• KIPS Transactions on Computer and Communication Systems
• /
• v.6 no.11
• /
• pp.437-444
• /
• 2017

Electric Pole is a supporting beam used for power transmission/distribution which is sensitive to external force change of environmental factors. Therefore, power facilities have many difficulties in terms of maintenance/conservation from external environmental changes and natural disasters that cause a great economic impact. The aerial wire cause elasticity due to the influence of temperature, or factors such as wind speed and wind direction, that weakens the electric pole. The situation may lead to many safety risk in day-to-day life. But, the safety assessment of the pole is carried out at the design stage, and aftermath is not considered. For the safety and maintenance purposes, it is very important to analyze the influence of weather factors on external forces periodically. In this paper, we analyze the acceleration data of the sensor nodes installed in electric pole for maintenance/safety purpose and use Kalman filter as noise compensation method. Fast Fourier Transform (FFT) is performed to analyze the influence of each meteorological factor, along with the meteorological factors on frequency components. The result of the analysis shows that the temperature, humidity, solar radiation, hour of daylight, air pressure, wind direction and wind speed were influential factors. In this paper, the influences of meteorological factors on frequency components are different, and it is thought that it can be an important factor in achieving the purpose of safety and maintenance.

• KIPS Transactions on Computer and Communication Systems
• /
• v.6 no.11
• /
• pp.445-452
• /
• 2017

Electric pole is a supporting beam used for power transmission/distribution which accelerometer are used for measuring a external force. The meteorological condition has various effects on the external forces of electric pole. One of them is the elasticity change of the aerial wire. It is very important to perform modelling. The acceleration sensor is converted into a pitch and a roll angle. The meteorological condition has a high correlation between variables, and selecting significant explanatory variables for modeling may result in the problem of over-fitting. We constructed high deviance explained model considering multicollinearity using the Generalized Additive Model which is one of the machine learning methods. As a result of the Variation Inflation Factor Test, we selected and fitted the significant variable as temperature, precipitation, wind speed, wind direction, air pressure, dewpoint, hours of daylight and cloud cover. It was noted that the Hours of daylight, cloud cover and air pressure has high explained value in explonatory variable. The average coefficient of determination (R-Squared) of the Generalized Additive Model was 0.69. The constructed model can help to predict the influence on the external forces of electric pole, and contribute to the purpose of securing safety on utility pole.

• The Journal of Korean society of community based occupational therapy
• /
• v.7 no.3
• /
• pp.13-21
• /
• 2017

Purpose : This article was to study mental health status through aggression and stress of children of multicultural families, marriage immigrant and to use them as baseline data. Method : We used questionnaire and collected them from 135 children of multicultural families who live in 2 city and 4 do. Frequency Analysis was used for Demographic Characteristics, t-test and one-way ANOVA for aggression and stress depending on demographic characteristics, and regression analysis for the factors. Result : The first, the total score of aggression of the children of multicultural families was 3.05 and the most high score was verbal aggression, 3.69. Stress was 3.66. The second, there was a significant difference between aggression depending on demographic characteristics and verbal aggression(p=.031) depending on age and anger(p=.011). There was also a significant difference between total aggression(p=.028) depending on economic level and physical aggression(p=.049), verbal aggression(p=.000), anger(p=.036), hostility(p=.042), and stress(p=.011). The third, we analysed the factors affecting aggression of children of multicultural families. There was a significant difference resulting from stepwise regression analysis(F=57.139, p<.001), the results showed a strong explanation of aggression by bad in economic status(p<.01), stress(p<.01), 10 years in age(p<.01), and 13 years in age(p<.01). Conclusion : Aggression depending on demographic characteristics of the children of multicultural families was caused by age, economic level, and stress.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.20 no.3
• /
• pp.125-140
• /
• 2017

Until recently, regression based spatial interpolation methods and Kriging based spatial interpolation methods have been largely used to estimate land price or housing price, but less attention has been paid on comparing the performance of these spatial interpolation methods. In this regard, this research applied regression based spatial interpolators and Kriging based spatial interpolators for estimating the land prices in Dalseo-gu, Daegu metropolitan city and evaluated the accuracy of eight spatial interpolators. OLS, SLM, SEM, and GWR were used as regression based spatial interpolators while SK, OK, UK, and CK were employed as Kriging based spatial interpolators. The global accuracy was statistically evaluated by RMSE, adjusted RMSE, and COD. The relative accuracy was visually compared by three-dimensional residual error map and scatterplot. Results from statistical and visual analyses indicate that GWR reflecting the spatial non-stationarity was a relatively more accurate spatial predictor to estimate land prices in the study area than SAR and Kriging based spatial interpolators considering the spatial dependence. The findings from this research will contribute to the secondary research into analyzing the urban spatial structure with land prices.

• Science of Emotion and Sensibility
• /
• v.20 no.3
• /
• pp.61-70
• /
• 2017

Physiological responses have been measured to recognize emotion. Although physiological responses have been interrelated between organs, their connectivities have been less considered for emotion recognizing. The connectivities have been assumed to enhance emotion recognition. Specially, autonomic nervous system is physiologically modulated by the interrelated functioning. Therefore, this study has been tried to analyze connectivities between heart and respiration and to find the significantly connected variables for emotion recognition. The eighteen subjects(10 male, age $24.72{\pm}2.47$) participated in the experiment. The participants were asked to listen to predetermined sound stimuli (arousal, relaxation, negative, positive) for evoking emotion. The bio-signals of heart and respiration were measured according to sound stimuli. HRV (heart rate variability) and BRV (breathing rate variability) spectrum were obtained from spectrum analysis of ECG (electrocardiogram) and RSP (respiration). The synchronization of HRV and BRV spectrum was analyzed according to each emotion. Statistical significance of relationship between them was tested by one-way ANOVA. There were significant relation of synchronization between HRV and BRV spectrum (synchronization of HF: F(3, 68) = 3.605, p = 0.018, ${\eta}^2_p=0.1372$, synchronization of LF: F(3, 68) = 5.075, p = 0.003, ${\eta}^2_p=0.1823$). HF difference of synchronization between ECG and RSP has been able to classify arousal from relaxation (p = 0.008, d = 1.4274) and LF's has negative from positive (p = 0.002, d = 1.7377). Therefore, it was confirmed that the heart and respiration to recognize the dimensional emotion by connectivity.

• Journal of Hospice and Palliative Care
• /
• v.18 no.1
• /
• pp.42-50
• /
• 2015

Purpose: This study was aimed to analyze how social workers understand the rights for elderly patient and family caregiver to make end-of-life (EOL) care decisions and their roles the decision making process. Methods: The study employed a quantitative research method of collecting data from a structured questionnaire that was filled out by 334 social workers at long-term care facilities. Data were analyzed by descriptive statistics, mean differences, correlation between variables, using SPSS 20.0 program. Results: The mean score for the understanding the rights to an EOL care decision was $3.46{\pm}0.69$ and of their own roles $3.48{\pm}0.84$. The level of understanding significantly differed by social workers' experience of assisting a process to make an EOL care decision such as advance directives and life sustaining treatment, work experience, and the number of beds. Positive correlation was observed between the level of understanding of the rights for EOL care decisions and of social workers' roles (Pearson r=0.329, P<0.001). Conclusion: This study proposes development of an education program for social workers and devising standards for the EOL care decision making process to protect elderly patients, family caregivers as well as social workers in a long term care facility.

• Journal of Korean Society of Transportation
• /
• v.33 no.3
• /
• pp.237-246
• /
• 2015

Operation and Maintenance in road infrastructure is repetitive carbon emissions activities to preserve the road in its originally constructed condition. In the view of road planning and construction, operation, and maintenance of life cycle, operation and maintenance stage quantification of carbon emissions is very important because it is easily accessible activities to reduce carbon emissions in road infrastructure that existing and new road. However, carbon emissions estimation in operation and maintenance stage is yet to do, because data collection is so hard and carbon emissions estimation methodology is very complicated. In this study, a basic unit of carbon emission in the operation and maintenance stage of the road infrastructure was developed in order to get the quantitative determination of carbon that occurring. Carbon emissions of the Expressway and Common state road was calculated by using the basic unit of carbon emission and application plan of basic unit of carbon emission are presented.

• Culinary science and hospitality research
• /
• v.21 no.3
• /
• pp.155-167
• /
• 2015

This study aims to examine the knowledge level of culinary staff members regarding the restaurant country- oforigin labeling system by developing a scale to investigate and evaluate such knowledge levels. The empirical study targeted culinary staff members with over 7 years of experience in 10 luxury hotels in Seoul who were approached through the convenience sampling method, which was conducted for 14 days from November 14th to 27th, 2014. A total of 192 self-administered questionnaires were collected, of which 186 questionnaires(93%) were used for the final analysis. For investigation and analysis, a frequency analysis was carried out to look into population statistics and the level of knowledge using the SPSS 18.0 statistics program. One-way ANOVA and t-test were carried out to investigate differences in knowledge levels of restaurant country-of-origin labeling system with reference to academic background, job position, and hotel management type. As the result, the average correct answer rate of the culinary staff members for a total of 21 questions was 39.85% and there were no significant differences based on the academic background. However, the correct answer rate was higher for respondents that held high positions and had independently managed hotels, suggesting that knowledge varied depending on job position and management type. In conclusion, it is suggested that in order to improve the level of knowledge of the restaurant country-of-origin labeling system among culinary staff members there is a need to enhance training and continuous supervision to apply the knowledge to work in future. In addition to this, this study discussed the limits of the study and the orientation of further research.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.54 no.4
• /
• pp.50-58
• /
• 2017

Recently, some methods of converging heterogeneous fire sensor data have been proposed for effective fire detection, but the rule-based methods have low adaptability and accuracy, and the fuzzy inference methods suffer from detection speed and accuracy by lack of consideration for images. In addition, a few image-based deep learning methods were researched, but it was too difficult to rapidly recognize the fire event in absence of cameras or out of scope of a camera in practical situations. In this paper, we propose a novel fire detection system combining a deep learning algorithm based on CNN and fuzzy inference engine based on heterogeneous fire sensor data including temperature, humidity, gas, and smoke density. we show it is possible for the proposed system to rapidly detect fire by utilizing images and to decide fire in a reliable way by utilizing multi-sensor data. Also, we apply distributed computing architecture to fire detection algorithm in order to avoid concentration of computing power on a server and to enhance scalability as a result. Finally, we prove the performance of the system through two experiments by means of NIST's fire dynamics simulator in both cases of an explosively spreading fire and a gradually growing fire.

• Journal of The Korean Association For Science Education
• /
• v.37 no.1
• /
• pp.25-37
• /
• 2017

The purpose of this study is to analyze the effects on particulate concept formation based on abductive reasoning model for elementary science class. For this study, an author selected two groups in the sixth grade. One group is an ordinary textbook-based control group (N=26) and the other group is an abductive reasoning model-based treatment group (N=26). After twelve lessons, the scores of Concepts Test for Gas were analyzed by t-test and two-way ANOVA. The result of t-test showed both the control and treatment groups have higher score than before they take the lesson. But after the lesson, an author found out that the treatment group had higher score than that of the control group. And compared to the number of particles expressed, the number of the treatment group were higher than that of the control class. The two-way ANOVA result revealed that the interaction effect between their cognitive level and treatment was not significant. And regardless of the level of cognition, the scores of treatment group are higher than those of control group. Therefore, abductive reasoning model-based elementary science class were found to be more effective for particulate concept formation. Based on the results, an author concluded that abductive reasoning model is very effective in teaching particulate concepts to elementary students.