• Journal of Preventive Medicine and Public Health
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• v.37 no.3
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• pp.246-252
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• 2004

Objectives : Cancer is the leading cause of death in Korea. Therefore, a National Cancer Screening Program (NCSP) was launched in 1999. This study planned to evaluate the performance of the NCSP to identifying the influencing factors in relation to characteristic public health centers. Methods : To analyze the performance, the database of the NCSP records for 2002 was used. The performance index was measured by the goal achievement rate, which was defined by the real number of screenees against the expected number of screenees. Also, a survey was conducted by a self-administered questionnaire to identify the factors associated with the goal achievement rate. The questionnaire was divided into two sections. In the first section, the individual characteristics of the program coordinator in each public health center were measured, and second section was comprised of questions about the organizational characteristics associated with the NCSP. A total of 121 subjects from 241 public health centers completed the questionnaire. Results : Of the 121 public health centers (50.2% response rate), the average goal achievement rate was 72.8%. The results of the regression model showed that public health centers located in rural area (parameter estimates=38.2) and had great support from a head of center or province (parameter estimates=0.20) and tended to have higher goal achievement rates. However, the characteristics of the program coordinator, especially their knowledge of and attitude toward cancer screening, were not significantly related to the goal achievement rates. Conclusions : It appears that the most important associated factors to the goal achievement rate in the NSCP were the location of the public health center and the support for the NCSP from the head of the center or province.

• Journal of the Korea Institute of Building Construction
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• v.17 no.3
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• pp.287-294
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• 2017

Recently the construction project is becoming large-sized, complicated, and modernize. This has increased the uncertainty of construction risk. Therefore, studies should be followed regarding scientifically identifying the risk factors, quantifying the frequency and severity of risk factors in order to develop a model that can quantitatively evaluate and manage the risk for response the increased risk in construction. To address the problem, this study analyze the probability distribution of risk causes, the probability of occurrence and frequency of the specific risk level through Monte Carlo simulation method based on the accident data caused at construction sites. In the end, this study derives quantitative analysis by analyzing the amount of risk and probability distributions of accident causes. The results of this study will be a basis for future quantitative risk management models and risk management research.

• Smart Structures and Systems
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• v.11 no.4
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• pp.411-433
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• 2013

Due to its easy operation and wide applicability, the ambient vibration method is commonly adopted to determine the cable force by first identifying the cable frequencies from the vibration signals. With given vibration length and flexural rigidity, an analytical or empirical formula is then used with these cable frequencies to calculate the cable force. It is, however, usually difficult to decide the two required parameters, especially the vibration length due to uncertain boundary constraints. To tackle this problem, a new concept of combining the modal frequencies and mode shape ratios is fully explored in this study for developing an accurate method merely based on ambient vibration measurements. A simply supported beam model with an axial tension is adopted and the effective vibration length of cable is then independently determined based on the mode shape ratios identified from the synchronized measurements. With the effective vibration length obtained and the identified modal frequencies, the cable force and flexural rigidity can then be solved using simple linear regression techniques. The feasibility and accuracy of the proposed method is extensively verified with demonstrative numerical examples and actual applications to different cable-stayed bridges. Furthermore, several important issues in engineering practice such as the number of sensors and selection of modes are also thoroughly investigated.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.11 no.2
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• pp.51-55
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• 1997

The high frequency fluorescnet lighting systems are widely used because of their high luminous efficacy. However, the performance of the fluorescnet lamp at high frequency reveals significant changes depending upon operating frequency, lamp shape, lamp voltage and current while adapting either an electronic or an magnetic ballast. Therefore the matching between the fluorescent lamp and the ballast is the major concern in designing a lighting system. In this paper, high frequency characteristics of the FHF32W lamp is measured in a range of frequencies from 12kHz to 50kHz. And we presented a model of a fluorescnet lamp with non-linear impedance depending on the lamp current. Finally, after identifying the operating condition under negative imped¬ance behavior as lamp current changing, we proposed a method of choosing the optimal parameter of a high frequency fluorescnet lamp and the result is analyzed.

• Journal of Information Technology Services
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• v.14 no.3
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• pp.99-116
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• 2015

The concept of Cloud computing has been introduced in the IT field over 10 years and industry has been expanding constantly. However, compare to the maturity of global market, Korea cloud computing industry is only in the early stage. Even the Korea has advantages in technology infrastructure; the pace of Korea cloud computing market growth is taking a serious downturn. Under these circumstances, it is needed to be discussing that strategy for expanding the cloud computing market size and for sustaining global competitiveness of local companies. Previous studies on plans for Korea cloud computing market has been conducted since 2009 and most of them are tend to examined in demand perspective. Thus, this study aims at identifying the priority of business challenges for making better performance in the market with service provider aspects. To analyze the important factors in the providing cloud computing service, ANP methodology was applied in this study. The network model including five clusters, security, stability, performance, consumer, and institution, was defined through literature review and expert survey was conducted to collect data. As a result of ANP analysis, 'Securing service reliability' was analyzed as the most important factor and followed by 'Preparing the range of legal liability', 'Preventing personal information leakage' and 'Preventing confidential information data leakage.' The priority of result indicates that service provider needs to focus on to make the secured service environment. This study has significance on analyzing the priority of business challenges in the service provider perspective. This study will provide useful guidelines to for establishing strategies in cloud computing market.

• Journal of Korean society of Dental Hygiene
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• v.18 no.4
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• pp.501-513
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• 2018

Objectives: The purpose of this study was to investigate the perception of workplace bullying in the dental hygienists to use them as basic data for improving the organizational relationship of the dental hygienists. Methods: The subjects were 302 dental hygienists that had been working at the dental clinics and dental hospitals. These data were analyzed by SPSS Version 20.0 (IBM Co., Armonk, NY, USA). Factor analysis was used for exploratory and confirmatory data. Independent t-test and one-way ANOVA were used to find out mean differences for verbal violence, improper business, and improper work environment according to characteristics of subjects. Results: Upon the study results, there were statistically significant differences between the verbal violence and working patterns according to the mean differences of the subfactors on subject's characteristics. Also, there were statistically significant differences between the improper work environment and all of subject's characteristics. However, there was no statistically significant differences in the improper business. The factor analysis showed that the Workplace Bullying of the dental hygienists was composed of three elements, namely 'verbal violence', 'improper business,' and 'improper work environment.' The validity of the model examined by a confirmatory factor analysis satisfied most of the relevant requirements. The Cronbach's aplha shows a good reliability. Conclusions: In conclusion, it was proven that dental hygienist's Workplace Bullying measurement tool had high validity and reliability. Furthermore, this study can be used to improve dental hygienists' organizational relationship. Therefore, by identifying the recognition of the dental hygienists, this study can contribute to affect a positive influence in the dental hospitals.

• Journal of the Korean Society of Safety
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• v.11 no.4
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• pp.135-142
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• 1996

Prolonged in-plant personnel exposure to high noise levels results in permant hearing damage. There are no way to correct this hearing damage by treatment or use of hearing aids. Therefore, every employer is responsible for providing a workplace free of such hazards as excessive noise. This study was carried out to evalute and predict a given noise environment based on specific limit as the noise guarantee for a newly-founded petrochemical plant. The maximum total sound level should not exceed 85dBA in the work area, except where the area is defined as a restricted area and 70dBA at the plant boundary. Prediction of the noise levels within the plant area for a newly-founded petrochemical plant was achieved by dividing all plant area into 20m$\times$20m regular grid spaces and noise level inside the area or unit that in-plant personel exposure to high noise levels was estimated computed into 5m$\times$5m regular grid spaces. The noise level at the grid point that was propagated from each of the noise sources(equipments) computed using the methematical formula was defined as follows : $SPL_2$=$SPL_1-20log{\frac{r_2}{r_1}}$(dB) where $SPL_1$ =sound pressure level at distance $r_1$ from the source $SPL_2$=sound pressure level at distance $r_2$ from the source As a result, the equipments exceeded noise limit or irritaring noise levels were identified on the specific grid coordinates. As for equipments in the area that show high noise levels, appropriate counter-measures for noise control (by barriers, enclosure, silencers, or the change of equipments, for example) should be reviewed. Methods for identifying sources of noise applied in this study should be the model for prediction of the noise levels for any newly-founded plant.

• Smart Media Journal
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• v.6 no.3
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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.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.778-804
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• 2017

Software today has become an inseparable part of our life. In order to achieve the ever demanding needs of customers, it has to rapidly evolve and include a number of changes. In this paper, our aim is to study the relationship of object oriented metrics with change proneness attribute of a class. Prediction models based on this study can help us in identifying change prone classes of a software. We can then focus our efforts on these change prone classes during testing to yield a better quality software. Previously, researchers have used statistical methods for predicting change prone classes. But machine learning methods are rarely used for identification of change prone classes. In our study, we evaluate and compare the performances of ten machine learning methods with the statistical method. This evaluation is based on two open source software systems developed in Java language. We also validated the developed prediction models using other software data set in the same domain (3D modelling). The performance of the predicted models was evaluated using receiver operating characteristic analysis. The results indicate that the machine learning methods are at par with the statistical method for prediction of change prone classes. Another analysis showed that the models constructed for a software can also be used to predict change prone nature of classes of another software in the same domain. This study would help developers in performing effective regression testing at low cost and effort. It will also help the developers to design an effective model that results in less change prone classes, hence better maintenance.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.751-770
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• 2018

Web applications are indispensable in the software industry and continuously evolve either meeting a newer criteria and/or including new functionalities. However, despite assuring quality via testing, what hinders a straightforward development is the presence of defects. Several factors contribute to defects and are often minimized at high expense in terms of man-hours. Thus, detection of fault proneness in early phases of software development is important. Therefore, a fault prediction model for identifying fault-prone classes in a web application is highly desired. In this work, we compare 14 machine learning techniques to analyse the relationship between object oriented metrics and fault prediction in web applications. The study is carried out using various releases of Apache Click and Apache Rave datasets. En-route to the predictive analysis, the input basis set for each release is first optimized using filter based correlation feature selection (CFS) method. It is found that the LCOM3, WMC, NPM and DAM metrics are the most significant predictors. The statistical analysis of these metrics also finds good conformity with the CFS evaluation and affirms the role of these metrics in the defect prediction of web applications. The overall predictive ability of different fault prediction models is first ranked using Friedman technique and then statistically compared using Nemenyi post-hoc analysis. The results not only upholds the predictive capability of machine learning models for faulty classes using web applications, but also finds that ensemble algorithms are most appropriate for defect prediction in Apache datasets. Further, we also derive a consensus between the metrics selected by the CFS technique and the statistical analysis of the datasets.