• The Journal of Korean Academy of Prosthodontics
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• v.47 no.3
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• pp.328-334
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• 2009

Statement of problem: Pier abutments act as a Class I fulcrum lever system when the teeth are incorporated in a fixed partial denture with rigid connectors. Therefore non-rigid connector incorporated into the fixed partial denture might reduce the stresses created by the leverage. Purpose: The purpose of this study was to evaluate, by means of finite element method, the effects of non-rigid connectors and supporting alveolar bone level on stress distribution for fixed partial dentures with pier abutments. Material and methods: A 2-dimensional finite element model simulating a 5-unit metal ceramic fixed partial denture with a pier abutment with rigid or non-rigid designs, the connector was located at the distal region of the second premolar, was developed. In the model, the lower canine, second premolar, and second molar served as abutments. Four types of alveolar bone condition were employed. One was normal bone condition and others were supporting bone reduced 20% height at one abutment. Two different loading conditions, each 150 N on 1st premolar and 1st molar and 300N on 1st molar, were used. Results: Two types of FPD were displaced apically. The amount of displacement decreased in an almost linear slope away from the loaded point. Non-rigid design tended to cause the higher stresses in supporting bone of premolar and molar abutments and the lower stresses in that of canine than rigid design. Alveolar bone loss increased the stresses in supporting bone of corresponding abutment. Conclusion: Careful evaluation of the retentive capacity of retainers and the periodontal condition of abutments may be required for the prosthetic design of fixed partial denture with a pier abutment.

• Journal of dental hygiene science
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• v.13 no.4
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• pp.471-479
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• 2013

This study is trying to grasp the stress of the male high school students and the correlation between the stress according to the academic and economic level and oral parafunctional habits, emphasizing the need for the education of oral parafunctional habits, providing the basic data in order to accomplish correctly until the oral health of the oral maxillofacial region. From May 2013 till July 2013, a self administered survey was conducted by the selected by convenience sampling from subjects of 1, 2 grade of two high school located in Chungnam, Korea. The study results were as follow: 1) Among five areas of stress, the stress of school life was the highest as 2.11 points and the stress of home problem was the lowest as 1.51 points; 2) the stress by class showed that grade 2 was higher than grade 1 in all areas. The stress of the school life (2.21) (p<0.01), interpersonal relationship (p<0.01), and own problem (p<0.05) showed the significant difference; 3) The significance analysis results between the five areas of stress according to the stress of latent variable and the oral parafunctional habits all showed the significant difference (p<0.001). The correlation between the stress and the oral parafunctional habits showed a weak negative correlation as -0.30, and the stress of the school life, own problem, environment problem, and interpersonal relationship showed very strong correlations more than 0.7; 4) Fit measures test result of stress, academic level, and family economic level model all showed more than 0.9 in good of fit index, adjusted goodness of fit index, normed fit index and root mean square residual and root mean square error of approximation values is all estimated less than 0.1, so it showed good model. From this study, it can be concluded that there is the correlation between stress and oral parafunctional habits.

• Journal of Korean Society of Forest Science
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• v.109 no.3
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• pp.259-270
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• 2020

Extreme weather events, such as heat and drought, have occurred frequently over the past two decades. This has led to continuous reports of cases of forest damage due to physiological stress, not pest damage. In 2014, pine trees were collectively damaged in the forest genetic resources reserve of Sogwang-ri, Uljin, South Korea. An investigation was launched to determine the causes of the dieback, so that a forest management plan could be prepared to deal with the current dieback, and to prevent future damage. This study aimedto 1) understand the topographic and structural characteristics of the area which experienced pine tree dieback, 2) identify the main causes of the dieback, and 3) predict future risk areas through the use of machine-learning techniques. A model for identifying risk areas was developed using 14 explanatory variables, including location, elevation, slope, and age class. When three machine-learning techniques-Decision Tree, Random Forest (RF), and Support Vector Machine (SVM) were applied to the model, RF and SVM showed higher predictability scores, with accuracies over 93%. Our analysis of the variable set showed that the topographical areas most vulnerable to pine dieback were those with high altitudes, high daily solar radiation, and limited water availability. We also found that, when it came to forest stand characteristics, pine trees with high vertical stand densities (5-15 m high) and higher age classes experienced a higher risk of dieback. The RF and SVM models predicted that 9.5% or 115 ha of the Geumgang Pine Forest are at high risk for pine dieback. Our study suggests the need for further investigation into the vulnerable areas of the Geumgang Pine Forest, and also for climate change adaptive forest management steps to protect those areas which remain undamaged.

• Sijohaknonchong
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• v.20
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• pp.141-171
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• 2004

Even though only 3 sijo are in high school textbook. through these 3 sijo each type can be understood in that each represents pyung sijo, sasul sijo, and present sijo. To learn with learner-centered activities, which aim for full knowledge acquisition regarding literary works, as the preparing stage, students can learn what theyll learn by teachers. Sijo are, so to speak, formed with three chapters, and stand for the world that is colorless, scentless, and flavorless. So, the theme can be found with ease. Compared with other genres, sijo can be formed creating background with ease. Moreover, sijo are not too long, so learners can paraphrase it. Sijo that express private experiences with the everyday language can be related to other genres or everyday language. So, sijo are last to present. In the teaching phase, on the gradation of concretion and gradation, writing or presentation activities are presented. After classroom, learners keep a reaction journal. In the phase of concretion and gradation, learners can apprehend that typical differences of the emotions of poetic speakers is from typical differences, even though emotions of poetic speakers of (1)$\cdot$(2)$\cdot$(3) that is each stand for pyung sijo, sasul sijo, and present sijo are roughly summarized loneliness, desolateness, and gloominess. Moreover, these typical differences are from social, political. and cultural settings, namely, the differences of contexts. In this teaching model. learners should prepare for content regarding context and text before the class. Teachers should act as an assistant to help learners pre-understand their subjective experiences and imaginations.

• 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 Korean Society of Forest Science
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• v.110 no.4
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• pp.504-515
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• 2021

To examine the relationship between environmental factors influencing the habitat of Aconitum austrokoreense Koidz., this study employed the MexEnt model to evaluate 21 environmental factors. Fourteen environmental factors having an AUC of at least 0.6 were found to be the age of stand, growing stock, altitude, topography, topographic wetness index, solar radiation, soil texture, mean temperature in January, mean temperature in April, mean annual temperature, mean rainfall in January, mean rainfall in August, and mean annual rainfall. Based on the response curves of the 14 descriptive factors, Aconitum austrokoreense Koidz. on the Baekun Mountain were deemed more suitable for sites at an altitude of 600 m or lower, and habitats were not significantly affected by the inclination angle. The preferred conditions were high stand density, sites close to valleys, and distribution in the northwestern direction. Under the five-age class system, the species were more likely to be observed for lower classes. The preferred solar radiation in this study was 1.2 MJ/m². The species were less likely to be observed when the topographic wetness index fell below the reference value of 4.5, and were more likely observed above 7.5 (reference of threshold). Soil analysis showed that Aconitum austrokoreense Koidz. was more likely to thrive in sandy loam than clay. Suitable conditions were a mean January temperature of - 4.4℃ to -2.5℃, mean April temperature of 8.8℃-10.0℃, and mean annual temperature of 9.6℃-11.0℃. Aconitum austrokoreense Koidz. was first observed in sites with a mean annual rainfall of 1,670- 1,720 mm, and a mean August rainfall of at least 350 mm. Therefore, sites with increasing rainfall of up to 390 mm were preferred. The area of potential habitats having distributive significance of 75% or higher was 202 ha, or 1.8% of the area covered in this study.

• Journal of the Korean Society of Child Welfare
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• no.59
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• pp.209-234
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• 2017

This study viewed schools as a cause of students dropping out and posited that dropping out of high school would vary depending on the characteristics and influencing factors of the school from which students were dropping out. Therefore, focusing on schools, we longitudinally investigated the change patterns of school dropout across high schools in the country, and the types of changes in dropping out of high school. In addition, we predicted the general characteristics of schools according to the type of school students were dropping out from, looked at the changes in the major factors (i.e., school violence and school counseling) affecting school dropout, and reviewed schools' long-term efforts and outcomes in relation to school dropout. For this purpose, KERIS EDSS's "Secondary School Information Disclosure Data" were used. The final model included data collected five years20122016) from high schools across the country. The results were as follows. First, in order to examine the longitudinal change patterns of dropping out of high schools, a latent growth models analysis was conducted, and it revealed that, as time passed, the dropout rate decreased. Second, growth mixture modeling was used to explore types according to the change patterns of the school students were dropping out from. The results showed three types: the "remaining in school" type, the "gradually decreasing school dropout" type, and the "increasing school dropping out". Third, the multinomial logistic regression was conducted to predict the general characteristics of schools by type. The results showed that public schools, vocational schools, and schools with a large number of students who have below the basic levels in Korean, English and mathematics were more likely to belong to the "increasing school dropout" type. Further, the larger the total number of students, the higher the probability of belonging to the "remaining in school" type or the "gradually decreasing school dropout" type. Lastly, growth mixture modeling was used to analyze the trend of school violence and school counseling according to the three types. The focus was on the "gradually decreasing school dropout" type. In the case of the "gradually decreasing school dropout" type, it was found that as time passed, the number of school violence cases and the number of offenders gradually decreased. In addition, in terms of change in school counseling the results revealed that the number of placement of professional counselors in schools increased every year and peer counseling was continuously promoted, which may account for the "gradually decreasing school dropout" type.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.5
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• pp.431-449
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• 2022

As many tunnels generally have been constructed, various experiences and techniques have been accumulated for tunnel design as well as tunnel construction. Hence, there are not a few cases that, for some usual tunnel design works, it is sufficient to perform the design by only modifying or supplementing previous similar design cases unless a tunnel has a unique structure or in geological conditions. In particular, for a tunnel blast design, it is reasonable to refer to previous similar design cases because the blast design in the stage of design is a preliminary design, considering that it is general to perform additional blast design through test blasts prior to the start of tunnel excavation. Meanwhile, entering the industry 4.0 era, artificial intelligence (AI) of which availability is surging across whole industry sector is broadly utilized to tunnel and blasting. For a drill and blast tunnel, AI is mainly applied for the estimation of blast vibration and rock mass classification, etc. however, there are few cases where it is applied to blast pattern design. Thus, this study attempts to automate tunnel blast design by means of machine learning, a branch of artificial intelligence. For this, the data related to a blast design was collected from 25 tunnel design reports for learning as well as 2 additional reports for the test, and from which 4 design parameters, i.e., rock mass class, road type and cross sectional area of upper section as well as bench section as input data as well as16 design elements, i.e., blast cut type, specific charge, the number of drill holes, and spacing and burden for each blast hole group, etc. as output. Based on this design data, three machine learning models, i.e., XGBoost, ANN, SVM, were tested and XGBoost was chosen as the best model and the results show a generally similar trend to an actual design when assumed design parameters were input. It is not enough yet to perform the whole blast design using the results from this study, however, it is planned that additional studies will be carried out to make it possible to put it to practical use after collecting more sufficient blast design data and supplementing detailed machine learning processes.

• Communications of Mathematical Education
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• v.36 no.3
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• pp.313-334
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• 2022

The purpose of this study is to analyze the changes in mathematical learning through applying STEAM education according to social needs for out-of-school youth. For this purpose, we developed a teaching and learning model and program for mathematics and music STEAM education, and we implemented and analyzed the changes of affective area and problem-solving strategies. The analysis results of characteristic in affective area are as follows: first, the activity-oriented class of mathematics and music STEAM education aroused interest in mathematics. Second, providing opportunities for mathematics and music STEAM education instilled a positive perception of the value of mathematics and STEAM education. Third, the autonomous communication-oriented learning environment of mathematics and music STEAM education improved confidence and motivation to learn in mathematics. The analysis results of the characteristic in problem-solving strategy are as follows: first, through the STEAM education with mathematics and music, a conceptual understanding of internally and externally dividing points was formed, and a given problem was expressed and solved in a formula. Second, the functional correspondence relationship was understood, and the given problem was described and solved with symbols associated with the function. The suggestions of the study are as follows: first, based on the teaching and learning model and results of this study, various STEAM education programs for out-of-school youth should be developed and expanded to foster future competencies and provide new changes for out-of-school youth. Second, it can be used for research on the development of teaching and learning materials for convergence elective subjects in the high school credit system by referring to the mathematics and music convergence STEAM program of this study. As the subjects and fields of STEAM education are diversified and organized, students in need of receiving educational opportunities will be reduced, and there will be a world where the name of out-of-school youth and alternative education will not be necessary. Therefore, it is expected that development of teaching and learning programs created by interest in education of out-of-school youth will be used as an innovative idea in school education to achieve a virtuous cycle.

• Journal of Korean Society of Forest Science
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• v.111 no.2
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• pp.311-318
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• 2022

We conducted this study to derive the site index, which is a criterion for the planting of Robinia pseudoacacia, a honey plant, and to investigate the diameter distribution change by derived site index. We applied the Chapman-Richards equation model to estimate the site index of the Robinia pseudoacacia stand. The site index was distributed within the range of 16-22 when the base age was 30 years. The fitness index of the site index estimation model was low, but we judged that there was no problem in the application because the residual distribution of the equation had not shifted to one side. We used the Weibull diameter distribution function to determine the diameter distribution of the Robinia pseudoacacia stand by site index. We used the mean diameter and the dominant tree height as independent variables to present the diameter distribution, and our analysis procedure was to estimate and recover the parameters of the Weibull diameter distribution function. We used the mean diameter and the dominant tree height of the Robinia pseudoacacia stand to show distribution by diameter class, and the fitness index for dbh distribution estimation was about 80.5%. As a result of schematizing the diameter distribution by site indices as a 30-year-old, we found that the higher the site index, the more the curve of the diameter distribution moved to the right. This suggests that if the plantation were to be established in a high site index stand, considering the suitable trees on the site, the growth of Robinia pseudoacacia woul d become active, and not onl y the production of wood but al so the production of honey would increase. We therefore anticipate that the site index classification table and curve of this Robinia pseudoacacia stand will become the standard for decision making in the plantation and management of this tree.