• Journal of the East Asian Society of Dietary Life
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• v.23 no.5
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• pp.645-653
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• 2013

The results of examining total soluble solid, reducing sugar, antioxidant and sensory properties regarding LTD (Liriope Tuber Dried), LTSD (Liriope Tuber Steamed and Dried), LTASD (Liriope Tuber Alcohol-Steamed and Dried), LTDR (LTD Roasted), LTSDR (LTSD Roasted) and LTASDR (LTASD Roasted) are as follows : Total soluble solid content of the roasted samples (LTDR, LTSDR and LTASDR) was more than those of all dried samples (LTD, LTSD and LTASD). According to roasting conditions, total sugar and reducing sugar are significantly greater than the raw and dried sample (LTD) in all heat-treated samples. The browning index was significantly higher in all roasted samples. In particular, LTASDR had a high browning index. Further, the antioxidative activity of the roasted LT samples were higher than that of all dried LT samples. In particular, the LTASDR sample showed significantly high figures in DPPH scavenging activity, ABTS scavenging activity, Nitrite scavenging activity and xanthine oxidase inhibitory activity. Sensory properties showed an increased acceptance in all evaluation items among roasted samples. In this study, hot water extracts of steamed or alcohol-steamed roasted LT samples had a higher antioxidative effect than that of LTD or LTDR and attained positive results by getting high scores in the overall sensory evaluation. Therefore, when using Liriope tuber in making beverages or herbal recipes, it is appropriate to dry and roast before steaming or spreading with alcohol when treating LT.

• Proceedings of the Korean Geotechical Society Conference
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• 1998.05a
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• pp.35-81
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• 1998

Distinct Element Method(DEM) has a great advantage to model the discontinuous behaviour of jointed rock masses such as rotation, sliding, and separation of rock blocks. Geometrical data of joints by a field monitoring is not enough to model the jointed rock mass though the results of DE analysis for the jointed rock mass is most sensitive to the distributional properties of joints. Also, it is important to use a properly joint law in evaluating the stability of a jointed rock mass because the joint is considered as the contact between blocks in DEM. In this study, a stochastic modelling technique is developed and the dilatant rock joint is numerically modelled in order to consider th geometrical and mechanical properties of joints in DE analysis. The stochastic modelling technique provides a assemblage of rock blocks by reproducing the joint distribution from insufficient joint data. Numerical Modelling of joint dilatancy in a edge-edge contact of DEM enable to consider not only mechanical properties but also various boundary conditions of joint. Preprocess Procedure for a stochastic DE model is composed of a statistical process of raw data of joints, a joint generation, and a block boundary generation. This stochastic DE model is used to analyze the effect of deviations of geometrical joint parameters on .the behaviour of jointed rock masses. This modelling method may be one tool for the consistency of DE analysis because it keeps the objectivity of the numerical model. In the joint constitutive law with a dilatancy, the normal and shear behaviour of a joint are fully coupled due to dilatation. It is easy to quantify the input Parameters used in the joint law from laboratory tests. The boundary effect on the behaviour of a joint is verified from shear tests under CNL and CNS using the numerical model of a single joint. The numerical model developed is applied to jointed rock masses to evaluate the effect of joint dilation on tunnel stability.

• Journal of IKEEE
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• v.27 no.1
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• pp.116-121
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• 2023

In this paper, we develop a deep learning structure for a complex microbial incubator that applies deep learning prediction result information. The proposed complex microbial incubator consists of pre-processing of complex microbial data, conversion of complex microbial data structure, design of deep learning network, learning of the designed deep learning network, and GUI development applied to the prototype. In the complex microbial data preprocessing, one-hot encoding is performed on the amount of molasses, nutrients, plant extract, salt, etc. required for microbial culture, and the maximum-minimum normalization method for the pH concentration measured as a result of the culture and the number of microbial cells to preprocess the data. In the complex microbial data structure conversion, the preprocessed data is converted into a graph structure by connecting the water temperature and the number of microbial cells, and then expressed as an adjacency matrix and attribute information to be used as input data for a deep learning network. In deep learning network design, complex microbial data is learned by designing a graph convolutional network specialized for graph structures. The designed deep learning network uses a cosine loss function to proceed with learning in the direction of minimizing the error that occurs during learning. GUI development applied to the prototype shows the target pH concentration (3.8 or less) and the number of cells (10⁸ or more) of complex microorganisms in an order suitable for culturing according to the water temperature selected by the user. In order to evaluate the performance of the proposed microbial incubator, the results of experiments conducted by authorized testing institutes showed that the average pH was 3.7 and the number of cells of complex microorganisms was 1.7 × 10⁸. Therefore, the effectiveness of the deep learning structure for the complex microbial incubator applying the deep learning prediction result information proposed in this paper was proven.