• The korean journal of orthodontics
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• v.53 no.3
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• pp.150-162
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• 2023

Objective: To investigate craniofacial differences in individuals with hypodontia and explore the relationship between craniofacial features and the number of congenitally missing teeth. Methods: A cross-sectional study was conducted among 261 Chinese patients (males, 124; females, 137; age, 7-24 years), divided into four groups (without hypodontia: no teeth missing, mild: one or two missing teeth, moderate: three to five missing teeth, severe: six or more missing teeth) according to the number of congenitally missing teeth. Differences in cephalometric measurements among the groups were analyzed. Further, multivariate linear regression and smooth curve fitting were performed to evaluate the relationship between the number of congenitally missing teeth and the cephalometric measurements. Results: In patients with hypodontia, SNA, NA-AP, FH-NA, ANB, Wits, ANS-Me/N-Me, GoGn-SN, UL-EP, and LL-EP significantly decreased, while Pog-NB, AB-NP, N-ANS, and S-Go/N-Me significantly increased. In multivariate linear regression analysis, SNB, Pog-NB, and S-Go/N-Me were positively related to the number of congenitally missing teeth. In contrast, NA-AP, FH-NA, ANB, Wits, N-Me, ANS-Me, ANS-Me/N-Me, GoGn-SN, SGn-FH (Y-axis), UL-EP, and LL-EP were negatively related, with absolute values of regression coefficients ranging from 0.147 to 0.357. Further, NA-AP, Pog-NB, S-Go/N-Me, and GoGn-SN showed the same tendency in both sexes, whereas UL-EP and LL-EP were different. Conclusions: Compared with controls, patients with hypodontia tend toward a Class III skeletal relationship, reduced lower anterior face height, flatter mandibular plane, and more retrusive lips. The number of congenitally missing teeth had a greater effect on certain characteristics of craniofacial morphology in males than in females.

• Journal of Satellite, Information and Communications
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• v.11 no.4
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• pp.94-101
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• 2016

Satellite data for remote sensing technology has limitations, especially with visible range sensor, cloud and/or other environmental factors cause missing data. In this study, using land surface temperature data from the MODerate resolution Imaging Spectro-radiometer(MODIS), we developed retrieving methods for satellite missing data and developed three methods; mean bias, regression analysis and local variation method. These methods used the previous day data as reference data. In order to validate these methods, we selected a specific measurement ratio using artificial missing data from 2014 to 2015. The local variation method showed low accuracy with root mean square error(RMSE) more than 2 K in some cases, and the regression analysis method showed reliable results in most cases with small RMSE values, 1.13 K, approximately. RMSE with the mean bias method was similar to RMSE with the regression analysis method, 1.32 K, approximately.

• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.499-514
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• 2007

It is a common practice to use the auxiliary variables to impute missing values from item nonresponse in surveys. Sometimes there are few auxiliary variables for missing value imputation, but if spatial and time autocorrelations exist, we should use these correlations for better results. Recently, Lee et al. (2006) showed that spatial autocorrelation could be efficiently used for missing value imputation when spatial autocorrelation existed, using the data from the farm household economy data in Gangwon-do, 2002. In this paper, we present au evaluation of spatial and space-time nonresponse imputation methods when there exist spatial and time autocorrelations using the monthly data during 2000-2002 from the same data previously used by Lee et al. (2006). We show that space-time imputation method is more efficient than the other through the numerical simulations.

• The korean journal of orthodontics
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• v.51 no.5
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• pp.355-362
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• 2021

Objective: This study aimed to establish the prevalence and patterns of nonsyndromic tooth agenesis in patients referred to a tertiary health care facility. Methods: The intraoral records and panoramic radiographs of 9,874 patients aged 12-22 years were evaluated. The study group included 716 patients (371 male, 345 female) with non-syndromic agenesis of at least one tooth (except the third molars). The study data were assessed using descriptive statistics, chi-square test, and Mann-Whitney U test, while patterns were evaluated using a tooth agenesis code (TAC) tool. Results: A total of 1,627 congenitally missing teeth, were found in patients with non-syndromic tooth agenesis, with an average of 2.27 missing teeth per patient. The prevalence of tooth agenesis was 7.25%, and the most commonly missing teeth were the left mandibular second premolars (10.17%). The age group comparison revealed no significant difference in the median number of missing teeth per patient according to the cutoff values for ages between 12 and 22 years. When the missing teeth were examined separately according to quadrants, 114 different tooth agenesis patterns (upper right quadrant = 28, upper left quadrant = 27, lower left quadrant = 31, and lower right quadrant = 28) were identified, and 81 of these patterns appeared only once. Conclusions: This study highlights the benefits of applying the TAC tool in a large sample population. The application of the TAC tool in such studies will enable the development of template treatment plans by determining homogenous patterns of tooth agenesis in certain populations.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.2
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• pp.67-78
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• 2019

Accurate electric load forecasting is very important in the efficient operation of the smart grid. Recently, due to the development of IT technology, many works for constructing accurate forecasting models have been developed based on big data processing using artificial intelligence techniques. These forecasting models usually utilize external factors such as temperature, humidity and historical electric load as independent variables. However, due to diverse internal and external factors, historical electrical load contains many missing data, which makes it very difficult to construct an accurate forecasting model. To solve this problem, in this paper, we propose a random forest-based missing data recovery scheme and construct an electric load forecasting model based on multilayer perceptron using the estimated values of missing data and external factors. We demonstrate the performance of our proposed scheme via various experiments.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.91-97
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• 2021

This paper uses a data extension technique originally designed for the rule refinement problem to handling incomplete data. This technique is characterized in that each event can have a weight indicating importance, and each variable can be expressed as a probability value. Since the key problem in this paper is to find the probability that is closest to the missing value and replace the missing value with the probability, three different algorithms are used to find the probability for the missing value and then store it in this data structure format. And, after learning to classify each information area with the SVM classification algorithm for evaluation of each probability structure, it compares with the original information and measures how much they match each other. The three algorithms for the imputation probability of the missing value use the same data structure, but have different characteristics in the approach method, so it is expected that it can be used for various purposes depending on the application field.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.4
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• pp.601-608
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• 2023

Mobile robots are widely used in industries because mobile robots perform tasks in various environments. In order to carry out tasks, determining the precise location of the robot in real-time is important due to the need for path generation and obstacle detection. In particular, when mobile robots autonomously navigate in indoor environments and carry out assigned tasks within pre-determined areas, highly precise positioning performance is required. However, mobile robots frequently experience data missing in wireless communication environments. The robots need to rely on predictive techniques to autonomously determine the mobile robot positions and continue performing mobile robot tasks. In this paper, we propose an extended Kalman filter-based algorithm to enhance the accuracy of mobile robot localization and address the issue of data missing. Trilateration algorithm relies on measurements taken at that moment, resulting in inaccurate localization performance. In contrast, the proposed algorithm uses residual values of predicted measurements in data missing environments, making precise mobile robot position estimation. We conducted simulations in terms of data missing to verify the superior performance of the proposed algorithm.

• Korean Journal of Culture and Social Issue
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• v.15 no.4
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• pp.487-505
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• 2009

We focused on the missing confirmation domain of the hasteful behavior. We tried to identify the variety of hasteful behavior and the effects of missing confirmation behavior domain of the hasteful behavior on driving behaviors. 388 drivers responded to Hasteful Behavior Questionnaire, Driver Behavior Questionnaire(DBQ), and Driving Experience Questions. Data which have missing values among them were removed, 374 data were analyzed. As a result of factor analysis, hasteful behavior consist of time pressure, uncomfortableness, isolation, boring condition, and expecting rewards, and the DBQ consist of violation, error, and lapse. The components of hasteful behavior was divided into the missing confirmation behavior and the need for achievement domain by the second factor analysis and difference verification of coefficient of correlation. The missing confirmation behavior domain of hasteful behavior had significant influence on error and lapse. The isolation of the missing confirmation behavior domain had a negative effect, and the uncomfortableness of the missing confirmation domain had a positive effect on violation. The time pressure had a negative effect, and the isolation and the uncomfortableness had a positive effect on error and lapse.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.120-130
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• 2021

The spinal cord or CSF surgery is a very complex process. It requires continuous pre and post-surgery evaluation to have a better ability to diagnose the disease. To detect automatically the suspected areas of tumors and symptoms of CSF leakage during the development of the tumor inside of the brain. We propose a new method based on using computer software that generates statistical results through data gathered during surgeries and operations. We performed statistical computation and data collection through the Google Source for the UK National Cancer Database. The purpose of this study is to address the above problems related to the accuracy of missing hybrid KNN values and finding the distance of tumor in terms of brain cancer or CSF images. This research aims to create a framework that can classify the damaged area of cancer or tumors using high-dimensional image segmentation and Laplace transformation method. A high-dimensional image segmentation method is implemented by software modelling techniques with measures the width, percentage, and size of cells within the brain, as well as enhance the efficiency of the hybrid KNN algorithm and Laplace transformation make it deal the non-zero values in terms of missing values form with the using of Frobenius Matrix for deal the space into non-zero values. Our proposed algorithm takes the longest values of KNN (K = 1-100), which is successfully demonstrated in a 4-dimensional modulation method that monitors the lighting field that can be used in the field of light emission. Conclusion: This approach dramatically improves the efficiency of hybrid KNN method and the detection of tumor region using 4-D segmentation method. The simulation results verified the performance of the proposed method is improved by 92% sensitivity of 60% specificity and 70.50% accuracy respectively.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.821-822
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• 2008

This paper presents a new demosaicking method based on weighted sum in the wavelet domain. In our method, the missing wavelet coefficients in lowest frequency subband are obtained by weighted sum. Since detail coefficients have large values at the edge region, these values are used as weighting factors. Detail coefficients are replaced by the coefficients in the corresponding subbands. Experimental results show that the proposed method generates good performance.