• Journal of the Korean GEO-environmental Society
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• v.22 no.10
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• pp.5-12
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• 2021

The deterioration of facilities is an unavoidable phenomenon. For the management of aging facilities, cracks can be detected and tracked, and the condition of the facilities can be indirectly inferred. Therefore, crack detection plays a crucial role in the management of aged facilities. Conventional maintenances are conducted using the crack detection results. For example, maintenance activities to prevent further deterioration can be performed. However, currently, most crack detection relies only on human judgment, so if the area of the facility is large, cost and time are excessively used, and different judgment results may occur depending on the expert's competence, it causes reliability problems. This paper proposes a concrete crack detection framework based on machine learning to overcome these limitations. Fully automated concrete crack detection was possible through the proposed framework, which showed a high accuracy of 96%. It is expected that effective and efficient management will be possible through the proposed framework in this paper.

• Journal of dental hygiene science
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• v.21 no.3
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• pp.185-197
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• 2021

Background: This study aimed at investigating the meal regularity, health, and oral health habits of single Korean households to understand the impact of these factors on the risk of metabolic syndrome, in addition to preventing and managing metabolic syndrome. Methods: Using raw data from the 8th Korea National Health and Nutrition Examination Survey (2019), 274 study subjects, aged 19 to 64, were selected primarily from single adult households. Complex sample statistical analysis was performed using the Predictive Analytics Software Statistics ver. 18.0 program. Results: Regarding the meal regularity in single-person households in Korea, the younger group outperformed the middle-aged group, and those who drank more than once a month performed better than those who drank less than once a month. In terms of oral health, regardless of the age and the income level, participants who ate three meals a day had a higher rate of speech problems and chewing difficulties than those who ate irregularly or regularly on a regular day. Factors influencing the risk of developing metabolic syndrome were age, speech problems, and frequency of toothbrushing. Compared to the younger group, there were 0.361 times more people in the middle-aged group; and compared to those without speech problems, there were 1.161 more people with speech problem. Compared to those who tooth brushed more than four times a day, there were 1.284 more people who tooth brushed 2 to 3 times a day and there were 5.673 times more people who tooth brushed less than once. Conclusion: Based on the study results, it is necessary to implement a program that can plan and apply customized management measures and prevent metabolic syndrome by improving and correcting the health and oral health behaviors of single-person households in Korea. Therefore, active mediation measures, such as support and publicity at the local or national level, should be planned.

• Journal of Information Science Theory and Practice
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• v.6 no.4
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• pp.6-16
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• 2018

This article shows an approach to the study of two fundamental aspects of the prepublication of scientific manuscripts in specialized repositories (arXiv). The first refers to the size of the interaction of "standard papers" in journals appearing in the Web of Science (WoS)-now Clarivate Analytics-and "non-standard papers" (manuscripts appearing in arXiv). Specifically, we analyze the citations found in the WoS to articles in arXiv. The second aspect is how publication in arXiv affects the citation count of authors. The question is whether or not prepublishing in arXiv benefits authors from the point of view of increasing their citations, or rather produces a dispersion, which would diminish the relevance of their publications in evaluation processes. Data have been collected from arXiv, the websites of the journals, Google Scholar, and WoS following a specific ad hoc procedure. The number of citations in journal articles published in WoS to preprints in arXiv is not large. We show that citation counts from regular papers and preprints using different sources (arXiv, the journal's website, WoS) give completely different results. This suggests a rather scattered picture of citations that could distort the citation count of a given article against the author's interest. However, the number of WoS references to arXiv preprints is small, minimizing this potential negative effect.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.15-26
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• 2023

The purpose of this study is to determine the artificial intelligence-based degradation index from the image of the cross-section of the microstructure taken with a scanning electron microscope of the specimen obtained by the creep test of DA-5161 SX, a nickel-based superalloy used as a material for high-temperature parts. It proposes a new method of quantification and proposes a model that predicts degradation based on Bayesian inference without destroying components of high-temperature parts of operating equipment and a creep life prediction model that predicts Larson-Miller Parameter (LMP). It is proposed that the new degradation indexing method that infers a consistent representative value from a small amount of images based on the geometrical characteristics of the gamma prime phase, a nickel-base superalloy microstructure, and the prediction method of degradation index and LMP with information on the environmental conditions of the material without destroying high-temperature parts.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.1-7
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• 2022

With the development of artificial intelligence, efforts to incorporate neuroscience mining with AI have increased. Neuroscience mining, also known as NSM, expands on this concept by combining computational neuroscience and business analytics. Using fNIRS (functional near-infrared spectroscopy)-based experiment dataset, we have investigated the potential of NSM in the context of the BPSC (business problem-solving creativity) prediction. Although BPSC is regarded as an essential business differentiator and a difficult cognitive resource to imitate, measuring it is a challenging task. In the context of NSM, appropriate methods for assessing and predicting BPSC are still in their infancy. In this sense, we propose a novel NSM method that systematically combines CNN, BiLSTM, and attention network for the sake of enhancing the BPSC prediction performance significantly. We utilized a dataset containing over 150 thousand fNIRS-measured data points to evaluate the validity of our proposed NSM method. Empirical evidence demonstrates that the proposed NSM method reveals the most robust performance when compared to benchmarking methods.

• Journal of the Society of Disaster Information
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• v.19 no.1
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• pp.69-75
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• 2023

Purpose: The risk of disaster from extreme weather events is increasing due to the increase in occurrence and the strength of heavy rains and storms from continued climate change. To reduce these risks, emergency weather information customized for the characteristics of the information users and related circumstances should be provided. Method: A first-stage emergency weather information delivery system has been developed to provide weather information to the disaster-risk area residents and the disaster response personnel. Novel methods to apply artificial intelligence to identify emergencies have been studied. The relationship between special weather reports from meteorological administration and disaster-related news articles has been analyzed to identify the significance of a pilot study using text analytic artificial intelligence. Result: The basis to identify the significance of the relations between disaster-related articles and special weather reports has been established and the possibility of the development of a real-world applicable system based on a broader analysis of data has been suggested. Conclusion: Through direct alert delivery of weather emergency alerts, a weather emergency alert system is expected to reduce the risk of damage from extreme weather situations.

• Smart Structures and Systems
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• v.31 no.4
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• pp.335-349
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• 2023

Bridges constantly undergo deterioration and damage, the most common ones being concrete damage and exposed rebar. Periodic inspection of bridges to identify damages can aid in their quick remediation. Likewise, identifying components can provide context for damage assessment and help gauge a bridge's state of interaction with its surroundings. Current inspection techniques rely on manual site visits, which can be time-consuming and costly. More recently, robotic inspection assisted by autonomous data analytics based on Computer Vision (CV) and Artificial Intelligence (AI) has been viewed as a suitable alternative to manual inspection because of its efficiency and accuracy. To aid research in this avenue, this study performs a comparative assessment of different architectures, loss functions, and ensembling strategies for the autonomous segmentation of bridge components and damages. The experiments lead to several interesting discoveries. Nested Reg-UNet architecture is found to outperform five other state-of-the-art architectures in both damage and component segmentation tasks. The architecture is built by combining a Nested UNet style dense configuration with a pretrained RegNet encoder. In terms of the mean Intersection over Union (mIoU) metric, the Nested Reg-UNet architecture provides an improvement of 2.86% on the damage segmentation task and 1.66% on the component segmentation task compared to the state-of-the-art UNet architecture. Furthermore, it is demonstrated that incorporating the Lovasz-Softmax loss function to counter class imbalance can boost performance by 3.44% in the component segmentation task over the most employed alternative, weighted Cross Entropy (wCE). Finally, weighted softmax ensembling is found to be quite effective when used synchronously with the Nested Reg-UNet architecture by providing mIoU improvement of 0.74% in the component segmentation task and 1.14% in the damage segmentation task over a single-architecture baseline. Overall, the best mIoU of 92.50% for the component segmentation task and 84.19% for the damage segmentation task validate the feasibility of these techniques for autonomous bridge component and damage segmentation using RGB images.

• Restorative Dentistry and Endodontics
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• v.46 no.3
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• pp.42.1-42.15
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• 2021

Objectives: This study aimed to analyze the main features of the 25 most-cited articles in minimally invasive access cavities. Materials and Methods: An electronic search was conducted on the Clarivate Analytics' Web of Science 'All Databases' to identify the most-cited articles related to this topic. Citation counts were cross-matched with data from Elsevier's Scopus and Google Scholar. Information about authors, contributing institutions and countries, year and journal of publication, study design and topic, access cavity, and keywords were analyzed. Results: The top 25 most-cited articles received a total of 572 (Web of Science), 1,160 (Google Scholar) and 631 (Scopus) citations. It was observed a positive significant association between the number of citations and age of publication (r = 0.6907, p < 0.0001); however, there was no significant association regarding citation density and age of publication (r = -0.2631, p = 0.2038). The Journal of Endodontics made the highest contribution (n = 15, 60%). The United States had the largest number of publications (n = 7) followed by Brazil (n = 4), with the most contributions from the University of Tennessee and Grande Rio University (n = 3), respectively. The highest number of most-cited articles were ex vivo studies (n = 16), and 'fracture resistance' was the major topic studied (n = 10). Conclusions: This study revealed a growing interest for researchers in the field of minimally invasive access cavities. Future trends are focused on the expansion of collaborative networks and the conduction of laboratory studies on under-investigated parameters.

• The Journal of Bigdata
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• v.6 no.2
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• pp.109-124
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• 2021

In this paper, the structural characteristics of Industry-University cooperation networks are analyzed using network analysis. Recent studies have shown that technological cooperation and joint research has a positive effect on R&D performance. In order to boost innovation performance, various types of cooperative activities and governmental policy supports for major R&D stakeholders(i.e. universities, laboratories, etc.) are provided. However, despite these efforts, the outcome is still insufficient, so it is time to prepare for a plan to build an innovative network to strengthen university-centered Industry-University cooperation activities. Specifically, this study builds the networks according to the form of Industry-University cooperations(i.e. patent, paper, joint research, and technology transfer), and different types of Industry-University cooperation networks are analyzed from a statistical viewpoint by using QAP correlation and regression analyses. The analysis results show that joint research network is closely related to paper network, and is related to other Industry-University cooperation networks. This study is expected to shed a light on supporting innovation activities such as establishing Industry-University cooperation strategies and discovering cooperative partners necessary for creating new growth engines for universities.

• 한국노년학
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• v.39 no.4
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• pp.883-893
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• 2019

The purpose of this study is to analyze the characteristics of the elderly patients with depression who were admitted to the hospital with intentional self-harm. 3,280 patients were selected from KCDC database(2011-2015) using STATA 12.0. Analysis results show that gender(female), residence(micropolitan city), result of suicide(death), risk factors(financial problems, psychological problems, physical disease, conflicts with family, place(non-residence) method of suicide(poisoning) were statistically significant. The hospital should detect the elderly patient with depression when they admitted.