• Journal of Information Technology Services
• /
• v.17 no.1
• /
• pp.91-103
• /
• 2018

This study developed a prediction model using machine learning technology and predicted the success of health consulting by using life log data generated through u-Health service. The model index of the Random Forest model was the highest using. As a result of analyzing the Random Forest model, blood pressure was the most influential factor in the success or failure of metabolic syndrome in the subjects of u-Health service, followed by triglycerides, body weight, blood sugar, high cholesterol, and medication appear. muscular, basal metabolic rate and high-density lipoprotein cholesterol were increased; waist circumference, Blood sugar and triglyceride were decreased. Further, biometrics and health behavior improved. After nine months of u-health services, the number of subjects with four or more factors for metabolic syndrome decreased by 28.6%; 3.7% of regular drinkers stopped drinking; 23.2% of subjects who rarely exercised began to exercise twice a week or more; and 20.0% of smokers stopped smoking. If the predictive model developed in this study is linked with CBR, it can be used as case study data of CBR with high probability of success in the prediction model to improve the compliance of the subject and to improve the qualitative effect of counseling for the improvement of the metabolic syndrome.

• Environmental Engineering Research
• /
• v.14 no.1
• /
• pp.53-60
• /
• 2009

The main objective was to develop and assess a dynamic fate and transport model for lead in air, soil, sediment, water and vegetation. Daejeon was chosen as the study area for its relatively high contamination and emission levels. The model was assessed by comparing model predictions with measured concentrations in multi-media and atmospheric deposition flux. Given a lead concentration in air, the model could predict the concentrations in water and soil within a factor of five. Sensitivity analysis indicated that effective compartment volumes, rain intensity, scavenging ratio, run off, and foliar uptake were critical to accurate model prediction. Important implications include that restriction of air emission may be necessary in the future to protect the soil quality objective as the contamination level in soil is predicted to steadily increase at the present emission level and that direct discharge of lead into the water body was insignificant as compared to atmospheric deposition fluxes. The results strongly indicated that atmospheric emission governs the quality of the whole environment. Use of the model developed in this study would provide quantitative and integrated understanding of the cross-media characteristics and assessment of the relationships of the contamination levels among the multi-media environment.

• Journal of Biosystems Engineering
• /
• v.19 no.1
• /
• pp.22-32
• /
• 1994

The combine harvester is considered as an important but complicated and costly machine. The appropriate size of combine has to be developed to use efficiently in Korea. But the combine is such a complicated machine that a complete design model to develop a new type is impossible without understanding the relationship between each factor. The combine capacity is generally limited by the cleaning shoe performance. So a design model for a cleaning shoe has to be developed first for the complete combine design. The objective of this research was to develop a cleaning model of head-feeding combine to predict grain separation from chaff and broken straw on a sieve. A developed physically based model can explain the situation which can happen during separation process. A test apparatus based on the field going machine was developed. The test materials were paddy rice and barley. The data obtained were analyzed by the hand and the video camera. The developed model was verified as an adequate model through the test with $R^2$ of 0.934 and 0.837. The model can be used to evaluate design and operation alternatives of combine and also applied to the automatic control of separation unit of combine with a loss monitering sensors.

• International Journal of Aeronautical and Space Sciences
• /
• v.18 no.4
• /
• pp.651-661
• /
• 2017

A truss-braced wing (TBW) aircraft has recently received increasing attention due to higher aerodynamic efficiency compared to conventional cantilever wing aircraft. For conceptual TBW aircraft design, we developed a propulsion-and-airframe integrated design environment by replacing a semi-empirical turbofan engine model with a thermodynamic cycle-based one built upon the numerical propulsion system simulation (NPSS). The constructed NPSS model benefitted TBW aircraft design study, as it could handle engine installation effects influencing engine fuel efficiency. The NPSS model also contributed to broadening TBW aircraft design space, for it provided turbofan engine design variables involving a technology factor reflecting progress in propulsion technology. To effectively consolidate the NPSS propulsion model with the TBW airframe model, we devised a rapid, approximate substitute of the NPSS model by reduced-order modeling (ROM) to resolve difficulties in model integration. In addition, we formed an artificial neural network (ANN) that associates engine component attributes evaluated by object-oriented weight analysis of turbine engine (WATE++) with engine design variables to determine engine weight and size, both of which bring together the propulsion and airframe system models. Through propulsion-andairframe design space exploration, we optimized TBW aircraft design for fuel saving and revealed that a simple engine model neglecting engine installation effects may overestimate TBW aircraft performance.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1997.10a
• /
• pp.454-457
• /
• 1997

The purpose of this study is to develop an optimal model, using existing models, that is able to estimate the amount of spatter utilizing artificial neural network in the short circuit transfer mode of gas metal arc (GMA) welding. The amount of spatter generated during welding can become a barometer which represents the process stability of metal transfer in GMA welding, and it depends on some factors which constitute a periodic waveforms of welding current and arc voltage in short circuit GMA welding. So, the 12 factors, which could express the characteristics for the waveforms, and the amount of spatter are used as input and output variables of the neural network, respectively. Two neural network models to estimate the amount of spatter are proposed: A neural network model, where arc extinction is not considered, and a combined neural network model where it is considered. In order to reduce the calculation time it take to produce an output, the input vector and hidden layers for each model are optimized using the correlation coefficients between each factor and the amount of spattcr. The est~mation performance of each optimized model to the amount of spatter IS assessed and compared to the est~mation performance of the model proposed by Kang. Also, through the evaluation for the estimation performance of each optimized model, it is shown that the combined neural network model can almost perfectly predict the amount of spatter.

• Journal of the Korean Society of Hazard Mitigation
• /
• v.11 no.3
• /
• pp.1-8
• /
• 2011

In this study, a wind hazard map over Korea peninsula based on geographical information is developed, which consists of the surface roughness model, the topographical effect model and the homogeneous wind model. The surface roughness model is assessed to evaluate the effect of the surface roughness on the wind field near ground. The topographical effect model is assessed to quantify the effect of the speed-up caused by topology, which is calculated by adopting the topographical effect factor in Korea building code (2005). The homogeneous wind map is created either by a frequency analysis method for meteorological data or a typhoon simulation. The results show that the wind hazard map can be applied to the determination of insurance premium as well as the assessment of loss and damage.

• Journal of the Korean GEO-environmental Society
• /
• v.20 no.6
• /
• pp.15-22
• /
• 2019

In this study, the hazard areas are identified by using the Newmark displacement model, which is a predictive model for identifying the areas at risk of landslide triggered by earthquakes, based on the results of field survey and laboratory test, and literature data. The Newmark displacement model mainly utilizes earthquake and slope related data, and the safety of slope stability derived from LSMAP, which is a landslide prediction program. Backyang Mt. in Busan where the landslide has already occurred, was chosen as the study area of this research. As a result of this study, the area of landslide prone zone identified by using the Newmark displacement model without earthquake factor is about 1.15 times larger than that identified by using LSMAP.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.45 no.4
• /
• pp.118-126
• /
• 2022

This study attempts a comparison between AHP(Analytic Hierarchy Process) in which the importance weight is structured by individual subjective values and regression model with importance weight based on statistical theory in determining the importance weight of casual model. The casual model is designed by for students' satisfaction with university, and SERVQUAL modeling methodology is applied to derive factors affecting students' satisfaction with university. By comparison of importance weights for regression model and AHP, the following characteristics are observed. 1) the lower the degree of satisfaction of the factor, the higher the importance weight of AHP, 2) the importance weight of AHP has tendency to decrease as the standard deviation(or p-value) increases. degree of decreases. the second sampling is conducted to double-check the above observations. This study empirically checks that the importance weight of AHP has a relationship with the mean and standard deviation(or p-value) of independence variables, but can not reveal how exactly the relationship is. Further research is needed to clarify the relationship with long-term perspective.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.18 no.2
• /
• pp.51-58
• /
• 2023

This paper proposes a deep learning method for estimating the heart rate from facial videos. Our proposed method estimates remote photoplethysmography (rPPG) signals to predict the heart rate. Although there have been proposed several methods for estimating rPPG signals, most previous methods can not be utilized in low-power single board computers due to their computational complexity. To address this problem, we construct a lightweight student model and employ a knowledge distillation technique to reduce the performance degradation of a deeper network model. The teacher model consists of 795k parameters, whereas the student model only contains 24k parameters, and therefore, the inference time was reduced with the factor of 10. By distilling the knowledge of the intermediate feature maps of the teacher model, we improved the accuracy of the student model for estimating the heart rate. Experiments were conducted on the UBFC-rPPG dataset to demonstrate the effectiveness of the proposed method. Moreover, we collected our own dataset to verify the accuracy and processing time of the proposed method on a real-world dataset. Experimental results on a NVIDIA Jetson Nano board demonstrate that our proposed method can infer the heart rate in real time with the mean absolute error of 2.5183 bpm.

• Journal of Korean Academy of Nursing
• /
• v.30 no.5
• /
• pp.1265-1278
• /
• 2000

This study was carried out to assess the perception of decisional balance of Korean subjects about 4 health behaviors and to identify the influencing factor of decisional balance for exercise acquisition, smoking cessation, mammography screening and Kegel's exercise acquisition. All are representative health behaviors nurses can intervene in Korea based on the Transtheoretical model. Convenient samples of 2,484 subjects (191; exercise, 169; smoking cessation, 1903; mammography screening and 221; Kegel's exercise) were selected from cities and counties over 9 provinces throughout Korea, and the data was collected from January 1, 1999 to February 29, 2000. The research instrument were the Decisional Balance Measure for Exercise (Marcus & Owen., 1992), Smoking Cessation (Velicer et al., 1985), Mammography Screening (Rakowski et al.,1992) and Kegel Exercise (Lim, 1999) and Stage of Change Measure for Exercise (Marcus et al, 1992), Smoking Cessation (DiClemente et al., 1991), Mammography Screening (Rakowski et al.,1992) and Kegel's Exercise (Lim, 1999). The data was analyzed by the SAS Program. The results are as follows; 1. According to the stage of change measure, 2,484 subjects were distributed in each stage of change for four health behaviors: 1,233 subjects (49.8%), 745 subjects (30.2%), 113 subjects (4.7%), 156 subjects (6.5%), and 216 (8.7%) belonged to the pre- contemplation stage, contemplation stage, preparation stage, action stage and maintenance stage. They were all series of stages of change in their efforts to do health behavior. 2. Factor analysis identified 3 factors (1 of Pros, 2 of Cons) for the exercise, 4 factors for smoking cessation (2 of Pros, 2 of Cons), 2 factors (1 of Pros, 1 of Cons) for the mammogram screening and 2 factors (1 of Pros, 1 of Cons) for Kegel's exercise of decisional balance. 3. The analysis of variance and multiple comparison analysis showed that for all 4 samples, the Cons of changing the problem behaviors outweighed the Pros for subjects who were in the pre- contemplation stage, The opposite was true for subjects in action and maintenance stage. 4. Through the discriminant analysis, it was found that one factor of Pros for exercise, one factor of Cons for smoking cessation, 1 factor of Cons for mammogram screening and one factor of Cons for Kegel's exercise were the more influencing factors, than others in discriminating the stages of change. Results are consistent with the applications of the Transtheoretical model, which have been used to understand how people change health behaviors. This results provide some evidence that subject's report of his/her health behavior corresponds to beliefs about usefulness of related health behaviors. The results of this study have implications for patients' health education and health intervention strategies. The findings of this study give useful information for nursing educators for 4 health behaviors, especially the factors relating to decision making in the different stages of change.