• Journal of Korea Water Resources Association
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• v.41 no.12
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• pp.1219-1230
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• 2008

Stream inflows induced by flood runoffs have a higher density than the ambient reservoir water because of a lower water temperature and elevated suspended sediment(SS) concentration. As the propagation of density currents that formed by density difference between inflow and ambient water affects reservoir water quality and ecosystem, an understanding of reservoir density current is essential for an optimization of filed monitoring, analysis and forecast of SS and nutrient transport, and their proper management and control. This study was aimed to quantify the characteristics of inflow density current including plunge depth($d_p$) and distance($X_p$), separation depth($d_s$), interflow thickness($h_i$), arrival time to dam($t_a$), reduction ratio(${\beta}$) of SS contained stream inflow for different flood magnitude in Daecheong Reservoir with a validated two-dimensional(2D) numerical model. 10 different flood scenarios corresponding to inflow densimetric Froude number($Fr_i$) range from 0.920 to 9.205 were set up based on the hydrograph obtained from June 13 to July 3, 2004. A fully developed stratification condition was assumed as an initial water temperature profile. Higher $Fr_i$(inertia-to-buoyancy ratio) resulted in a greater $d_p,\;X_p,\;d_s,\;h_i$, and faster propagation of interflow, while the effect of reservoir geometry on these characteristics was significant. The Hebbert equation that estimates $d_p$ assuming steady-state flow condition with triangular cross section substantially over-estimated the $d_p$ because it does not consider the spatial variation of reservoir geometry and water surface changes during flood events. The ${\beta}$ values between inflow and dam sites were decreased as $Fr_i$ increased, but reversed after $Fr_i$>9.0 because of turbulent mixing effect. The results provides a practical and effective prediction measures for reservoir operators to first capture the behavior of turbidity inflow.

• Korean Journal of Medicinal Crop Science
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• v.1 no.2
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• pp.137-157
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• 1993

This study was carried out to investigate the optimal condition for multiple propagation through leaf tissue culture and to apply anther culture techniques to Pulsatilla koreana Nakai breeding. Leaf and anther of Pulsatilla koreana Nakai were cultured on MS, MT, LS and $B_5$ media supplemented with several growth regulators and nitrogen sources under various conditions. For callus induction and differentiation from the Pulsatilla koreana leaf segments were more effective in the combination of zeatin and auxin than auxin alone. The color of the callus was green when treated with IBA alone. Shoot differentiation was more effective when treated with zeatin than auxin alone, especially the best hormoal combination for shoot differentiation was zeatin 1.0mg/l +NAA 0.1mg/l, while 2,4-D inhibited shoot differentiation. The appeared rate of S pollen was 35% in vivo, while that of S pollen by low temperature$(4^{\circ}C)$ pretreatment for 4 days was increased by 53% and the optimum culture time for callus induction from anther was uni-nucleate stage. $B_5$ basal medium supplemented with NAA 0.5mg/l and zeatin 1 mg/l was the most effective on callus formation and the best results of plant regeneration were obtained from combination of NAA 0.5mg/l and zeatin 0.5mg/l in anther culture. $NH_{4}NO_3$ as more effectives as the nitrogen source than $KNO_3$ and the combination with zeatin 2.0mg /L was the best effective. The best combination for plant regeneration in callus induced from anther was $NH_{4}NO_3$ 1650mg/l + $KNO_3$ 3800mg/l + zeatin 2.0mg/l. Ploidy level of anther-derived plants appeared 28% haploid, 47% diploid and the others were triploid, tetraploid and mixploid. In compare with E.S.T, M.D.H and P.X banding patterns were distinguished among callus, haploid and diploid plants in electrophoresis.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.201-220
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• 2019

Fake news has emerged as a significant issue over the last few years, igniting discussions and research on how to solve this problem. In particular, studies on automated fact-checking and fake news detection using artificial intelligence and text analysis techniques have drawn attention. Fake news detection research entails a form of document classification; thus, document classification techniques have been widely used in this type of research. However, document summarization techniques have been inconspicuous in this field. At the same time, automatic news summarization services have become popular, and a recent study found that the use of news summarized through abstractive summarization has strengthened the predictive performance of fake news detection models. Therefore, the need to study the integration of document summarization technology in the domestic news data environment has become evident. In order to examine the effect of extractive summarization on the fake news detection model, we first summarized news articles through extractive summarization. Second, we created a summarized news-based detection model. Finally, we compared our model with the full-text-based detection model. The study found that BPN(Back Propagation Neural Network) and SVM(Support Vector Machine) did not exhibit a large difference in performance; however, for DT(Decision Tree), the full-text-based model demonstrated a somewhat better performance. In the case of LR(Logistic Regression), our model exhibited the superior performance. Nonetheless, the results did not show a statistically significant difference between our model and the full-text-based model. Therefore, when the summary is applied, at least the core information of the fake news is preserved, and the LR-based model can confirm the possibility of performance improvement. This study features an experimental application of extractive summarization in fake news detection research by employing various machine-learning algorithms. The study's limitations are, essentially, the relatively small amount of data and the lack of comparison between various summarization technologies. Therefore, an in-depth analysis that applies various analytical techniques to a larger data volume would be helpful in the future.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.30 no.6
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• pp.306-318
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• 2018

In order to evaluate the seismic capacity of massive vertical type breakwaters which have intensively been deployed along the coast of South Korea over the last two decades, we carry out the preliminary numerical simulation against the PoHang, GyeongJu, Hachinohe 1, Hachinohe 2, Ofunato, and artificial seismic waves based on the measured time series of ground acceleration. Numerical result shows that significant sliding can be resulted in once non-negligible portion of seismic energy is shifted toward the longer period during its propagation process toward the ground surface in a form of shear wave. It is well known that during these propagation process, shear waves due to the seismic activity would be amplified, and non-negligible portion of seismic energy be shifted toward the longer period. Among these, the shift of seismic energy toward the longer period is induced by the viscosity and internal friction intrinsic in the soil. On the other hand, the amplification of shear waves can be attributed to the fact that the shear modulus is getting smaller toward the ground surface following the descending effective stress toward the ground surface. And the weakened intensity of soil as the number of attacking shear waves are accumulated can also contribute these phenomenon (Das, 1993). In this rationale, we constitute the numerical model using the model by Hardin and Drnevich (1972) for the weakened shear modulus as shear waves go on, and shear wave equation, in the numerical integration of which $Newmark-{\beta}$ method and Modified Newton-Raphson method are evoked to take nonlinear stress-strain relationship into account. It is shown that the numerical model proposed in this study could duplicate the well known features of seismic shear waves such as that a great deal of probability mass is shifted toward the larger amplitude and longer period when shear waves propagate toward the ground surface.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.465-486
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• 2021

Purpose: Since most disaster information systems are centered on non-disabled people, the reality is that there is a lack of disaster information delivery systems for the vulnerable, such as the disabled, the elderly, and children, who are relatively vulnerable to disasters. The purpose of the service is to improve the safety of the disabled and the elderly by eliminating blind spots of informatization and establishing customized disaster information services to respond to disasters through IoT-based integrated control technology. Method: The model at the core of this study is the disaster alert propagation model and evacuation support model, and it shall be developed by reflecting the behavioral characteristics of the disabled and the elderly in the event of a disaster. The disaster alert propagation model spreads disaster situations collected using IoT technology, and the evacuation support model uses geomagnetic field-based measuring technology to identify the user's indoor location and help the disabled and the elderly evacuate safely. Results: Demonstration model demonstration resulted in an efficient qualitative evaluation of indoor location accuracy, such as the suitability of evacuation route guidance and satisfaction of services from the user's perspective. Conclusion: Disaster information and evacuation support services were established for the safety vulnerable groups of mobile app for model verification. The disaster situation was demonstrated through experts in the related fields and the disabled by limiting it to the fire situation. It was evaluated as "satisfaction" in the adequacy of disaster information delivery and evacuation support, and its functional satisfaction and user UI were evaluated as "normal" due to the nature of the pilot model. Through this, the disaster information and evacuation support services presented in this study were evaluated to support the safety vulnerable groups to a faster disaster evacuation without missing the golden time of disaster evacuation.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.457-468
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• 2020

Problems arising during pile design works for plant construction, civil and architecture work are mostly come from uncertainty of geotechnical characteristics. In particular, obtaining the N-value measured through the Standard Penetration Test (SPT) is the most important data. However, it is difficult to obtain N-value by drilling investigation throughout the all target area. There are many constraints such as licensing, time, cost, equipment access and residential complaints etc. it is impossible to obtain geotechnical characteristics through drilling investigation within a short bidding period in overseas. The geotechnical characteristics at non-drilling investigation points are usually determined by the engineer's empirical judgment, which can leads to errors in pile design and quantity calculation causing construction delay and cost increase. It would be possible to overcome this problem if N-value could be predicted at the non-drilling investigation points using limited minimum drilling investigation data. This study was conducted to predicted the N-value using an Artificial Neural Network (ANN) which one of the Artificial intelligence (AI) method. An Artificial Neural Network treats a limited amount of geotechnical characteristics as a biological logic process, providing more reliable results for input variables. The purpose of this study is to predict N-value at the non-drilling investigation points through patterns which is studied by multi-layer perceptron and error back-propagation algorithms using the minimum geotechnical data. It has been reviewed the reliability of the values that predicted by AI method compared to the measured values, and we were able to confirm the high reliability as a result. To solving geotechnical uncertainty, we will perform sensitivity analysis of input variables to increase learning effect in next steps and it may need some technical update of program. We hope that our study will be helpful to design works in the future.

• Journal of the Daesoon Academy of Sciences
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• v.33
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• pp.91-145
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• 2019

This study aims to reveal that Daesoon Jinrihoe Yeoju Headquarters Temple Complex is a sacred place of Gaebyeokgongsa (the Reordering Works of the Great Opening) through the logic of the energy of form in Feng-Shui studies. The Headquarters Temple Complex can illuminate the lamp of coexistence, emerge as a place for cultivation, and support the era of human nobility with Gucheonsangje (the Supreme God of the Ninth Heaven) as an object of faith. Virtuous Concordance of Yin and Yang, Harmonious Union between Divine Beings and Human Beings, the Resolution of Grievances for Mutual Beneficence, and Perfected Unification with Dao are the mission statements of this great site. For this purpose, it is necessary to investigate the headquarters according to integral Feng-Shui Theory. Doing so can provide proof that the geographic location, landscape, yin-yang harmonizing, and flowing veins of terrestrial energy at Headquarters Temple Complex are all profoundly auspicious. At the same time, this data also allows further study into the interactions of dragon-veins, energy hubs, surrounding mountains, and watercourses, which reveal how Daesoon Jinrihoe Yeoju Headquarters Temple Complex promotes the basic works of propagation, edification, and cultivation and three societal works of charity aid, social welfare, and education for the purpose of global propagation, saving beings, and building an earthly paradise by reforming humanity and engaging in spiritual civilization. This must be done on site with proper Feng-Shui in order to open up the era of human nobility upon the Great Opening of the Later World. As the center of the religious order, Daesoon Jinrihoe, Yeoju Headquarter Temple Complex has the general Feng-Shui characteristic of Baesanimsu (a back supported by a mountain and a front facing water). Through discussing the Feng-Shui of Daesoon Jinrihoe's Yeoju Headquarters Temple Complex as the center of humankind's resolution of grievances for mutual beneficence, this study would explore growth-supporting land that delivers future rewards through Feng-Shui symbolism and the ethical practice of grateful reciprocation of favors for mutual beneficence. This exploration will reveal how the geographical features and conditions of the Yeoju Headquarters Temple Complex make it a place fit for spiritual cultivation. It is a miraculous luminous court surrounded by mountains, where auspicious signs in eight directions gather. Its veins of terrestrial energy harmonize with clean water energy as it is affectionately situated within its natural environment. Its location corresponds with the Feng-Shui theory of dragon-veins, energy hubs, surrounding mountains, and watercourses. Thus, with regards to the Feng-Shui of Daesoon Jinrihoe's Yeoju Headquarters Temple Complex, this study examines the flows of mountains and waters and focuses on how the site is based on the logic of Feng-Shui. More generally, the geographical features of the surrounding mountains are likewise examined. An analysis of the relationship between Poguk (布局) of Sasinsa (animal symbols of the four directions, four gods, including blue dragon of the east, red phoenix of the south, white tiger of the west, and black tortoise of the north) and the location will be provided while focusing on the Yeoju Headquarters Temple Complex. This study supports the feasibility of further Feng-Shui studies of the Yeoju Headquarters Temple Complex based on traditional geomancy books that focusing on Hyeonggi (Energy of Form) Theory.

• Journal of Mushroom
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• v.3 no.4
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• pp.140-144
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• 2005

This study was conducted to verify the cause of suppression symptom in mycelial growth during summer and to be able to establish a countermeasure. Cultivation of Flammulina velutipes was experimented with varying elapsed time of 0, 3, 6, 9 hours after mixing the sawdust media and two kinds of water temperature (normal water, $24^{\circ}C$ and cold water, $6^{\circ}C$) for mixing sawdust media. There were trends of increased media temperature from $24^{\circ}C$ to $31^{\circ}C$ and decreased pH from 6.5 to 5.2~5.6 with varying elapsed time from mixing the media to sterilization. Bacterial density also increased with bacterial density in Medium $24^{\circ}C$ being 1.9~4.1 times higher than that in Medium 6. Growth of F. velutipes was delayed with dual culture of bacteria isolated from sawdust media. Total nitrogen content of sawdust media was lowered by elapsed time from mixing the media to sterilization. The use of normal water($24^{\circ}C$) delayed mushroom growth by 1~2 days compared with that of cold water($6^{\circ}C$). Furthermore, mycelial growth of F. velutipes in the bottle cultivation ceased 9 hours after mixing the media. Primordia formation of F. velutipes was delayed by 1~3 days by elapsed time after mixing sawdust media, while fruit-body yield decreased by 7~12% 6 hours after mixing the media. Fruit-body yield was more stabilized with the use of cold water($6^{\circ}C$) than with that of normal water($24^{\circ}C$). Results showed that it is effective to use cold water as low as $6^{\circ}C$ in mixing media for F. velutipes cultivation, especially during summer when environmental temperature is high, high pressure sterilization after bottling work can prevent bacterial propagation in the media and can stabilize media ingredient.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.127-142
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• 2016

Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.

• Journal of Korea Water Resources Association
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• v.42 no.12
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• pp.1079-1089
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• 2009

The objective of this study is to develop the accurate, robust and high resolution two-dimensional numerical model that solves the computationally difficult hydraulic problems, including the wave front propagation over dry bed and abrupt change in bathymetry. The developed model in this study solves the conservative form of the two-dimensional shallow water equations using an unsplit finite volume scheme and HLLC approximate Riemann solvers to compute the interface fluxes. Bed-slope term is discretized by the divergence theorem in the framework of FVM for application of unsplit scheme. Accurate and stable SGM, in conjunction with the MUSCL which is second-order-accurate both in space and time, is adopted to balance with fluxes and source terms. The exact C-property is shown to be satisfied for balancing the fluxes and the source terms. Since the spurious oscillations in second-order schemes are inherent, an efficient slope limiting technique is used to supply TVD property. The accuracy, conservation property and application of developed model are verified by comparing numerical solution with analytical solution and experimental data through the simulations of one-dimensional dam break flow without bed slope, steady transcritical flow over a hump and two-dimensional dam break flow with a constriction.