• The korean journal of orthodontics
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• v.49 no.4
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• pp.246-253
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• 2019

Objective: To evaluate the effectiveness of three different caries-preventing agents on artificial caries in a Streptococcus mutans-based caries model. Methods: Sixty-five caries-free human molar enamel blocks were treated with a demineralization solution and a remineralization solution. The specimens were assigned to the following groups according to the caries-protective product applied: group A, chlorhexidine varnish; group B, fluoride-releasing chemically cured sealant; group C, fluoride-releasing lightcured sealant; group D, positive control (specimens that were subjected to de- and remineralization cycles without treatment with any caries-protective agents); and group E, negative control (specimens that were not subjected to de- and remineralization cycles). Samples in groups A-D were stored in demineralization solution with S. mutans and thereafter in artificial saliva. This procedure was performed for 30 days. Average fluorescence loss (${\Delta}F$) and surface size of the lesions were measured using quantitative light-induced fluorescence at baseline and on the 7th, 14th, and 30th days. Results: After 30 days, group A demonstrated a significant increase in ΔF and the surface size of the lesions, no significant difference in comparison with the positive control group, and a significant difference in comparison with the negative control group. Group B showed no significant changes in both parameters at any of the measurement points. While group C showed increased ${\Delta}F$ after 14 days, no significant fluorescence change was observed after 30 days. Conclusions: Both fluoride-releasing sealants (chemically or light-cured) show anti-cariogenic effects, but the use of chlorhexidine varnish for the purpose of caries protection needs to be reconsidered.

• Smart Structures and Systems
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• v.24 no.1
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• pp.53-65
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• 2019

Misaligned wind-wave and seismic loading render offshore wind turbines suffering from excessive bi-directional vibration. However, most of existing research in this field focused on unidirectional vibration mitigation, which is insufficient for research and real application. Based on the authors' previous work (Sun and Jahangiri 2018), the present study uses a three dimensional pendulum tuned mass damper (3d-PTMD) to mitigate the nacelle structural response in the fore-aft and side-side directions under wind, wave and near-fault ground motions. An analytical model of the offshore wind turbine coupled with the 3d-PTMD is established wherein the interaction between the blades and the tower is modelled. Aerodynamic loading is computed using the Blade Element Momentum (BEM) method where the Prandtl's tip loss factor and the Glauert correction are considered. Wave loading is computed using Morison equation in collaboration with the strip theory. Performance of the 3d-PTMD is examined on a National Renewable Energy Lab (NREL) monopile 5 MW baseline wind turbine under misaligned wind-wave and near-fault ground motions. The robustness of the mitigation performance of the 3d-PTMD under system variations is studied. Dual linear TMDs are used for comparison. Research results show that the 3d-PTMD responds more rapidly and provides better mitigation of the bi-directional response caused by misaligned wind, wave and near-fault ground motions. Under system variations, the 3d-PTMD is found to be more robust than the dual linear TMDs to overcome the detuning effect. Moreover, the 3d-PTMD with a mass ratio of 2% can mitigate the short-term fatigue damage of the offshore wind turbine tower by up to 90%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.5
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• pp.495-507
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• 2019

Data sparsity is one of the main challenges for the recommender system. The recommender system contains massive data in which only a small part is the observed data and the others are missing data. Most studies assume that missing data is randomly missing from the dataset. Therefore, they only use observed data to train recommendation model, then recommend items to users. In actual case, however, missing data do not lost randomly. In our research, treat these missing data as negative examples of users' interest. Three sample methods are seamlessly integrated into SVD++ algorithm and then propose SVD++_W, SVD++_R and SVD++_KNN algorithm. Experimental results show that proposed sample methods effectively improve the precision in Top-N recommendation over the baseline algorithms. Among the three improved algorithms, SVD++_KNN has the best performance, which shows that the KNN sample method is a more effective way to extract the negative examples of the users' interest.

• Yonsei Medical Journal
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• v.59 no.9
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• pp.1057-1063
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• 2018

Purpose: Coronary artery spasm (CAS) and diabetes mellitus (DM) are implicated in endothelial dysfunction, and insulin resistance (IR) is a major etiological cause of type 2 DM. However, the association between CAS and IR in non-diabetic individuals has not been elucidated. The aim of the present study was to evaluate the impact of IR on CAS in patients without DM. Materials and Methods: A total of 330 eligible patients without DM and coronary artery disease who underwent acetylcholine (Ach) provocation test were enrolled in this study. Inclusion criteria included both hemoglobin A1c <6.0% and fasting glucose level <110 mg/dL without type 2 DM. Patients were divided into quartile groups according the level of homeostasis model assessment of insulin resistance (HOMA-IR): 1Q (n=82; HOMA-IR<1.35), 2Q (n=82; $1.35{\leq}HOMA-IR<1.93$), 3Q (n=83; $1.93{\leq}HOMA-IR<2.73$), and 4Q (n=83; $HOMA-IR{\geq}2.73$). Results: In the present study, the higher HOMA-IR group (3Q and 4Q) was older and had higher body mass index, fasting blood glucose, serum insulin, hemoglobin A1c, total cholesterol, and triglyceride levels than the lower HOMA-IR group (1Q). Also, poor IR (3Q and 4Q) was considerably associated with frequent CAS. Compared with Q1, the hazard ratios for Q3 and Q4 were 3.55 (95% CI: 1.79-7.03, p<0.001) and 2.12 (95% CI: 1.07-4.21, p=0.031), respectively, after adjustment of baseline risk confounders. Also, diffuse spasm and accompanying chest pain during Ach test were more strongly associated with IR patients with CAS. Conclusion: HOMA-IR was significantly negatively correlated with reference diameter measured after nitroglycerin and significantly positively correlated with diffuse spasm and chest pain.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.23-30
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• 2021

An Automated Industry and Occupation Coding System assigns statistical classification code to the enormous amount of natural language data collected from people who write about their industry and occupation. Unlike previous studies that applied information retrieval, we propose a system that does not need an index database and gives proper code regardless of the level of classification. Also, we show our model, which utilized KoBERT that achieves high performance in natural language downstream tasks with deep learning, outperforms baseline. Our method achieves 95.65%, 91.51%, and 97.66% in Occupation/Industry Code Classification of Population and Housing Census, and Industry Code Classification of Census on Basic Characteristics of Establishments. Moreover, we also demonstrate future improvements through error analysis in the respect of data and modeling.

• Journal of Information Science Theory and Practice
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• v.9 no.2
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• pp.1-17
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• 2021

Pseudo relevance feedback (PRF) is a powerful query expansion (QE) technique that prepares queries using the top k pseudorelevant documents and choosing expansion elements. Traditional PRF frameworks have robustly handled vocabulary mismatch corresponding to user queries and pertinent documents; nevertheless, expansion elements are chosen, disregarding similarity to the original query's elements. Word embedding (WE) schemes comprise techniques of significant interest concerning QE, that falls within the information retrieval domain. Deep averaging networks (DANs) defines a framework relying on average word presence passed through multiple linear layers. The complete query is understandably represented using the average vector comprising the query terms. The vector may be employed for determining expansion elements pertinent to the entire query. In this study, we suggest a DANs-based technique that augments PRF frameworks by integrating WE similarities to facilitate Arabic information retrieval. The technique is based on the fundamental that the top pseudo-relevant document set is assessed to determine candidate element distribution and select expansion terms appropriately, considering their similarity to the average vector representing the initial query elements. The Word2Vec model is selected for executing the experiments on a standard Arabic TREC 2001/2002 set. The majority of the evaluations indicate that the PRF implementation in the present study offers a significant performance improvement compared to that of the baseline PRF frameworks.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.717-723
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• 2020

Determination of local tie vectors between the space geodetic techniques such as VLBI (Very Long Baseline Interferometer), SLR (Satellite Laser Ranging), DORIS (Doppler Orbit determination and Radiopositioning Integrated on Satellite), GNSS (Global Navigation Satellite System) is essential for combination of ITRF (International Terrestrial Reference Frame). Therefore, it is required to compute IVP (Invariant Point) position of each space geodetic technique with high accuracy. In this study, we have computed Sejong VLBI IVP position by using updated mathematical model for adjustment computation so that the improvement on efficiency and reliability in computation are obtained. The measurements used for this study are the coordinates of reflective targets on the VLBI antenna and their accuracies are set to 1.5 mm for each component. The results show that the position of VLBI IVP together with its standard deviation is successfully estimated when they are compared with those of the results from previous study. However, it is notable that additional terrestrial surveying should be performed so that realistic measurement errors are incorporated in the adjustment computation process.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.179-188
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• 2022

There have been many successful researches on a bearing fault diagnosis based on Deep Learning, but there is still a critical issue of the data distribution difference between training data and test data from their different working conditions causing performance degradation in applying those methods to the machines in the field. As a solution, a data adaptation method has been proposed and showed a good result, but each and every approach is strictly limited to a specific applying scenario or presupposition, which makes it still difficult to be used as a real-world application. Therefore, in this study, we have proposed a method that, using a data transformation with MFCCs and a simple CNN architecture, can perform a robust diagnosis on a target domain data without an additional learning or tuning on the model generated from a source domain data and conducted an experiment and analysis on the proposed method with the CWRU bearing dataset, which is one of the representative datasests for bearing fault diagnosis. The experimental results showed that our method achieved an equal performance to those of transfer learning based methods and a better performance by at least 15% compared to that of an input transformation based baseline method.

• The Journal of Korean Academy of Sensory Integration
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• v.19 no.3
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• pp.44-60
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• 2021

Objective : The purpose of this study was to identify the characteristics of domestic single-subject research related to the participation of students with intellectual disabilities in classes. Methods : We investigated a total of five papers to determine the features and qualitative levels of the papers being analyzed. Results : A total of 12 subjects were studied, and experiments were conducted at all stages. All research used a multiple baseline design. The independent variables were the positive behavior support approach (2), the strength-oriented activities approach, the self-decision teaching and learning model approach, and the self-monitoring approach. As a dependent variables, there were four pieces set up exclusively for class participation behavior, and one that was set up mixed with class interruptions behaviors. The qualitative level of the studies to be analyzed was 100% of the high level. Conclusion : Through this study, single-subject studies that applied interventions related to participation in class for intellectual disabilities in elementary schools have confirmed that effective interventions were applied and that the quality levels were reliable.

• Journal of Korea Multimedia Society
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• v.25 no.3
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• pp.493-501
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• 2022

This paper is a study on context-sensitive spelling error correction and uses the Korean WordNet (KorLex)[1] that defines the relationship between words as a graph to improve the performance of the correction[2] based on the vector information of the word embedded in the correction technique. The Korean WordNet replaced WordNet[3] developed at Princeton University in the United States and was additionally constructed for Korean. In order to learn a semantic network in graph form or to use it for learned vector information, it is necessary to transform it into a vector form by embedding learning. For transformation, we list the nodes (limited number) in a line format like a sentence in a graph in the form of a network before the training input. One of the learning techniques that use this strategy is Deepwalk[4]. DeepWalk is used to learn graphs between words in the Korean WordNet. The graph embedding information is used in concatenation with the word vector information of the learned language model for correction, and the final correction word is determined by the cosine distance value between the vectors. In this paper, In order to test whether the information of graph embedding affects the improvement of the performance of context- sensitive spelling error correction, a confused word pair was constructed and tested from the perspective of Word Sense Disambiguation(WSD). In the experimental results, the average correction performance of all confused word pairs was improved by 2.24% compared to the baseline correction performance.