• The korean journal of orthodontics
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• v.44 no.3
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• pp.119-127
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• 2014

Objective: To evaluate the long-term effects of self-ligating brackets (SLBs) on transverse dimensions of arches and skeletal and soft tissues and to quantitatively evaluate the treatment outcome after non-extraction treatment with SLBs. Methods: The sample consisted of 24 (18 female and six male) subjects, with a mean age of $14.23{\pm}2.19$ years, who received treatment with the Damon$^{(R)}$3 appliances. Complete records including cephalometric radiographs and plaster models were obtained before treatment (T1), immediately after treatment (T2), six months after treatment (T3), and two years (T4) after treatment. Digital study models were generated. Twenty lateral cephalometric, six frontal cephalometric, and eight dental cast measurements were examined. The Peer Assessment Rating index was used to measure the treatment outcome. The Wilcoxon test was applied for statistical analysis of the changes. Results: There were significant increases in all transverse dental cast measurements with active treatment. There was some significant relapse in the long term, particularly in maxillary width (p < 0.05). Statistically significant increases were found in nasal (p < 0.001), maxillary base, upper molar, lower intercanine, and antigonial (p < 0.05) widths in T1-T2. Lower incisors were proclined and protruded in T1-T2. Conclusions: SLBs correct crowding by mechanisms involving incisor proclination and protrusion and expansion of the dental arches, without induction of clinically significant changes in hard and soft tissues of the face.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.18-29
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• 2019

In the field of Human-Computer Interaction design, persuasive design has gradually been applied to the system development and design process, especially for mobile application design. However, most mobile applications have hitherto a very short using lifecycle. Especially, design features with long-term persuasive effectiveness remain to be further researched and developed. In this study, we focused on investigating and identifying the durative persuasive design characteristics through a data mining process and evaluating the durative effectiveness through a long-term observation process. Total five hundred healthcare-related mobile applications were selected from Apple iTunes Store and a mixed method was conducted to extract the most common persuasive design characteristics. Based on the results of extraction, a representative healthcare-related mobile application was selected as experimental subject. Total one hundred and twenty participants were observed during a six-months experiment and the monitoring data of app usage of all participants was collected once a week. According to the evaluation model for behavior change identification process, participants with habit formation features were proved to have a significant long-term perception level for ten persuasive design characteristics. Further interview research was performed to investigate the participant's long-term perceptions on those characteristics for the purpose of identifying the durative persuasions. The results indicated that a long-term durative effectiveness can be observed and healthcare-related apps designed with those characteristics could have durative effectiveness. This study may contribute to the improvement of future mobile application designs in user experience and durative persuasion, as well as bringing future benefits for both mobile application developers and users.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10a
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• pp.80-81
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• 2007

• The Journal of Advanced Prosthodontics
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• v.6 no.4
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• pp.245-252
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• 2014

PURPOSE. To present a literature review on implant overdentures after a brief survey of bone loss after extraction of all teeth. MATERIALS AND METHODS. Papers on alveolar bone loss and implant overdentures have been studied for a narrative review. RESULTS. Bone loss of the alveolar process after tooth extraction occurs with great individual variation, impossible to predict at the time of extraction. The simplest way to prevent bone loss is to avoid extraction of all teeth. To keep a few teeth and use them or their roots for a tooth or root-supported overdenture substantially reduces bone loss. Jaws with implant-supported prostheses show less bone loss than jaws with conventional dentures. Mandibular 2-implant overdentures provide patients with better outcomes than do conventional dentures, regarding satisfaction, chewing ability and oral-health-related quality of life. There is no strong evidence for the superiority of one overdenture retention-system over the others regarding patient satisfaction, survival, peri-implant bone loss and relevant clinical factors. Mandibular single midline implant overdentures have shown promising results but long-term results are not yet available. For a maxillary overdenture 4 to 6 implants splinted with a bar provide high survival both for implants and overdenture. CONCLUSION. In edentulous mandibles, 2-implant overdentures provide excellent long-term success and survival, including patient satisfaction and improved oral functions. To further reduce the costs a single midline implant overdenture can be a promising option. In the maxilla, overdentures supported on 4 to 6 implants splinted with a bar have demonstrated good functional results.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.2
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• pp.103-109
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• 2015

In this paper, a novel method for the classification of term and preterm birth is proposed based on time-frequency analysis of electrohysterogram (EHG) using multivariate empirical mode decomposition (MEMD). EHG is a promising study for preterm birth prediction, because it is low-cost and accurate compared to other preterm birth prediction methods, such as tocodynamometry (TOCO). Previous studies on preterm birth prediction applied prefilterings based on Fourier analysis of an EHG, followed by feature extraction and classification, even though Fourier analysis is suboptimal to biomedical signals, such as EHG, because of its nonlinearity and nonstationarity. Therefore, the proposed method applies prefiltering based on MEMD instead of Fourier-based prefilters before extracting the sample entropy feature and classifying the term and preterm birth groups. For the evaluation, the Physionet term-preterm EHG database was used where the proposed method and Fourier prefiltering-based method were adopted for comparative study. The result showed that the area under curve (AUC) of the receiver operating characteristic (ROC) was increased by 0.0351 when MEMD was used instead of the Fourier-based prefilter.

• JSTS:Journal of Semiconductor Technology and Science
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• v.12 no.1
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• pp.46-52
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• 2012

Separate extraction of source ($R_S$) and drain ($R_D$) resistances caused by process, layout variations and long term degradation is very important in modeling and characterization of MOSFETs. In this work, we propose "Avalanche Hot-Source Method (AHSM)" for simple separated extraction of $R_S$ and $R_D$ in a single device. In AHSM, the high field region near the drain works as a new source for abundant carriers governing the current-voltage relationship in the MOSFET at high drain bias. We applied AHSM to n-channel MOSFETs as single-finger type with different channel width/length (W/L) combinations and verified its usefulness in the extraction of $R_S$ and $R_D$. We also confirmed that there is a negligible drift in the threshold voltage ($V_T$) and the subthreshold slope (SSW) even after application of the method to devices under practical conditions.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.41-51
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• 2023

In this paper, we propose a technique of multi-time window feature extraction for anger detection in gait data. In the previous gait-based emotion recognition methods, the pedestrian's stride, time taken for one stride, walking speed, and forward tilt angles of the neck and thorax are calculated. Then, minimum, mean, and maximum values are calculated for the entire interval to use them as features. However, each feature does not always change uniformly over the entire interval but sometimes changes locally. Therefore, we propose a multi-time window feature extraction technique that can extract both global and local features, from long-term to short-term. In addition, we also propose an ensemble model that consists of multiple classifiers. Each classifier is trained with features extracted from different multi-time windows. To verify the effectiveness of the proposed feature extraction technique and ensemble model, a public three-dimensional gait dataset was used. The simulation results demonstrate that the proposed ensemble model achieves the best performance compared to machine learning models trained with existing feature extraction techniques for four performance evaluation metrics.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.42 no.4
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• pp.205-208
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• 2016

Actinomycosis is an infection caused by the actinomyces genus and is associated with trauma or previous infection. A 58-year-old male patient was referred from a private dental clinic for root extraction of the lower right molar. The x-ray showed fractured root-like material distal to the distal root of the lower right second molar. A biopsy during extraction of the root-like material was performed, which revealed a sequestrum with actinomycosis by a pathological examination. In this case, the radiopacity of the suspicious lesion was higher than that of the surrounding alveolar bone, which confused it with the root tip. The diagnosis of actinomycosis required long-term antimicrobial therapy, which is very different from simple extraction or removal of sequestrum.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.91-94
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• 2021

As deep learning technologies becoming developed, realistic fake videos synthesized by deep learning models called "Deepfake" videos became even more difficult to distinguish from original videos. As fake news or Deepfake blackmailing are causing confusion and serious problems, this paper suggests a novel model detecting Deepfake videos. We chose Residual Convolutional Neural Network (Resnet50) as an extraction model and Long Short-Term Memory (LSTM) which is a form of Recurrent Neural Network (RNN) as a classification model. We adopted cosine similarity with hinge loss to train our extraction model in embedding the features of Deepfake and original video. The result in this paper demonstrates that temporal features in the videos are essential for detecting Deepfake videos.

• Journal of the Korea Organic Resources Recycling Association
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• v.11 no.4
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• pp.90-96
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• 2003

This study was performed to evaluate effect of extraction mode on SVE efficiency for fuel-contaminated soil. A gas station was selected for this study. As a result of pressure test in well head, soil texture of contaminated site under the gas station was very different from site to site. SVE system was operated in intermittent mode (1hr extraction / 3hr rest) or continuous mode. Capacity of air blower was $1m^3/min$. Extration mode test was conducted in two severe contaminated sites. In both two sites, cumulative TPHgas mass of intermittent extraction mode was higher than that of continuous mode. Considering long term operation of SVE in a field, in general, it was thought that intermittent extraction mode was effective in view of vaporized TPHgas mass and electrical cost.