• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.965-976
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• 2015

Determining relevant variables for a regression model is important in regression analysis. Recently, a variable selection methods using a penalized likelihood with various penalty functions (e.g. LASSO and SCAD) have been widely studied in simple statistical models such as linear models and generalized linear models. The advantage of these methods is that they select important variables and estimate regression coefficients, simultaneously; therefore, they delete insignificant variables by estimating their coefficients as zero. We study how to select proper variables based on penalized hierarchical likelihood (HL) in semi-parametric frailty models that allow three penalty functions, LASSO, SCAD and HL. For the variable selection we develop a new function in the "frailtyHL" R package. Our methods are illustrated with breast cancer survival data from the Medical Center at Chonnam National University in Korea. We compare the results from three variable-selection methods and discuss advantages and disadvantages.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.521-529
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• 2020

Forest fires are one of the most important environmental risks that have adverse effects on many aspects of life, such as the economy, environment, and health. The early detection, quick prediction, and rapid response of forest fires can play an essential role in saving property and life from forest fire risks. For the rapid discovery of forest fires, there is a method using meteorological data obtained from local sensors installed in each area by the Meteorological Agency. Meteorological conditions (e.g., temperature, wind) influence forest fires. This study evaluated a Data Mining (DM) approach to predict the burned area of forest fires. Five DM models, e.g., Stochastic Gradient Descent (SGD), Support Vector Machines (SVM), Decision Tree (DT), Random Forests (RF), and Deep Neural Network (DNN), and four feature selection setups (using spatial, temporal, and weather attributes), were tested on recent real-world data collected from Gyeonggi-do area over the last five years. As a result of the experiment, a DNN model using only meteorological data showed the best performance. The proposed model was more effective in predicting the burned area of small forest fires, which are more frequent. This knowledge derived from the proposed prediction model is particularly useful for improving firefighting resource management.

• Journal of the Korean Geographical Society
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• v.50 no.3
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• pp.277-287
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• 2015

This paper addresses limitations of land-change modeling application in the context of REDD (Reducing Emissions from Deforestation and forest Degradation). REDD is an international conservation policy that aims to protect forests via carbon credit generation and trading. In REDD, carbon credits are generated only if there is measurable quantied carbon sequestration activities that are additional to business-as-usual (BAU). A "reference level" is defined as simulated baseline carbon emissions for the future under a BAU scenario, and predictive land-change modeling plays an important role in constructing reference levels. It is tested in this research how predictive accuracies of two land-change models, namely Geographic Emission Benchmark (GEB) and GEOMOD, vary with respect to different spatial scales: Xishuangbanna prefecture and Yunnan province. The accuracies are measured by Figure of Merit. In this Chinese case study, it turns out that GEB's better performance is mainly due to quantity (e.g., how many hectares of forest will be converted to agricultural land?) rather than spatial allocation (e.g., where will the conversion happen?). As both quantity and allocation are crucial in REDD reference level setting it appears to be fundamental to systematically analyze accuracies of quantity and allocation independently in pursuit of accurate reference levels.

• The Journal of Small Business Innovation
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• v.20 no.2
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• pp.51-66
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• 2017

Online and wireless communications have dramatically changed the contents industry marketplace. Content transactions are now instantaneous as distribution channels move from the 'mart' to smart platforms, creating opportunities for content creators large and small. Yet with opportunity comes the threat of imbalance in the industry ecosystem. In order to ensure the health and diversity of an industry that relies so heavily on the welfare of small creative enterprises, it is essential to establish rules for the fair transaction of content rights. Several structural forces may work against such rules: first, the industry consists of a large number of small distributor intermediary businesses (e.g. major publishers); second, end distributors (e. g. platforms) maintain a superior, monopsony position; and third, economic valuation of content is difficult. In terms of acquisition business model, rights transactions can be classified into three general models: (1) license model, (2) original acquisition model, and (3) monopsony model. This study explores the publishing industry in detail, considering key statutes and their operation across the models. From analysis of Korea and the US statutes and case law, and decisions of the Fair Trade Commission (FTC) of Korea, we offer evaluation criteria for discerning between fair and unfair content rights transactions. We further recommend industry practice that may enhance the likelihood for fair content rights transactions, and thus a thriving publishing ecosystem.

• Korean Chemical Engineering Research
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• v.55 no.6
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• pp.807-815
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• 2017

This study investigated the removal efficiency of residual pentane from paclitaxel according to the drying temperature in the case of rotary evaporation, and performed a kinetic and thermodynamic analysis of the drying process. At all the temperatures (25, 30, 35, 40, and $45^{\circ}C$), a large amount of the residual solvent was initially removed during the drying, and the drying efficiency increased when increasing the drying temperature. Five drying models (Newton, Page, modified Page, Henderson and Pabis, Geometric) were then used for the kinetic analysis, where the Henderson and Pabis model showed the highest coefficient of determination ($r^2$) and lowest root mean square deviation (RMSD), indicating that these models were the most suitable. Furthermore, in the thermodynamic analysis of the rotary evaporation, the activation energy ($E_a$) was 4.9815 kJ/mol and the standard Gibbs free energy change (${\Delta}G^0$) was negative, whereas the standard enthalpy change (${\Delta}H^0$) and standard entropy change (${\Delta}S^0$) were both positive, indicating that the drying process was spontaneous, endothermic, and irreversible.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1295-1303
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• 2022

As the role of water distribution networks (WDNs) becomes more important, identifying abnormal events (e.g., pipe burst) rapidly and accurately is required. Since existing approaches such as field equipment-based detection methods have several limitations, model-based methods (e.g., machine learning based detection model) that identify abnormal events using hydraulic simulation models have been developed. However, no previous work has examined the impact of data uncertainties on the results. Thus, this study compares the effects of measurement error-induced pressure data uncertainty in WDNs. An artificial neural network (ANN) is used to predict nodal pressures and measurement errors are generated by using cumulative density function inverse sampling method that follows Gaussian distribution. Total of nine conditions (3 input datasets × 3 output datasets) are considered in the ANN model to investigate the impact of measurement error size on the prediction results. The results have shown that higher data uncertainty decreased ANN model's prediction accuracy. Also, the measurement error of output data had more impact on the model performance than input data that for a same measurement error size on the input and output data, the prediction accuracy was 72.25% and 38.61%, respectively. Thus, to increase ANN models prediction performance, reducing the magnitude of measurement errors of the output pressure node is considered to be more important than input node.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.13
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• pp.5317-5324
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• 2014

Polymorphisms in the endothelial nitric oxide synthase (eNOS) gene may influence the risk of cancer, but the results are still debatable. Therefore, we performed a systematic review to provide a more complete picture and conducted a meta-analysis to derive a precise estimation. We searched PubMed, EMBASE, EBSCO, Google Scholar and China National Knowledge Infrastructure (CNKI) databases until April 2014 to identify eligible studies. Thirty-one studies with cancer patients and controls were included in the meta-analysis. Overall, the polled analysis revealed that the T-786C polymorphism was significantly associated with increased cancer risk under multiple genetic models (C vs T: OR=1.135, 95%CI=1.048-1.228; CC vs TT: OR=1.278, 95%CI=1.045-1.562; TC vsTT: OR=1.136, 95%CI=1.023-1.261; CC+TC vs TT: OR=1.159, 95%CI=1.047-1.281; CC vs TC+TT: OR=1.204, 95%CI= 1.003-1.447). G894T was associated with significant risk for females (TT vs GG: OR=1.414, 95%CI=1.056-1.892; TT vs GT+GG: OR=1.356, 95%CI=1.108-1.661) and for breast cancer (T vs G: OR=1.097, 95%CI=1.001-1.203; TT vs GG: OR=1.346, 95%CI=1.012-1.789; TT vs GT+GG: OR=1.269, 95%CI=1.028-1.566). Increased susceptibility was revealed for prostate cancer with 4a/b (ba vs bb: OR=1.338, 95%CI=1.013-1.768; aa+ba vs bb: OR=1.474, 95%CI=1.002-2.170). This meta-analysis indicated that the eNOS T-786C polymorphism is associated with elevated cancer risk; the G894T polymorphism contributes to susceptibility to breast cancer and cancer generally in females; and the 4a/b polymorphism may be associated with prostate cancer risk.

• Journal of the Korean Society for Library and Information Science
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• v.51 no.2
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• pp.49-71
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• 2017

FRBR Library Reference Model is a consolidated model of FR Family Model - FRBR, FRAD, FRSAD developed by IFLA in early 2016. This means the creation of upper model for the consistency of FR individual models. It can be used for the expanding of the discussions on FRBR model in Korea that have been mainly discussed only for the first group of FRBR model. In addition, it can be an opportunity to apply the whole FR Family model to domestic literature in a comprehensive manner. In this study, we analyze the draft of FRBR LRM and apply the model to the Korean classic materials. There are various work, expression, and manifestation related to a work of korean classic material, so it is good to represent the structure of bibliographic information using FR Family model. We built on our experiment using $Prot{\acute{e}}g{\acute{e}}$ to design korean classic materials' ontology modeling, which propose a new orientation of korean classic materials' organizing based on FRBR LRM. As a result, we find out the applicability of the FRBR LRM model on upcoming bibliographical data control environment.

• Earthquakes and Structures
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• v.15 no.6
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• pp.655-665
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• 2018

Shield tunnels are widely used throughout the world. However, their seismic performance has not been well studied. This paper focuses on the seismic response of a large scale model tunnel in compacted sand. A 9.3 m long, 3.7 m wide and 2.5 m high rigid box was filled with sand so as to simulate the sandy soil surrounding the tunnel. The setup was excited on a large-scale shake table. The model tunnel used was a 1:8 scaled model with a cross-sectional diameter of 900 mm. The effective shock absorbing layer (SAL) on the seismic response of the model tunnel was also investigated. The thickness of the tunnel lining is 60 mm. The earthquake motion recorded from the Kobe earthquake waves was used. The ground motions were scaled to have the same peak accelerations. A total of three peak accelerations were considered (i.e., 0.1 g, 0.2 g and 0.4 g). During the tests, the strain, acceleration and soil pressure on the surface of the tunnel were measured. In order to investigate the effect of shock absorbing layer on the dynamic response of the sand- tunnel system, two tunnel models were set up, one with and one without the shock absorbing layer of foam board were used. The results shows the longitudinal direction acceleration of the model tunnel with a shock absorbing layer were lower than those of model tunnel without the shock absorbing layer, Which indicates that the shock absorbing layer has a beneficial effect on the acceleration reduction. In addition, the shock absorbing layer has influence on the hoop strain and earth pressure of the model tunnel, this the effect of shock absorbing layer to the model tunnel will be discussed in the paper.

• Journal of Periodontal and Implant Science
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• v.53 no.4
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• pp.295-305
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• 2023

Purpose: Various methods have been proposed to achieve the nearly complete decontamination of the surface of implants affected by peri-implantitis. We investigated the in vitro debridement efficiency of multiple decontamination methods (Gracey curettes [GC], glycine air-polishing [G-Air], erythritol air-polishing [E-Air] and titanium brushes [TiB]) using a novel spectrophotometric ink-model in 3 different bone defect settings (30°, 60°, and 90°). Methods: Forty-five dental implants were stained with indelible ink and mounted in resin models, which simulated standardised peri-implantitis defects with different bone defect angulations (30°, 60°, and 90°). After each run of instrumentation, the implants were removed from the resin model, and the ink was dissolved in ethanol (97%). A spectrophotometric analysis was performed to detect colour remnants in order to measure the cumulative uncleaned surface area of the implants. Scanning electron microscopy images were taken to assess micromorphological surface changes. Results: Generally, the 60° bone defects were the easiest to debride, and the 30° defects were the most difficult (ink absorption peak: 0.26±0.04 for 60° defects; 0.32±0.06 for 30° defects; 0.27±0.04 for 90° defects). The most effective debridement method was TiB, independently of the bone defect type (TiB vs. GC: P<0.0001; TiB vs. G-Air: P=0.0017; TiB vs. GE-Air: P=0.0007). GE-Air appeared to be the least efficient method for biofilm debridement. Conclusions: T-brushes seem to be a promising decontamination method compared to the other techniques, whereas G-Air was less aggressive on the implant surface. The use of a spectrophotometric model was shown to be a novel but promising assessment method for in vitro ink studies.