• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.38 no.6
• /
• pp.859-865
• /
• 2018

A construction of infrastructures and base station on the moon could be undertaken by linking with the regions where construction materials and energy could be supplied on site. It is necessary to detect craters on the lunar surface and gather their topological information in advance, which forms permanent shaded regions (PSR) in which rich ice deposits might be available. In this study, an effective method for automatic detection of lunar craters on the moon surface is taken into consideration by employing a latest version of deep-learning algorithm. A training of a deep-learning algorithm is performed by involving the still images of 90000 taken from the LRO orbiter on operation by NASA and the label data involving position and size of partly craters shown in each image. the Faster RCNN algorithm, which is a latest version of deep-learning algorithms, is applied for a deep-learning training. The trained deep-learning code was used for automatic detection of craters which had not been trained. As results, it is shown that a lot of erroneous information for crater's positions and sizes labelled by NASA has been automatically revised and many other craters not labelled has been detected. Therefore, it could be possible to automatically produce regional maps of crater density and topological information on the moon which could be changed through time and should be highly valuable in engineering consideration for lunar construction.

• Journal of Korean Academy of Oral Health
• /
• v.42 no.4
• /
• pp.118-123
• /
• 2018

Objective: This study aimed to investigate the effects of fluoride-containing acidic tea beverages on bovine teeth surfaces. Methods: The pH values and fluoride concentrations of 11 kinds of mineral water and 23 kinds of tea were measured. The fluoride-containing Seopyenje black tea beverages with pH 3.48 were chosen for the experimental group. Distilled water that did not contain fluoride and had the same pH value as that of the Seopyenje black teas served as the positive control. The Jeju Samdasoo mineral waters with neural pH (pH 7.52) and no fluoride were chosen as the negative control. Bovine teeth in each group were submerged for 10 minutes. Thereafter, surface microhardness of each bovine teeth was measured and the results were analyzed. Results: The pH value range was 6.94-8.09 ($mean=7.37{\pm}0.08$) for the 11 kinds of mineral water and 3.48-6.74 ($5.62{\pm}0.05$) for the 23 tea beverages. Titratable acidity of the Seopyenje black tea beverages was higher than that of the distilled water mixed with citric acid, and the pH was 5.5 and 7.0, respectively. The fluoride content of the 11 kinds of mineral water was 0-1.09 ppm ($mean=0.44{\pm}0.02ppm$) that of the 23 tea beverages was and 0-0.74 ppm ($0.64{\pm}0.06ppm$). In terms of enamel microhardness reduction of the bovine teeth, the tea beverages had the largest effects. There was no significant difference in microhardness reduction between the tea beverages and distilled water mixed with citric acid (P>0.05). Conversely, a significant difference was found between the kinds of mineral water and tea beverages, and also between mineral water and distilled water mixed with citric acid (P<0.05). Conclusions: Acidic tea beverages appeared to erode the surfaces of bovine teeth. The amount of fluoride in the tea beverages was not enough to inhibit erosion. Therefore, frequent intakes of acidic tea beverage are likely to increase erosion on the surfaces of bovine teeth, by affecting the enamel microhardness of teeth.

• Journal of Ocean Engineering and Technology
• /
• v.32 no.6
• /
• pp.447-457
• /
• 2018

In this study, a two-dimensional fully nonlinear transient wave numerical tank was developed using a desingularized indirect boundary integral equation method. The desingularized indirect boundary integral equation method is simpler and faster than the conventional boundary element method because special treatment is not required to compute the boundary integral. Numerical simulations were carried out in the time domain using the fourth order Runge-Kutta method. A mixed Eulerian-Lagrangian approach was adapted to reconstruct the free surface at each time step. A numerical damping zone was used to minimize the reflective wave in the downstream region. The interpolating method of a Gaussian radial basis function-type artificial neural network was used to calculate the gradient of the free surface elevation without element connectivity. The desingularized indirect boundary integral equation using an isolated point source and radial basis function has no need for information about the element connectivity and is a meshless method that is numerically more flexible. In order to validate the accuracy of the numerical wave tank based on the desingularized indirect boundary integral equation method and meshless technique, several numerical simulations were carried out. First, a comparison with numerical results according to the type of desingularized source was carried out and confirmed that continuous line sources can be replaced by simply isolated sources. In addition, a propagation simulation of a $2^{nd}$-order Stokes wave was carried out and compared with an analytical solution. Finally, simulations of propagating waves in shallow water and propagating waves over a submerged bar were also carried and compared with published data.

• Journal of Life Science
• /
• v.28 no.12
• /
• pp.1536-1544
• /
• 2018

Depression is a common, serious, and recurring mental disorder. The pathogenesis of depression involves many factors such as environmental factor, genetic factor and alteration of structure and function in neurobiological systems. Increasing evidence supports that epigenetic alteration may be associated with depression. The epigenetics is explained as the mechanisms by which environmental factor causes changes in chromatin structure and alters gene expression without changing DNA base sequence. DNA methylation and histone modification involving histone acetylation and methylation are the main epigenetic mechanisms. Animal studies have shown that stressful environment such as early life stress can leave persistent epigenetic marks in the genome, which alter gene expression and influence neural and behavioral function through adulthood. A potentially important gene in depression is brain-derived neurotrophic factor (BDNF). BDNF plays a central role in depression and antidepressant action. In studies of the rodent, exposure to stress at prenatal, postnatal, and adult stages alters BDNF expression through histone modification and DNA methylation of the BDNF gene which results in anxiety and depressive-like behavior. This review discusses recent advances in the study of the epigenetic mechanisms that contribute to depression, particularly histone modification and DNA methylation of the BDNF gene, that may help in the development of new targets for depression treatment.

• Journal of Intelligence and Information Systems
• /
• v.24 no.4
• /
• pp.155-170
• /
• 2018

Predicting a company's financial bankruptcy is traditionally one of the most crucial forecasting problems in business analytics. In previous studies, prediction models have been proposed by applying or combining statistical and machine learning-based techniques. In this paper, we propose a novel intelligent prediction model based on the simulated annealing which is one of the well-known optimization techniques. The simulated annealing is known to have comparable optimization performance to the genetic algorithms. Nevertheless, since there has been little research on the prediction and classification of business decision-making problems using the simulated annealing, it is meaningful to confirm the usefulness of the proposed model in business analytics. In this study, we use the combined model of simulated annealing and machine learning to select the input features of the bankruptcy prediction model. Typical types of combining optimization and machine learning techniques are feature selection, feature weighting, and instance selection. This study proposes a combining model for feature selection, which has been studied the most. In order to confirm the superiority of the proposed model in this study, we apply the real-world financial data of the Korean companies and analyze the results. The results show that the predictive accuracy of the proposed model is better than that of the naïve model. Notably, the performance is significantly improved as compared with the traditional decision tree, random forests, artificial neural network, SVM, and logistic regression analysis.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.37 no.4
• /
• pp.219-231
• /
• 2019

The specifications of signboards are set for each type of signboards, but the shape and size of the signboard actually installed are not uniform. In addition, because the colors of the signboard are not defined, so various colors are applied to the signboard. Methods for recognizing signboards can be thought of as similar methods of recognizing road signs and license plates, but due to the nature of the signboards, there are limitations in that the signboards can not be recognized in a way similar to road signs and license plates. In this study, we proposed a methodology for recognizing plate-type signboards, which are the main targets of illegal and old signboards, and automatically extracting areas of signboards, using the deep learning-based Faster R-CNN algorithm. The process of recognizing flat type signboards through signboard images captured by using smartphone cameras is divided into two sequences. First, the type of signboard was recognized using deep learning to recognize flat type signboards in various types of signboard images, and the result showed an accuracy of about 71%. Next, when the boundary recognition algorithm for the signboards was applied to recognize the boundary area of the flat type signboard, the boundary of flat type signboard was recognized with an accuracy of 85%.

• Korean Journal of Poultry Science
• /
• v.46 no.3
• /
• pp.185-191
• /
• 2019

Nucleoporin 210 (Nup210) is associated with several physiological processes including muscle and neural cell differentiation, autoimmune diseases, and peripheral T cell homeostasis. Chicken Nup210 (chNup210) gene was originally identified as one of the differentially expressed genes (DEGs) in the kidney tissues of chicken. To elucidate the role of Nup210 in metabolic disease of chicken, we studied the molecular characteristics of chNup210 and analyzed its gene expression under the stimulation of Toll-like receptor 3 (TLR3) ligands. The Nup210 genomic DNA and amino acid sequences of various species including fowls, fishes, and mammals were retrieved from the Ensemble database and subjected to bioinformatics analyses. The expression of Nup210 from several chicken tissues was probed through qRT-PCR, and chicken fibroblast DF-1 cell line was used to determine the change in expression of chNup210 after stimulation with TLR3 ligand, polyinosinic-polycytidylic acid (poly (I:C)). The chNup210 gene was highly expressed in chicken lung and spleen tissues. Although highly conserved among the species, chNup210 was evolutionary clustered in the same clade as that of duck compared to other mammals. Furthermore, this study revealed that chNup210 is expressed in TLR3 signaling pathway and provides fundamental information on Nup210 expression in chicken. Future studies that offer insight into the involvement of chNup210 in the chicken innate immune response against viral infection are recommended.

• The Journal of Society for e-Business Studies
• /
• v.24 no.3
• /
• pp.19-33
• /
• 2019

Recently, manufacturers have been struggling to efficiently use production equipment as their production methods become more sophisticated and complex. Typical factors hindering the efficiency of the manufacturing process include setup cost due to job change. Especially, in the process of using expensive production equipment such as semiconductor / LCD process, efficient use of equipment is very important. Balancing the tradeoff between meeting the deadline and minimizing setup cost incurred by changes of work type is crucial planning task. In this study, we developed a scheduling model to achieve the goal of minimizing the duedate and setup costs by using reinforcement learning in parallel machines with duedate and work preparation costs. The proposed model is a Deep Q-Network (DQN) scheduling model and is a reinforcement learning-based model. To validate the effectiveness of our proposed model, we compared it against the heuristic model and DNN(deep neural network) based model. It was confirmed that our proposed DQN method causes less due date violation and setup costs than the benchmark methods.

• The Journal of Bigdata
• /
• v.3 no.2
• /
• pp.59-70
• /
• 2018

In order to provide stable district heat supplying service to the certain limited residential area, it is the most important to forecast the short-term future demand more accurately and produce and supply heat in efficient way. However, it is very difficult to develop a universal heat demand forecasting model that can be applied to general situations because the factors affecting the heat consumption are very diverse and the consumption patterns are changed according to individual consumers and regional characteristics. In particular, considering all of the various variables that can affect heat demand does not help improve performance in terms of accuracy and versatility. Therefore, this study aims to develop a demand forecasting model using deep learning based on only limited information that can be acquired in real time. A demand forecasting model was developed by learning the artificial neural network of the Tensorflow using past data consisting only of the outdoor temperature of the area and date as input variables. The performance of the proposed model was evaluated by comparing the accuracy of demand predicted with the previous regression model. The proposed heat demand forecasting model in this research showed that it is possible to enhance the accuracy using only limited variables which can be secured in real time. For the demand forecasting in a certain region, the proposed model can be customized by adding some features which can reflect the regional characteristics.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.4
• /
• pp.435-447
• /
• 2019

In order to prevent unnecessary re-admission, it is necessary to intensively manage the groups with high probability of re-admission. For this, it is necessary to develop a re-admission prediction model. Two - year discharge summary data of one university hospital were collected from 2016 to 2017 to develop a predictive model of re-admission. In this case, the re-admitted patients were defined as those who were discharged more than once during the study period. We conducted descriptive statistics and crosstab analysis to identify the characteristics of rehospitalized patients. The re-admission prediction model was developed using logistic regression, neural network, and decision tree. AUC (Area Under Curve) was used for model evaluation. The logistic regression model was selected as the final re-admission predictive model because the AUC was the best at 0.81. The main variables affecting the selected rehospitalization in the logistic regression model were Residental regions, Age, CCS, Charlson Index Score, Discharge Dept., Via ER, LOS, Operation, Sex, Total payment, and Insurance. The model developed in this study was limited to generalization because it was two years data of one hospital. It is necessary to develop a model that can collect and generalize long-term data from various hospitals in the future. Furthermore, it is necessary to develop a model that can predict the re-admission that was not planned.