• Journal of The Korean Association For Science Education
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• v.28 no.7
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• pp.768-778
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• 2008

The most important factor in students' growth and development is the teacher. Therefore in-service science teacher education to develop the professionality is important as well as the selection of new excellence teachers. Our research is on the development and application of new education program on science experiments where in-service teachers become the lecturers in the program and provide information that is bound to the context of real lessons. This program is consisted of following 10 steps of work, which was implemented in 5 months: sharing the philosophy of the program, selecting science experiments, first application of the experiments, discussion on the first application, learning how to edit the movie clips of the lesson, second application of the experiments, in depth discussion on the second application, developing the experiment package, giving lecture to other science teachers, and evaluating the program. We describe the process of the program developed and implemented in detail to suggest a model of science teacher education program on science experiments and discuss educational implications. This program is characterized by the emphasis of the context closely linked to the real lessons, the problem solving in a real situation, and the collaboration of teachers, professors and science education researcher in a teacher education.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.77-88
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• 2021

Duck industry had a rapid growth in recent years. Nevertheless, researches to improve duck house environment are still not sufficient enough. Moisture generation of duck house litter is an important factor because it may cause severe illness and low productivity. However, the measuring process is difficult because it could be disturbed with animal excrements and other factors. Therefore, it has to be calculated according to the environmental data around the duck house litter. To cut through all these procedures, we built several machine learning regression model forecasting moisture generation of litter by measured environment data (air temperature, relative humidity, wind velocity and water contents). 5 models (Multi Linear Regression, k-Nearest Neighbors, Support Vector Regression, Random Forest and Deep Neural Network). have been selected for regression. By using R-Square, RMSE and MAE as evaluation metrics, the best accurate model was estimated according to the variables for each machine learning model. In addition, to address the small amount of data acquired through lab experiments, bootstrapping method, a technique utilized in statistics, was used. As a result, the most accurate model selected was Random Forest, with parameters of n-estimator 200 by bootstrapping the original data nine times.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.125-132
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• 2023

In this study, we compared the performance of machine learning models for predicting Vapor Pressure Deficits (VPD) in greenhouses that affect pore function and photosynthesis as well as plant growth due to nutrient absorption of plants. For VPD prediction, the correlation between the environmental elements in and outside the greenhouse and the temporal elements of the time series data was confirmed, and how the highly correlated elements affect VPD was confirmed. Before analyzing the performance of the prediction model, the amount and interval of analysis time series data (1 day, 3 days, 7 days) and interval (20 minutes, 1 hour) were checked to adjust the amount and interval of data. Finally, four machine learning prediction models (XGB Regressor, LGBM Regressor, Random Forest Regressor, etc.) were applied to compare the prediction performance by model. As a result of the prediction of the model, when data of 1 day at 20 minute intervals were used, the highest prediction performance was 0.008 for MAE and 0.011 for RMSE in LGBM. In addition, it was confirmed that the factor that most influences VPD prediction after 20 minutes was VPD (VPD_y__71) from the past 20 minutes rather than environmental factors. Using the results of this study, it is possible to increase crop productivity through VPD prediction, condensation of greenhouses, and prevention of disease occurrence. In the future, it can be used not only in predicting environmental data of greenhouses, but also in various fields such as production prediction and smart farm control models.

• 한국신재생에너지학회:학술대회논문집
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• 2010.11a
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• pp.38.2-38.2
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• 2010

Biomimetc is a new domain of learning that proposes a solution getting clues from nature. There seems to be a sign of this phenomenon in fields of Renewable Energy. Foe example, Wind power was imitate the whale's fin that was improve efficiency of generating energy. This study focused on the photovoltaic generation as the instance of applying biomimetic. Efficiency is the most important factor in field of Photovoltaic generation. When given solar cell taking the sun light, most important fields of the study are absorb more light and increase the quantity of generation. For improving efficiency, the solar cell were builded up textures of taking a pyramid form, such a surface structure taking a role for remaining the light. This effects do the role as increasing absorbing efficiency. Such phenomenon calls Light Trapping, locking up the light on the surface of solar cell for a long time. Light is a vital factor to plants in the nature. Plants grow up through the photosynthesis that absorbing light for growth and propagation. So, plants make a effort how can absorb more the light in poor surroundings. This study set up a goal that imitates the minute surface structure of plants and applies to the existing solar cells's surface structure, so it can improve the efficiency of absorbing light. We used Light Tools software analyzing geometrical optics to analyze efficiency about new designed textures on the computer. We made a comparison between existing textures and new designed textures. Consequently, new designed textures were advanced efficiency, absorbing rates of light increasing about 7 percent. In comparison with existing and new textures, advancing about 20 percent in the efficient aspect.

• The Journal of Pediatrics of Korean Medicine
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• v.16 no.1
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• pp.105-124
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• 2002

With the progress of civilization, the disorders due to the stress, which derived from the social-structural complexity and diversity, are on an increasing trend in our times. Accordingly, the accurate diagnosis and appropriate treatment for them are required. Especially in the current years, children's disorders delivered by the emotional problems keep increasing. In this research, the researcher tried to figure out the cause of the children's stress and its treatment, studied the theories of the stress in the modem medicine and the sever emotions in oriental medicine, and came to the conclusion as follows: 1. The stress can be defined as the combination of the reaction to noxious stimuli and its defense mechanism of the body, In oriental medicine, it is considered as pathological notions which includes seven emotions as the internal factor, six evils as the external factor and other foods, expectoration, ecchymoma as the non-internal/external factors. 2. Children usually get stressed by various reasons in a growth process such as schooling, relationship with friends, the opposite sex of family, or change of surroundings, and these can cause the various disorders. 3. In the study of the children's stress symptoms, it is found that the silent reaction is uncommon. It usually appeared in both reactions: firs, physical reactions such as stomachache, vomiting, headache, neural frequent urination, bronchial asthma or excessive respiration and/or, second, behavioral reactions such as a decline of performance, alimentary disorder, e.g. anorexia nervosa or bulimia, sleep disorder, e.g. nightmare or panic in sleep, anthrophobia, refusal to a school attendance or hyperactiveness. Besides, the peculiar mental disorder such as paroxysm of anger, tic, autism, nocturnal enuresis, lack of attentiveness, impediment in linguistic development, learning difficulty, intellectual decline, etc. can be appeared, and the heavy stress during the babyhood can cause the regression of behavior or the immaturity of formation of character. 4. The appropriate treatments for the children's stress are Osteopathy, Manpulation, Aroma Therapy, Alexander Technique, Autonomic Never Control Treatment, Biofeedback, Chiropractic, Dance Therapy, Feldenkrasis Technique, Gravity Therapy, Homepathy, Aquatherapy, Hypnotherapy, Naturopathy and Meditation.

• KDI Journal of Economic Policy
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• v.25 no.2
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• pp.3-53
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• 2003

Using the plant level panel data on Korean manufacturing during 1990-98 period, this study tries to assess the role of entry and exit in enhancing aggregate productivity, both qualitatively and quantitatively. Main findings of this study are summarized as follows. First, plant entry and exit rates in Korean manufacturing seem quite high: they are higher than in the U.S. or several developing countries for which comparable studies exist. Second, in line with existing studies on other countries, plant turnovers reflect underlying productivity differential in Korean manufacturing, with the "shadow of death" effect as well as selection and learning effects all present. Third, plant entry and exit account for as much as 45 and 65 percent in manufacturing productivity growth during cyclical upturn and downturn, respectively. The findings of this study show that the entry and exit of plants has been an important source of productivity growth in Korean manufacturing. Plant birth and death are mainly a process of resource reallocation from plants with relatively low and declining productivity to a group of heterogeneous plants, some of which have the potential to become highly efficient in future. The most obvious lesson from this study is that it is important to establish policy or institutional environment where efficient businesses can succeed and inefficient businesses fail.

• The Journal of Bigdata
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• v.9 no.1
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• pp.31-47
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• 2024

With the advancement of artificial intelligence, the travel and hospitality industry is also adopting AI and machine learning technologies for various purposes. In the tourism industry, demand forecasting is recognized as a very important factor, as it directly impacts service efficiency and revenue maximization. Demand forecasting requires the consideration of time-varying data flows, which is why statistical techniques and machine learning models are used. In recent years, variations and integration of existing models have been studied to account for the diversity of demand forecasting data and the complexity of the natural world, which have been reported to improve forecasting performance concerning uncertainty and variability. This study also proposes a new model that integrates various machine-learning approaches to improve the accuracy of hotel sales demand forecasting. Specifically, this study proposes a new time series forecasting model based on XGBoost that selectively utilizes a local model by clustering with DTW K-means and a global model using the entire data to improve forecasting performance. The hotel demand forecasting model that selectively utilizes global and regional models proposed in this study is expected to impact the growth of the hotel and travel industry positively and can be applied to forecasting in other business fields in the future.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.157-172
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• 2023

The recent Fourth Industrial Revolution is accelerating changes due to digital transformation. According to this trend, the existing start-up paradigm is changing, and new business models based on new technologies and creative ideas are emerging. In addition, the diversity of mentoring relationships and environments such as online mentoring, reverse mentoring, group mentoring, and multiple mentoring is also increasing. However, most mentors in their 50s and 60s, who are mainly active in the start-up field, have been able to help mentees a lot based on their own experience and expertise, but they are having difficulty responding to the changing environment due to a lack of understanding and experience of new technologies and environments. To cope with these changes well, mentors must constantly study, acquire and apply the latest technologies to improve their understanding of new technologies and the environment. In addition, it is necessary to have an understanding and respect for the diversity of mentoring relationships and environments, and to maximize the effectiveness of mentoring by actively utilizing them. Therefore, mentors should recognize that they directly affect the growth and development of mentees, constantly acquire new knowledge and skills to maintain and develop expertise, and actively deliver their knowledge and experiences to mentees. Therefore, in this study, was tried to empirically analyze the relationship between mentoring's influence on mentor's job satisfaction through mentor's personal learning and self-efficacy. The results of the empirical analysis were as follows. Among the functions of mentoring, career function and role modeling were found to have a positive effect on both personal learning and self-efficacy, which are parameters, and job satisfaction, which is a dependent variable. On the other hand, psychological and social functions have a positive effect on personal learning, but they do not have an effect on self-efficacy and job satisfaction. In addition, as a result of analyzing the mediating effect, all mediating effects were confirmed for career functions, and only the mediating effect of self-efficacy was confirmed for role modeling. Through this study, mentoring is an important factor in promoting job satisfaction, personal learning and self-efficacy, and this study can be said to be academically and practically meaningful in that it confirmed personal learning and self-efficacy as factors that increase mentor's job satisfaction, and the focus of mentoring research was shifted from mentee to mentor to study the impact of mentoring on mentors.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.1
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• pp.40-49
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• 2004

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Self-Organizing Polynomial Neural Networks(SOPNN), discuss a comprehensive design methodology and carry out a series of numeric experiments. The conventional SOPNN is based on the extended Group Method of Data Handling(GMDH) method and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons (or nodes) located in each layer through a growth process of the network. Moreover it does not guarantee that the SOPNN generated through learning has the optimal network architecture. But the proposed GA-based SOPNN enable the architecture to be a structurally more optimized network, and to be much more flexible and preferable neural network than the conventional SOPNN. In order to generate the structurally optimized SOPNN, GA-based design procedure at each stage (layer) of SOPNN leads to the selection of preferred nodes (or PNs) with optimal parameters- such as the number of input variables, input variables, and the order of the polynomial-available within SOPNN. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. A detailed design procedure is discussed in detail. To evaluate the performance of the GA-based SOPNN, the model is experimented with using two time series data (gas furnace and NOx emission process data of gas turbine power plant). A comparative analysis shows that the proposed GA-based SOPNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• The Journal of Information Systems
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• v.17 no.2
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• pp.1-26
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• 2008

SCM is one of the important key issues in Internet-based business environment. This study intends to suggest a research model to measure the influence of environmental, organizational, information technology factors on organizational performance using the four perspectives of balanced scorecard (BSC). 9 independent variables and 4 dependent variables were adopted from existing literature review. 103 companies data were collected by survey. Four hypotheses in this study were generated to analyze the positive relationship of environmental, organizational, information systems on organizational performance with 4 perspectives of BSC. The results of hypothesis testing show as follows. First, organizational performance of learning and growth perspective has a positive influence of IS maturity, process innovation, top management support, use of SCM package, IS interoperability, and objectives sharing. Second organizational performance of infernal process perspective has a positive influence of process innovation, IS interoperability, objectives sharing, top management support, use of SCM package, competitiveness, and IS maturity. Third, organizational performance of customer perspective has a positive influence of IS interoperability, objectives sharing, process innovation, IS maturity, competitiveness, and use of SCM package. Finally, organizational performance of financial perspective has a positive influence of process in innovation, use of SCM package, IS maturity, objectives sharing, IS interoperability, and top management support. The contribution of this study is that it provides a conceptual framework and empirical evidences of the causal relationship between environmental, organizational, IS factor and organizational performance with 4 perspectives of BSC.