was proven.

• Knowledge Management Research
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• v.22 no.3
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• pp.273-293
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• 2021

Due to the COVID-19 pandemic, the size of the e-commerce has been increased rapidly. This pandemic, which made contact-less communication culture in everyday life made the e-commerce market to be opened even to the consumers who would hesitate to purchase and pay by electronic device without any personal contacts and seeing or touching the real products. Consumers who have experienced the easy access and convenience of the online purchase would continue to take those advantages even after the pandemic. During this time of transformation, however, the size of information source for the consumers has become even shrunk into a flat screen and limited to visual only. To provide differentiated and competitive information on products, companies are adopting AR/VR and steaming technologies but the reviews from the honest users need to be recognized as important in that it is regarded as strong as the well refined product information provided by marketing professionals of the company and companies may obtain useful insight for product development, marketing and sales strategies. Then from the consumer's point of view, if the ratings of reviews are widely diverged how consumers would process the review information before purchase? Are non-converged ratings always unreliable and worthless? In this study, we analyzed how consumer's regulatory focus moderate the attitude to process the diverged information. This experiment was designed as a 2x2 factorial study to see how the variance of product review ratings (high vs. low) for cosmetics affects product attitudes by the consumers' regulatory focus (prevention focus vs. improvement focus). As a result of the study, it was found that prevention-focused consumers showed high product attitude when the review variance was low, whereas promotion-focused consumers showed high product attitude when the review variance was high. With such a study, this thesis can explain that even if a product with exactly the same average rating, the converged or diverged review can be interpreted differently by customer's regulatory focus. This paper has a theoretical contribution to elucidate the mechanism of consumer's information process when the information is not converged. In practice, as reviews and sales records of each product are accumulated, as an one of applied knowledge management types with big data, companies may develop and provide even reinforced customer experience by providing personalized and optimized products and review information.

• Journal of the Society of Disaster Information
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• v.15 no.2
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• pp.249-258
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• 2019

Purpose: The purpose of this study is to attain the correlation analysis and thereby to deduce the uniaxial compressive strength of rock specimens through the elastic wave velocity and the elastic modulus among the physical characteristics measured from the rock specimens collected during drilling investigations in Seoul and Gyeonggi region. Method: Experiments were conducted in the laboratory with 119 granite specimens in order to derive the correlation between the compressive strength of the rocks and elastic wave velocity and elastic modulus. Results: In the case of granite, the results of the analysis of the interaction between the compressive strength of a rock and the elastic wave velocity and elastic modulus were found to be less reliable in the relation equation as a whole. And it is believed that the estimation of the compressive strength by the elastic wave velocity and elastic modulus is less used because of the composition of non-homogeneous particles of granite. Conclusion: In this study, the analysis of correlation between the compressive strength of a rock and the elastic wave velocity and elastic modulus was performed with simple regression analysis and multiple regression analysis. The coefficient determination ($R^2$) of simple regression analysis was shown between 0.61 and 0.67. Multiple regression analysis was 0.71. Thus, using multiple regression analysis when estimating compressive strength can increase the reliability of the correlation. Also, in the future, a variety of statistical analysis techniques such as recovery analysis, and artificial neural network analysis, and big data analysis can lead to more reliable results when estimating the compressive sterength of a rock based on the elastic wave velocity and elastic modulus.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.199-208
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• 2019

As the number of available satellites increases and technology advances, image information outputs are becoming increasingly diverse and a large amount of data is accumulating. In this study, we propose a change detection method for high-resolution satellite images that uses transfer learning and a deep learning network to overcome the limit caused by insufficient training data via the use of pre-trained information. The deep learning network used in this study comprises convolutional layers to extract the spatial and spectral information and convolutional long-short term memory layers to analyze the time series information. To use the learned information, the two initial convolutional layers of the change detection network are designed to use learned values from 40,000 patches of the ISPRS (International Society for Photogrammertry and Remote Sensing) dataset as initial values. In addition, 2D (2-Dimensional) and 3D (3-dimensional) kernels were used to find the optimized structure for the high-resolution satellite images. The experimental results for the KOMPSAT-3A (KOrean Multi-Purpose SATllite-3A) satellite images show that this change detection method can effectively extract changed/unchanged pixels but is less sensitive to changes due to shadow and relief displacements. In addition, the change detection accuracy of two sites was improved by using 3D kernels. This is because a 3D kernel can consider not only the spatial information but also the spectral information. This study indicates that we can effectively detect changes in high-resolution satellite images using the constructed image information and deep learning network. In future work, a pre-trained change detection network will be applied to newly obtained images to extend the scope of the application.

• Journal of Mushroom
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• v.17 no.3
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• pp.119-125
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• 2019

This study aims to report the results for the analysis of the growth environment by applying smart farm technology to "Chunchu No 2" farmers in order to develop an optimal growth model for precision cultivation of bottle-grown oyster mushrooms. The temperature, humidity, carbon dioxide concentration, and illumination data were collected and analyzed using an environmental sensor installed to obtain growth environment data from the oyster mushroom cultivator. Analysis of the collected temperature data revealed that the temperature at the time of granulation was $19.5^{\circ}C$ after scraping, and the mushroom was generated and maintained at about $21^{\circ}C$ until the bottle was flipped. When the fruiting body grew and approached harvest time, mushrooms were harvested while maintaining the temperature between $14^{\circ}C$ and $18^{\circ}C$. The humidity was maintained at almost 100% during the complete growth stage. Carbon dioxide concentration gradually increased until 3 days after the beginning of cultivation, and then increased rapidly to almost 5,500 ppm. From the 6th day, carbon dioxide concentration was gradually decreased through ventilation and was maintained at 1,600 ppm during harvest. Light intensity of 8 lux was irradiated up to day 6 after seeding, and growth was then continued while periodically irradiating 4 lux light. The fruiting body characteristics of "Chunchu No 2" cultivated in the farmhouse were as follows: pileus diameter of 26.5 mm and thickness of 4.9 mm, stipe thickness of 8.9 mm, and length of 68.7 mm. The fruiting body yield was 166.8 g/850 ml, and the individual weight was 12.8 g/10 units.