Soo-Beom Lee

Comparison of cortical bone thickness and root proximity at maxillary and mandibular interradicular sites for orthodontic mini‐implant placement

OBJECTIVES To compare maxillary and mandibular cortical bone thickness and rootic proximity for optimal mini-implant placement. SETTING AND SAMPLE POPULATION CT images from 14 men and 14 women were used to evaluate buccal interradicular cortical bone thickness and root proximity from mesial of the central incisor to the 2nd molar. Cortical bone thickness was measured at 0 degrees , 15 degrees , 30 degrees , and 45 degrees angles relative to the root surface using three-dimensional images. RESULTS For the cortical bone thickness, there was no statistically significant difference between the maxilla and the mandible in the anterior area; however, there was a significant difference in the posterior area. Cortical bone in the maxilla, mesial and distal to canine interradicular sites, was thickest while thickness in the mandible exhibited a gradual anterior to posterior increase. Cortical bone thickness in the maxilla increased as both level and angle increased, while the cortical bone thickness in the mandible was greatest at 4 mm from the alveolar crest. Root proximity mesial and distal to 2nd premolar interradicular sites was greatest. CONCLUSION Based on our results, cortical bone thickness depends on the interradicular site rather than sex or individual differences.

Estimation of the optimal number of cluster-heads in sensor network

A sensor network system consisting of a large number of small sensors with low-power can be an effective tool for collection and integration of data by each sensor in a variety of environments. The collected data by each sensor node is communicated through the network to a single base station that uses all collected data to determine properties of the data. Clustering sensors into groups, yields that sensors communicate information only to cluster heads and then the cluster-heads communicate the aggregated information to the base station. We estimate the optimal number of cluster-heads among randomized sensors in a bounded region. We derive solutions for the values of parameters of our algorithm that minimize the total energy spent in the wireless sensor network when all sensors communicate data from the cluster-heads to the base station. Computer simulation shows that the energy consumption reduce as the optimal number of cluster-heads for the proposed method.

A biometric study of C‐shaped root canal systems in mandibular second molars using cone‐beam computed tomography

AIM To investigate the configuration of C-shaped canals in mandibular second molars, canal wall thickness and the orientation of the thinnest area at 1-mm intervals from the canal orifice to the apex by using cone-beam computed tomographic (CBCT) images. METHODOLOGY Three-dimensional CBCT images of 92 Korean mandibular second molars having C-shaped root canals were analysed to determine their configuration using a modification of Meltons classification, as well as the thinnest walls and their location. Associations between configuration type and distance from the canal orifice to the apex, as well as associations between the directional orientation of the thinnest root wall and distance from the canal orifice to the apex, were assessed by Fishers exact test. Because serial measurements of minimum wall thicknesses were correlated with individual teeth, a mixed-effects analysis was applied. RESULTS The most common configuration types were Meltons type I in the coronal region and Meltons type III in the apical region. Mean thicknesses of the thinnest root canal walls were 1.39 ± 0.38, 0.85 ± 0.25 and 0.77 ± 0.20 mm in the coronal, middle and apical regions, respectively. The thicker the root canal walls at the orifice region, the greater the decrease in thickness towards the apical region (P < 0.05), with the linguo-central root area being the thinnest. The pattern of decreasing thickness from the orifice to the apex formed a nonlinear cubic curve. CONCLUSIONS The most prevalent configuration types were Meltons type I (coronal region) and type III (apical region). The linguo-central root area was the thinnest in C-shaped root canals of Korean mandibular second molars. These anatomical variations should be considered during surgical or nonsurgical endodontic procedures.

Bayesian approach with the power prior for road safety analysis

Drawing inference from current data could be more reliable if similar data based on previous studies are used. We propose a full Bayesian approach with the power prior to utilize these data. The power prior is constructed by raising the likelihood function of the historical data to the power where . The power prior is a useful informative prior in Bayesian inference. We use the power prior to estimate regression coefficients and to calculate the accident reduction factors of some covariates including median strips and guardrails. We also compare our method with the empirical Bayes method. We demonstrate our results with several sets of real data. The data were collected for two rural national roads of Korea in the year 2002. The computations are executed with the Metropolis–Hastings algorithm which is a popular technique in the Markov chain and Monte Carlo methods.

Probabilistic Model of Traffic Breakdown with Random Propagation of Disturbance for ITS Application

In this paper, a probabilistic model of vehicular traffic breakdown applicable to Intelligent Transportation Systems (ITS) is presented. When traffic demand exceeds freeway capacity, the so-called breakdown occurs and the congestion begins. While preventing the breakdown is a major concern of traffic operation, the mechanism of breakdown is not thoroughly explained and most of the research regarding traffic breakdown rely on empirical analysis. To further our understanding of traffic breakdown, this paper explains the phenomenon of traffic breakdown in terms of random propagation of traffic disturbance and proposes a probabilistic model of breakdown. A Monte-Carlo simulation is also conducted to investigate the characteristics of the proposed model.

Development of traffic accidents prediction model with intelligent system theory

It is important to clarify the relationship between traffic accidents and various influencing factors in order to reduce the number of traffic accidents. This study developed a traffic accident frequency prediction model using multi-linear regression and quantification theories which are commonly applied in the field of traffic safety to verify the influences of various factors in the traffic accident frequency. The data was collected on the Korean National Highway 17 which shows the highest accident frequency and fatality in Chonbuk Province. In order to minimize the uncertainty of the data, the fuzzy theory and neural network theory were applied. The neural network theory can provide fair learning performance by modeling the human neural system mathematically. In conclusion, this study focused on the practicability of the fuzzy reasoning theory and the neural network theory for traffic safety analysis.

Development of Traffic Accident Frequency Prediction Model in Urban Signalized Intersections with Fuzzy Reasoning and Neural Network Theories

This study is to suggest a methodology to overcome the uncertainty and lack of reliability of data. The fuzzy reasoning model and the neural network model were developed in order to overcome the potential lack of reliability which may occur during the process of data collection. According to the result of comparison with the Poisson regression model, the suggested models showed better performance in the accuracy of the accident frequency prediction. It means that the more accurate accident frequency prediction model can be developed by the process of the uncertainty of raw data and the adjustment of errors in data by learning. Among the suggested models, the performance of the neural network model was better than that of the fuzzy reasoning model. The suggested models can evaluate the safety of signalized intersections in operation and/or planning, and ultimately contribute the reduction of accidents.

Freeway Crash Frequency Model Development Based on the Classification of Geometric Alignment Type

This paper presents how one can investigate the effects on crash occurrence of freeway geometric design elements including the horizontal, vertical alignment and road environment. At present, the available research results for the most part involve geometric data analysis that are obtained along a relatively long section of freeway, and, because of the long section`s diverse geometric conditions, the results tend to miss the specific local geometric impacts on vehicle crashes. In this regard, this research attempts to establish vehicle crash models based on a set of freeway geometric patterns whose crash generating characteristics are identical because they are homogeneous in terms of producing the same vehicle operating speeds, and subsequently their actual relationships are described by providing statistical analysis made in this research. Also each standard is comprised of part of straight, curve and continuous curve. This research has revealed that each type of model has different relation between accident and geometry structure. This research results should be useful for doing more reasonable highway designs and safety audit analysis.

Determination of optimal locations for the variable message signs by the genetic algorithm

The Variable Message Signs (VMS) are useful way to reduce the socio-economic costs due to the traffic congestions and delays by providing the information on traffic condition to drivers. This paper provided a methodology to determine the locations of VMSs in terms of the minimization of the delay by applying the genetic algorithm. The optimal number of VMSs was also derived by the economic analysis based on the cost and the benefit. The simulation considered the variation of traffic volume, the frequency and duration of the incident, and the traffic conversion in order to reflect the real situation.

Development of route choice behavior model using linkage of neural network and genetic algorithm with trip information

The decision-making behaviors of drivers have been clarified, as various researches on traffic behavior have been conducted until today. However, existing models set limits to examine non-linearity, ambiguity and the errors, which are occurred by variety of space-time contained in human conscious structure and process of decision-making. In particular, it is true that the route choice behavior of drivers is affected by external factors, such as past experience and change in diverse road environment information. However, taking into account the fact that various traffic problems are not being solved, it is proven that there are limitations in clearly describing traffic behavior. Accordingly, in order to construct a model that considers obscurity and non-linearity in the decision-making process of drivers, a model through neural network linkage and genetic algorithm was proposed in this study. In addition, this study was focused on proving the effectiveness of neural network theory and genetic algorithm, and studying the appropriateness of applying them into the field of transportation.