• The Journal of the Korea Contents Association
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• v.19 no.4
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• pp.446-457
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• 2019

There are many local resources industrialization projects for regional development in South Korea but a lack of method for being adapted for project planning from survey of local resources to commercialization. In this research, we composed local resource commercialization process with a case study of Chusa Kimjeonghee cultural assets commercialization project considering regional characteristics of resources, employment, and needs. This research is composed with three parts such as resource assessment, main manufacturing area selection, and project contents development. In first step, local resource lists are gathered and graded by expert survey with the item of importance and industrialization possibility. In second step, we proposed project asssistant coefficient with numbers of enterprises and employment data by manufacturing are gathered in national and target area level. In third step, we propose questionnaire survey items for developing project contents based on customer needs. Finally we summarized project results and induced implications for future cultural assets commercialization projects.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.2
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• pp.176-182
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• 2019

Objective: This study was conducted to investigate basic information on genetic structure and characteristics of Limousin population in Hungary. Obtained results will be taken into consideration when adopting the new breeding strategy by the Association of Hungarian Limousin and Blonde d'Aquitaine Breeders (AHLBB). Methods: Genetic diversity and phylogenetic relationship of 3,443 Limousin cattle from 16 different herds were investigated by performing genotyping using 18 microsatellite markers. Amplified DNA was genotyped using an automated genetic analyzer. Results: Mean of effective alleles ($n_e$) of the populations was 3.77. Population C had the lowest number of effective alleles (3.01) and the lowest inbreeding coefficient ($F_{IS}$) value (-0.15). Principal component analysis of estimated genetic distance ($F_{ST}$) values (p<0.000) revealed two herds (C and E) distinct from the majority of other Limousin herds. The pairwise $F_{ST}$ values of population C compared to the others (0.066 to 0.120) fell into the range of moderate genetic distance: 0.050 to 0.150, while population E displayed also moderate genetic distance ($F_{ST}$ values in range 0.052 to 0.064) but only to six populations (G, H, J, L, N, and P). $F_{ST(C-E)}$ was 0.148, all other pairs -excluding C and E herds- displayed low genetic distance ($F_{ST}$<0.049). Population D, F, I, J, K, L, N, O, and P carried private alleles, which alleles belonged to 1.1% of the individuals. Most probable number of clusters (K) were 2 and 7 determined by Structure and BAPS software. Conclusion: This study showed useful genetic diversity and phylogenetic relationship data that can be utilized for the development of a new breeding strategy by AHLBB. The results presented could also contribute to the proper selection of animals for further whole genome scan studies of Limousins.

• Journal of Sleep Medicine
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• v.15 no.2
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• pp.48-54
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• 2018

Objectives: The aim of this study was to develop a predicting model for the optimal continuous positive airway pressure (CPAP) for obstructive sleep apnea (OSA) patient with obesity by using a machine learning. Methods: We retrospectively investigated the medical records of 162 OSA patients who had obesity [body mass index (BMI) ≥ 25] and undertaken successful CPAP titration study. We divided the data to a training set (90%) and a test set (10%), randomly. We made a random forest model and a least absolute shrinkage and selection operator (lasso) regression model to predict the optimal pressure by using the training set, and then applied our models and previous reported equations to the test set. To compare the fitness of each models, we used a correlation coefficient (CC) and a mean absolute error (MAE). Results: The random forest model showed the best performance {CC 0.78 [95% confidence interval (CI) 0.43-0.93], MAE 1.20}. The lasso regression model also showed the improved result [CC 0.78 (95% CI 0.42-0.93), MAE 1.26] compared to the Hoffstein equation [CC 0.68 (95% CI 0.23-0.89), MAE 1.34] and the Choi's equation [CC 0.72 (95% CI 0.30-0.90), MAE 1.40]. Conclusions: Our random forest model and lasso model ($26.213+0.084{\times}BMI+0.004{\times}$apnea-hypopnea index+$0.004{\times}oxygen$ desaturation index-$0.215{\times}mean$ oxygen saturation) showed the improved performance compared to the previous reported equations. The further study for other subgroup or phenotype of OSA is required.

• Journal of the Korean Society of Propulsion Engineers
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• v.25 no.3
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• pp.42-55
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• 2021

In this study, the effect of nozzle performance according to the selection of the center line equation. Three of S-type nozzles and three of double S-type nozzles were designed using the curve equation and design parameters, and the nozzle shielding performance was evaluated using the shielding ratio definition. In order to analyze the internal flow of the nozzle, the characteristics of the velocity distribution and pressure distribution were studied, and the nozzle performance was evaluated through the total thrust ratio(f) and the nozzle insulation efficiency coefficient(η). On the other hand, the centerline with a sharply change in curvature at the entrance has a low nozzle performance and a high shielding rate. The double S-type nozzle is excellent nozzle performance and shielding rate by using a smooth centerline at the first curvature.

• Smart Structures and Systems
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• v.26 no.6
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• pp.681-692
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• 2020

Conventional Piezoelectric Energy Harvesters (CPEH) have been extensively studied for maximizing their electrical output through material selection, geometric and structural optimization, and adoption of efficient interface circuits. In this paper, the performance of Stepped Piezoelectric Energy Harvester (SPEH) under harmonic base excitation is studied analytically, numerically and experimentally. The motivation is to compare the energy harvesting performance of CPEH and SPEHs with the same characteristics (resonant frequency). The results of this study challenge the notion of achieving higher voltage and power output through incorporation of geometric discontinuities such as step sections in the harvester beams. A CPEH consists of substrate material with a patch of piezoelectric material bonded over it and a tip mass at the free end to tune the resonant frequency. A SPEH is designed by introducing a step section near the root of substrate beam to induce higher dynamic strain for maximizing the electrical output. The incorporation of step section reduces the stiffness and consequently, a lower tip mass is used with SPEH to match the resonant frequency to that of CPEH. Moreover, the electromechanical coupling coefficient, forcing function and damping are significantly influenced because of the inclusion of step section, which consequently affects harvester's output. Three different configurations of SPEHs characterized by the same resonant frequency as that of CPEH are designed and analyzed using linear electromechanical model and their performances are compared. The variation of strain on the harvester beams is obtained using finite element analysis. The prototypes of CPEH and SPEHs are fabricated and experimentally tested. It is shown that the power output from SPEHs is lower than the CPEH. When the prototypes with resonant frequencies in the range of 56-56.5 Hz are tested at 1 m/s2, three SPEHs generate power output of 482 μW, 424 μW and 228 μW when compared with 674 μW from CPEH. It is concluded that the advantage of increasing dynamic strain using step section is negated by increase in damping and decrease in forcing function. However, SPEHs show slightly better performance in terms of specific power and thus making them suitable for practical scenarios where the ratio of power to system mass is critical.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.12
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• pp.62-69
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• 2020

The demand for industrial robots is proliferating with production automation. Industrial robots are used in various fields, such as logistics, welding, and assembly. Generally, six degrees of freedom are required to move freely in space. However, the palletizing robot used for material management and logistics systems typically has four degrees of freedom. In designing such robots, their main parts, such as motors and reducers, need to be adequately selected while satisfying payload requirements and speed. Hence, this study proposes a practical method for selecting the major parts based on dynamic analysis using ADAMS. First, the acceleration torques for the robot motion were found from the analysis, and then the friction torques were evaluated. This study introduces a constant-speed torque constant instead of friction coefficient. The RMS torque and maximum power of each motor were found considering the above torques. After that, this study recommends the major specifications of all motors and reducers. The proposed method was applied to a palletizing robot to verify the suitability of the pre-selected main parts. The verification result shows that the proposed method can be successfully applied to the early design stage of industrial robots.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.1
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• pp.103-116
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• 2021

Analysis of runoff is substantial for effective water management in the watershed. Runoff occurs by reaction of a watershed to the rainfall and has non-linearity and uncertainty due to the complex relation of weather and watershed factors. ANN (Artificial Neural Network), which learns from the data, is one of the machine learning technique known as a proper model to interpret non-linear data. The performance of ANN is affected by the ANN's structure, the number of hidden layer nodes, learning rate, and activation function. Especially, the activation function has a role to deliver the information entered and decides the way of making output. Therefore, It is important to apply appropriate activation functions according to the problem to solve. In this paper, ANN models were constructed to estimate runoff with different activation functions and each model was compared and evaluated. Sigmoid, Hyperbolic tangent, ReLU (Rectified Linear Unit), ELU (Exponential Linear Unit) functions were applied to the hidden layer, and Identity, ReLU, Softplus functions applied to the output layer. The statistical parameters including coefficient of determination, NSE (Nash and Sutcliffe Efficiency), NSEln (modified NSE), and PBIAS (Percent BIAS) were utilized to evaluate the ANN models. From the result, applications of Hyperbolic tangent function and ELU function to the hidden layer and Identity function to the output layer show competent performance rather than other functions which demonstrated the function selection in the ANN structure can affect the performance of ANN.

• Journal of Nutrition and Health
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• v.54 no.2
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• pp.129-138
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• 2021

To set the estimated energy requirement (EER) in Dietary Reference Intakes for Koreans (KDRI), we need the coefficient by physical activity stage, as determined by the physical activity level(PAL). Thus, there has been demand for a tool to calculate PAL based on the physical activity diary. This study was undertaken to develop a physical activity (PA) classification table for Koreans, using the 2011 Compendium of physical activities in the United States. The PA classification table for Koreans contains 262 codes, and values of the metabolic equivalent of task (MET) for specific activities. Of these, 243 PAs which do not have Korean specific data or information, were selected from the 2011 Compendium of PAs that originated in the United States; another 19 PAs were selected from the previous research data of Koreans. The PA classification table is codified to facilitate the selection of energy values corresponding to each PA. The code for each PA consists of a single letter alphabet (activity category) and four numeric codes that display the activity type (2 digit number), activity intensity (1 digit number), and specific activities (1 digit number). In addition, the intensity (sedentary behavior, low, middle and high) of specific PA and its rate of energy expenditure in MET are presented together. The activity categories are divided into 4 areas: Daily Activity (A), Movement (B), Occupation (C), and Exercise and Sports (D). The developed PA classification table can be applied to quantify the energy cost of PA for adults in research or practice, and to assess energy expenditure and physical activity levels based on self-reported PA.

• Journal of Korean Academy of Nursing
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• v.52 no.1
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• pp.52-65
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• 2022

Purpose: This study was conducted to develop a scale to measure spiritual distress in cancer patients. Methods: A total of 69 preliminary items for the spiritaul distress assessment tool (SDAT) were compiled, based on a literature review, selection of empirically relevant items through concept analysis of hybrid models, confirmation of content validity by experts, cognitive interviews, and a pretest. Self-administered questionnaires were collected between April 1 and July 31, 2018, from 225 cancer patients at four medical institutions and one nursing home. The data were analyzed using item analysis, exploratory factor analysis, convergent and discriminant validity, and Pearson correlation for criterion validity. Reliability was tested by Cronbash's α coefficient. Results: The final version of the SDAT consisted of 20 items. Five-factors, loss of peace, burden of family, avoidance of confronting death, guilt and remorse, regret for not being able to apololgize and forgive were extracted, and showed 62.8% of total variance. The factors were confirmed through convergent and discriminant validity. Criterion validity was confirmed by functional assessment chronic illness therapy spiritual well-being scale 12 (FACIT-Sp12). The overall Cronbach's α was .91, and the coefficients of each subscale ranged from .78~.83. Conclusion: The SDAT for cancer patients is valid and reliable. It is suggested that the tool can be used to measure spiritual distress in cancer patients.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.131-138
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• 2022

Novel Coronavirus (COVID-19) is viewed as one of the main general wellbeing theaters on the worldwide level all over the planet. Because of the abrupt idea of the flare-up and the irresistible force of the infection, it causes individuals tension, melancholy, and other pressure responses. The avoidance and control of the novel Covid pneumonia have moved into an imperative stage. It is fundamental to early foresee and figure of infection episode during this troublesome opportunity to control of its grimness and mortality. The entire world is investing unimaginable amounts of energy to fight against the spread of this lethal infection. In this paper, we utilized machine learning and deep learning techniques for analyzing what is going on utilizing countries shared information and for detecting the climate factors that effect on spreading Covid-19, such as humidity, sunny hours, temperature and wind speed for understanding its regular dramatic way of behaving alongside the forecast of future reachability of the COVID-2019 around the world. We utilized data collected and produced by Kaggle and the Johns Hopkins Center for Systems Science. The dataset has 25 attributes and 9566 objects. Our Experiment consists of two phases. In phase one, we preprocessed dataset for DL model and features were decreased to four features humidity, sunny hours, temperature and wind speed by utilized the Pearson Correlation Coefficient technique (correlation attributes feature selection). In phase two, we utilized the traditional famous six machine learning techniques for numerical datasets, and Dense Net deep learning model to predict and detect the climatic factor that aide to disease outbreak. We validated the model by using confusion matrix (CM) and measured the performance by four different metrics: accuracy, f-measure, recall, and precision.