• Spatial Information Research
• /
• v.6 no.2
• /
• pp.247-256
• /
• 1998

The objective of this research was to develop habitat suitability models for wild boar (Sus Scrafa) in Mt. Sulak National Park and Mt. Jumbong Natural Forest Reserve. The study area is covered-with climax temperate hardwood forests ot'mainly Mongolian oak ($\textit{Quercus mongolica}$), and has diverse wildlife species including wild boars. Three suitability models - summer, fall, and annual models - were developed. These models were based on slope, aspect, forest types, forest year classes, distance from streams and trails. Habitat data collected through telemetry were used for the models. The accuracy of the models was tested by comparing observed traces of wild boar in Mt. Jurnbong, and most traces were on suitable areas on the suitability maps.

• Water for future
• /
• v.23 no.1
• /
• pp.99-107
• /
• 1990

The statistical analysis based on the past hydrologic data required to set up the water resources development plan and design the hydraulic structres rationally. Because hydrologic events have random factors implied, the sotchastic analysis is necessary. In this paper, same order of stochastic models of monthly runoff data(multivariate AR(1) and AR(2) models, univariate AR(1) and AR(2) models) are applied to compare the statistical characteristics. The other purpose of this paper is to compare the monthly series, which is generated by univariate and multivariate models. By comparing and estimating of each simulated series, it is known that the multivariate models, including the time and spatial colinearity, are better in prediction than univariate models in the analysis of monthly flow at south Han river basin.

• Bulletin of the Korean Chemical Society
• /
• v.17 no.8
• /
• pp.735-744
• /
• 1996

We present results of molecular dynamic (MD) simulations for the thermodynamic and structural properties of liquid n-alkanes, from n-butane to n-heptadecane, using three different models Ⅰ-Ⅲ. Two of the three classes of models are collapsed atomic models while the third class is an atomistically detailed model. Model Ⅰ is the original Ryckaert and Bellemans' collapsed atomic model [Discuss. Faraday Soc. 1978, 66, 95] and model Ⅱ is the expanded collapsed model which includes C-C bond stretching and C-C-C bond angle bending potentials in addition to Lennard-Jones and torsional potentials of model Ⅰ. In model Ⅲ all the carbon and hydrogen atoms in the monomeric units are represented explicitly for the alkane molecules. Excellent agreement of the results of our MD simulations of model Ⅰ for n-butane with those of Edberg et al.[J. Chem. Phys. 1986, 84, 6933], who used a different algorithm confirms the validity of our algorithms for MD simulations of model Ⅱ for 14 liquid n-alkanes and of models Ⅰ and Ⅲ for liquid n-butane, n-decane, and n-heptadecane. The thermodynamic and structural properties of models Ⅰ and Ⅱ are very similar to each other and the thermodynamic properties of model Ⅲ for the three n-alkanes are not much different from those of models Ⅰ and Ⅱ. However, the structural properties of model Ⅲ are very different from those of models Ⅰ and Ⅱ as observed by comparing the radial distribution functions, the average end-to-end distances and the root-mean-squared radii of gyrations.

• Journal of the Korean Society of Safety
• /
• v.34 no.4
• /
• pp.59-67
• /
• 2019

Since it is really hard to construct process models based on prior process knowledges, various statistical approaches have been employed to build fault diagnosis models. However, the crucial drawback of these approaches is that the solutions may vary according to the fault magnitude, even if the same fault occurs. In this study, the parameter monitoring approach is suggested. When a fault occurs in a chemical process, this leads to trigger the change of a process model and the monitoring parameters of process models is able to provide the efficient fault diagnosis model. A few important variables are selected and their predictive models are constructed by partial least square (PLS) method. The Euclidean norms of parameters of PLS models are estimated and a fault diagnosis can be performed as comparing with parameters of PLS models based on normal operational conditions. To improve the monitoring performance, cumulative summation (CUSUM) control chart is employed and the changes of model parameters are recorded to identify the type of an unknown fault. To verify the efficacy of the proposed model, Tennessee Eastman (TE) process is tested and this model can be easily applied to other complex processes.

• Journal of Climate Change Research
• /
• v.6 no.4
• /
• pp.389-397
• /
• 2015

The changes in the teleconnection associated with El Nin?o-Southern Oscillation (ENSO) over the East Asia and North Pacific under greenhouse warming are analyzed herein by comparing the Historical run (1970/1971~1999/2000) and the Representative Concentration Pathway (RCP) 4.5 run with 31 climate models, participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5). It is found that CMIP5 models have diverse systematic errors in simulating the ENSO teleconnection pattern from model to model. Therefore, we select 21 models based on the models' performance in simulating teleconnection pattern in the present climate. It is shown that CMIP5 models tend to project an overall weaker teleconnection pattern associated with ENSO over East Asia in the future climate than that in the present climate. It can be also noted that the cyclonic flow over the North Pacific is weakened and shifted eastward. However, uncertainties for the ENSO teleconnection changes still exist, suggesting that much consistent agreements on this future teleconnections associated with ENSO should be taken in a further study.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.39 no.5
• /
• pp.297-311
• /
• 2021

Around the world, there is an increasing interest in Digital Twin cities. Although geospatial data is critical for building a digital twin city, currently-established spatial data cannot be used directly for its implementation. Integration of geospatial data is vital in order to construct and simulate the virtual space. Existing studies for data integration have focused on data transformation. The conversion method is fundamental and convenient, but the information loss during this process remains a limitation. With this, standardization of the data model is an approach to solve the integration problem while hurdling conversion limitations. However, the standardization within indoor space data models is still insufficient compared to 3D building and city models. Therefore, in this study, we present a comparative analysis of data models commonly used in indoor space modeling as a basis for establishing a generic indoor space feature model. By comparing five models of IFC (Industry Foundation Classes), CityGML (City Geographic Markup Language), AIIM (ArcGIS Indoors Information Model), IMDF (Indoor Mapping Data Format), and OmniClass, we identify essential elements for modeling indoor space and the feature classes commonly included in the models. The proposed generic model can serve as a basis for developing further indoor feature models through specifying minimum required structure and feature classes.

• Journal of Korean Dental Science
• /
• v.15 no.2
• /
• pp.132-140
• /
• 2022

Purpose: Dental diagnostic records derived from study models are a popular method of obtaining reliable and vital information. Conventional plaster models are the most common method, however, they are being gradually replaced by digital impressions as technology advances. Moreover, three-dimensional dental models are becoming increasingly common in dental offices, and various methods are available for obtaining them. This study aimed to evaluate the accuracy of the measurement of dental digital models by comparing them with conventional plaster and to determine their clinical validity. Materials and Methods: The study was conducted on 16 patients' maxillary and mandibular dental models. Tooth size (TS), intercanine width (ICW), intermolar width (IMW), and Bolton analysis were taken by using a digital caliper on a plaster model obtained from each patient, while intraoral scans were manually measured using two digital analysis software. A one-way analysis of variance test was used to compare the dental measurements of the three methods. Result: No significant differences were reported between the TS, the ICW and IMW, and the Bolton analysis through the conventional and two digital groups. Conclusion: Measurements of TS, arch width, and Bolton analysis produced from digital models have shown acceptable clinical validity. No significant differences were observed between the three dental measurement techniques.

• Health Policy and Management
• /
• v.26 no.4
• /
• pp.359-372
• /
• 2016

Background: The purpose of this study was to develop risk-adjustment models for acute stroke mortality that were based on data from Health Insurance Review and Assessment Service (HIRA) dataset and to evaluate the validity of these models for comparing hospital performance. Methods: We identified prognostic factors of acute stroke mortality through literature review. On the basis of the avaliable data, the following factors was included in risk adjustment models: age, sex, stroke subtype, stroke severity, and comorbid conditions. Survey data in 2014 was used for development and 2012 dataset was analysed for validation. Prediction models of acute stroke mortality by stroke type were developed using logistic regression. Model performance was evaluated using C-statistics, $R^2$ values, and Hosmer-Lemeshow goodness-of-fit statistics. Results: We excluded some of the clinical factors such as mental status, vital sign, and lab finding from risk adjustment model because there is no avaliable data. The ischemic stroke model with age, sex, and stroke severity (categorical) showed good performance (C-statistic=0.881, Hosmer-Lemeshow test p=0.371). The hemorrhagic stroke model with age, sex, stroke subtype, and stroke severity (categorical) also showed good performance (C-statistic=0.867, Hosmer-Lemeshow test p=0.850). Conclusion: Among risk adjustment models we recommend the model including age, sex, stroke severity, and stroke subtype for HIRA assessment. However, this model may be inappropriate for comparing hospital performance due to several methodological weaknesses such as lack of clinical information, variations across hospitals in the coding of comorbidities, inability to discriminate between comorbidity and complication, missing of stroke severity, and small case number of hospitals. Therefore, further studies are needed to enhance the validity of the risk adjustment model of acute stroke mortality.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.14 no.1
• /
• pp.85-93
• /
• 2015

Previous studies have estimated crash prediction models with the fixed effect model which assumes the fixed value of coefficients without considering characteristics of each intersections. However the fixed effect model would estimate under estimation of the standard error resulted in over estimation of t-value. In order to overcome these shortcomings, the random effect model can be used with considering heterogeneity of AADT, geometric information and unobserved factors. In this study, data collections from 89 intersections in Daejeon and estimates of crash prediction models were conducted using the random and fixed effect negative binomial regression model for comparison and analysis of two models. As a result of model estimates, AADT, speed limits, number of lanes, exclusive right turn pockets and front traffic signal were found to be significant. For comparing statistical significance of two models, the random effect model could be better statistical significance with -1537.802 of log-likelihood at convergence comparing with -1691.327 for the fixed effect model. Also likelihood ration value was computed as 0.279 for the random effect model and 0.207 for the fixed effect model. This mean that the random effect model can be improved for statistical significance of models comparing with the fixed effect model.

• Geomechanics and Engineering
• /
• v.25 no.1
• /
• pp.1-16
• /
• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.