• International journal of advanced smart convergence
• /
• v.11 no.4
• /
• pp.68-80
• /
• 2022

Since the launch of AI, technology development to implement complete and sophisticated AI functions has continued. In efforts to develop technologies for complete automation, Machine Learning techniques and deep learning techniques are mainly used. These techniques deal with supervised learning, unsupervised learning, and reinforcement learning as internal technical elements, and use the Big-data Analysis method again to set the cornerstone for decision-making. In addition, established decision-making is being improved through subsequent repetition and renewal of decision-making standards. In other words, big data analysis, which enables data classification and recognition/recognition, is important enough to be called a key technical element of AI function. Therefore, big data analysis itself is important and requires sophisticated analysis. In this study, among various tools that can analyze big data, we will use a Python program to find out what variables can affect addiction according to smartphone use in a decision tree environment. We the Python program checks whether data classification by decision tree shows the same performance as other tools, and sees if it can give reliability to decision-making about the addictiveness of smartphone use. Through the results of this study, it can be seen that there is no problem in performing big data analysis using any of the various statistical tools such as Python and R when analyzing big data.

• The Journal of the Korea Contents Association
• /
• v.21 no.9
• /
• pp.458-470
• /
• 2021

In Korea, there are many cases where policy decisions are not made even though it is a big social issue. Usually, the reasons for such delay in policy making are cases where it is delayed to go through an appropriate policy procedure, and there are cases where the consent of stakeholders is not obtained. But, this study intends to suggest that a few policy delay in Korea can be explained by intentional non-decision-making by the government. Non-decision making is being made as a strategic and active strategy of the government. In this study, the case of Lotte Shopping Mall in Sangam was analyzed from the point of view of non-decision making. As a result of analysis based on factors such as the existence of elite, purpose, policy tool, policy process, and result, it can be assumed that the policy delay of the Lotte Shopping Mall was caused by the government's active strategic decision. The government's strategic non-decision-making, which is distinct from passive administration, needs to be reviewed in various ways in the future.

• The Research Journal of the Costume Culture
• /
• v.12 no.3
• /
• pp.468-484
• /
• 2004

The purposes of this study were (1) to segment female college students' apparel shoppers into unique apparel buying decision-making style groups; and (2) to profile for each segment in terms of personal characteristics (material values, change seeking tendency and prestige sensitivity) and fashion information sources. Data were collected through questionnaires from convenient sample of 290 female college students. As a result of cluster analysis and univariate analysis of variance, distinctive consumer decision-making style groups of consumers were identified; Value-maximizing Recreational Shoppers, Brand-Maximizing Emotional Shoppers, and Apathetic Shoppers. These three groups were compared as to the effect of personal characteristics variables and fashion information sources through univariate analysis of variance and chi-square statistics. The result showed that personal characteristics (material values, change seeking tendency and prestige sensitivity) and fashion-information sources do influence the consumer decision-making styles and that these three groups were unique in their decision-making characteristics showing that consumer decision-making styles can be a good segmentation base for apparel market.

• Proceedings of the KSME Conference
• /
• 2000.11b
• /
• pp.216-221
• /
• 2000

Most Ice making companies make simple efforts to make products and fail to introduce improvements into the system against huh cost of products. The work presented here is an implementation of ice making method to improve both energy efficiency and productivity. In this present investigation, several ice making cycles are designed and calculated to evaluate COP, ice making time and electric energy consumption. Results obtained shows that COP is improved with more efficient use of time for ice making and electric consumption. Therefore this can offer the opportunity for more efficient energy consumption and productivity in ice making.

• Journal of Korea Multimedia Society
• /
• v.20 no.7
• /
• pp.1136-1147
• /
• 2017

In this study, we investigated the effect of users' emoticon usage on their satisfaction with image making and self-expression in MIM communication. We defined internal attraction, social appeal, and communication ability as personal image making factors due to the use of emoticons in MIM communication. And we defined the research hypotheses based on the definition of intimacy, openness, and social reality as relational image making factors. In MIM communication, we analyzed the questionnaire about emoticon users. As a result, emoticon usage was found to have a positive and significant effect on self-expression in relation image making rather than personal image making. The study implies that the use of emoticons can lead to image making in MIM communication and satisfy the satisfaction of self expression.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.13 no.6
• /
• pp.455-461
• /
• 2001

Domestic ice making companies make effort to obtain products and neglect to introduce low cost product improvements with energy savings. The work presented here is an implementation of ice making method to improve both energy efficiency and productivity. In this present investigation, several ice making cycles are proposed for higher efficiency in the system. COP(Coefficient of Performance), ice making time and electric energy consumption are evaluated and compared with the conventional system. Results shows that COP is improved with more efficient use of time for ice making and electric consumption. Therefore, this can offer an opportunity for more efficient use of energy and higher productivity in ice making.

• Hwankyungkyoyuk
• /
• v.15 no.1
• /
• pp.125-136
• /
• 2002

The ultimate goal of environmental education is to train the civilian who positively participates in solving environmental problems. To do above, not only accurate knowledge but also right value about environmental problems are needed. It is reasonable decision making that choose the first of all alternatives to solve the problems by accurate knowledge and right value of an individual. Teaching reasonable decision making in environmental education is related to raise the participant civilian toward regional environmental problem solving Simulation game helps that students have a opportunity to practice decision making skills about regional problems and give self-confidence to their decision making ability. So, the aim of this study is to present simulation games which is fit to elementary environmental education. The first one is for group decision making, the second one is for individual decision making. These can make a conclusion, winner and loser of games. But last one is open-ended game and aims to make explicit a variety of opinions, issues and conflicts to problem.

• Journal of Internet Computing and Services
• /
• v.25 no.4
• /
• pp.7-21
• /
• 2024

There are various machine learning techniques such as Reinforcement Learning, Deep Learning, Neural Network Learning, and so on. In recent, Large Language Models (LLMs) are popularly used for Generative AI based on Reinforcement Learning. It makes decisions with the most optimal rewards through the fine tuning process in a particular situation. Unfortunately, LLMs can not provide any explanation for how they reach the goal because the training is based on learning of black-box AI. Reinforcement Learning as black-box AI is based on graph-evolving structure for deriving enhanced solution through adjustment by human feedback or reinforced data. In this research, for mutually exclusive decision-making, Mutually Exclusive Learning (MEL) is proposed to provide explanations of the chosen goals that are achieved by a decision on both ends with specified conditions. In MEL, decision-making process is based on the tree-based structure that can provide processes of pruning branches that are used as explanations of how to achieve the goals. The goal can be reached by trade-off among mutually exclusive alternatives according to the specific contextual conditions. Therefore, the tree-based structure is adopted to provide feasible solutions with the explanations based on the pruning branches. The sequence of pruning processes can be used to provide the explanations of the inferences and ways to reach the goals, as Explainable AI (XAI). The learning process is based on the pruning branches according to the multi-dimensional contextual conditions. To deep-dive the search, they are composed of time window to determine the temporal perspective, depth of phases for lookahead and decision criteria to prune branches. The goal depends on the policy of the pruning branches, which can be dynamically changed by configured situation with the specific multi-dimensional contextual conditions at a particular moment. The explanation is represented by the chosen episode among the decision alternatives according to configured situations. In this research, MEL adopts the tree-based learning model to provide explanation for the goal derived with specific conditions. Therefore, as an example of mutually exclusive problems, employment process is proposed to demonstrate the decision-making process of how to reach the goal and explanation by the pruning branches. Finally, further study is discussed to verify the effectiveness of MEL with experiments.

• Journal of Internet Computing and Services
• /
• v.21 no.1
• /
• pp.237-246
• /
• 2020

this paper, a data modeling method based on decision-making conditions is proposed for making combat and battlefield management systems to be intelligent, which are also a decision-making support system. A picture of a robot seeing and perceiving like humans and arriving a point it wanted can be understood and be felt in body. However, we can't find an example of implementing a decision-making which is the most important element in human cognitive action. Although the agent arrives at a designated office instead of human, it doesn't support a decision of whether raising the market price is appropriate or doing a counter-attack is smart. After we reviewed a current situation and problem in control & command of military, in order to collect a big data for making a machine staff's advice to be possible, we propose a data modeling prototype based on decision-making conditions as a method to change a current control & command system. In addition, a decision-making tree method is applied as an example of the decision making that the reformed control & command system equipped with the proposed data modeling will do. This paper can contribute in giving us an insight of how a future AI decision-making staff approaches to us.

• Journal of Korea Foundry Society
• /
• v.15 no.2
• /
• pp.164-174
• /
• 1995

New metallic powder making processes, named "Centrifugal Emulsification Process(CEP)" and "Mixer and Settler(MS)" have been developed to synthesize rapid solidified metallic powders. Through CEP and MS processings, the high temperature metals as well as the low temperature alloys are manufactured. Also, the effects of rapid solidification on the undercooling, solidification rate and crystallization behaviors can be evaluated effectively through the processes. The standard deviations of the synthesized typical Pb-Sn eutectic powders are 1.63 and 1.51 for CEP and MS respectively, and the average size of the MS powders was $18{\mu}m$. The possibility of the customized not only size and shape control but microstructure control was also shown. Both of the new methods can be applied to continuous powder making processes.