• Journal of Sensor Science and Technology
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• v.32 no.3
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• pp.164-168
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• 2023

Accurate and reliable weather forecasts for temperature, relative humidity, and precipitation using advanced transformer models and IoT are essential in various fields related to global climate change. We propose a novel weather prediction device that integrates state-of-the-art transformer models and IoT techniques to improve prediction accuracy and real-time processing. The proposed system demonstrated high reliability and performance, offering valuable insights for industries and sectors that rely on accurate weather information, including agriculture, transportation, and emergency response planning. The integration of transformer models with the IoT signifies a substantial advancement in weather and climate modeling.

• Korean Journal of Child Studies
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• v.20 no.4
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• pp.119-139
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• 1999

The present study developed teaching-learning activities to improve personal intelligences and to investigate their effectiveness. Significant differences were found in both interpersonal and intrapersonal intelligence. Positive effects appeared in cooperative attitudes, capacity to care for and understand others, and in reflective thinking abilities. The results suggest the importance of a balanced educational curriculum in order to enhance human affective aspects, self-esteem, cooperative classroom climate formation and the moral and character development of students. Consistent and systematic investigation is needed on multiple intelligences theory, development of teaching-learning activities, their longitudinal effects, and a fair assessment instrument.

• Journal of Korea Water Resources Association
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• v.57 no.7
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• pp.471-479
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• 2024

Flood damage is becoming more serious due to the heavy rainfall caused by climate change. Physically based hydrological models have been utilized to predict stream water level variability and provide flood forecasting. Recently, hydrological simulations using machine learning and deep learning algorithms based on nonlinear relationships between hydrological data have been getting attention. In this study, the Long Short-Term Memory (LSTM) algorithm is used to predict the water level of the Seomjin River watershed. In addition, Climate Prediction Center morphing method (CMORPH)-based gridded precipitation data is applied as input data for the algorithm to overcome for the limitations of ground data. The water level prediction results of the LSTM algorithm coupling with the CMORPH data showed that the mean CC was 0.98, RMSE was 0.07 m, and NSE was 0.97. It is expected that deep learning and remote data can be used together to overcome for the shortcomings of ground observation data and to obtain reliable prediction results.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.1021-1032
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• 2021

This research identifies the main factors of the psychological climate that directly affect the performance of banking employees in Vietnam. Besides, this research also takes into consideration the differences in gender, age, educational level, and income on working performance. A survey was obtained from 207 employees working at joint-stock commercial banks and the analysis was handled with SPSS 20 software supports. The result shows that the measurement scales meet the requirements of validity and reliability. Regression analysis demonstrates that there are four factors directly affecting the working performance: friendliness, personal development and learning opportunities, straight and open communication, and the support from the senior management. These four factors have created a healthy psychological climate in the banks, where employees will feel comfortable and happy to improve work performance. Furthermore, this research has found that the higher the income, the more efficiently employees will work. The results of this research contribute to the measurement scale of working environment factors. At the same time, this research also proposes some recommendations for organizational managers to build a reasonable working environment that can inspire a sense of mental comfort for employees to work at their full capacity and to achieve the highest performance.

• English Language & Literature Teaching
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• v.12 no.3
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• pp.79-98
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• 2006

As one way of changing a teacher-fronted, grammar-based reading class into a meaningful, fun-creating one, this paper compared teacher- fronted reading with student-centered reading framed upon cooperative learning. In a study in which each type of reading method was conducted for college students in an EFL reading course for a period of one semester, data were gathered via questionnaires. The results showed that though each type of reading instruction came with its own strengths and weaknesses, student-centered reading instruction was preferred for various reasons. Most important, through an active participation in cooperative work, subjects were motivated and interested in L2 reading much, were exposed to various reading strategies and skills, and practiced them in a friendly, low-anxiety learning climate.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.811-816
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• 2023

With the emergence of a climate crisis, climate disasters have recently been clearly felt in Korea. In particular, the typhoon 'Hinnamno' in the summer of 2022 made many people feel a sense of crisis with its formidable power. In this situation, children are likely to suffer great damage even in small crises due to their lack of experience and ability to cope with disaster situations. In this paper, we introduce a disaster response learning application that supports children's disaster response training. Designed based on research results on situational learning theory and child disaster safety education, this system produces various episodes and trains them to encounter disaster situations. Children can participate in the episode by choosing options during the episode, which is reflected in the picture diary after the episode is completed. By providing information naturally in the picture diary, children can access how to cope with disaster situations. Through this system, children are expected to develop their judgment in disaster situations that they can encounter and have the ability to secure basic safety outside of adult help.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.307-314
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• 2023

As supercomputing and hardware technology advances, climate prediction models are improving. The Korean Meteorological Administration adopted GloSea5 from the UK Met Office and now operates an updated GloSea6 tailored to Korean weather. Universities and research institutions use Low-GloSea6 on smaller servers, improving accessibility and research efficiency. In this paper, profiling Low-GloSea6 on smaller servers identified the tri_sor_dp_dp subroutine in the tri_sor.F90 atmospheric model as a CPU-intensive hotspot. Applying linear regression, a type of machine learning, to this function showed promise. After removing outliers, the linear regression model achieved an RMSE of 2.7665e-08 and an MAE of 1.4958e-08, outperforming Lasso and ElasticNet regression methods. This suggests the potential for machine learning in optimizing identified hotspots during Low-GloSea6 execution.

• Smart Media Journal
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• v.12 no.10
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• pp.93-101
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• 2023

Climate change has emerged as a global problem, with frequent temperature increases, droughts, and floods, and it is predicted that it will have a great impact on the characteristics and productivity of crops. Cnidium officinale is used not only as traditionally used herbal medicines, but also as various industrial raw materials such as health functional foods, natural medicines, and living materials, but productivity is decreasing due to threats such as continuous crop damage and climate change. Therefore, this paper proposes a model that can predict the physiologically active ingredient index according to the climate change scenario of Cnidium officinale, a representative medicinal crop vulnerable to climate change. In this paper, data was first augmented using the CTGAN algorithm to solve the problem of data imbalance in the collection of environment information, physiological reactions, and physiological active ingredient information. Column Shape and Column Pair Trends were used to measure augmented data quality, and overall quality of 88% was achieved on average. In addition, five models RF, SVR, XGBoost, AdaBoost, and LightBGM were used to predict phenol and flavonoid content by dividing them into ground and underground using augmented data. As a result of model evaluation, the XGBoost model showed the best performance in predicting the physiological active ingredients of the sacrum, and it was confirmed to be about twice as accurate as the SVR model.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.368-375
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• 2023

In this study, we examined the error characteristic and bias correction method for one-month temperature forecast data produced through joint development between the Rural Development Administration and the H ong Kong University of Science and Technology. For this purpose, hindcast data from 2013 to 2021, weather observation data, and various environmental information were collected and error characteristics under various environmental conditions were analyzed. In the case of maximum and minimum temperatures, the higher the elevation and latitude, the larger the forecast error. On average, the RMSE of the forecast data corrected by the linear regression model and the XGBoost decreased by 0.203, 0.438 (maximum temperature) and 0.069, 0.390 (minimum temperature), respectively, compared to the uncorrected forecast data. Overall, XGBoost showed better error improvement than the linear regression model. Through this study, it was found that errors in prediction data are affected by topographical conditions, and that machine learning methods such as XGBoost can effectively improve errors by considering various environmental factors.

• Journal of IKEEE
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• v.26 no.1
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• pp.56-61
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• 2022

Various efforts have been made worldwide to respond to environmental problems such as climate change. Research on artificial intelligence (AI)-based energy management has been widely conducted as the most effective way to alleviate the climate change problem. In particular, buildings that account for more than 20% of the total energy delivered worldwide have been focused as a target for energy management using the building energy management system (BEMS). In this paper, we propose a multi-armed bandit (MAB)-based energy management algorithm that can efficiently decide the energy consumption level of the lighting system in each room of the building, while minimizing the discomfort levels of occupants of each room.