• Imaging Science in Dentistry
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• v.52 no.2
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• pp.133-140
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• 2022

Purpose: The aim of this review was to systematically analyze the available literature on the correlation between the gray values (GVs) of cone-beam computed tomography (CBCT) and the Hounsfield units (HUs) of computed tomography (CT) for assessing bone mineral density. Materials and Methods: A literature search was carried out in PubMed, Cochrane Library, Google Scholar, Scopus, and LILACS for studies published through September 2021. In vitro, in vivo, and animal studies that analyzed the correlations GVs of CBCT and HUs of CT were included in this review. The review was prepared according to the PRISMA checklist for systematic reviews, and the risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. A quantitative analysis was performed using a fixed-effects model. Results: The literature search identified a total of 5,955 studies, of which 14 studies were included for the qualitative analysis and 2 studies for the quantitative analysis. A positive correlation was observed between the GVs of CBCT and HUs of CT. Out of the 14 studies, 100% had low risks of bias for the domains of patient selection, index test, and reference standards, while 95% of studies had a low risk of bias for the domain of flow and timing. The fixed-effects meta-analysis performed for Pearson correlation coefficients between CBCT and CT showed a moderate positive correlation (r=0.669; 95% CI, 0.388 to 0.836; P<0.05). Conclusion: The available evidence showed a positive correlation between the GVs of CBCT and HUs of CT.

• Asia-Pacific Journal of Business
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• v.13 no.3
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• pp.19-34
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• 2022

Purpose - The purpose of this study is to examine whether a psychological concept enhances healthcare users' service experience. Specifically, the study proposes and empirically examines a model of perceived control in which the user's sense of control is postulated as exerting positive influences upon his/her motivation, self-efficacy associated with his/her role as a patient, and satisfaction with his/her medical service experience. Methodology - Data were collected by a professional research company, using an online survey method. Participants of the study included adults nineteen years or older who had visited a medical service institute at least once during the previous one-year period. For the test of the research hypotheses, structural equation modeling using AMOS was used. Findings - Findings of this study denote a unique insight into the users' comprehension of medical service experiences and their behaviors. First, the concept of perceived control is identified as a factor that enhances the quality of individuals' medical service experiences. A sense of control directly influences medical users' self-efficacy to comply with doctor's recommendations, their motivation to comply with doctor's recommendations, and their satisfaction with the medical service experience. Second, one's perceived self-efficacy is found to exert positive influences upon both motivation and satisfaction. Third, one's motivation to comply with the doctor's recommendation is found to exert a positive influence upon one's satisfaction. Additionally, perceived control is found to exert an indirect influence upon medical service users' satisfaction through the mediation of both self-efficacy and motivation. Research Implications - The findings of the study support the notion that perception of control among medial service users enhances their service experience as patients. The main thrust of this study suggests that it is necessary for healthcare practitioners to consider implementing service encounter strategies that purposefully enhance the sense of control among their patients. The identification of significant inter-relationships among perceived control, motivation, self-efficacy, and satisfaction among medical service customers should also serve as a meaningful seed for further research pursuits.

• Journal of Korean Association for Spatial Structures
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• v.22 no.3
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• pp.33-40
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• 2022

Recently, cultural heritages in South Korea gain many interests of restoration and preservation from the government since many of that have been severely damaged during earthquakes. Many previous studies in both terms of experimental and analytical approaches have been done to examine structural behavior and decide appropriate methods of preservation. Being motivated by such researches, this research aims to investigate a religious stone pagoda dated back to the Goryeo Dynasty in Korea. The structure consists of a granite stone foundation and baked bricks, which resembles the shape of traditional pagodas. In order to examine the structural behavior of the pagoda, an analytical model is implemented using ANSYS, a comprehensive engineering simulation platform. For the time history analysis of the pagoda, several earthquake excitations are chosen and input to simulation modeling. Seismic response of the tower such as time domain, natural frequency, modal shapes and peak acceleration measured at each layer are presented and discussed. In addition, the amplification ratio of the tower is calculated from the accelerations of each layer to determine tower stability in accordance with Korean seismic design guide. The determination and evaluation of status and response of the brick tower by simulation analysis play an important role in the preservation of history as well as valuable architectural heritages in South Korea.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.235-248
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• 2022

Through the research on the design evaluation index of parent-child interactive game furniture, it is convenient for designers to quantitatively analyze the design advantages and disadvantages of related products, which is of positive help to control and improve the design quality. Combined with AHP and TOPSIS, this study proposes the evaluation model of three design criteria and 26 design indexes. After expert scoring, calculation, and consistency test of each index, the weight value of each design index is obtained, and the index is classified according to the importance of each index. Finally, eight essential indicators, eleven secondary indicators, and seven general indicators are classified. A case study was conducted with TOPSIS, and the design samples of three parent-child climbing game furniture were analyzed. Finally, the three samples' relative proximity was 0.505, 0281, and 0.640, respectively. The research shows that the AHP-TOPSIS method can scientifically and effectively sort and screen the advantages and disadvantages of design schemes and provide a reference for the research and development of related products.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• Human Ecology Research
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• v.60 no.3
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• pp.471-481
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• 2022

This study aimed at examining the mediation effects of depressive symptoms on the association between social activity participation and marital satisfaction among couples in later life. The study included 1,196 married couples aged 65 or above who participated in the 7th Korean Longitudinal Study of Aging in 2018. The study variables were husbands' and wives' social activity participation, marital satisfaction, and depressive symptoms. Covariates were individual age, education level, subjective health, couples' household income, and number of children. The Actor-Partner Interdependence Mediation Model (APIMeM) and bootstrapping techniques were used to test the significance of the mediating impacts of depressive symptoms based on the dyadic data structure. The results showed that for both husbands and wives, active participation in social activities had significant effects on increasing marital satisfaction through lowering depressive symptoms. However, only the participation of husbands in social activities was positively associated with their own marital satisfaction by reducing their own and their partner's depressive symptoms. These findings suggest that the active social participation of individuals salient for promoting the quality of marital relationships in later life, particularly for preventing their own and their spouse's depressive symptoms.

• Smart Structures and Systems
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• v.31 no.4
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• pp.421-436
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• 2023

This paper presents a vibration displacement measurement and damage identification method for a space truss structure from its vibration videos. Features from Accelerated Segment Test (FAST) algorithm is combined with adaptive threshold strategy to detect the feature points of high quality within the Region of Interest (ROI), around each node of the truss structure. Then these points are tracked by Kanade-Lucas-Tomasi (KLT) algorithm along the video frame sequences to obtain the vibration displacement time histories. For some cases with the image plane not parallel to the truss structural plane, the scale factors cannot be applied directly. Therefore, these videos are processed with homography transformation. After scale factor adaptation, tracking results are expressed in physical units and compared with ground truth data. The main operational frequencies and the corresponding mode shapes are identified by using Subspace Stochastic Identification (SSI) from the obtained vibration displacement responses and compared with ground truth data. Structural damages are quantified by elemental stiffness reductions. A Bayesian inference-based objective function is constructed based on natural frequencies to identify the damage by model updating. The Success-History based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE) is applied to minimise the objective function by tuning the damage parameter of each element. The locations and severities of damage in each case are then identified. The accuracy and effectiveness are verified by comparison of the identified results with the ground truth data.

• Journal of the Korean Geotechnical Society
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• v.23 no.1
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• pp.67-76
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• 2007

Flat DMT penetration was analyzed using a finite element model based on a large strain formulation. The ABAQUS/Explicit, a commercial finite element method, was used to study the flat DMT penetration in soils. Then, because the very large mesh distortion occurred due to the penetration of flat DMT, the adaptive meshing technique was utilized to maintain a high quality mesh configuration. The undrained shear strength obtained from the flat DMT is estimated using only the horizontal stress index ($K_{D}$) and so it became necessary to examine using the analysis results obtained from the penetration of the flat DMT. Analysis results show that in normally consolidated region of $K_{D}=2$, the results obtained from the correlations proposed by Marchetti show good agreement with those estimated from the finite element method. The present analysis also shows that in overconsolidated region of $K_{D}>2$, the results obtained from the relationships proposed by Kamei and Iwasaki show good agreement with those provided by the penetration analysis.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.167-174
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• 2009

The meaning of Database in order to manage the data which is huge in the meeting of the record which logically had become the fire tube or file 'efficiently' is widely used from the place which controls a many double meaning data. Like this data base it creates, it manages, the programs which send an answer back according to demand of the user as DBMS it calls. Like this it will be able to grasp the quality level of the data base software product which is important index from the research which index it buys it defined. Also, in order to produce the result of index it selects the collection item which is necessary and collection and analysis it leads and what kind of defect types occur substantially mainly, and it confirmed and the test and evaluation model in about data base software and a tentative instance it developed it analyzed.

• Korean Journal of Cognitive Science
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• v.34 no.3
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• pp.197-226
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• 2023

Generative Text Summarization is one of the Natural Language Processing tasks. It generates a short abbreviated summary while preserving the content of the long text. ROUGE is a widely used lexical-overlap based metric for text summarization models in generative summarization benchmarks. Although it shows very high performance, the studies report that 30% of the generated summary and the text are still inconsistent. This paper proposes a methodology for evaluating the performance of the summary model without using the correct summary. AggreFACT is a human-annotated dataset that classifies the types of errors in neural text summarization models. Among all the test candidates, the two cases, generation summary, and when errors occurred throughout the summary showed the highest correlation results. We observed that the proposed evaluation score showed a high correlation with models finetuned with BART and PEGASUS, which is pretrained with a large-scale Transformer structure.