• 한국산학기술학회논문지
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• 제14권8호
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• pp.3748-3759
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• 2013

본 연구의 목적은 DINA(deterministic-input, noisy "and" gate)모형에서 최대우도(maximum likelihood: ML), 최대사후확률(maximum a posteriori: MAP), 사후기대(expected a posteriori: EAP)방법들의 분류 정확성이 어느 정도인가를 알아보는 것이다. 연구 목적을 달성하기 위하여 다양한 모의실험 조건들[인지요소의 수(K= 5, 7), 응시생 능력분포(고능력, 중간능력, 저능력 집단), 검사 길이(J= 15, 30, 45)]에 따라 모의자료를 생성했다. 응시생 분류 정확성을 평가하기 위한 준거로 참 인지요소(true ${\alpha}$)와 ML, MAP, EAP방법으로 추정된 인지요소가 어느 정도 일치하는지를 계산했다. 본 연구의 주요결과를 요약하면 다음과 같다. 첫째, 본 연구에서 설정한 검사 조건에서 ML, MAP방법보다 EAP방법의 정확일치도 평균이 높았다. 둘째, 다른 검사 조건이 동일할 때, 인지요소의 수가 증가하면 ML, MAP, EAP방법 모두에서 정확일치도 평균이 낮아졌다. 셋째, 동일한 검사 길이에서 사전분포로 고능력, 중간능력, 저능력 집단을 각각 가정했을 때 ML, MAP방법보다 EAP방법의 정확일치도 평균이 높았다. 넷째, 동일한 응시생 능력분포에서 검사 길이가 증가하면 ML, MAP, EAP방법 모두에서 정확일치도 평균이 높아졌다. 인지요소의 수에 따라 응시생을 정확하게 분류하기 위한 적절한 검사 길이를 보면, 인지요소의 수가 5, 7개이고 이에 대응하는 검사 길이가 각각 30, 45문항일 때 본 연구에서 설정한 높은 분류 정확성 기준에 부합하는 것으로 나타났다.

• Communications for Statistical Applications and Methods
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• 제23권4호
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• pp.269-285
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• 2016

In this paper, a simple step-stress accelerated life test (ALT) under progressive type-II censoring is considered. Progressive type-II censoring and accelerated life testing are provided to decrease the lifetime of testing and lower test expenses. The cumulative exposure model is assumed when the lifetime of test units follows an extension of the exponential distribution. Maximum likelihood estimates (MLEs) and Bayes estimates (BEs) of the model parameters are also obtained. In addition, a real dataset is analyzed to illustrate the proposed procedures. Approximate, bootstrap and credible confidence intervals (CIs) of the estimators are then derived. Finally, the accuracy of the MLEs and BEs for the model parameters is investigated through simulation studies.

• Journal of Microbiology and Biotechnology
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• 제22권1호
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• pp.34-45
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• 2012

A thermophilic Bacillus stearothermophilus F1 produces an extremely thermostable serine protease. The F1 protease sequence was used to predict its three-dimensional (3D) structure to provide better insights into the relationship between the protein structure and biological function and to identify opportunities for protein engineering. The final model was evaluated to ensure its accuracy using three independent methods: Procheck, Verify3D, and Errat. The predicted 3D structure of F1 protease was compared with the crystal structure of serine proteases from mesophilic bacteria and archaea, and led to the identification of features that were related to protein stabilization. Higher thermostability correlated with an increased number of residues that were involved in ion pairs or networks of ion pairs. Therefore, the mutants W200R and D58S were designed using site-directed mutagenesis to investigate F1 protease stability. The effects of addition and disruption of ion pair networks on the activity and various stabilities of mutant F1 proteases were compared with those of the wild-type F1 protease.

• Imaging Science in Dentistry
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• 제53권4호
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• pp.271-281
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• 2023

Purpose: The objective of this scoping review was to investigate the applicability and performance of various convolutional neural network (CNN) models in tooth numbering on panoramic radiographs, achieved through classification, detection, and segmentation tasks. Materials and Methods: An online search was performed of the PubMed, Science Direct, and Scopus databases. Based on the selection process, 12 studies were included in this review. Results: Eleven studies utilized a CNN model for detection tasks, 5 for classification tasks, and 3 for segmentation tasks in the context of tooth numbering on panoramic radiographs. Most of these studies revealed high performance of various CNN models in automating tooth numbering. However, several studies also highlighted limitations of CNNs, such as the presence of false positives and false negatives in identifying decayed teeth, teeth with crown prosthetics, teeth adjacent to edentulous areas, dental implants, root remnants, wisdom teeth, and root canal-treated teeth. These limitations can be overcome by ensuring both the quality and quantity of datasets, as well as optimizing the CNN architecture. Conclusion: CNNs have demonstrated high performance in automated tooth numbering on panoramic radiographs. Future development of CNN-based models for this purpose should also consider different stages of dentition, such as the primary and mixed dentition stages, as well as the presence of various tooth conditions. Ultimately, an optimized CNN architecture can serve as the foundation for an automated tooth numbering system and for further artificial intelligence research on panoramic radiographs for a variety of purposes.

• The Journal of Advanced Prosthodontics
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• 제16권4호
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• pp.201-211
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• 2024

PURPOSE. The aim of this prospective clinical study was to compare the influence of palatal vault forms on accuracy and speed of intraoral (IO) scans in completely edentulous cases. MATERIALS AND METHODS. Based on the palatal vault form, participants were divided into three equal groups (n = 10 each); Class I: moderate; Class II: deep; Class III: flat palatal vault. A reference model was created for each patient using polyvinylsiloxane impression material. The poured models were digitized using an extraoral scanner. The resultant data were imported as a solid CAD file into 3D analysis software (GOM Inspect 2018; Gom GmbH, Braunschweig, Germany) and aligned using the software's coordinate system to determine its X, Y, and Z axes. Five digital impressions (DIs) of maxilla were captured for each patient using an intraoral scanner (TRIOS; 3Shape A/S, Copenhagen, Denmark) and the resultant Standard Tessellation Language (STL) scan files served as test models. Trueness was evaluated by calculating arithmetic mean deviation (AMD) of the vault area between reference and test files while precision was evaluated by calculating AMD between captured scans to measure repeatability of scan acquisition. The scan time taken for each participant was also recorded. RESULTS. There was no significant difference in trueness and precision among the groups (P = .806 and .950, respectively). Average scan time for Class I and III palatal vaults was 1 min 13 seconds and 1 min 37 seconds, respectively, while class II deep palatal vaults showed the highest scan time of 5 mins. CONCLUSION. Palatal vault form in edentulous cases has an influence on scan time. However, it does not have a substantial impact on the accuracy of the acquired scans.