• Journal of the Korean Association of Geographic Information Studies
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• v.4 no.1
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• pp.34-46
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• 2001

The Boolean logic analysis method using GIS as a spatial decision support system(SDSS) contains two problems. One is losing a lots of informations in analysis process, the other is unable to reflect of different weights between analysis items. The purpose of this study is to provide a new decision-making model for site analysis, that provides a rational and systemic way using fuzzy sets theory and analytic hierarchy process(AHP) theory. According to this study of technopark in Pohang, Boolean logic method did not reflect the influence of the differently weighted items and selected only 8.0% to 16.1% of the area for suitable sites for residence, commercial/research, park/green uses. The fuzzy sets theory and AHP theory method were able to reflect the influence of differently weighted items and selected 32.9% to 37.4% of the area for the best sites, and also provided more other kinds of informations. The results of this study show that GIS system using fuzzy sets theory and AHP proess method provides a more flexible and objective solutions for site analysis.

• Proceedings of the KAIS Fall Conference
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• 2006.05a
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• pp.608-610
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• 2006

Supply Chain Management(SCM) system is a critical investment that can affect future competitiveness and performance of a company. When adopting a new SCM system, organizations experience increasing difficulty in decision making because information technology is changing so rapidly these days. Therefore, organizations have been looking for industry standards and proven methods of selection that they can utilize to choose the best SCM system. To select an optimum solution, we need to consider a number of different quantitative and qualitative factors such as cost, user interface and convenience, reference site, and so on. In this study, we propose a solution selection model of SCM systems using Fuzzy AHP to maximize the return on investment in information technology. The proposed model can systematically construct the objectives of SCM system selection to support the business goals.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.4
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• pp.269-276
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• 2013

Information and communication technology (ICT) is a promising solution for mitigating road traffic congestion. ICT allows road users and vehicles to be managed based on real-time road status information. In Tanzania, traffic congestion causes losses of TZS 655 billion per year. The main objective of this study was to develop an optimal approach for integrating real-time road information (RRI) to mitigate traffic congestion. Our research survey focused on three cities that are highly affected by traffic congestion, i.e., Arusha, Mwanza, and Dar es Salaam. The results showed that ICT is not yet utilized fully to solve road traffic congestion. Thus, we established a possible approach for Tanzania based on an analysis of road traffic data provided by organizations responsible for road traffic management and road users. Furthermore, we evaluated the available road information management techniques to test their suitability for use in Tanzania. Using the floating car data technique, fuzzy logic was implemented for real-time traffic level detection and decision making. Based on this solution, we propose a RRI system architecture, which considers the effective utilization of readily available communication technology in Tanzania.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.1060-1071
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• 2014

Signals of the Electroencephalogram (EEG) can reflect the electrical background activity of the brain generated by the cerebral cortex nerve cells. This has been the mostly utilized signal, which helps in effective analysis of brain functions by supervised learning methods. In this paper, an approach for improving the accuracy of EEG signal classification is presented to detect epileptic seizures. Moreover, Independent Component Analysis (ICA) is incorporated as a preprocessing step and Short Time Fourier Transform (STFT) is used for denoising the signal adequately. Feature extraction of EEG signals is accomplished on the basis of three parameters namely, Standard Deviation, Correlation Dimension and Lyapunov Exponents. The Artificial Neural Network (ANN) is trained by incorporating Levenberg-Marquardt(LM) training algorithm into the backpropagation algorithm that results in high classification accuracy. Experimental results reveal that the methodology will improve the clinical service of the EEG recording and also provide better decision making in epileptic seizure detection than the existing techniques. The proposed EEG signal classification using feed forward Backpropagation Neural Network performs better than to the EEG signal classification using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier in terms of accuracy, sensitivity, and specificity.

• Advances in nano research
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• v.17 no.2
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• pp.109-124
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• 2024

In the era of smart manufacturing, precise prediction of springback-a common issue in ultra-thin sheet metal forming- and forming limits are critical for ensuring high-quality production and minimizing waste. This paper presents a novel approach that leverages the Internet of Things (IoT) and Artificial Neural Networks (ANN) to enhance springback and forming limits prediction accuracy. By integrating IoT-enabled sensors and devices, real-time data on material properties, forming conditions, and environmental factors are collected and transmitted to a central processing unit. This data serves as the input for an ANN model, which is trained with crystal plasticity simulations and experimental data to predict springback with high precision. Our proposed system not only provides continuous monitoring and adaptive learning capabilities but also facilitates real-time decision-making in manufacturing processes. Experimental results demonstrate significant improvements in prediction accuracy compared to traditional methods, highlighting the potential of IoT and ANN integration in advancing smart manufacturing. This approach promises to revolutionize quality control and operational efficiency in the industry, paving the way for more intelligent and responsive manufacturing systems.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1189-1198
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• 2015

For the effects of the global financial crisis cause hedge funds to have a strong influence on financial markets, it is needed to study new approach method to predict hedge fund survival. This paper proposes to organize fuzzy neural network using hedge fund data as input to predict hedge fund survival. The variables of hedge fund data are ambiguous to analyze and have internal uncertainty and these characteristics make it challenging to predict their survival from the past records. The object of this study is to evaluate the predictability of fuzzy neural network which uses grades of membership to predict survival. The results of this study show that proposed system is effective to predict the hedge funds survival and can be a desirable solution which helps investors to support decision-making.

• Korean Journal of Health Education and Promotion
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• v.30 no.5
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• pp.189-199
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• 2013

Objectives: This paper aims to explain principles of gist-based health message design and discuss their implications for health promotion and public health education. Methods: After reviewing Reyna and Brainerd's Fuzzy Trace Theory(FTT), the authors explicate how to transform FTT into a practical guidance of health message design. Our explication is based upon FTT's reasoning that human intuition, rather than analysis, takes a primary role in message recall and comprehension, followed by judgment and decision making. We expect gist-based message design to be appropriate to serve such intuition. Results: Four principles of gist-based message design are offered: (1) provision of qualitative, as well as quantitative, information of gist, (2) inclusion of visual images corresponding to gist, (3) use of effective message formats to emphasize the gist (4) inclusion of relevant reasons and contextutal information. Conclusions: Gist-based message design has theoretical and practical implications for health promotion, specifically in the field of public health education, the press and governmental communication toward the public, and provider-patient communication in medical settings.

• Journal of the Korean Society of Marine Environment & Safety
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• v.21 no.5
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• pp.531-537
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• 2015

In order to maximize power output from the solar panels, one needs to keep the panels aligned with the sun. So solar tracker having high reliability must be designed. This paper cares about the design and evaluation of a two-axis solar tracker system based on fuzzy logic control with LabVIEW. The research focus on planning mechanical parts, making an intelligent controller which controls and monitors all parameters via user interface implemented of a fuzzy decision support system for control of photovoltaic panel movement. We also develop a real solar tracker system and analyze the influence indexes such as environment, weather, season, and light condition. The solar tracker is tested in real condition and all parameters related to the system operation are recorded and analyzed. The developed solar tracking system got a much higher efficiency about 38 % compare to fixed solar panel although the weather condition is affected a lot to the solar panel. So we confirmed the our auto tracking system is more effective and can allow more energy to be produced.

• Korean Business Review
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• v.15
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• pp.99-114
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• 2002

Recently several business models concerning e-Business has been introduced. But the different environment for each business requires the business model which is contingent to its specific situation. We, therefore, need to develop the e-Business models considering environment factors such as capital size, technology level, collection ability and amount of information, profit or target customers, etc. There can be several ways to create the value of an e-Business firm. A way among them is to develop limited area by focusing on core parts of the firm. This way leads for the firm to search the investment priority in order to solve the problem, which is to set a proper production and investment level for concentrating on competitively excellent areas of the firm. In this paper, we propose a method to decide the investment priority effectively when making a decision using fuzzy information. The method by our model is to minimize tolerances of given business fuzzy goals.

• Journal of Korean Society of Transportation
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• v.15 no.4
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• pp.143-163
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• 1997

Incident management is significant far the traffic management systems. The management of incidents determines the smoothness of freeway operations. The dynamic nature of incidents and the uncertainty associated with them require solutions based on the incident operator's judgment. Fuzz systems attempt to adapt such human expertise and are designed to replicate the decision making capability of on operator. Fuzzy systems process complex traffic information, and transmit it in a simplified, understandable form to human traffic operators. In this study, fuzzy rules were developed based on data from real incidents on Santa Monica Freeway in LosAngeles. The fuzzy rules ail linguistic based, and hence, user-friendly. A comparison of the results from the linguistic model with the real incident durations indicate that the outputs from the model reliably correspond to real incident durations conditions. The model reliably predicts the freeway incident duration. The modes can thus be used as an effective management tool for freeway incident response systems. The approach could be applied to other problems regarding dispatch systems in transportation.