• 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.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.57-78
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• 2023

COVID-19, which started in Wuhan, China in November 2019, spread beyond China in 2020 and spread worldwide in March 2020. It is important to prevent a highly contagious virus like COVID-19 in advance and to actively treat it when confirmed, but it is more important to identify the confirmed fact quickly and prevent its spread since it is a virus that spreads quickly. However, PCR test to check for infection is costly and time consuming, and self-kit test is also easy to access, but the cost of the kit is not easy to receive every time. Therefore, if it is possible to determine whether or not a person is positive for COVID-19 based on the sound of a cough so that anyone can use it easily, anyone can easily check whether or not they are confirmed at anytime, anywhere, and it can have great economic advantages. In this study, an experiment was conducted on a method to identify whether or not COVID-19 was confirmed based on a cough sound. Cough sound features were extracted through MFCC, Mel-Spectrogram, and spectral contrast. For the quality of cough sound, noisy data was deleted through SNR, and only the cough sound was extracted from the voice file through chunk. Since the objective is COVID-19 positive and negative classification, learning was performed through XGBoost, LightGBM, and FCNN algorithms, which are often used for classification, and the results were compared. Additionally, we conducted a comparative experiment on the performance of the model using multidimensional vectors obtained by converting cough sounds into both images and vectors. The experimental results showed that the LightGBM model utilizing features obtained by converting basic information about health status and cough sounds into multidimensional vectors through MFCC, Mel-Spectogram, Spectral contrast, and Spectrogram achieved the highest accuracy of 0.74.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

• Journal of Korean Society of Archives and Records Management
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• v.1 no.1
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• pp.37-52
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• 2001

1. establishment and development of China archives science: With the centuries-old history of archives and archives management, early China archives science came into being in 1930s, and the research pushed forward by archives enterprise has made great achievements since then. 1.1 Expanding research fields: Foundation

• Applied Microscopy
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• v.35 no.3
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• pp.135-152
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• 2005

Prefrontal cortex is a psychological and metaphysical cortex, which deals with feeling, memory, planning, attention, personality, etc. And it also integrates above-mentioned events with motor control and locomotor activities. Prefrontal cortex works as a highest CNS center, since the above mentioned functions are very important for one's successful life, and further more they are upgraded every moments through memory and learning. Many of these highest functions are supposed to be generated via forebrain basal nuclei (caudate nucleus, fundus striati nucleus, accumbens septi nucleus, septal nucleus, etc.). In this experiment, prefrontal efferent terminals within basal forebrain nuclei were ultrastructurally studied. Spraque Dawley rats, weighing $250{\sim}300g$ each, were anesthetized and their heads were fixed on the stereotaxic apparatus (experimental model, David Kopf Co.). Rats were incised their scalp, perforated a 3mm-wide hole on the right side of skull at the 11mm anterior point from the frontal O point (Ref. 13, Fig. 1), suctioned out the prefrontal cortex including cortex of the frontal pole, with suction instrument. Two days following the operations, small tissue blocks of basal forebrain nuclei were punched out, fixed in 1% glutaraldehyde-1% paraformaldehyde solution followed by 2% osmium tetroxide solutions. Ultrathin sections were stained with 1% borax-toluidin blue solution, and the stained sections were obserbed with an electron microscope. Degenerating axon terminals were found within all the basal forbrain nuclei. Numbers of degenerated terminals were largest in the caudate nucleus, next in order, in the fundus striati nucleus, in the accumbens septi nucleus, and the least in the septal nucleus. Only axospinous terminals were degenerated within the caudate nucleus and the fundus striati nucleus, and they showed the characters of striatal motor control system. Axodendritic and axospinous terminals were degenerated within the accumbens septi nucleus and the lateral septal nucleus, and they showed the characters of visceral limbic system. Prefrontal role in integrating the limbic system with the striatal system, en route basal forebrain nuclei, was discussed.

• 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.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1053-1066
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• 2020

Sulfur dioxide (SO₂) is primarily released through industrial, residential, and transportation activities, and creates secondary air pollutants through chemical reactions in the atmosphere. Long-term exposure to SO₂ can result in a negative effect on the human body causing respiratory or cardiovascular disease, which makes the effective and continuous monitoring of SO₂ crucial. In South Korea, SO₂ monitoring at ground stations has been performed, but this does not provide spatially continuous information of SO₂ concentrations. Thus, this research estimated spatially continuous ground-level SO₂ concentrations at 1 km resolution over South Korea through the synergistic use of satellite data and numerical models. A stacking ensemble approach, fusing multiple machine learning algorithms at two levels (i.e., base and meta), was adopted for ground-level SO₂ estimation using data from January 2015 to April 2019. Random forest and extreme gradient boosting were used as based models and multiple linear regression was adopted for the meta-model. The cross-validation results showed that the meta-model produced the improved performance by 25% compared to the base models, resulting in the correlation coefficient of 0.48 and root-mean-square-error of 0.0032 ppm. In addition, the temporal transferability of the approach was evaluated for one-year data which were not used in the model development. The spatial distribution of ground-level SO₂ concentrations based on the proposed model agreed with the general seasonality of SO₂ and the temporal patterns of emission sources.

• Management & Information Systems Review
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• v.36 no.3
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• pp.131-150
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• 2017

The purpose of this study is to observe the spread pattern of open source software (OSS) while establishing relations with surrounding actors during its operation period. In order to investigate the change pattern of participants in the OSS, we use a netnography on the basis of online data, which can trace the change patterns of the OSS depending on the passage of time. For this, the cases of three OSSs (e.g. jQuery, MooTools, and YUI), which are JavaScript frameworks, were compared, and the corresponding data were collected from the open application programming interface (API) of GitHub as well as blog and web searches. This research utilizes the translation process of the actor-network theory to categorize the stages of the change patterns on the OSS translation process. In the project commencement stage, we identified the type of three different OSS-related actors and defined associated relationships among them. The period, when a master commences a project at first, is refined through the course for the maintenance of source codes with persons concerned (i.e. project growth stage). Thereafter, the period when the users have gone through the observation and learning period by being exposed to promotion activities and codes usage respectively, and becoming to active participants, is regarded as the 'leap of participants' stage. Our results emphasize the importance of promotion processes in participants' selection of the OSS for participation and confirm the crowding-out effect that the rapid speed of OSS development retarded the emergence of participants.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.14 no.1
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• pp.12-25
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• 2003

Objectives：As increasing number of new antidepressants have been being introduced in clinical practice, pharmacological understanding has been broadened. These changes mandate new information and theories to be incorporated into the treatment process of children with depressive disorders. In light of newly coming knowledge, this review intended to recapitulate the characteristics of new antidepressants and to consider the pivotal issues to develope guidelines for the treatment of depression in childhood and adolescence. Methods：Searching the Pub-Med online database for the articles with the key words of 'new', 'antidepressants' and 'children' ninety-seven headings of review articles were obtained. The author selected the articles of pertinent subjects in terms of either treatment guideline or psychopharmacology of new antidepressants. When required, articles about the clinical effectiveness of individual antidepressants were separatedly searched. In addition, the safety information of new antidepressants was acquired by browsing the official sites of the United States Food and Drugs Administration and Department of Health and Human Services. Results：1) For the clinical course, treatment phase, and treatment outcome, the reviews or treatment guidelines adopted the information from adult treatment guidelines. 2) Systematic and critical reviews unambiguously concluded that selective serotonin reuptake inhibitors(SSRIs) excelled tricyclic antidepressants( TCAs) for both efficacy and side effect profiles, and were recommend for the first-line choice for the treatment of children with depressive disorders. 3) New antidepressants generally lacked treatment experiences and randomized controlled clinical trials. 4) SSRIs and other new antidepressants, when used together, might result in pharmacokinetic and/or pharmacodynamic drug-to-drug interaction. 5) The difference of the clinical effectiveness of antidepressants between children and adults should be addressed from developmental aspects, which required further evidence. Conclusion：Treatment guidelines for the pharmacological treatment of childhood and adolescence depression could be constructed on the basis of clinical trial findings and practical experiences. Treatment guidelines are to best serve as the frame of reference for a clinician to make reasonable decisions for a particular therapeutic situation. In order to fulfill this role, guidelines should be updated as soon as new research data become available.

• 대한공업교육학회지
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• v.44 no.2
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• pp.104-122
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• 2019

In the era of the fourth industrial revolution, maker education is drawing attention as a method of student-led education. At a time when interest in maker education is also growing in technology education, figuring out what stage of concern(SoC) a middle school technology teacher is critical to effective implementation. This study analyzed SoC in maker education by layer sampling among 400 middle school technology teachers using Concerns-based adoption model. SoC was then obtained by measuring the origin using the SoCQ and then presenting it as a SOCQ profile. Gender, training experience with two lower variables were analyzed using t verification, working cities, teaching experience with more than three lower variables were analyzed using one-way ANOVA. Studies showed that SoC in maker education of middle school technology teachers showed the most similar characteristics to that of non-users. The difference in concern depending on gender was that male teachers were more concerned in maker education than female teachers. The difference in concern depending on the working city was that teachers working in the township were more concerned in the maker education than teachers working in the large city, and the difference in concern depending on the teaching career was higher among teachers with middle experience than those with low and high experience. There was also a higher stage of concern in maker education than in teachers without training experience. Therefore, it is necessary to provide middle school technology teachers with an introduction to the maker education and various information, teaching, learning and evaluation data to enhance overall concern and to support the use and evaluation of the maker education in the classroom by providing various teacher training and consulting on the maker education in the future. Further, through further study, we should conduct study that analyzes both Stage of Concern, Level of Use and Innovation Configuration, to put in the effort for effective settlement of maker education.