• Journal of Preventive Medicine and Public Health
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• 제53권3호
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• pp.171-174
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• 2020

The coronavirus disease 2019 (COVID-19) pandemic has not only caused significant challenges for health systems all over the globe but also fueled the surge of numerous rumors, hoaxes, and misinformation, regarding the etiology, outcomes, prevention, and cure of the disease. Such spread of misinformation is masking healthy behaviors and promoting erroneous practices that increase the spread of the virus and ultimately result in poor physical and mental health outcomes among individuals. Myriad incidents of mishaps caused by these rumors have been reported globally. To address this issue, the frontline healthcare providers should be equipped with the most recent research findings and accurate information. The mass media, healthcare organization, community-based organizations, and other important stakeholders should build strategic partnerships and launch common platforms for disseminating authentic public health messages. Also, advanced technologies like natural language processing or data mining approaches should be applied in the detection and removal of online content with no scientific basis from all social media platforms. Furthermore, these practices should be controlled with regulatory and law enforcement measures alongside ensuring telemedicine-based services providing accurate information on COVID-19.

• 지역사회간호학회지
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• 제30권2호
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• pp.109-118
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• 2019

Purpose: The purpose of this study is to identify factors affecting the quality of life among community-dwelling older women with urinary incontinence (UI). Methods: A cross-sectional study was conducted with 475 women aged 60 or over who were recruited from 10 primary health care facilities in rural Korea. Data were collected using a structured questionnaire consisting of socio-demographic, health-related, and UI-related characteristics. The quality of life was assessed using Incontinence Quality of Life (I-QOL). SPSS/WIN 23.0 program was used to analyze descriptive statistics, $x^2$ test, t-test, ANOVA, Pearson's Correlation, and hierarchical regression. Results: Of 475 subjects, 180 (37.9%) had urinary incontinence. The mean scores of I-QOL of women with and without UI were 76.87 and 94.77, respectively. The results of hierarchical regression analysis show that the Questionnaire for Urinary Incontinence Diagnosis total score was the greatest influencing factor, followed by age and the International Consultation on Incontinence Questionnaire-Short Form total score. Conclusion: The study revealed that factors related to UI symptoms are more likely to have impact on the quality of life in women with UI. It suggests that early detection or management of UI is important in improving the quality of life of women with UI.

• The Journal of Asian Finance, Economics and Business
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• 제9권10호
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• pp.215-225
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• 2022

This study investigates employees' online reviews, conducts in-depth text topic mining, effectively summarizes the dimensions of employer brand value, and seeks effective ways to build employer brands from a multi-dimensional perspective. This study employs samples of employer reviews, filter keywords according to word frequency-inverse document frequency, builds a review network containing the same keywords, explore the community and summarize the theme dimensions. Simultaneously, it makes a dynamic comparison and analysis of the employer brand value dimension of different industries and enterprises. The study shows that the community exploration theme can be summarized into 11 dimensions of employer brand value, and the dimensions of employer brand value are significantly different across industries and among different enterprises within the industry. The attention to the employer brand value dimension has a significant time change. Various industries pay increasing attention to the dimension of work intensity and career development, while employers pay steady attention to the dimension of welfare benefits. The findings of this study suggest that seeking the heterogeneity of employer brand resources from the multi-dimensional differences and changes is an effective way to improve the competitiveness of enterprises in the human capital market.

• 한국지하수토양환경학회지:지하수토양환경
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• 제27권3호
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• pp.1-10
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• 2022

Most wells developed in Jeju island before the enactment of the Groundwater Management Ordinance in 2002 are vulnerable to aquifer contamination due to inflow of upper groundwater having the high concentration of nitrate nitrogen, likely due to incomplete grouting in upper section of the wells. Although these wells require entire reinstallation, it is often necessary to rehabilitate the existing wells due to various constraints. Therefore, to identified the inflow section of contaminants, the thermal level sensor (TLS) technique was firstly applied for three wells, which enables to monitor temperature variations in every 50 cm depth. Then, the grouting material was injected to the upper section to prevent the inflow of upper contaminated groundwater into the entire aquifer. By applying TLS technique, it was found that the temperature deviations in the upper groundwater inflow section decreased sharply. Moreover, both the change in the concentration of nitrate nitrogen in the rainy/dry seasons and the average concentrations were found to decrease rapidly after grouting material injection. Consequently, the application of TLS proposed in the study turned out to be appropriate to prevent aquifer contamination.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제22권8호
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• pp.357-362
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• 2016

소셜 네트워크나 바이오 네트워크는 인터랙션이 가능한 오브젝트들이 관계를 맺음으로써 형성되는 복잡 네트워크이다. 실세계에 존재하는 복잡 네트워크는 커뮤니티 구조로 구성되어 있으며, 이 커뮤니티 구조를 자동으로 발견하는 것은 그 네트워크를 제어하고 이해하는데 있어서 중요한 기술이다. 하지만 이런 네트워크들은 시간에 따라 오브젝트들의 인터랙션에 의해 그 네트워크의 구조와 위상이 불특정하게 변화한다. 이런 동적 네트워크에서 노드들 간에 인터랙션을 기반으로 한 커뮤니티 구조를 발견하는 것은 높은 시간 복잡도 연산이 요구되며, 반복된 계산을 비효율적으로 처리하는 문제점이 있다. 따라서 본 연구에서는 동적 네트워크에서 인터랙션 기반 커뮤니티 구조를 점진적으로 발견하는 기법을 제안한다. 제안하는 기법은 이전 네트워크에서 변화한 요소들을 인지하고, 이전 커뮤니티 그룹 구조를 점진적으로 재활용함으로써 효율적인 커뮤니티 발견이 가능하다.

• 스마트미디어저널
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• 제6권3호
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• pp.41-48
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• 2017

Event detection using social media has been widespread since social network services have been an active communication channel for connecting with others, diffusing news message. Especially, the real-time characteristic of social media has created the opportunity for supporting for real-time applications/systems. Social network such as Twitter is the potential data source to explore useful information by mining messages posted by the user community. This paper proposed a novel system for temporal event detection by analyzing social data. As a result, this information can be used by first responders, decision makers, or news agents to gain insight of the situation. The proposed approach takes advantages of deep learning methods that play core techniques on the main tasks including informative data identifying from a noisy environment and temporal event detection. The former is the responsibility of Convolutional Neural Network model trained from labeled Twitter data. The latter is for event detection supported by Recurrent Neural Network module. We demonstrated our approach and experimental results on the case study of earthquake situations. Our system is more adaptive than other systems used traditional methods since deep learning enables to extract the features of data without spending lots of time constructing feature by hand. This benefit makes our approach adaptive to extend to a new context of practice. Moreover, the proposed system promised to respond to acceptable delay within several minutes that will helpful mean for supporting news channel agents or belief plan in case of disaster events.

• 한국정밀공학회지
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• 제6권4호
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• pp.71-83
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• 1989

The optimization in the machining process has been a long-standing goal of the manufacturing community. The optimization is composed of two main subjects;one is to select an optimum cutting condition, and the other is to detect the emergency situation and take necessary actions in real-time base. This paper proposes a reliable and practical guide system whose purpose is the optimization of cutting conditions, and the detection of tool failure in the machining process. The optimal cutting conditions are determined through the estimation of tool wear rate and the establishment of access- ible field from the measured cutting temperature and force. Tool breakage is detected by the normal force component acting on minor flank face extracted from on-line sensed feed force and radial force. In experiments, the proposed guide system has proved availability for the decision of reliable cutting conditions for the given tool-work system and the detection of tool breakage in ordinary cutting environments.

• Environmental Analysis Health and Toxicology
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• 제23권4호
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• pp.231-245
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• 2008

Various methods for detecting changes in response behaviors of indicator specimens are presented for monitoring effects of toxic treatments. The movement patterns of individuals are quantitatively characterized by statistical (i.e., ANOVA, multivariate analysis) and computational (i.e., fractal dimension, Fourier transform) methods. Extraction of information in complex behavioral data is further illustrated by techniques in ecological informatics. Multi-Layer Perceptron and Self-Organizing Map are applied for detection and patterning of response behaviors of indicator specimens. The recent techniques of Wavelet analysis and line detection by Recurrent Self-Organizing Map are additionally discussed as an efficient tool for checking time-series movement data. Behavioral monitoring could be established as new methodology in integrative ecological assessment, tilling the gap between large-scale (e.g., community structure) and small-scale (e.g., molecular response) measurements.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 춘계정기학술대회
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• pp.293-304
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• 2001

frauds detection is a difficult problem, requiring huge computer resources and complicated search activities. researchers have struggled with the problem. Even though a flew research approaches have claimed that their solution is much bettor than others, research community has not found 'the best solution'well fitting every fraud. Because of the evolving nature of the frauds, a Revel and self-adapting method should be devised. In this research a new approach is suggested to solving frauds in insurance claims and credit card transaction. Based on evolutionary computing approach, the method is itself self-adjusting and evolving enough to generate a new set of decision-making rules. We believe that this new approach will provide a promising alternative to conventional ones, in terms of computation performance and classification accuracy.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 춘계학술발표대회
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• pp.9-11
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• 2022

Community detection is a hot topic in social network analysis, and many existing studies use graph theory analysis methods to detect communities. This paper focuses on detecting absolute fair maximal balanced cliques in signed attributed social networks, which can satisfy ensuring the fairness of complex networks and break the bottleneck of the "information cocoon".