• The Journal of the Acoustical Society of Korea
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• v.41 no.3
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• pp.278-286
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• 2022

In this study, a 33-mode Free-Flooded Ring (FFR) transducer was designed to apply piezoelectric single crystal PIN-PMN-PT, which has high piezoelectric constants and electromechanical coupling coefficient. To ensure low-frequency high transmitting sensitivity characteristics with a small size of FFR transducer, the commercial FFR transducer based on piezoelectric ceramics was compared. To develop the FFR transducer with broadband characteristics, a piezoelectric segmented ring structure inserted with inactive elements was applied. The oil-filled structure was applied to minimize the change of acoustic characteristics of the ring transducer. It was verified that the transmitting voltage response, underwater impedance, and beam pattern matched the finite element numerical simulation results well through an acoustic test. The difference in the transmitting voltage response between the measured and the simulated results is about 1.3 dB in cavity mode and about 0.3 dB in radial mode. The fabricated FFR transducer had a higher transmitting voltage response compared to the commercial transducer, but the diameter was reduced by about 17 %. From this study, it was confirmed that the feasibility of a single crystal-applied FFR transducer with compact size and high-power characteristics. The effectiveness of the performance prediction by simulation was also confirmed.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.4
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• pp.593-601
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• 2017

In this paper, we constructed the model of production/investment, distribution, and screening by using variables that can be considered at each stage according to the value chain stage of the movie industry. To increase the predictive power of the model, a regression analysis was used to derive meaningful variables. Based on the given variables, we compared the difference in predictive power between the artificial neural network, which is a machine learning analysis method, and the decision tree analysis method. As a result, the accuracy of artificial neural network was higher than that of decision trees when all variables were added in production/ investment model and distribution model. However, decision trees were more accurate when selected variables were applied according to regression analysis results. In the screening model, the accuracy of the artificial neural network was higher than the accuracy of the decision tree regardless of whether the regression analysis result was reflected or not. This paper has an implication which we tried to improve the performance of movie prediction model by using machine learning analysis. In addition, we tried to overcome a limitation of linear approach by reflecting the results of regression analysis to ANN and decision tree model.

• Journal of Cardiovascular Imaging
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• v.30 no.3
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• pp.185-196
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• 2022

BACKGROUND: Two-dimensional (2D) strain provides more predictive power than ejection fraction (EF) in patients with ST-elevation myocardial infarction (STEMI). 3D strain and EF are also expected to have better clinical usefulness and overcome several inherent limitations of 2D strain. We aimed to clarify the prognostic significance of 3D strain analysis in patients with STEMI. METHODS: Patients who underwent successful revascularization for STEMI were retrospectively recruited. In addition to conventional parameters, 3D EF, global longitudinal strain (GLS), global area strain (GAS), as well as 2D GLS were obtained. We constructed a composite outcome consisting of all-cause death or re-hospitalization for acute heart failure or ventricular arrhythmia. RESULTS: Of 632 STEMI patients, 545 patients (86.2%) had a reliable 3D strain analysis. During median follow-up of 49.5 months, 55 (10.1%) patients experienced the adverse outcome. Left ventricle EF, 2D GLS, 3D EF, 3D GLS, and 3D GAS were significantly associated with poor outcomes. (all, p < 0.001) The maximum likelihood-ratio test was performed to evaluate the additional prognostic value of 2D GLS or 3D GLS over the prognostic model consisting of clinical characteristics and EF, and the likelihood ratio was 15.9 for 2D GLS (p < 0.001) and 1.49 for 3D GLS (p = 0.22). CONCLUSIONS: The predictive power of 3D strain was slightly lower than the 2D strain. Although we can obtain 3D strains, volume, and EF simultaneously in same cycle, the clinical implications of 3D strains in STEMI need to be investigated further.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Journal of Preventive Medicine and Public Health
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• v.31 no.1 s.60
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• pp.91-103
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• 1998

Under experimental conditions, UVB radiation, a type of ultra violet radiation, has shown to .elate with the occurrence of skin erythema (sun-burn) in human and skin cancer in experimental animal. Cumulative exposure to UVB is also believed to be at least partly responsible for the 'aging' process of the skin in human. It has also been observed to have an effect of altering DNA (deoxyribonucleic acid). UVB radiation is both an initiator and a promoter of non-melanoma skin cancer. Meta-analysis is a new discipline that critically reviews and statistically combines the results of previous researches. A recent review of meta-analysis in the field of public health emphasized its growing importance. Using a meta-analysis in this study, we explored more reliable dose-response relationships between UVB radiation and skin cancer incidence. We estimated skin cancer incidence using measured UVB radiation dose at a local area of Seoul (Shin chou-dong). The studies showing the dose-response relationships between UVB radiation and non-melanoma skin cancer incidence were searched and selected for a meta-analysis. The data for 7 reported epidemiological studies of three counties (USA, England, Australia) were pooled to estimated the risk. We estimated rate of incidence change of skin cancer using pooled data by meta-analysis method, and exponential and power models. Using either model, the regression coefficients for UVB did not differ significantly by gender and age. In each analysis of variance, non-melanoma skin cancer incidence after removing the gender and age and UVB effects was significant (p>0.01). The coefficients for UVB dose were estimated $2.07\times10^{-6}$ by the exponential model and 2.49 by the power model. At a local area of Seoul (Shinchon-Dong), BAF value were estimated 1.90 and 2.51 by the exponential and power model, respectively. The estimated BAP value were increased statistical power than that of primary studies that using a meta-analysis method.

• Journal of the korean academy of Pediatric Dentistry
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• v.28 no.1
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• pp.16-24
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• 2001

The purpose of this study was to evaluate the specificity, sensitivity, and diagnostic power of caries activity test using laser fluorescence. The subjects of this study were 50 children of $7\sim9$ years old. Fluorescence from initial carious lesion of teeth illuminated by an argon laser(480nm) was observed and photographed with barrier filter. Visual examination for the dDfFtT rate and Streptococcus mutans colony counting was done to evaluate correlation with caries activity test using laser fluorescence. Data analysis was accomplished by Axelsson's method. The results from the present study can be summarized as follows: 1. There was positive correlation $(\gamma=0.48)$ between laser fluorescence test and Streptococcus mutans count. And also positive correlation $(\gamma=0.39)$ exists between laser fluorescence test and dDfFtT rate (P<0.01). 2. Positive correlation $(\gamma=0.27)$ between Streptococcus mutans colony count and dDfFtT rate was found(P<0.05). 3. When dDfFtT rate was defined to standard testing method, the specificity, senstivity, and diagnostic power of laser fluorescence test were 44.4%, 85.7%, and 87.8%. 4. When dDfFtT rate was defined to standard testing method, the specificity, senstivity, and diagnostic power of S. mutans colony counting were 77.8%, 92.9%, 84.8%. 5. When S. mutans colony counting was defined to standard testing method, sensitivity, specificity and diagnostic power of laser fluorescence test were 40.0%, 84.8%, 95.1%. In regard to above results, laser fluorescence test considered to be accurate and reliable method for determining caries activity because of it's close relationship with caries susceptibility test and caries experience measurements. And it was also considered to be practical because it would be simple, inexpensive, and time saving method.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.6
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• pp.423-433
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• 2019

In order to predict the seabed topography change due to the construction of offshore wind power structures in the west-southern sea of Korea, field observations for tides, tidal currents, suspended sediment concentrations and seabed sediments were carried out at the same time. These data could be used for numerical simulation. In numerical experiments, the empirical constants for the suspended sediment flux were determined by the trial and error method. When a concentration distribution factor was 0.1 and a proportional constant was 0.05 in the suspended sediment equilibrium concentration formulae, the calculated suspended sediment concentrations were reasonably similar with the observed ones. Also, it was appropriate for the open boundary conditions of the suspended sediment when the south-east boundary corner was 11.0 times, the south-west was 0.5 times, the westnorth 1.0 times, the north-west was 1.0 times and the north-east was 1.0 times, respectively, using the time series of the observed suspended sediment concentrations. In this case, the depth change was smooth and not intermittent around the open boundaries. From these calibrations, the annual water depth change before and after construction of the offshore wind power structures was shown under 1 cm. The reason was that the used numerical model for the large scale grid could not reproduce a local scour phenomenon and they showed almost no significant velocity change over ± 2 cm/s because the jacket structures with small size diameter, about 1 m, were a water-permeable. Therefore, it was natural that there was a slight change on seabed topography in the study area.

• Korean Journal of Occupational Health Nursing
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• v.11 no.1
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• pp.13-30
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• 2002

Industrial shift workers feels suffer mental stresses which are caused by unfamiliar day sleep, noisy environment, sleeping disorder by bright light, unusual contacts with family, difficulty in meeting with friends or having formal social meetings and other social limitations such as the use of transportation. Such stresses influence health of the workers negatively. Thus the health promotion policy for shift workers should be made considering the workers' ways of living and shift work specially. This study attempted to provide basic information for development of the health promotion program for industrial shift workers by examining predictive factors influencing health promotion behaviors of those workers. In designing the study, three power generation plants located in Pusan and south Kyungsang province were randomly selected and therefrom 280 workers at central control, boiler and turbine rooms and environmental chemistry parts whose processes require shift works were sampled as subjects of the study. Data were collected two times from September 17 to October 8, 1999 using questionnaires with helps of safety and health managers of the plants. The questionnaires were distributed through mails or direct visits. Means for the study included the measurement tool of health promotion behavior provided by Park(1995), the tool of self-efficacy measurement by Suh(1995), the tool of internal locus of control measurement by Oh(1987), the measurement tool of perceived health state by Park(1995) and the tool of social support measurement by Paek(1995). The collected data were analyzed using SPSS program. Controlling factors of the subjects were evaluated in terms of frequency and percentage ratio Perceived factors and health promotion behaviors of the subjects were done so in terms of mean and standard deviation, and average mark and standard deviation, respectively. Relations between controlling and perceived factors were analyzed using t-test and ANOVA and those between perceived factors and the performance of health promotion behaviors, using Pearson's Correlation Coefficient. The performance of health promotion behaviors was tested using t-test, ANOVA and post multi-comparison (Scheffe test). Predictive factors of health promotion behavior were examined through the Stepwise Multiple Regression Analysis. Results of the study are summarized as follows. 1. The performance of health promotion behaviors by the subjects was evaluated as having the value of mean, $161.27{\pm}26.73$ points(min.:60, max.:240) and average mark, $2.68{\pm}0.44$ points(min.:1, max.:4). When the performance was analyzed according to related aspects, it showed the highest level in harmonious relation with average mark, $3.15{\pm}.56$ points, followed by hygienic life($3.03{\pm}.55$), self-realization ($2.84{\pm}.55$), emotional support($2.73{\pm}.61$), regular meals($2.71{\pm}.76$), self-control($2.62{\pm}.63$), health diet($2.62{\pm}.56$), rest and sleep($2.60{\pm}.59$), exercise and activity($2.53{\pm}.57$), diet control($2.52{\pm}.56$) and special health management($2.06{\pm}.65$). 2. In relations between perceived factors of the subjects(self-efficacy, internal locus of control, perceived health state) and the performance of health promotion behaviors, the performance was found having significantly pure relations with self-efficacy (r=.524, P=.000), internal locus of control (r=.225, P=.000) and perceived health state(r=.244, P=.000). The higher each evaluated point of the three factors was, the higher the performance was in level. 3. When relations between the controlling factors(demography-based social, health-related, job-related and human relations characteristics) and the performance of health promotion behaviors were analyzed, the performance showed significant differences according to marital status (t=2.09, P= .03), religion(F=3.93, P= .00) and participation in religious activities (F=8.10, P= .00) out of demography-based characteristics, medical examination results (F=7.20, P= .00) and methods of the collection of health knowledge and information(F=3.41, P= .01) and methods of desired health education(F=3.41, P= .01) out of health-related characteristics, detrimental factors perception(F=4.49, P= .01) and job satisfaction(F=8.41, P= .00) out of job-related characteristics and social support(F=14.69, P= .00) out of human relations characteristics. 4. The factor which is a variable predicting best the performance of health promotion behaviors by the subjects was the self-efficacy accounting for 27.4% of the prediction, followed by participation in religious activities, social support, job satisfaction, received health state and internal locus of control in order all of which totally account for 41.0%. In conclusion, the predictive factor which most influence the performance of health promotion behaviors by shift workers was self-efficacy. To promote the sense, therefore, it is necessary to develop the nursing intervention program considering predictive factors as variables identified in this study. Further industrial nurses should play their roles actively to help shift workers increase their capability of self-management of health.

• Tunnel and Underground Space
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• v.30 no.4
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• pp.359-381
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• 2020

Within the framework of DECOVALEX-2019 Task D, full-scale engineered barriers experiment (FEBEX) at Grimsel Test Site was numerically simulated to investigate an applicability of implemented Barcelona basic model (BBM) into TOUGH2-MP/FLAC3D simulator, which was developed for the prediction of the coupled thermo-hydro-mechanical behavior of bentonite buffer. And the calculated heater power, temperature, relative humidity, total stress, saturation, water content and dry density were compared with in situ data monitored in the various sections. In general, the calculated heater power and temperature provided a fairly good agreement with experimental observations, however, the difference between power of heater #1 and that of heater #2 could not captured in the numerical analysis. It is necessary to consider lamprophyre with low thermal conductivity around heater #1 and non-simplified installation progresses of bentonite blocks in the tunnel for better modeling results. The evolutions and distributions of relative humidity were well reproduced, but hydraulic model needs to be modified because the re-saturation process was relatively fast near the heaters. In case of stress evolutions due to the thermal and hydraulic expansions, the computed stress was in good agreement with the data. But, the stress is slightly higher than the measured in situ data at the early stage of the operation, because gap between rock mass and bentonite blocks have not been considered in the numerical simulations. The calculated distribution of saturation, water content, and dry density along the radial distance showed good agreement with the observations after the first and final dismantling. The calculated dry density near the center of the FEBEX tunnel and heaters were overestimated compared with the observations. As a result, the saturation and water content were underestimated with the measurements. Therefore, numerical model of permeability is needed to modify for the production of better numerical results. It will be possible to produce the better analysis results and more realistically predict the coupled THM behavior in the bentonite blocks by performing the additional studies and modifying the numerical model based on the results of this study.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.