• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.387-393
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• 2022

The article reflects short-term perspectives on the use of information and communication technologies in the training of teachers for higher education. Education is characterized by conservatism, so aspects of systematic development of the industry are relevant to this cluster of social activity. Therefore, forecasting the introduction of innovative elements of ICT training is in demand for the educational environment. Forecasting educational trends are most relevant exactly in the issues of training future teachers of higher education because these specialists are actually the first to implement the acquired professional skills in pedagogical activities. The article aims to consider the existing potential of ICT-based learning, its implementation in the coming years, and promising innovative educational elements that may become relevant for the educational space in the future. The tasks of scientific exploration are to show the optimal formats of synergy between traditional and innovative models of learning. Based on already existing experience, extrapolation of conditions of educational process organization with modeling realities of using information and communication technologies in various learning dimensions should be carried out. Educational trends for the next 3 years are a rather tentative forecast because, as demonstrated by the events associated with the COVID-19 pandemic, the socio-cultural space is very changeable. Consequently, the dynamism of the educational environment dictates the need for a value-based awareness of the information society and the practical use of technological advances. Thus, information and communication technologies are a manifestation of innovative educational strategies of today and become an important component along with traditional aspects of educational process organization. Future higher education teachers should develop a training strategy taking into account the expediency of the ICT component.

• Journal of the Korean housing association
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• v.21 no.5
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• pp.73-81
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• 2010

With rapid development of various information and communication technologies, to forecast future became important for coping with new environment. Experts in each field of study are forecasting future society, and considerable life scenarios are derived in the process. Life scenarios help people to approach and understand future circumstances easily. Therefore, to study future housing with life scenarios as materials will be helpful to establish the direction to the development of current housing. The purpose of this research is to examine what characterizes the housing functions and life behaviors of future house and what is changing from the housing functions and life behaviors of past and present. Content analysis was used as research method. The subject was 10 future forecasting books which reflects daily life in the house, and 1 episode relating residential space as 1 analysis unit, the total of 213 episodes were analyzed as materials. As a result, most of the life behaviors in the house are expected to be performed by robots instead of humans in the future. On the other hand, partial life behaviors are already being performed mostly by computer system, and another partial life behaviors show that the role-performance of them are not being totally by robots but partially with human.

• Archives of design research
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• v.16 no.3
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• pp.81-90
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• 2003

The purpose of the study is to propose a new way of classification for products and to forecast the future of products through the physical factor and the mental factor as human natures. For the purpose of the study, the research was carried out in three ways. Firstly, the study considered the evolutional process of products through human natures. At this stage, the study defined that the physical ability and the mental ability of human are the cores of the product's evolution. Secondly, for understanding human evolution, the study set up two types of future humans . Finally, the study classified products by the physical factor and the mental factor as human natures with the aspect of embryology. As the results, the study illustrated two different species of products and their futures.

• Journal of Korea Society of Digital Industry and Information Management
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• v.4 no.3
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• pp.115-126
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• 2008

This thesis is the research for methods to forecast of dynamic changes at retail stores in the ubiquitous era, and, through adopting RFID based technology, to minimize cost of operation of stores and bringing up better customer service. The core methods had been researched and studied are to build future-oriented stores by providing store system with better customer's convenience based on referring to case studies of future-oriented stores and efficient inventory management method enabling more profitable store, and adopting Smart Carts, Smart Shelf and e-POP and environment sensors as a method in order to provide more improved customer service.

• International Journal of Quality Innovation
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• v.9 no.3
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• pp.1-14
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• 2008

Patient satisfaction with the Emergency Department(ED) in a hospital is related to the length of stay, and especially to the amount of waiting time for medical treatments. ED overcrowding decreases quality and efficiency, therefore affecting hospitals' profitability. This paper presents a forecasting and simulation model for resource management of the ED at Moses H. Cone Memorial Hospital. A linear regression forecasting model is proposed to predict the number of ED patient arrivals, and then a simulation model is provided to estimate the length of stay of ED patients, system throughput, and the utilization of resources such as triage nurses, patient beds, registered nurses, and medical doctors. The near future load level of each resource is presented using the proposed models.

• Journal of the Korea Computer Industry Society
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• v.6 no.2
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• pp.241-248
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• 2005

Conventional estimating methods forecast the future that it usually using the past statistical numerical value. In order to forecast the farming price, it must need many effort and accuracy knowledge. Therefore, to solve the these problems, this paper to improve forecasting farming price using fuzzy rules and neural network as a preprocessing. Also, we developed an intelligent farming expert system for real time forecasting as a postprocessing about unexpectable conditions. Computer simulation results proved reducing pricing error which proposed farming price expecting system better than conventional demand forecasting system does not using fuzzy rules.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.25 no.4
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• pp.178-186
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• 2017

Air travel has become an essential part of the global society and its sustainable development is expected. Airlines profit structure and network operation will be influenced by internalization of external costs like emission charge. This additional cost of the airlines will be directly pose air ticket fare increase and demand of air passenger will be decreased. EU-ETS is a part of environmental binding to airlines fly to EU territory airports. This study analyzes the impact of emission charges by application of EU-ETS on airlines network change. For long-term forecast, a reliable estimation of the future price of carbon dioxide (CO2) will be used.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.127-129
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• 2005

Construction of wind power plant is increasing rapidly because Jeju island is known as the most suitable place for wind power plant. Rut wind power plant is difficult electric power control and it has a rapid electric power fluctuation. Such a problem has a bad influence on electric power network in small electric network like Jeju. Therefore, we forecast the amount of wind power plant construction by weather information and the rate of utilization for existing facility. We investigate the contribution degree for electric Power demand, economic effect, the case of power network influence. So we forecast influence of wind power plant for Jeju power network's operation in the near future.

• Health Policy and Management
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• v.12 no.3
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• pp.54-76
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• 2002

This study investigated how much EVA which evaluate firm's value can explain hospital bankruptcy prediction as a explanatory variable including financial indicators in Korea. In this study, artificial neural network and logit regression which are traditional statistical were used as the model for bankruptcy prediction. Data used in this study were financial and economic value added indicators of 34 bankrupt and -:4 non-bankrupt hospitals from the Database of Korean Health Industry Development Institute. The main results of this study were as follows: First, there was a significant difference between the financial variable model including EVA and the financial variable model excluding EVA in pre-bankruptcy analysis. Second, EVA could forecast bankruptcy hospitals up to 83% by the logistic analysis. Third, the EVA model outperformed the financial model in terms of the predictive power of hospital bankruptcy. Fourth, The predictive power of neural network model of hospital bankruptcy was more powerful than the legit model. After all the result of this study will be useful to future study on EVA to evaluate bankruptcy hospitals forecast.

• Culinary science and hospitality research
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• v.5 no.1
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• pp.89-101
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• 1999

This study attempts to identify the most accurate quantitative forecasting technique for measuring the future level of demand for food ＆ beverage in super deluxe hotel in Seoul, which will subsequently lead to determining the optimal level of purchasing food ＆ beverage. This study, in detail, examines the food purchasing system of H hotel, reviews three rigorous univariate time series models and identify the most accurate forecasting technique. The monthly data ranging from January 1990 to December 1997 (96 observations) were used for the empirical analysis and the 1998 data were left for the comparison with the ex post forecast results. In order to measure the accuracy, MAPE, MAD and RMSE were used as criteria. In this study, Box-Jenkins model was turned out to be the most accurate technique for forecasting hotel food ＆ beverage demand among selected models generating 3.8％ forecast error in average.