• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.9
• /
• pp.5585-5593
• /
• 2014

This study tested the hypothesis that the significance of the heterogeneous dispersion parameter in safety performance function (SPF) used to estimate the expected crashes is affected by the endogenous heterogeneous prior distributions, and analyzed the impacts of the mis-specified dispersion parameter on the evaluation results for traffic safety countermeasures. In particular, this study simulated the Poisson means based on the heterogeneous dispersion parameters and estimated the SPFs using both the negative binomial (NB) model and the heterogeneous negative binomial (HNB) model for analyzing the impacts of the model mis-specification on the mean and dispersion functions in SPF. In addition, this study analyzed the characteristics of errors in the crash reduction factors (CRFs) obtained when the two models are used to estimate the posterior means and variances, which are essentially estimated through the estimated hyper-parameters in the heterogeneous prior distributions. The simulation study results showed that a mis-estimation on the heterogeneous dispersion parameters through the NB model does not affect the coefficient of the mean functions, but the variances of the prior distribution are seriously mis-estimated when the NB model is used to develop SPFs without considering the heterogeneity in dispersion. Consequently, when the NB model is used erroneously to estimate the prior distributions with heterogeneous dispersion parameters, the mis-estimated posterior mean can produce large errors in CRFs up to 120%.

• Korean Chemical Engineering Research
• /
• v.58 no.4
• /
• pp.588-595
• /
• 2020

Prediction and control of nitrogen oxides (NO_x) emission is of great interest in industry due to stricter environmental regulations. Herein, we propose an artificial intelligence (AI)-based framework for prediction of NO_x emission. The framework includes pre-processing of data for training of neural networks and evaluation of the AI-based models. In this work, Long-Short-Term Memory (LSTM), one of the recurrent neural networks, was adopted to reflect the time series characteristics of NO_x emissions. A decision tree was used to determine a time window of LSTM prior to training of the network. The neural network was trained with operational data from a heating furnace. The optimal model was obtained by optimizing hyper-parameters. The LSTM model provided a reliable prediction of NO_x emission for both training and test data, showing an accuracy of 93% or more. The application of the proposed AI-based framework will provide new opportunities for predicting the emission of various air pollutants with time series characteristics.

• Journal of Oral Medicine and Pain
• /
• v.37 no.4
• /
• pp.243-250
• /
• 2012

Trigeminal nerve injuries due to invasive dental procedures such as implant surgery and extraction is one of the most serious issues in dentistry and may provoke medico-legal problems. Thus, for objective and reliable assessment of nerve injury, a need of QST (quantitative sensory testing) is emphasized and thermal threshold test is an essential part of QST, reported to have acceptable reliability in the orofacial region. This pilot study aimed to evaluate thermal thresholds for limited cases of trigeminal nerve injures. The study investigated 18 clinical cases with trigeminal nerve injuries who visited Department of Oral Medicine, Dankook Univeristy Dental Hospital during the period from May 2011 to Oct 2012. Thermal thresholds was measured by Thermal Sensory Analyzer, TSA-II (Medoc, Israel). Their CDT(cold detection threshold) was significantly decreased in the affected sides compared to the unaffected sides. Other parameters such as WDT(warm detection threshold), CPT(cold pain threshold) and HPT(heat pain threshold) did not show statistical difference between the affected and unaffected sides. Further researches are required to compare thermal thresholds relative to types of nerve deficits such as thermal hyper- or hypoesthesia and hyper- or hypoalgesia for larger sample.

• Microbiology and Biotechnology Letters
• /
• v.26 no.2
• /
• pp.179-186
• /
• 1998

Dextransucrase hyper-producing Leuconostoc mesenteroides B-512FMCM and dextransucrase constitutive mutants B-742CB and B-1355C catalyzed the transfer of glucose from sucrose to other carbohydrates which were present or were added to the reaction digests. When the acceptor was a maltose, gentiobiose, lactose or raffinose, there was produced a series of oligosaccharide acceptor products or single product based on the kinds of enzymes and reaction conditions. To obtain the quantitative information about the yield and the distribution of acceptor products and dextran two experimental parameters were studied: a) the ratio of acceptor to sucrose and b) the amount of enzyme at constant carbohydrate concentration (100 mM). As the amount of enzyme increased, the synthesis of acceptor products (of maltose or gentiobiose) increased, and the formation of dextran decreased. As the ratio of acceptor to sucrose increased, the amount of dextran and the number of acceptor-products decreased and the amount of acceptor-products increased. When maltose or gentiobiose was an acceptor, the glucose from sucrose was transferred to the C-6 hydroxyl group of the nonreducing-end glucose residue of accepters to give a homologous series of isomaltosyl dextrins. In case of lactose or raffinose, there was produced only one acceptor product from B-512FMCM dextransucrase reaction. In the lactose acceptor reaction, the glucose from sucrose was transferred to the C-2 hydroxyl of the reducing end glucose residue of lactose. To get a series of oligosaccharides from lactose or raffinose acceptor reaction we used B-742CB dextransucrase or B-1355C alternansucrase with 500 mM sucrose in reaction digest.

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.44 no.7
• /
• pp.961-969
• /
• 2015

To investigate the effect of n-3 fatty acid from Bokbunja (Rubus coreanus Miq.) seed oil (BSO), we examined improvement of plasma triglycerides and cholesterol in vivo. Five-week-old ICR mice were divided into five groups of six mice each; Control, high fat diet (HFD) control (negative control), salmon oil control (positive control, HFD+commercial n-3 fatty acid), and BSO experimental groups (HFD+1 g/60 kg BW/d, HFD+2 g/60 kg BW/d). After 4 weeks of BSO treatment, we measured serum triglyceride and cholesterol levels. The levels of low-density lipoprotein/very-low-density lipoprotein-cholesterol, high-density lipoprotein-cholesterol, and total cholesterol were significantly (P<0.05) reduced in the group fed BSO at 2 g/60 kg BW/d compared to the negative control. Levels of triglycerides, which are similar to cholesterol, were also significantly (P<0.05) reduced in the same group. To investigate further, we tested blood coagulation parameters. Prothrombin time (PT) and activated partial thromboplastin time (aPTT) were not significantly different among the five groups according to BSO. However, the 2 g/60 kg BW/d BSO group treated with PT and aPTT showed a tendency to live longer than the negative control. Taken together, BSO might improve blood homeostasis mediated via hypo-lipidemic and anti-coagulation activities.

• Journal of Navigation and Port Research
• /
• v.44 no.3
• /
• pp.187-194
• /
• 2020

It is very important to forecast freight volume accurately to establish major port policies and future operation plans. Thus, related studies are being conducted because of this importance. In this paper, stepwise regression analysis and artificial neural network model were analyzed to compare the predictive power of each model on Gwangyang Port, the largest domestic port for coal and iron ore transportation. Data of a total of 121 months J anuary 2009-J anuary 2019 were used. Factors affecting coal and iron ore trade volume were selected and classified into supply-related factors and market/economy-related factors. In the stepwise regression analysis, the tonnage of ships entering the port, coal price, and dollar exchange rate were selected as the final variables in case of the Gwangyang Port coal volume forecasting model. In the iron ore volume forecasting model, the tonnage of ships entering the port and the price of iron ore were selected as the final variables. In the analysis using the artificial neural network model, trial-and-error method that various Hyper-parameters affecting the performance of the model were selected to identify the most optimal model used. The analysis results showed that the artificial neural network model had better predictive performance than the stepwise regression analysis. The model which showed the most excellent performance was the Gwangyang Port Coal Volume Forecasting Artificial Neural Network Model. In comparing forecasted values by various predictive models and actually measured values, the artificial neural network model showed closer values to the actual highest point and the lowest point than the stepwise regression analysis.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.3
• /
• pp.58-64
• /
• 2021

As the number of registered vehicles increases, traffic congestion will worsen worse, which may act as an inhibitory factor for urban social and economic development. Through accurate traffic flow prediction, various AI techniques have been used to prevent traffic congestion. This paper uses the data from a VDS (Vehicle Detection System) as input variables. This study predicted traffic flow in five levels (free flow, somewhat delayed, delayed, somewhat congested, and congested), rather than predicting traffic flow in two levels (free flow and congested). The Catboost model, which is a machine-learning algorithm, was used in this study. This model predicts traffic flow in five levels and compares and analyzes the accuracy of the prediction with other algorithms. In addition, the preprocessed model that went through RandomizedSerachCv and One-Hot Encoding was compared with the naive one. As a result, the Catboost model without any hyper-parameter showed the highest accuracy of 93%. Overall, the Catboost model analyzes and predicts a large number of categorical traffic data better than any other machine learning and deep learning models, and the initial set parameters are optimized for Catboost.

• Advanced Industrial SCIence
• /
• v.3 no.2
• /
• pp.8-16
• /
• 2024

This paper shows the system of drug classification, the goal of this is to foretell the apt drug for the patients based on their demographic and physiological traits. The dataset consists of various attributes like Age, Sex, BP (Blood Pressure), Cholesterol Level, and Na_to_K (Sodium to Potassium ratio), with the objective to determine the kind of drug being given. The models used in this paper are K-Nearest Neighbors (KNN), Logistic Regression and Random Forest. Further to fine-tune hyper parameters using 5-fold cross-validation, GridSearchCV was used and each model was trained and tested on the dataset. To assess the performance of each model both with and without hyper parameter tuning evaluation metrics like accuracy, confusion matrices, and classification reports were used and the accuracy of the models without GridSearchCV was 0.7, 0.875, 0.975 and with GridSearchCV was 0.75, 1.0, 0.975. According to GridSearchCV Logistic Regression is the most suitable model for drug classification among the three-model used followed by the K-Nearest Neighbors. Also, Na_to_K is an essential feature in predicting the outcome.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.11
• /
• pp.1-6
• /
• 2017

Stents are widely used as the most common method of treating coronary artery disease with implants in the form of a metal mesh. The blood flow is normalized by inserting a stent into the narrowed or clogged areas of the human body. In this study, the mechanical characteristics of a stent are investigated according to the variations of its design parameters by the Taguchi method and finite element analysis. A stent model of the Palmaz-Schatz type was used for the analysis. In the analysis, an elasto-plastic material model was adopted for the stent and a hyper-elastic model was used for the balloon. The main interest of this study is to investigate the effects of the design parameters which reduce the possibility of restenosis by adjusting the recoil amount. A Taguchi orthogonal array was constructed on the model of the stent. The thickness and length and angle of the slot were selected as the design parameters. The amounts of radial recoil and longitudinal recoil were calculated by finite element analysis. The statistical analysis using the Taguchi method showed that optimizing the shape of the stent could reduce the possibility of restenosis. The optimized shape showed improvements of recoil in the radial and longitudinal directions of ~1% and ~0.1%, respectively, compared to the default model.

• Geomechanics and Engineering
• /
• v.35 no.2
• /
• pp.121-133
• /
• 2023

The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.