• Journal of Biomedical Engineering Research
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• v.40 no.3
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• pp.105-115
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• 2019

Brain tumor surgery may be difficult, but it is also incredibly important. The technological improvements for traditional brain tumor surgeries have always been a focus to improve the precision of surgery and release the potential of the technology in this important area of the body. The need for precision during brain tumor surgery has led to an increase in Robotic-assisted surgeries (RAS). One of the challenges to the widespread acceptance of RAS in the neurosurgery is to recognize invisible tumor accurately. Therefore, it is important to detect brain tumor size and location because surgeon tries to remove as much tumor as possible. In this paper, we proposed brain tumor detection procedures for MRI (Magnetic Resonance Imaging) system. A method of automatic brain tumor detection is needed to accurately target the location of the lesion during brain tumor surgery and to report the location and size of the lesion. In the qualitative assessment, the proposed method showed better results than those obtained with other brain tumor detection methods. Comparisons among all assessment criteria indicated that the proposed method was significantly superior to the threshold method with respect to all assessment criteria. The proposed method was effective for detecting brain tumor.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.375-388
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• 2021

This paper aims to help prevent the spread of COVID-19 by analyzing confirmed cases of COVID-19 in Daejeon. A high volume of visitors, downtown areas, and psychological fatigue with prolonged social distancing were considered as risk factors associated with the spread of COVID-19. We considered the weekly confirmed cases in each administrative district as a response variable. Explanatory variables were the number of passengers getting off at a bus station in each administrative district and the elapsed time since the Korean government had imposed distancing in daily life. We employed a mixed-effects zero-inflated Poisson regression model because the number of cases was repeatedly measured with excess zero-count data. We conducted k-means clustering to identify three groups of administrative districts having different characteristics in terms of the number of bars, the population size, and the distance to the closest college. Considering that the number of confirmed cases might vary depending on districts' characteristics, the clustering information was incorporated as a categorical explanatory variable. We found that Covid-19 was more prevalent as population size increased and a district is downtown. As the number of passengers getting off at a downtown district increased, the confirmed cases significantly increased.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.354-360
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• 2021

People hope to live a healthy and happy life achieving satisfaction by striking a good work-life balance. Therefore, there is a growing interest in well-aging which means living happily to a healthy old age without worry. This study identified important factors related to well-aging by analyzing news articles published in Korea. Using Python-based web crawling, 1,199 articles were collected on the news service of portal site Daum till November 2020, and 374 articles were selected which matched the subject of the study. The frequency analysis results of text mining showed keywords such as 'elderly', 'health', 'skin', 'well-aging', 'product', 'person', 'aging', 'female', 'domestic' and 'retirement' as important keywords. Besides, a social network analysis with 45 important keywords revealed strong connections in the order of 'skin-wrinkle', 'skin-aging' and 'old-health'. The result of the CONCOR analysis showed that 45 main keywords were composed of eight clusters of 'life and happiness', 'disease and death', 'nutrition and exercise', 'healing', 'health', and 'elderly services'.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.285-301
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• 2023

Due to the increasing demand and importance of non-face-to-face education, open online learning platforms are getting interests both domestically and internationally. These platforms exhibit different characteristics from online courses by universities and other educational institutions. In particular, students engaged in these platforms can receive more learner autonomy, and the development of tools to assist learning is required. From the past, researchers have attempted to utilize process mining to understand realistic study behaviors and derive learning patterns. However, it has a deficiency to employ it to the open online learning platforms. Moreover, existing research has primarily focused on the process model perspective, including process model discovery, but lacks a method for the process pattern and instance perspectives. In this study, we propose a method to identify learning patterns within an open online learning platform using process mining techniques. To achieve this, we suggest three different viewpoints, e.g., model-level, variant-level, and instance-level, to comprehend the learning patterns, and various techniques are employed, such as process discovery, conformance checking, autoencoder-based clustering, and predictive approaches. To validate this method, we collected a learning log of machine learning-related courses on a domestic open education platform. The results unveiled a spaghetti-like process model that can be differentiated into a standard learning pattern and three abnormal patterns. Furthermore, as a result of deriving a pattern classification model, our model achieved a high accuracy of 0.86 when predicting the pattern of instances based on the initial 30% of the entire flow. This study contributes to systematically analyze learners' patterns using process mining.

• Korean Journal of Ecology and Environment
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• v.56 no.2
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• pp.161-171
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• 2023

The medium-large cladoceran species Simocephalus spp. predominantly occur in habitats with developed aquatic vegetation. Accordingly, due to Simocephalus' high contribution to zooplankton community biomass in the lake's littoral zone and wetland habitats, estimating their biomass is important to understand the matter cycling based on biological interactions within the aquatic food web. In this study, we reviewed the length-weight regression equations used previously to estimate Simocephalus biomass, directly measured S. serrulatus' body specification (length, width and area) and their biomass(dry weight) using instruments such as a microscopic digital camera and a microscale, and performed regression analysis between each other. When S. serrulatus biomass was estimated using the equation (Kawabata and Urabe, 1998) presented in 『Biomonitoring Survey and Assessment Manual』, Korea, errors between estimates and measures were relatively large compared to the S. serrulatus species-specific biomass estimate equation developed by Lemke and Benke (2003). In addition, both equations showed not only increasing trends in error (estimate-measure) with increasing S. serrulatus' body length, but also in error variance among similar-sized individuals. The results of regression analysis with dry weight by body specifications indicated that the most appropriate equation for estimating the biomass of S. serrulatus was derived from the width-dry weight exponential regression equation (R²=0.9555). The review and development study of such species-specific biomass estimation equations for zooplankton can be used as a tool to understand their role and function in aquatic ecosystem food webs.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.25 no.4
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• pp.97-105
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• 2020

The genus Navicula, with its notably high species diversity, is one of the most important genera of the diatom assemblages of the tidal flats. In the present study, Navicula spartinetensis was firstly observed from Yellow Sea including both of Korean and Chinese tidal flats. Morphological description was also made based on the LM and SEM observation. Samples were collected from four locations in Korea, two in October 2006, one in July 2007, and one in July 2018, and seven location in China from June to July 2018. N. spartinetensis was firstly described by Sullvian & Reimer in 1975; Cells are lanceolate with narrow valve faces, 20-30 ㎛ long, 5-6 ㎛ wide, and the density of striae is 12-13 in 10 ㎛, and the terminal raphe ending curved in the same direction. N. spartinetensis has been previously reported from Europe and South America, and the present study has expanded its distribution to the Northeast Asia. In conclusion, the diversity of Korean marine benthic diatoms is still underestimated thus extensive further study of diatom taxonomy is needed.

• Journal of Life Science
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• v.33 no.7
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• pp.538-548
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• 2023

The research has been conducted on the isolation of antimicrobial compounds from plant natural extracts and their potential application in oral health care products. This study aimed to investigate the antimicrobial mechanism by analyzing the changes in gene expression of Streptococcus mutans, a major oral pathogen, in response to complex compounds extracted from Aralia continentalis and Arctii Semen using organic solvents. Transcriptome analysis (RNA-seq) revealed that both natural extracts commonly upregulated or downregulated the expression of various genes associated with different metabolic and physiological activities. Three genes (SMU_1584c, SMU_2133c, SMU_921), particularly SMU_921 (rcrR), known as a transcription activator of two sugar phosphotransferase systems (PTS) involved in sugar transport and biofilm formation, exhibited consistent high expression levels. Additionally, component analysis of the A. continentalis extract was performed to compare its effects on gene expression changes with the A. Semen extract, and two active compounds were identified through gas chromatography-mass spectrometry (GC-MS) analysis of the active fraction. The n-hexane fraction (ACEH) from the A. continentalis extract exhibited antibacterial specificity against S. mutans, leading to a significant reduction in the viable cell counts of Streptococcus sanguinis and Streptococcus gordonii among the tested multi-species bacterial communities. These findings suggest the broad-spectrum antibacterial activity of the A. continentalis extract and provide essential foundational data for the development of customized antimicrobial materials by elucidating the antibacterial mechanism of the identified active compounds.

• Asia Marketing Journal
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• v.13 no.4
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• pp.139-158
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• 2012

This study was intended to classify tie-in promotion tools by the criteria of benefit-fit between consumer and tie-in promotions. Tie-in promotion tools include tie-in price reductions, tie-in coupons, tie-in memberships, tie-in contests, tie-in sweepstakes, tangible and intangible tie-in premiums, tie-in payment terms, tie-in samples, tie-in events(culture event, charity event, experience event) and tie-in fund·rebates. The fit between consumer pursuit benefit and tie-in promotion supplying benefit was used as a classification criteria on the basis of Lee et al.'s study in 2011. For the experiment, one stimuli and 12 scenarioes were developed. 100 pieces of data were obtained for each scenario. As a result, benefit fit was subsequently divided into two factors: hedonic-benefit fit and utilitarian-benefit fit. Tie-in promotion tools were then classified into 4 types: high hedonic benefit-added, high utilitarian benefit-added, low hedonic benefit-added, and low utilitarian benefit-added. In previous research, tie-in promotion type was mainly divided by the evaluative criteria on company's viewpoint such as horizontal/vertical or intra-company/ inter-company, which reflects mutual exclusiveness between two criteria. Whereas, in this study, tie-in promotion type was divided by evaluative criteria on consumer's viewpoint such as hedonic- benefit fit/utilitarian-benefit fit. The classifications in this study practically reflect benefit-added of tie-in promotion type superadded one benefit coexisting two benefits.

• Journal of the Economic Geographical Society of Korea
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• v.27 no.1
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• pp.38-52
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• 2024

Digital transformation industry contributes to the improvement of productivity in overall industrial production, the smart green industry for carbon neutrality and sustainable growth is growing as a future industry. The purpose of this paper is to explore the status and role of the industry in the future industry innovation ecosystem through the analysis of the growth drivers and location pattern changes of the smart green industry. The industry is on the rise in both metropolitan and non-metropolitan areas, and the growth of the industry can be seen in non-metropolitan and non-urban areas. In particular, due to the smart green industrial complex pilot project, the creation of Gwangju Jeonnam Innovation City, and the promotion of new and renewable energy policies, the emergence of core aggregation areas (HH type) in the coastal areas of Honam and Chungcheongnam-do, and the formation of isolated centers (HL type) in the Gyeongsang region, new and renewable energy production companies are being accumulated in non-metropolitan areas. Therefore, the smart green industry is expected to promote the formation of various specialized spokes in non-urban areas in the future industrial innovation ecosystem that forms a multipolar hub-spoke network structure, where policy factors are the triggers for growth.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.