• Smart Media Journal
• /
• v.12 no.10
• /
• pp.63-70
• /
• 2023

Recently, energy consumption for heating costs, which is 35% of smart farm energy costs, has increased, requiring energy consumption efficiency, and the importance of new and renewable energy is increasing due to concerns about the realization of electricity bills. Renewable energy belongs to hydropower, wind, and solar power, of which solar energy is a power generation technology that converts it into electrical energy, and this technology has less impact on the environment and is simple to maintain. In this study, based on the greenhouse heat storage tank and heat pump data, the factors that affect the heat storage tank are selected and a heat storage tank supply temperature prediction model is developed. It is predicted using Long Short-Term Memory (LSTM), which is effective for time series data analysis and prediction, and XGBoost model, which is superior to other ensemble learning techniques. By predicting the temperature of the heat pump heat storage tank, energy consumption may be optimized and system operation may be optimized. In addition, we intend to link it to the smart farm energy integrated operation system, such as reducing heating and cooling costs and improving the energy independence of farmers due to the use of solar power. By managing the supply of waste heat energy through the platform and deriving the maximum heating load and energy values required for crop growth by season and time, an optimal energy management plan is derived based on this.

• Geomechanics and Engineering
• /
• v.35 no.2
• /
• pp.121-133
• /
• 2023

The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.6
• /
• pp.1143-1150
• /
• 2023

The global climate crisis and the implementation of low-carbon policies have led to a growing interest in renewable energy and a growing number of related industries. Among them, solar power is attracting attention as a representative eco-friendly energy that does not deplete and does not emit pollutants or greenhouse gases. As a result, the supplement of solar power facility is increasing all over the world. However, solar power is easily affected by the environment such as geography and weather, so accurate solar power forecast is important for stable operation and efficient management. However, it is very hard to predict the exact amount of solar power using statistical methods. In addition, the conventional prediction methods have focused on only short- or long-term prediction, which causes to take long time to obtain various prediction models with different prediction horizons. Therefore, this study utilizes a many-to-many structure of a recurrent neural network (RNN) to integrate short-term and long-term predictions of solar power generation. We compare various RNN-based very short- and long-term prediction methods for solar power in terms of MSE and R² values.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.6
• /
• pp.1321-1330
• /
• 2023

This study attempts to address the problem of 3D pose estimation for multiple human objects through a single image generated during the character development process that can be used in augmented reality. In the existing top-down method, all objects in the image are first detected, and then each is reconstructed independently. The problem is that inconsistent results may occur due to overlap or depth order mismatch between the reconstructed objects. The goal of this study is to solve these problems and develop a single network that provides consistent 3D reconstruction of all humans in a scene. Integrating a human body model based on the SMPL parametric system into a top-down framework became an important choice. Through this, two types of collision loss based on distance field and loss that considers depth order were introduced. The first loss prevents overlap between reconstructed people, and the second loss adjusts the depth ordering of people to render occlusion inference and annotated instance segmentation consistently. This method allows depth information to be provided to the network without explicit 3D annotation of the image. Experimental results show that this study's methodology performs better than existing methods on standard 3D pose benchmarks, and the proposed losses enable more consistent reconstruction from natural images.

• Information Systems Review
• /
• v.20 no.4
• /
• pp.79-96
• /
• 2018

This study started by focusing on the internalization of the technology appraisal model into the credit rating model to increase the discriminative power of the credit rating model not only for SMEs but also for all companies, reflecting the items related to the financial stability of the enterprises among the technology appraisal items. Therefore, it is aimed to verify whether the technology appraisal model can be applied to identify high-stability SMEs in advance. We classified companies into industries (manufacturing vs. non-manufacturing) and the age of company (initial vs. non-initial), and defined as a high-stability company that has achieved an average debt ratio less than 1/2 of the group for three years. The C5.0 was applied to verify the discriminant power of the model. As a result of the analysis, there is a difference in importance according to the type of industry and the age of company at the sub-item level, but in the mid-item level the R&D capability was a key variable for discriminating high-stability SMEs. In the early stage of establishment, the funding capacity (diversification of funding methods, capital structure and capital cost which taking into account profitability) is an important variable in financial stability. However, we concluded that technology development infrastructure, which enables continuous performance as the age of company increase, becomes an important variable affecting financial stability. The classification accuracy of the model according to the age of company and industry is 71~91%, and it is confirmed that it is possible to identify high-stability SMEs by using technology appraisal items.

• Journal of Korean Home Economics Education Association
• /
• v.35 no.3
• /
• pp.169-190
• /
• 2023

Uzbekistan, one of the top five cotton-producing countries in the world, primarily focuses its textile and fashion industry on raw cotton exports and the sewing industry. For Uzbekistan to achieve high added value, it is essential for the textile and fashion industry, which is currently at the CMT(cut, make, and trim) stage, to upgrade to OEM (original equipment manufacturing), ODM (original design manufacturing), and OBM (original brand manufacturing). South Korea recognizes Uzbekistan as a potential manufacturing base and trading partner and has invested Official Development Assistance (ODA) funds for the development of Uzbekistan's textiles and apparel sector. This study aims to evaluate Uzbekistan's fashion higher education in the context of global competitiveness and measure the need and prospects for education ODA from the Korean government in this field. Comprehensive investigations, including surveys of academics, industry experts, and government officials, in-depth interviews, and focus group interviews, were conducted to understand Uzbekistan's current fashion education environment. According to the research results, despite the textile and fashion sectors playing a pivotal role in the Uzbek economy, there is room for improvement in the curricula and teaching and learning methods of the fashion higher education programs. This study holds significance as foundational data for establishing education ODA strategies.

• Journal of the Korean earth science society
• /
• v.28 no.3
• /
• pp.289-297
• /
• 2007

The purpose of this study was to analyze the temperament of the science gifted and to identify a relationship between temperament and test-anxiety of the students. The participants were composed of 92 middle school the science gifted who had been educated for the gifted in science educational institution of university and 97 general students in their first-year of middle school. A revised dimensions of temperament survey (DOTS-R) was used for data collection. This study revealed that the science gifted displayed higher concentricity, persistence, and approach-temperament than those of general students. On the other hand, general students were higher than the science gifted at activity, flexibility and positive mood. In the analysis of superior temperament, the science gifted were superior to general students in persistence, while general students were superior to the science gifted in flexibility. The Results of correlation with temperament and test-anxiety was as following. There was close correlation between approach-temperament and test-anxiety of the science gifted. Persistence was the same. While general students were not close correlation between concentricity and test-anxiety. Also science gifted and general students was close correlation between activity and test-anxiety. This mean that activity brings about a disturbing factor of test-anxiety. According to the results of superior temperament frequency analysis, persistence is superior temperament of the science gifted. While flexibility was superior temperament of general students. This study expects to making the use of providing appropriate teaching and learning strategies for the science gifted.

• Child Health Nursing Research
• /
• v.9 no.2
• /
• pp.206-220
• /
• 2003

Purpose: this study was intended to search the relationship between perception of the infant temperament in mother of infant at the age of 1～12 months and maternal confidence and satisfaction in performing maternal role, and to submit a basic data to establish a nursing intervention program which is helpful for determination of infant development and performing maternal role promotion by identify variables associated with infant temperament. Method: The subjects of this study were 300 mothers of infant at the age of 1～12 months who visited well baby clinic in 4 hospitals in Busan city and Kyoung-Nam province. Final analysis was performed in 293 cases. Seven cases was excluded in this study because of its inappropriate data collection. The data was collected from 1st July to 15th August 2002. The questionaries which were fill-up by mother were collected. Infant temperament was measured by using the tool of 'what my baby is like'(WBL) which was developed by Priham et. al.(1994) and translated by Bang(1999). The scale of postpartum self evaluation which was developed by Lederman et al(1981) and translated by Lee(1992) was used for the confidence and satisfaction of maternal role. All statistical analyses were performed using SPSS-PC for window, version 10.0: frequency, percentage, minimum, maximum, mean, SD, t-test, ANOVA, Post-hoc test(Scheffe's test), Pearson Correlation Coefficients. Result: The mean score of maternal perception of the infant temperament was 6.17±1.04, and mother recognized her infant as positive. The mean score of confidence of maternal role was 2.89± .41 and this revealed in an average level. The mean score of satisfaction of maternal role was 3.29± .51 and this revealed in a higher level. There was a weak significant positive correlation between the score of maternal perception of infant temperament and confidence of maternal role(r=0.176, P= .003), but there was no significant correlation between satisfaction of maternal role(P> .05). It revealed the more maternal perception of the infant temperament as positive, the higher confidence of maternal role. There was a moderate significant positive correlation between confidence of maternal role and satisfaction of maternal role(r=0.410, P= .000). It revealed the more confidence of maternal role, the higher satisfaction of maternal role. The variables related with the score of maternal perception of infant temperament were the type of delivery (t=-2.600, P= .010), experience of learning baby care(t=2.382, P= .018), maternal perception on baby's health status(F=3.467, P= .033), maternal perception on her health status(F=3.467, P= .027), baby's age(F=3.080, P= .028). Conclusion: Our result showed the confidence of maternal role was increased as the maternal perception of infant temperament was positive, and conformed that the confidence of maternal role was also related with satisfaction of maternal role. Prenatal education, type of delivery, baby's age were also related with the maternal perception of infant temperament. So, nursing intervention program of developmental stage maybe necessary in order to help maternal perception of infant temperament as positive, and it will be increased the confidence of maternal role and satisfaction of performing maternal role which was considered as real indicate of achievement of maternal role.

• Journal of agricultural medicine and community health
• /
• v.43 no.4
• /
• pp.234-249
• /
• 2018

This study was performed to identify the impact of awareness and educational experiences on cardiopulmonary resuscitation in the ability to execute of cardiopulmonary resuscitation among Korean adults. This study used original data of 2014 Community Health Data Survey. 228,712 participants in this survey were resident in South Korea who is aged 19 or older on July 2014. Participants in this survey were sampled an average of 900 residents(target error ${\pm}3percent$) per community health center of Korea. Data were analyzed by using R 3.1.3 employing chi-squared test, fisher's exact analysis, and logistic regression analysis. Ability to execute CPR was significantly higher in males(3.34 time), higher the education level (1.61 times), the white color occupation (1.14 times), the higher the income level (1.07 times), the higher the education level (0.91 times), non-hypertensive patients (1.12 times), non-diabetic patients (1.16 times), non-dyslipidemic patients (0.86 times), non-stroke patients (0.30 times), CPR education experience group (3.25 times), CPR experience group with manikin-based training (4.30 times), higher subjective health status (1.08 times, 1.16 times) respectively. This study identified that awareness, educational experience, and mannequin-based learning experience of CPR impacted on the ability to execute CPR. Responding to education-related factors could contribute to reducing the rate of out-of-hospital acute cardiac arrest by improving the ability to execute CPR of the general public.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.4
• /
• pp.198-221
• /
• 2021

After large-scale reforestation in the 1960s and 1970s, forests in Korea have gradually been aging. Net ecosystem CO₂ exchange of old-growth forests is theoretically near zero; however, it can be a CO₂ sink or source depending on the intervention of disturbance or management. In this study, we report the CO₂ budget dynamics of the Gwangneung deciduous old-growth forest (GDK) in Korea and examined the following two questions: (1) is the preserved GDK indeed CO₂ neutral as theoretically known? and (2) can we explain the dynamics of CO₂ budget by the common mechanisms reported in the literature? To answer, we analyzed the 15-year long CO₂ flux data measured by eddy covariance technique along with other biometeorological data at the KoFlux GDK site from 2006 to 2020. The results showed that (1) GDK switched back-and-forth between sink and source of CO₂ but averaged to be a week CO₂ source (and turning to a moderate CO₂ source for the recent five years) and (2) the interannual variability of solar radiation, growing season length, and leaf area index showed a positive correlation with that of gross primary production (GPP) (R²=0.32~0.45); whereas the interannual variability of both air and surface temperature was not significantly correlated with that of ecosystem respiration (RE). Furthermore, the machine learning-based model trained using the dataset of early monitoring period (first 10 years) failed to reproduce the observed interannual variations of GPP and RE for the recent five years. Biomass data analysis suggests that carbon emissions from coarse woody debris may have contributed partly to the conversion to a moderate CO₂ source. To properly understand and interpret the long-term CO₂ budget dynamics of GDK, new framework of analysis and modeling based on complex systems science is needed. Also, it is important to maintain the flux monitoring and data quality along with the monitoring of coarse woody debris and disturbances.