• The Journal of Industrial Distribution & Business
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• v.13 no.8
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• pp.19-27
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• 2022

Purpose: CEOs develop policies through their effective decision-making while employees implement the policies so that a business realizes the expected returns. This research focuses on the importance of the CEO's sustainable leadership to distribute environmental education culture to improve employees' environmental performance. Research design, data and methodology: The PRISMA that is selected by the present research is an evidence-based minimum group of entities for reporting in systematic reviews and meta-analyses. The core focus of the concept is to note studies that evaluate the impacts of intervention and can also be utilized as a basis for writing systematic reviews rather than intervention evaluations. Results: The current investigation indicates that there are four kinds of suggestions (a. Increased organizational learning, b. Open communication, c. Participative decision making, d. Psychological empowerment) how the management should develop sustainable leadership for distributing green culture and improving employee green performance. Conclusions: Based on four solutions, the present research concludes that sustainable leadership for CEOs is not only of advantage in terms of protecting the environment and the people, but it fosters increased organizational learning. Increased organizational learning leads to better employee sustainable performance, which includes financial performance and the social and environmental initiatives the organization implements.

• Korean Chemical Engineering Research
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• v.58 no.4
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• pp.565-572
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• 2020

This study developed a software platform using machine learning of artificial intelligence to optimize the distillation column system. The distillation column is representative and core process in the petrochemical industry. Process stabilization is difficult due to various operating conditions and continuous process characteristics, and differences in process efficiency occur depending on operator skill. The process control based on the theoretical simulation was used to overcome this problem, but it has a limitation which it can't apply to complex processes and real-time systems. This study aims to develop an empirical simulation model based on machine learning and to suggest an optimal process operation method. The development of empirical simulations involves collecting big data from the actual process, feature extraction through data mining, and representative algorithm for the chemical process. Finally, the platform for the distillation column was developed with verification through a developed model and field tests. Through the developed platform, it is possible to predict the operating parameters and provided optimal operating conditions to achieve efficient process control. This study is the basic study applying the artificial intelligence machine learning technique for the chemical process. After application on a wide variety of processes and it can be utilized to the cornerstone of the smart factory of the industry 4.0.

• Journal of the Korea Fashion and Costume Design Association
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• v.17 no.1
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• pp.117-130
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• 2015

As environmental issues have become a worldwide concern after the 20th century, the idea and the term of 'green growth development' has become familiar to the public. After 2008, the green growth development dramatically became an important ideology in Korea; thus industries, studies and product investments in relation are in active progress. Following the trend, the latter major unit of the middle school textbook "Technology & Home Economics" was named the revision of elementary, middle and high school textbooks in 2009. The learning goal of 'green' or 'eco-friendly' of the revised edition of the textbook will guide the middle school students to have better understanding of the issues of clothing habits and the environment. Furthermore, students will be able to apply the 'green' concepts in their real life and put eco-friendly clothing habits into action. Thus, the practice of effective learning will depend on the quality of the current issue of the textbook. Therefore this study analyzes the eco-friendly contents of the semi-unit from 7 different textbooks and presents an example of textbook production to the preliminary teacher of home economics.

• Korean System Dynamics Review
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• v.13 no.4
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• pp.33-55
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• 2012

The research is to identify important diffusion factors and their effects on green car diffusion process using system dynamics perspectives and a causal-loop analysis. Through a deep review on previous research, we have found the important factors of green car diffusion process. Price, driving range, network effect, recharge system, fuel cost had important facilitation on consumer attraction and green car diffusion. Based on the review, we have constructed a causal loop diagram explaining hybrid car diffusion process. We have found 3 important reinforcing loops in the causal loop diagram. Loop for learning & economies of scale(supply side), loop for network effect(consumer side), and loop for battery development(technology side) had most significant roles in the whole diffusion process. Through a deliberate analysis on the 3 causal loops, we have found meaningful results. First, there seems to exist a critical mass in the diffusion. Second, of the 3 loops, the battery technology had most significant role. Third, not consumer installed base but sales must be a standard to decide whether the critical mass is achieved or not. Based on these findings, several meaningful implications are suggested for the government and corporations related to the green car industries.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2496-2512
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• 2021

The requirements for powerful computing capability, high capacity, low latency and low energy consumption of emerging services, pose severe challenges to the fifth-generation (5G) network. As a promising paradigm, mobile edge networks can provide services in proximity to users by deploying computing components and cache at the edge, which can effectively decrease service delay. However, the coexistence of heterogeneous services and the sharing of limited resources lead to the competition between various services for multiple resources. This paper considers two typical heterogeneous services: computing services and content delivery services, in order to properly configure resources, it is crucial to develop an effective offloading and caching strategies. Considering the high energy consumption of 5G base stations, this paper considers the hybrid energy supply model of traditional power grid and green energy. Therefore, it is necessary to design a reasonable association mechanism which can allocate more service load to base stations rich in green energy to improve the utilization of green energy. This paper formed the joint optimization problem of computing offloading, caching and resource allocation for heterogeneous services with the objective of minimizing the on-grid power consumption under the constraints of limited resources and QoS guarantee. Since the joint optimization problem is a mixed integer nonlinear programming problem that is impossible to solve, this paper uses deep reinforcement learning method to learn the optimal strategy through a lot of training. Extensive simulation experiments show that compared with other schemes, the proposed scheme can allocate resources to heterogeneous service according to the green energy distribution which can effectively reduce the traditional energy consumption.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.11 no.2
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• pp.114-122
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• 2008

Roof greening recently emerged to be an important issue of environmental policies in a city. To cover roofs of buildings with green vegetation gives chances not only to improve urban environmental function but also provides the opportunities of environmental learning and convenience for users in the building. This study aimed to give directions for roof greening plan to designers by acknowledging the relationships between ecological and utilizational effectiveness. 10 sites and 15 variables were adopted to measure the relationships. As a result, no positive correlations was found between them. One of the results in correlation analysis among variables, however, showed that the roof gardens have high utilizational effectiveness only when a green roof was made by focusing on ecological functions in addition to the concepts that will guarantee user's convenience such as accessibility, entrance and exit, facilities for convenience and learning. The results implies that a green roof has to be designed considering multi-functional effects. Correlation between species of vegetation and the number of daily users, average staying hours, and attending level for environment educational programs were not significant. These findings imply that when a green roof has to be take high utilizational effectiveness, both plentiful vegetation species and design concepts for users' convenience are should be considered.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.1
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• pp.14-21
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• 2021

Green networking has become a issue to reduce energy wastes and CO2 emission by adding energy managing mechanism to wired data networks. Energy consumption of the overall wired data networks is driven by access networks, expect for end devices. However, on a global scale, it is more difficult to manage centrally energy, measure and model the real energy use and energy savings potential of the access networks. This paper presented the multiple linear regression model to predict energy consumption of wired access networks using supervised learning of machine learning with data collected by existing investigated materials, actual measured values and results of many models. In addition, this work optimized the performance of it by various experiments and predict energy consumption of wired access networks. The performance evaluation of the regression model was achieved by well-knowned evaluation metrics.

• Korean Journal of Comparative Education
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• v.28 no.5
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• pp.47-69
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• 2018

The purpose of this study is to classify the lifelong learning policy implementation process of lifelong learning in Korea according to the policy making decision models and to suggest developmental tasks. Korea's lifelong learning policy came to a full-fledged start with the enactment of the Lifelong Education Act in 2000. The Lifelong Education Act proposed the establishment of an open educational system as a strategy to realize the lifelong learning society. According to the Lifelong Education Act, the Korean government has developed several lifelong education policies such as providing learning opportunity for the underprivileged, facilitating lifelong learning city project, building lifelong learning culture, recognizing of experiential learning result, funding lifelong learning hub university, launching lifelong learning supporting administrative etc. The Korean lifelong system is characterized as Allison's (1971) governmental/bureaucratic, Ziegler and Johnson's (1972) legislative, Griffin's(1987) social control and Green's (2000) state-led models which make policy through the coordination between the government and the parliament and control bureaucratic power and educational qualifications. Lifelong learning policies should be managed in terms of supply and demand at the learning market. In addition, the state has to strengthen lifelong learning through supporting NGOs' activities and adult learners' tuition fee for the disadvantaged group of people.

• Journal of Korean Society of Rural Planning
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• v.28 no.4
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• pp.105-117
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• 2022

This study tested SVM(support vector machine), RF(random forest), and ANN(artificial neural network) machine-learning models that can predict net irrigation water requirements in paddy fields. For the Jeonju and Jeongeup meteorological stations, the net irrigation water requirement was calculated using K-HAS from 1981 to 2021 and set as the label. For each algorithm, twelve models were constructed based on cumulative precipitation, precipitation, crop evapotranspiration, and month. Compared to the CE model, the R² of the CEP model was higher, and MAE, RMSE, and MSE were lower. Comprehensively considering learning performance and learning time, it is judged that the RF algorithm has the best usability and predictive power of five-days is better than three-days. The results of this study are expected to provide the scientific information necessary for the decision-making of on-site water managers is expected to be possible through the connection with weather forecast data. In the future, if the actual amount of irrigation and supply are measured, it is necessary to develop a learning model that reflects this.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.2
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• pp.681-687
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• 2013

This study examined whether the combination of the indoor temperature on the learning environment and the colors of the learning materials affect the learning performance. To do this, the condition of indoor temperature was divided into three conditions: the neutral condition which is the appropriate temperature condition of the learning activities ($22.5{\sim}24^{\circ}C$), the high-temperature condition (> $24^{\circ}C$), and the low-temperature condition (< $22.5^{\circ}C$). In addition, colors of red, blue, black, and green were used as the warm, cold, and neutral colors, and the verbal-working memory task was used as the learning task. As a result, it was not significant differences in the response time of the learning task, whereas, in the accuracy rate of the learning task, the performance was more accurate in red- and black-color conditions. These results could be interpreted as the saliency and color-temperature of the red color, and the familiarity and specificity of the black color.