• Journal of Korean Society for Quality Management
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• v.14 no.2
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• pp.40-46
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• 1986

The decision maker has the job of torecasting capital investments and operating expenses to aid the decision making in choosing and evaluating present and future alternatives. The estimating function eventually analysis, evaluates and choose the alternatives. The analysis stemmed originally from a preliminary design of some sort, and eventually plans are started to investigate investment possibilites. This study provide the descounted cash flow and the present worth method. Despite any choice of an analytical method, there remains the problem of predicting certain future events. Therefore, these models dealing with optimum plant sizing, equipment replacement, and lease or buy will be discussed.

• Journal of Convergence for Information Technology
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• v.11 no.8
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• pp.36-41
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• 2021

Although the technology for prediction or analysis using artificial intelligence is constantly developing, a black-box problem does not interpret the decision-making process. Therefore, the decision process of the AI model can not be interpreted from the user's point of view, which leads to unreliable results. We investigated the problems of artificial intelligence and explainable artificial intelligence using Blockchain to solve them. Data from the decision-making process of artificial intelligence models, which can be explained with Blockchain, are stored in Blockchain with time stamps, among other things. Blockchain provides anti-counterfeiting of the stored data, and due to the nature of Blockchain, it allows free access to data such as decision processes stored in blocks. The difficulty of creating explainable artificial intelligence models is a large part of the complexity of existing models. Therefore, using the point cloud to increase the efficiency of 3D data processing and the processing procedures will shorten the decision-making process to facilitate an explainable artificial intelligence model. To solve the oracle problem, which may lead to data falsification or corruption when storing data in the Blockchain, a blockchain artificial intelligence problem was solved by proposing a blockchain-based explainable artificial intelligence model that passes through an intermediary in the storage process.

• Journal of Environmental Impact Assessment
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• v.20 no.5
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• pp.663-673
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• 2011

In the face of accelerating biodiversity loss and its significance in our coexistence with nature, biodiversity is becoming more crucial in sustainable development perspective. To estimate biodiversity in the future which provides valuable information for decision making system especially in the national level, a quantitative approach must be studied forehand as a baseline of the present status. In this study, we developed a large-scale map of Plant Species Richness (PSR, typical indicator of biodiversity) for Young-dong and Pyung-chang provinces. Due to the accessibility of appropriate data and advance of modelling techniques, reduction of variables without deteriorating the predictive power is considered by applying Genetic algorithm. In addition, a number of Correctly Classified Instances (CCI) with 10-fold cross validation which indicates the predictive power, was carried out for evaluation. This study, as a fundamental baseline, will be beneficial in future land work as well as ecosystem restoration business or other relevant decision making agenda.

• Journal of the Korea Society for Simulation
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• v.5 no.1
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• pp.1-12
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• 1996

We propose a new approach to classifying a time series data into one of the autoregressive moving-average (ARMA) models. It is bases on two pattern recognition concepts for solving time series identification. The one is an extended sample autocorrelation function (ESACF). The other is a neural network-driven decision tree classifier(NNDTC) in which two pattern recognition techniques are tightly coupled : neural network and decision tree classfier. NNDTc consists of a set of nodes at which neural network-driven decision making is made whether the connecting subtrees should be pruned or not. Therefore, time series identification problem can be stated as solving a set of local decisions at nodes. The decision values of the nodes are provided by neural network functions attached to the corresponding nodes. Experimental results with a set of test data and real time series data show that the proposed approach can efficiently identify the time seires patterns with high precision compared to the previous approaches.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.828-834
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• 1989

On-Line optimization of process units has heretofore been restricted to the individual equipment level using linear approximate models. The advent of the low cost, high speed micro-computer coupled with the speed and robustness of an equation based exact simulator is making real-time optimization of entire process units a reality. The resultant implications for a decision system applied to day-to-day operations, point to a significant change in the way process plants will be managed in the future.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.07a
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• pp.310-311
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• 2015

Ships' tracking data are being monitored and collected by vessel traffic service center in real time. In this paper, we intend to contribute to vessel traffic service operators' decision making through extracting ships' tracking patterns and models based on these data. Support Vector Machine algorithm was used for vessel track modeling to handle and process the data sets and k-fold cross validation was used to select the proper parameters. Proposed data processing methods could support vessel traffic service operators' decision making on case of anomaly detection, calculation ships' dead reckoning positions and etc.

• The Journal of Korean Philosophical History
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• no.25
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• pp.99-135
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• 2009

This study is to investigate many kinds of conceptual models of social problem solving approach as well as decision making method, and then describes educational implications, especially for more effective method of teaching problem solving ability in order to reduce children' anti social behaviors and to be able to have their healthy and happy lives. Problem solving ability or decision making skills have been taken to goal of primary school curriculum, but is too cognitive or too centered to morality for student to get that kinds of skill or competency. As a result of new education method is developed on the basis of Socal Emotional Learning(SEL) as well as Emotional Intelligence which put on the importance on the role of emotion in the problem solving. This method have two distinctions. First, It has the background of culture specific views of emotion to be proper this method in our society. Second, It should be integrated into moral education as a part of school curriculum to establish secure and long term intervention.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.3
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• pp.7-22
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• 2014

By the publications of RDF and OWL, the Semantic Web is confirmed as a technology through which information in the Internet can be processed by machines. The focus of the Semantic Web study after then has moved to how to provide more useful information to users for their decision making beyond simple use of the structured data in ontologies. SWRL that makes logical inference possible by rules, and SWCL that formulates constraints under the Semantic Web environment are some of many efforts toward the achievement of that goal. Constraint represents a connection or a relationship between individual data in ontology. Based on SWCL, this paper tries to extend the language by adding one more type of constraint, implication constaint, in its repertoire. When users use binary variables to represent logical relationships in mathematical models, it requires and knowledge on the solver to solve the models. The use of implication constraint ease this difficulty. Its need, definition and relevant technical description is presented by the use of the optimal common attribute selection problem in product design.

• Journal of The Korean Association For Science Education
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• v.11 no.2
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• pp.87-101
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• 1991

Science-Technology-Society curricula have been developed in several countries for the last 20 years. Those curricula were focused on social aspects of science, i.e., value-laden knowledge and scientific enterprise, and society-related scientific and technological issues, i.e., energy, pollution, natural resources. The major teaching models employed in those curricula were problem solving and decision making, which required the following teaching techniques: teacher as a manager, small group discussion, controversy as a motivational force for substantive learning, and sufficient factual information into the discussion. Further researches are to be made to ascertain whether or not the expectations of the curricula might be realized in practice. It was shown that most Korean biology teachers considered the STS-related goal of science education as more important than the other goals. Based upon the findings, some recommendations for development of Korean STS curriculum were made as follows: 1. The contents of the STS curricula are to be organized with the integrated mode; 2. The major teaching models throughout the contents are to be problem solving and decision making. These are considered to provide students with the opportunities to involve in debates on practical issues and to draw consensus from them; 3. Some degree of flexibility should be provided on teachers' implementation of the curriculum in terms of contents, teaching techniques etc.; 4. To increase the practicality of the curriculum, teachers should be involved in the development of the curriculum and the relevant research; and 5. Contents to be included in STS curriculum were suggested by some science educators, but the more systematic study is required in this respect.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.4
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• pp.145-156
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• 2015

Incorporation of individual learning and forgetting behaviors within worker-task assignment models produces a mixed integer nonlinear program (MINLP) problem, which is difficult to solve as a NP hard due to its nonlinearity in the objective function. Previous studies commonly assume homogeneity among workers in workforce scheduling that takes account of learning and forgetting characteristics. This paper expands previous researches by considering heterogeneous individual learning/forgetting, and investigates the impact of worker heterogeneity in initial expertise, steady-state productivity, learning and forgetting on system performance to assist manager's decision-making in worker-task assignments without tackling complex MINLP models. In order to understand the performance implications of workforce heterogeneity, this paper examines analytically how heterogeneity in each of the four parameters of the exponential learning and forgetting (L/F) model affects system performance in three cases : consecutive assignments with no break, n breaks of s-length each, and total b break-periods occurred over T periods. The study presents the direction of change in worker performance under different assignment schedules as the variance in initial expertise, steady-state productivity, learning or forgetting increases. Thus, it implies whether having more heterogenous workforce in terms of each of four parameters in the L/F model is desired or not in different schedules from the perspective of system productivity measurement.