• Journal of the korean academy of Pediatric Dentistry
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• v.50 no.2
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• pp.179-191
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• 2023

The purpose of this study was to investigate the prevalence and patterns of congenitally missing teeth in permanent teeth excluding third molars, in patients aged 8 to 16 years who visited Pusan National University Dental Hospital from January 2010 to February 2021. This retrospective study evaluated tooth agenesis and the pattern of missing teeth represented by the tooth agenesis code by reviewing panoramic radiographs and electronic medical records of 11,759 patients, including 5,548 females and 6,211 males. The prevalence of congenitally missing teeth was 10.74% (females 11.95%, males 9.66%, p < 0.0001). Patients with tooth agenesis had an average of 2.22 missing teeth, and congenitally missing teeth occurred more frequently in the mandible (8.39%) than in the maxilla (4.52%, p < 0.0001). The mandibular second premolar (58.19%) was the most frequently missing tooth. The second premolar was the most frequently missing tooth in all quadrants (30.10%, 31.67%, 43.14%, and 35.59%) when a single tooth was absent, while the first and second premolars were the most commonly absent teeth (11.69%, 11.47%, 5.94%, and 5.24%) when two or more teeth were missing. In the relationship between maxillary-mandibular antagonistic quadrants and full mouth, the 1st to 4th place of the missing patterns were all involved with the 1st and 2nd premolars. This study can be clinically helpful in establishing a treatment plan for patients with missing teeth. In addition, it can be used as basic data for molecular biological research to find out the relationship between tooth agenesis and specific genes.

• Journal of Korean Society for Quality Management
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• v.22 no.1
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• pp.169-178
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• 1994

Nonparametric tests for comparing two treatments when data are subject to unequal patterns of censorship are discussed. Best precedence test proposed by Slud can be viewed as a nice alternative test comparing with weighted log-rank tests, not to mention the advantage of short experimental period. This research revises some missing parts of Slud's test and examines the asymptotic power of it under the nonproportional hazard alternatives through the simulation. The simulation studies show best precedence test has reasonable power in the sense of robustness under nonproportional hazard alternatives and could be recommended at such situation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5782-5799
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• 2018

With the development of GPS and the popularity of mobile devices with positioning capability, collecting massive amounts of trajectory data is feasible and easy. The daily trajectories of moving objects convey a concise overview of their behaviors. Different social roles have different trajectory patterns. Therefore, we can identify users or groups based on similar trajectory patterns by mining implicit life patterns. However, most existing daily trajectories mining studies mainly focus on the spatial and temporal analysis of raw trajectory data but missing the essential semantic information or behaviors. In this paper, we propose a novel trajectory semantics calculation method to identify groups that have similar behaviors. In our model, we first propose a fast and efficient approach for stay regions extraction from daily trajectories, then generate semantic trajectories by enriching the stay regions with semantic labels. To measure the similarity between semantic trajectories, we design a semantic similarity measure model based on spatial and temporal similarity factor. Furthermore, a pruning strategy is proposed to lighten tedious calculations and comparisons. We have conducted extensive experiments on real trajectory dataset of Geolife project, and the experimental results show our proposed method is both effective and efficient.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1035-1046
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• 2022

This study evaluated the applicability of multi-resolution data fusion for land-cover classification. In the applicability evaluation, a spatial time-series geostatistical deconvolution/fusion model (STGDFM) was applied as a multi-resolution data fusion model. The study area was selected as some agricultural lands in Iowa State, United States. As input data for multi-resolution data fusion, Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat satellite images were used considering the landscape of study area. Based on this, synthetic Landsat images were generated at the missing date of Landsat images by applying STGDFM. Then, land-cover classification was performed using both the acquired Landsat images and the STGDFM fusion results as input data. In particular, to evaluate the applicability of multi-resolution data fusion, two classification results using only Landsat images and using both Landsat images and fusion results were compared and evaluated. As a result, in the classification result using only Landsat images, the mixed patterns were prominent in the corn and soybean cultivation areas, which are the main land-cover type in study area. In addition, the mixed patterns between land-cover types of vegetation such as hay and grain areas and grass areas were presented to be large. On the other hand, in the classification result using both Landsat images and fusion results, these mixed patterns between land-cover types of vegetation as well as corn and soybean were greatly alleviated. Due to this, the classification accuracy was improved by about 20%p in the classification result using both Landsat images and fusion results. It was considered that the missing of the Landsat images could be compensated for by reflecting the time-series spectral information of the MODIS images in the fusion results through STGDFM. This study confirmed that multi-resolution data fusion can be effectively applied to land-cover classification.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.84-89
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• 2023

In water resource management, data prediction is performed using artificial intelligence, and companies, governments, and institutions continue to attempt to efficiently manage resources through this. LSTM is a model specialized for processing time series data, which can identify data patterns that change over time and has been attempted to predict groundwater level data. However, groundwater level data can cause sen-sor errors, missing values, or outliers, and these problems can degrade the performance of the LSTM model, and there is a need to improve data quality by processing them in the pretreatment stage. Therefore, in pre-dicting groundwater data, we will compare the LSTM model with the MSE and the model after normaliza-tion through distribution, and discuss the important process of analysis and data preprocessing according to the comparison results and changes in the results.

• The korean journal of orthodontics
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• v.51 no.5
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• pp.337-345
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• 2021

Objective: To investigate the phenotypes and predominant skeletodental pattern in pre-adolescent patients with Pierre-Robin sequence (PRS). Methods: The samples consisted of 26 Korean pre-adolescent PRS patients (11 boys and 15 girls; mean age at the investigation, 9.20 years) treated at the Department of Orthodontics, Seoul National University Dental Hospital between 1998 and 2019. Dental phenotypes, oral manifestation, cephalometric variables, and associated anomalies were investigated and statistically analyzed. Results: Congenitally missing teeth (CMT) were found in 34.6% of the patients (n = 9/26, 20 teeth, 2.22 teeth per patient) with 55.5% (n = 5/9) exhibiting bilaterally symmetric missing pattern. The mandibular incisors were the most common CMT (n = 11/20). Predominant skeletodental patterns included Class II relationship (57.7%), posteriorly positioned maxilla (76.9%) and mandible (92.3%), hyper-divergent pattern (92.3%), high gonial angle (65.4%), small mandibular body length to anterior cranial base ratio (65.4%), linguoversion of the maxillary incisors (76.9%), and linguoversion of the mandibular incisors (80.8%). Incomplete cleft palate (CP) of hard palate with complete CP of soft palate (61.5%) was the most frequently observed, followed by complete CP of hard and soft palate (19.2%) and CP of soft palate (19.2%) (p < 0.05). However, CP severity did not show a significant correlation with any cephalometric variables except incisor mandibular plane angle (p < 0.05). Five craniofacial and 15 extra-craniofacial anomalies were observed (53.8% patients); this implicated the need of routine screening. Conclusions: The results might provide primary data for individualized diagnosis and treatment planning for pre-adolescent PRS patients despite a single institution-based data.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.703-713
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• 2002

In longitudinal studies, outcomes are repeatedly measured over time for each subject. It is common to have missing values or dropouts for longitudinal data. In this study time trend in longitudinal data with dropouts is of concern. The confounding of time trend with dropout process is investigated through simulation studies. Some simulation results are reported for binary responses as well as continuous responses with patterns of dropouts varying. It has been found that time trend is not confounded with random dropout process for binary responses when it is estimated using GEE.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.11
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• pp.987-992
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• 2014

Register control of roll-to-roll printing system for printed electronics requires accurate measurement of register errors. The register marks used for the recognition of patterns position between layers have inherently defects due to low printability of register marks themselves, which brings out inaccurate register accuracy and consequently low performance of printed electronics devices. In this study, the compensation methods for the unrecognized or missing register data are proposed to improve the recognition and consequently the control performance of register accuracy in roll-to-roll printing equipment. The compensation methods using the prior data and the linear interpolation are proposed and compared with the case without compensation for the simulation as well as experiment. Only the linear interpolation method could successfully compensate the missing data and consequently improve the register control performance. We should apply the compensation process of the register errors to improve the register control accuracy in the roll-to-roll printing equipment.

• Nuclear Engineering and Technology
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• v.53 no.3
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• pp.888-893
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• 2021

The International Atomic Energy Agency has developed a tomographic imaging system for accomplishing the total fuel rod-by-rod verification time of fuel assemblies within the order of 1-2 h, however, there are still limitations for some fuel types. The aim of this study is to develop a deep learning-based denoising process resulting in increasing the tomographic image acquisition speed of fuel assembly compared to the conventional techniques. Convolutional AutoEncoder (CAE) was employed for denoising the low-quality images reconstructed by filtered back-projection (FBP) algorithm. The image data set was constructed by the Monte Carlo method with the FBP and ground truth (GT) images for 511 patterns of missing fuel rods. The de-noising performance of the CAE model was evaluated by comparing the pixel-by-pixel subtracted images between the GT and FBP images and the GT and CAE images; the average differences of the pixel values for the sample image 1, 2, and 3 were 7.7%, 28.0% and 44.7% for the FBP images, and 0.5%, 1.4% and 1.9% for the predicted image, respectively. Even for the FBP images not discriminable the source patterns, the CAE model could successfully estimate the patterns similarly with the GT image.

• Smart Structures and Systems
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• v.33 no.2
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• pp.93-103
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• 2024

Despite the rapid development of sensors, structural health monitoring (SHM) still faces challenges in monitoring due to the degradation of devices and harsh environmental loads. These challenges can lead to measurement errors, missing data, or outliers, which can affect the accuracy and reliability of SHM systems. To address this problem, this study proposes a classification method that detects anomaly patterns in sensor data. The proposed classification method involves several steps. First, data scaling is conducted to adjust the scale of the raw data, which may have different magnitudes and ranges. This step ensures that the data is on the same scale, facilitating the comparison of data across different sensors. Next, informative features in the time and frequency domains are extracted and used as input for a deep neural network model. The model can effectively detect the most probable anomaly pattern, allowing for the timely identification of potential issues. To demonstrate the effectiveness of the proposed method, it was applied to actual data obtained from a long-span cable-stayed bridge in China. The results of the study have successfully verified the proposed method's applicability to practical SHM systems for civil infrastructures. The method has the potential to significantly enhance the safety and reliability of civil infrastructures by detecting potential issues and anomalies at an early stage.