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Depending on the different types of raw materials used to produce hanji, a Korean traditional handmade paper, there can be significant differences in the durability and mechanical properties of the final product. In this study, near-infrared spectroscopy (NIR) combined with multivariate statistical methods were used to confirm the classification possibility of hanji based on the various type of raw materials. The hanji papers were prepared from paper mulberry trees, cooking agents, and mucilage. Altogether, a total of 60 hanji spectra were collected by NIR. Then, the 60 spectra were grouped into four categories: the control, paper mulberry, cooking agent, and mucilage type based on each of the types of raw materials contained in the hanji. Three different classification algorithms – partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), and random forest (RF) – were used to classify the hanji types. The best hanji material classification performance was obtained when the hanji samples were classified according to paper mulberry type, wherein the prediction accuracies of PLS-DA, SVM, and RF were 100%, 100%, and 98%, respectively. These results suggested that NIR in combination with multivariate statistical methods can be used for hanji material classification.

Aim To examine the effect of oral diabetes medication on the risk of dementia in an elderly cohort with type 2 diabetes. Methods This was a population-based cohort study using the Korean National Health Insurance claims data from 2002 to 2013. Elderly subjects (60 years of age or older) with and without type 2 diabetes were included; patients with new-onset type 2 diabetes were further divided into the oral diabetes medication group and no-medication group. Results Among 278,290 patients with type 2 diabetes, 56,587 developed dementia (20.3%) over 11 years of follow-up. Type 2 diabetes was associated with a 1.69-fold increased risk of dementia (95% CI 1.66–1.72). Among patients with newly diagnosed type 2 diabetes, the risk of dementia was lower in the oral diabetes medication group than in the no-medication group (adjusted hazard ratio [aHR], 0.79; 95% CI 0.77–0.81). Lower risk of dementia was particularly noticeable in all of the combination therapy groups and especially lower in the combination therapy group treated with dipeptidyl peptidase 4 inhibitor (aHR 0.48, 95% CI 0.45–0.51). Conclusion Overall, the use of oral diabetes medication in type 2 diabetes patients significantly decreased the risk of dementia.

Artificial intelligence (AI)-based diagnostic tools have been accepted in ophthalmology. The use of retinal images, such as fundus photographs, is a promising approach for the development of AI-based diagnostic platforms. Retinal pathologies usually occur in a broad spectrum of eye diseases, including neovascular or dry age-related macular degeneration, epiretinal membrane, rhegmatogenous retinal detachment, retinitis pigmentosa, macular hole, retinal vein occlusions, and diabetic retinopathy. Here, we report a fundus image-based AI model for differential diagnosis of retinal diseases. We classified retinal images with three convolutional neural network models: ResNet50, VGG19, and Inception v3. Furthermore, the performance of several dense (fully connected) layers was compared. The prediction accuracy for diagnosis of nine classes of eight retinal diseases and normal control was 87.42% in the ResNet50 model, which added a dense layer with 128 nodes. Furthermore, our AI tool augments ophthalmologist’s performance in the diagnosis of retinal disease. These results suggested that the fundus image-based AI tool is applicable for the medical diagnosis process of retinal diseases.

Honeybees play a crucial role in the agriculture industry because they pollinate approximately 75% of all flowering crops. However, every year, the number of honeybees continues to decrease. Consequently, numerous researchers in various fields have persistently attempted to solve this problem. Acoustic scene classification, using sounds recorded from beehives, is an approach that can be applied to detect changes inside beehives. This method can be used to determine intervals that threaten a beehive. Currently, studies on sound analysis, using deep learning algorithms integrated with various data preprocessing methods that extract features from sound signals, continue to be conducted. However, there is little insight into how deep learning algorithms recognize audio scenes, as demonstrated by studies on image recognition. Therefore, in this study, we used a mel spectrogram, mel-frequency cepstral coefficients (MFCCs), and a constant-Q transform to compare the performance of conventional machine learning models to that of convolutional neural network (CNN) models. We used the support vector machine, random forest, extreme gradient boosting, shallow CNN, and VGG-13 models. Using gradient-weighted class activation mapping (Grad-CAM), we conducted an analysis to determine how the best-performing CNN model recognized audio scenes. The results showed that the VGG-13 model, using MFCCs as input data, demonstrated the best accuracy (91.93%). Additionally, based on the precision, recall, and F1-score for each class, we established that sounds other than those from bees were effectively recognized. Further, we conducted an analysis to determine the MFCCs that are important for classification through the visualizations obtained by applying Grad-CAM to the VGG-13 model. We believe that our findings can be used to develop a monitoring system that can consistently detect abnormal conditions in beehives early by classifying the sounds inside beehives.

Many studies have attempted to predict chlorophyll-a concentrations using multiple regression models and validating them with a hold-out technique. In this study commonly used machine learning models, such as Support Vector Regression, Bagging, Random Forest, Extreme Gradient Boosting (XGBoost), Recurrent Neural Network (RNN), and Long&ndash, Short-Term Memory (LSTM), are used to build a new model to predict chlorophyll-a concentrations in the Nakdong River, Korea. We employed 1&ndash, step ahead recursive prediction to reflect the characteristics of the time series data. In order to increase the prediction accuracy, the model construction was based on forward variable selection. The fitted models were validated by means of cumulative learning and rolling window learning, as opposed to the hold&ndash, out technique. The best results were obtained when the chlorophyll-a concentration was predicted by combining the RNN model with the rolling window learning method. The results suggest that the selection of explanatory variables and 1&ndash, step ahead recursive prediction in the machine learning model are important processes for improving its prediction performance.

This study aims to compare the classification performance of statistical models on highly imbalanced kidney data. The health examination cohort database provided by the National Health Insurance Service in Korea is utilized to build models with various machine learning methods. The glomerular filtration rate (GFR) is used to diagnose chronic kidney disease (CKD). It is calculated using the Modification of Diet in Renal Disease method and classified into five stages (1, 2, 3A and 3B, 4, and 5). Different CKD stages based on the estimated GFR are considered as six classes of the response variable. This study utilizes two representative generalized linear models for classification, namely, multinomial logistic regression (multinomial LR) and ordinal logistic regression (ordinal LR), as well as two machine learning models, namely, random forest (RF) and autoencoder (AE). The classification performance of the four models is compared in terms of accuracy, sensitivity, specificity, precision, and F1-Measure. To find the best model that classifies CKD stages correctly, the data are divided into a 10-fold dataset with the same rate for each CKD stage. Results indicate that RF and AE show better performance in accuracy than the multinomial and ordinal LR models when classifying the response variable. However, when a highly imbalanced dataset is modeled, the accuracy of the model performance can distort the actual performance. This occurs because accuracy is high even if a statistical model classifies a minority class into a majority class. To solve this problem in performance interpretation, we not only consider accuracy from the confusion matrix but also sensitivity, specificity, precision, and F-1 measure for each class. To present classification performance with a single value for each model, we calculate the macro-average and micro-weighted values for each model. We conclude that AE is the best model classifying CKD stages correctly for all performance indices.

The use of deep-learning-based artificial intelligence (AI) is emerging in ophthalmology, with AI-mediated differential diagnosis of neovascular age-related macular degeneration (AMD) and dry AMD a promising methodology for precise treatment strategies and prognosis. Here, we developed deep learning algorithms and predicted diseases using 399 images of fundus. Based on feature extraction and classification with fully connected layers, we applied the Visual Geometry Group with 16 layers (VGG16) model of convolutional neural networks to classify new images. Image-data augmentation in our model was performed using Keras ImageDataGenerator, and the leave-one-out procedure was used for model cross-validation. The prediction and validation results obtained using the AI AMD diagnosis model showed relevant performance and suitability as well as better diagnostic accuracy than manual review by first-year residents. These results suggest the efficacy of this tool for early differential diagnosis of AMD in situations involving shortages of ophthalmology specialists and other medical devices.

The prediction of anaerobic digestion (AD) performance using numerical models, which are based on mathematics and kinetics, is being challenged by poor mechanistic understanding and the non-linear relationships between performance and operational parameters. This study demonstrated that various machine learning (ML) models using the 1-step ahead with the retraining method, which utilized AD performance data from prior states, can improve the prediction accuracy of ML models. For the four types of ML models studied, the 1-step ahead with the retraining method could improve the root mean square errors by 32–49% compared to the conventional multi-step ahead method, which was particularly noteworthy during the transition period when AD reactors were faced with loading shocks and showed inhibited methane yields. Moreover, the 1-step ahead with the retraining method showed the potential of achieving accurate predictions using a single input parameter, pH, which was considerably less labor-intensive to monitor than the other parameters often required in AD models (e.g., VSS). As such, the 1-step ahead with retraining method is suitable for efficient real-time prediction of AD performance in real-world operations.

An excessive increase in algae often has various undesirable effects on drinking water supply systems, thus proper management is necessary. Algal monitoring and classification is one of the fundamental steps in the management of algal blooms. Conventional microscopic methods have been most widely used for algal classification, but such approaches are time-consuming and labor-intensive. Thus, the development of alternative methods for rapid, but reliable algal classification is essential where an advanced machine learning technique, known as deep learning, is considered to provide a possible approach for rapid algal classification. In recent years, one of the deep learning techniques, namely the convolutional neural network (CNN), has been increasingly used for image classification in various fields, including algal classification. However, previous studies on algal classification have used CNNs that were arbitrarily chosen, and did not explore possible CNNs fitting algal image data. In this paper, neural architecture search (NAS), an automatic approach for the design of artificial neural networks (ANN), is used to find a best CNN model for the classification of eight algal genera in watersheds experiencing algal blooms, including three cyanobacteria (Microcystis sp., Oscillatoria sp., and Anabaena sp.), three diatoms (Fragilaria sp., Synedra sp., and two green algae (Staurastrum sp. and Pediastrum sp.). The developed CNN model effectively classified the algal genus with an F1-score of 0.95 for the eight genera. The results indicate that the CNN models developed from NAS can outperform conventional CNN development approaches, and would be an effective tool for rapid operational responses to algal bloom events. In addition, we introduce a generic framework that provides a guideline for the development of the machine learning models for algal image analysis. Finally, we present the experimental results from the real-world environments using the framework and NAS.

The role of hypertriglyceridemia (HTG) secondary to endocrine diseases in the occurrence of pancreatitis in dogs has not been fully investigated. The objective of the present study was to evaluate HTG as a mediator between endocrine diseases and pancreatitis in dogs. The study design was a retrospective case-control study. Medical records of dogs newly diagnosed with acutely presenting pancreatitis between 2012 and 2014 were reviewed for the presence or absence of hyperadrenocorticism (HAC), diabetes mellitus (DM), and hypothyroidism. A matched case-control analysis was performed, and the association between endocrine diseases and pancreatitis was evaluated using multiple logistic regression analysis. In dogs with pancreatitis, the odds of HAC (P < .001) and DM (P < .001) were 4.5 and 12.4 times that of dogs without pancreatitis, respectively. HTG significantly mediated the association between DM and pancreatitis but not between HAC and pancreatitis. Additional studies will be necessary to confirm these findings and to further elucidate the associations between endocrine diseases and pancreatitis.

Background: Smoking is known to be closely related with cancer and cardiovascular diseases. Studies have shown that smoking increases hemoglobin A1c levels in patients with diabetes. We aimed to explore the relationship between smoking and hemoglobin A1c levels in adults without diabetes based on body mass index values. Methods: In a general hospital in Korea, we conducted a questionnaire-based survey among 406 adults (both men and women) with no history of diabetes. We collected information about their age, sex, smoking habits, drinking habits, exercise regime, height, weight, waist circumference, and blood pressure and conducted routine biochemical tests. Subjects were divided into groups according to their body mass index values, hemoglobin A1c levels, and smoking habits. Subsequently, the relationship between smoking and hemoglobin A1c levels was assessed. Results: We determined a correlation between smoking and hemoglobin A1c levels in all the subjects particularly those with normal body mass index values (body mass index below 23.0 kg/m 2 ). Subjects with a history of smoking were odds ratio [OR] 1.091 more likely (95% confidence interval [CI], 1.006–1.183; P=0.03) to have hemoglobin A1c levels between 5.7% to 6.4% as compared to those who were OR 1.168 (95% CI, 1.054–1.294; P=0.003) likely to have hemoglobin A1c levels below 5.6%. Conclusion: In all the subjects including adults with normal body mass index values (body mass index below 23.0 kg/m 2 ), a statistically significant relationship between smoking and hemoglobin A1c levels was observed.

Sensitivity and uncertainty analysis of contaminant fate and transport modeling have received considerable attention in the literature. In this study, our objective is to elucidate the uncertainty pertaining to micropollutant modeling in the sediment-water column interface. Our sensitivity analysis suggests that not only partitioning coefficients of metals but also critical stress values for cohesive sediment affect greatly the predictions of suspended sediment and metal concentrations. Bayesian Monte Carlo is used to quantify the propagation of parameter uncertainty through the model and obtain the posterior parameter probabilities. The delineation of periods related to different river flow regimes allowed optimizing the characterization of cohesive sediment parameters and effectively reducing the overall model uncertainty. We conclude by offering prescriptive guidelines about how Bayesian inference techniques can be integrated with contaminant modeling and improve the methodological foundation of uncertainty analysis. Sensitivity and uncertainty analysis was performed for sediment-metal modeling.Suspended sediment predictions are sensitive to critical erosion stress.Sediment bed-water partitioning coefficient is critical for metal predictions.River flow dynamics affect contaminant fate and model parameter sensitivity.Strategies to improve uncertainty analysis of sediment-metal modeling are discussed.

Railway accidents are critical issues characterized by a large number of injuries and fatalities per accident due to massive public transport systems. This study proposes a new approach for evaluating the damages resulting from railway accidents using the two-part models (TPMs) such as the zero-inflated Poisson regression model (ZIP model) and the zero-inflated negative-binomial regression model (ZINB model) for the non-negative count measurements and the zero-inflated gamma regression model (ZIG model) and the zero-inflated log-normal regression model (ZILN model) for the semi-continuous measurements. The models are employed for the evaluation of the railway accidents on Korea Railroad, considering the accident damages, such as the train delay time, the number of trains delayed and the cost of considering the accident count responses, for the period 2008 to 2016. From the results obtained, we found that the human-related factors, the high-speed railway system or the Korea Train Express (KTX) and the number of casualties, are the main cost-escalating factors. The number of trains delayed and the amount of delay time tend to increase both the probability of incurring costs and the amount of cost. For better evaluation, the railway accident data should contain accurate information with less recurrence of zeros.

The quantitative analysis of the disaster effect on water supply systems can provide useful information for water supply system management. In this study, a total disaster index (TDI) was developed using open-source public data in 419 water treatment plants in Korea with 23 input variables. The TDI quantifies the possible effects or damage caused by three major disasters (typhoons, heavy rain, and earthquakes) on water supply systems. The four components (regional factor, risk factor, urgency factor, and response and recovery factor) were calculated using input variables to determine the disaster index (DI) of each disaster. The weight of the input variables was determined using principal component analysis (PCA), and the weights of the DI of three natural disasters and four components used to calculate the TDI were determined by the analytical hierarchy process (AHP). Specifically, two ensemble machine learning models, random forest (RF) and XGBoost (XGB), were used to develop models to predict the TDI. Both models predicted the TDI with the coefficient of determination and root-mean-square error-observations standard deviation ratio of 0.8435 and 0.3957 for the RF model and 0.8629 and 0.3703 for the XGB model, respectively. The relative importance analysis suggests that the number of input variables can be minimized, which improves the models&rsquo, practical applicability.

This study adopts two approaches to analyze the occurrence of algae at Haman Weir for Nakdong River; one is the traditional statistical method, such as logistic regression, while the other is machine learning technique, such as kNN, ANN, RF, Bagging, Boosting, and SVM. In order to compare the performance of the models, this study measured the accuracy, specificity, sensitivity, and AUC, which are representative model evaluation tools. The ROC curve is created by plotting association of sensitivity and (1-specificity). The AUC that is area of ROC curve represents sensitivity and specificity. This measure has two competitive advantages compared to other evaluation tools. One is that it is scale-invariant. It means that purpose of AUC is how well the model predicts. The other is that the AUC is classification-threshold-invariant. It shows that the AUC is independent of threshold because it is plotted association of sensitivity and (1-specificity) obtained by threshold. We chose AUC as a final model evaluation tool with two advantages. Also, variable selection was conducted using the Boruta algorithm. In addition, we tried to distinguish the better model by comparing the model with the variable selection method and the model without the variable selection method. As a result of the analysis, Boruta algorithm as a variable selection method suggested PO4-P, DO, BOD, NH3-N, Susp, pH, TOC, Temp, TN, and TP as significant explanatory variables. A comparison was made between the model with and without these selected variables. Among the models without variable selection method, the accuracy of RF analysis was highest, and ANN analysis showed the highest AUC. In conclusion, ANN analysis using the variable selection method showed the best performance among the models with and without variable selection method.

In this study, using public transport smart card data observed in Seoul, Korea, we provide empirical evidence related to the impact of the opening of a new railway line on passenger demand changes at transfer stations. An intervention time series modeling framework was applied to evaluate the travel demand patterns based upon massive card data collected before and after a new line opened. The results show that the new line opening has had meaningful intervention effects on passenger demand, but significant changes were not observed at all stations. This implies that the trends have been changed structurally and the changes may last for a long time. Consequently, it is found that the new line opening changed the trends of passenger ridership and these changes can be successfully explained by various intervention patterns derived from extensive Automatic Fare Collection (AFC) data. The intervention analysis will provide valuable information to transit agencies to enable them to make better decisions related to transit planning, pricing, and the deployment of new transit services.

In this study, we analyze the effect on the smoking status in the Seoul Metropolitan area using a multilevel logistic model with Community Health Survey data from the Korea Centers for Disease Control and Prevention. Intraclass correlation coefficient (ICC), profiling analysis and two types of predicted value were used to determine the appropriate multilevel analysis level. Sensitivity, specificity, percentage of correctly classified observations (PCC) and ROC curve evaluated model performance. We showed the applicability for multilevel analysis allowed for the possibility that different factors contribute to within group and between group variability using survey data.

To enhance transit ridership, Seoul introduced a transfer discount fare scheme that uses an automated fare collection system in Seoul 2004. The transfer discount fare system records all transfer information between rail and buses in transit smartcard data, which enabled us to explore an urban railway station's catchment area. In this study, we examined the geographic distribution of rail-to-bus transfer trips and their characteristics by using transit smartcard data. Mokdong station in Seoul was used as a case study to demonstrate the benefits of data mining for the depiction and easy evaluation of a station's catchment area. The results showed that the average transfer passenger traveled 1.7 km with five bus stops after boarding to access the business district during the morning peak hour. The cumulative distribution of alighting passengers by bus route helped with inferring the shape and size of the urban railway station's catchment area in each direction and depending on the time of day. We found that reliable transfer travel data constitute valuable information for evaluating an urban railway station's catchment area with respect to the type of land use and will help transit agencies with providing better transit services in terms of enhanced accessibility by changing bus headways and routes, as well as land use planners with evaluating transit-oriented development based on the expanded concept of a metro station's catchment area.

In this study, we provide empirical evidence of whether a spatial correlation among mode choices at the TAZ(Traffic Analysis Zone) level exists based on transit smart card data observed in Seoul, Korea. The results show that the areas with a higher probability that passengers choose to take a bus are clustered and that those regions have fewer metro stations than bus stations. We also found that the spatial correlation turned out to be statistically meaningful and provided an opportunity for the potential use of the spatial correlation in modeling mode choices. A reliable spatial interaction would constitute valuable information for transportation agencies in terms of their route planning and scheduling based on the transit smart card data.

PURPOSES : Traffic accidents at intersections have been increased annually so that it is required to examine the causations to reduce the accidents. However, the current existing accident models were developed mainly with non-linear regression models such as Poisson methods. These non-linear regression methods lack to reveal complicated causations for traffic accidents, though they are right choices to study randomness and non-linearity of accidents. Therefore, to reveal the complicated causations of traffic accidents, this study used structural equation methods(SEM). METHODS : SEM used in this study is a statistical technique for estimating causal relations using a combination of statistical data and qualitative causal assumptions. SEM allow exploratory modeling, meaning they are suited to theory development. The method is tested against the obtained measurement data to determine how well the model fits the data. Among the strengths of SEM is the ability to construct latent variables: variables which are not measured directly, but are estimated in the model from several measured variables. This allows the modeler to explicitly capture the unreliability of measurement in the model, which allows the structural relations between latent variables to be accurately estimated. RESULTS : The study results showed that causal factors could be grouped into 3. Factor 1 includes traffic variables, and Factor 2 contains turning traffic variables. Factor 3 consists of other road element variables such as speed limits or signal cycles. CONCLUSIONS : Non-linear regression models can be used to develop accident predictions models. However, they lack to estimate causal factors, because they select only few significant variables to raise the accuracy of the model performance. Compared to the regressions, SEM has merits to estimate causal factors affecting accidents, because it allows the structural relations between latent variables. Therefore, this study used SEM to estimate causal factors affecting accident at urban signalized intersections.

In this study, we identify the variables that affect the number of crime and spatial correlation in the Seoul metropolitan area, in addition, we measure the relative risk on the incidence of crime by a Poisson regression model. We suggest a statistical methodology to make a risk map for crime based on relative risk instead of the total event of crime by region using the Geographic Information System. To demonstrate the use and advantages of this methodology, this study presents an analyses of the total crime count in 25 wards in the Seoul metropolitan area.

Although Korea's coastal areas increasingly experience marine accident due to frequent ship encounters, increased vessel traffic and large vessel, there is a no specific model to evaluate the navigating vessel's risk for the given situation. The maritime transport environmental assessment is necessary due to the amended marine traffic law. However, marine safety diagnosis is now evaluated by foreign models. In this paper, therefore, we suggest a domestic model catering to and reflecting the characteristics of Korea's costal areas as well as those of vessel navigator's risk. We can evaluate subjective risks using this model, and can establish the model output as maritime risk exposure assessment system. We have performed analyses of variance and multiple comparison to identify the factor affecting subjective risks. As a result, measurable subjective risks of maritime traffic accident based on our suggested model can be expressed using the maritime risk exposure assessment system with geographic information system.최근 우리나라 연안 해역에서는 선박 간의 빈번한 조우 상황 , 선박 교통량 증대 및 대형화로 인해 해양사고의 잠재 위험이 증가하고 있지만 이러한 통항 선박의 위험도를 평가할 수 있는 모형은 개발되어 있지 않는 실정이다 . 또한 개정된 해상교통안전법에서는 해상교통 환경평가를 실시하도록 요구하고 있지만 이 진단 또한 외국평가모델에 의해 평가를 하고 있어 실제 우리나라 연안 해역 특성 및 선박운항자의 위험도 인식이 반영된 모델이 필요한 실정이다. 본 연구에서는 설문분석을 통해 선박운항자가 운항 중 처해 있는 상황에 대하여 선박운항자가 느끼는 주관적 위험도 인식에 대한 모형을 개발하고 해상위험도 표출시스템에 이식하기 위한 연구이다 . 다양한 요인들에 대해 분산분석과 다중비교 분석을 통해 주관적 위험도에 대한 요인들의 영향력을 통계적으로 확인하였다 . 본 연구에서는 이와같은 분석결과를 토대로 해상교통위험도를 측정할 수 있는 모형을 개발하고 , 이를 통해 지리정보시스템 기반의 해상위험도 표출 시스템에 적용하여 운항중인 선박에 대한 위험정도를 화면상에 표출하여 선박운항자들로 하여금 미리 위험을 회피하고자 하는데 활용될 수 있다 .

The Falling Weight Deflectormeter has been widely used in evaluating the structural adequacy of pavement structures. The deflections measured from the FWD are capable of estimating the stiffness of pavement layers and measuring the pavement responses in the pavement structure. The objective of paper is to develop the pavement response model using a partial least square regression technique based on the FWD deflection data. The partial least square regression method enables to solve the multicollinearity problem occurred in multiple regression model. It is also found that the pavement response model can be developed using the raw data when a partial least square regression was used.

In this study, we develop a methodology to estimate the effectiveness of ramp metering in reducing CO2 emissions. Ramp metering is one of several Intelligent Transportation Systems (ITS) applications to control traffic flow. In this paper in order to estimate the reduced emissions, we followed the procedure supported by the United Nations Framework Convention on Climate Change (UN-FCCC). And ramp metering causes some drivers to change their routes and affects CO2 emissions on the ramp and on the main lane. With these issues in mind, we performed Stated Preference analysis to determine the degree of driver preference for individual traffic situations.The Traffic Software Integrated System (TSIS) simulation program was used to verify the CO2 measurement model. The TSIS simulation showed that the reduction in CO2 emissions was 818.4 kg/h and that the decrease in the effectiveness was 7.3%. It also revealed that ramp metering reduced the CO2. emissions to 3,273.6kg emissions per day and 1,194.9 ton per year. In conclusion we found that ramp metering is effective in reducing CO2 emissions.

The thickness of anti-freezing layer has been empirically determined using the frost depth obtained from the freezing index and has not been generally considered as a structural layer in pavement design procedure. In fact, the anti-freezing layer makes a role in structural layer and enables to reduce the total thickness of pavement system. The objective of this study is to develop the statistical regression model for evaluating the structural capacity of anti-freezing layer using Falling Weight Deflectormeter(FWD) test data in asphalt pavements. The FWD testing was conducted at the embankment, cutting, and boundary area of various test sections to estimate the structural capacity of anti-freezing layer in different foundation condition. It is observed from this testing that the center deflections of pavement structure with anti-freezing layer are smaller than those without anti-freezing layer ranging from 0.4 to 82.6%. To determine the variables of statistical model, the correlation study has been conducted between various FWD deflection indexes and the anti-freezing layer thickness. It is found that the (%)( Damage Index(%)) is highly correlated with anti-freezing layer thickness. The (%) model were developed for evaluating structural capacity of anti-freezing layer using linear mixed-effect models.

Since Intelligent Transport Systems(ITS) is a complex project combining technology and system of transportation, information and communication and civil engineer, it needs a specialized management activities. However, it is difficult to judge who has responsibility for a shoddy and fault construction since the role between project management and supervision is very ambiguous. The aim of this study is propose an efficient project management and supervision for ITS reflecting the characteristics of ITS. Therefore, we suggest a statistical models using nonlinear regression model to prepare the project management and supervision compensation criteria, hence, we choose the realistic and validation statistical model.

Korea coastal area is highly potential dangerous zone of marine accident due to frequent ship`s encounters. VTS center can`t identify ship`s information because of beyond VHF range. It is also difficult of us to efficiently manage vessel traffic beyond VTS control area, so that it can`t prevent marine accident. Presently, korean government is conducting maritime traffic safety assessment according to enlargement of harbor & development of new port but do not have the system which provide danger of information depending on maritime traffic environment with real time. So it is necessary to develop evaluation index which can assess sea risk through the evaluation of maritime traffic environment. In this paper, on the basis of vessel navigator`s risk consciousness, we carried out survey & statistical analysis vessel navigator`s subjective risk depending on the LOA, crossing situation(, , ), overtaking, head-on situation, encountering vessel`s side, within or outside harbor, speed with other vessel(ex. same, fast or slow), speed difference with other vessel and distance with other vessel & propose basic expression to develop maritime traffic safety evaluation model. And by using this model, we can confirm that this proposing expression is suitable for domestic maritime traffic environment.

Knowing what factors into a player’s ability to affect the outcome of a sports game is crucial. This knowledgehelps determine the relative degree of contribution by each team member as well as sets appropriate annualsalaries. This study uses statistical analysis to investigate how much the outcome of a professional baseballgame is influenced by the records of individual players. We used the Lotte Giants’ data on 252 games playedbetween 2007 and 2008 that included environmental data(home or away games and opponents) as well aspitchers’ and batters’ data. Using a SAS Enterprise Miner, we performed a logistic regression analysis anddecision tree analysis on the data. The results obtained through the two analytic methods are comparedand discussed. Keywords: Decision tree analysis, logistic regression analysis, odds ratio, SAS Enterprise Miner. 1. Introduction The Korean Baseball League(KBL) was founded in 1982. The Lotte Giants(a professional baseballteam based in Busan, Korea) is one of the original franchises of the Korea Baseball Organiza-tion(KBO) league. The Lotte Giants won the league championship in 1984 and 1992, and weretwice the league runner-ups, in 1995 and 1999. Between 1982 and 2008, they played a total of3,263 games, 1,464 of which they won, 1,715 of which they lost, and 84 of which they tied, for awinning percentage of 0.449. In 2009, the Lotte Giants defended their title as Korea’s most popularprofessional baseball club and their total attendance increased to 1,380,018.There is existing literature of statistical analysis on Korean professional baseball data, for instance,Cho and Cho (2003) analyzed the Beane Count of baseball teams and its correlation with the winningaverage. They performed a cluster analysis and a regression analysis using the data of homerunsscored and allowed, and walks earned and allowed, which are the basic components of the BeaneCount. Kim (2004), meanwhile, pointed out how the criteria used for determining team rankings

Recently, foresight activity becomes more important. Corporate foresight focuses on corporate or organizational level foresight activities. As the companies recognize the importance of corporate foresight activities, lots of companies try to do foresight activities. However, there are many problems in the current corporate foresight; lack of the methodological groundings, lack of continuity, low level of diffusion of results, poor integration of foresight activities, and etc. To overcome such problems, academics and practitioners introduce frameworks and methodologies. This article also suggests 'funnel-shaped trend analysis methodology' and 'trend analysis system' to overcome such problems.

With the growing development of Information Technology (IT), various advanced research technologies have been applied to collect useful information in the area of transportation science. The Radio Frequency Identification (RFID) system is one of ITs useful in transportation research. Specifically, traffic count and travel time can simply be obtained from the RFID reader installed on a probe vehicle. The RFID technique, however, is a sample survey, not a complete enumeration since not all vehicles have a RFID tag. In this paper, we propose a reasonable and objective methodology to estimate total traffic volumes from developing a simple regression model based on the sample obtained from the RFID technique. We also suggest a method that assesses the optimal proportion of RFID tags needed to provide reliable estimates of traffic volume. This will help transportation agencies to optimise a budget with limited funds set aside for RFID projects.