• International conference on construction engineering and project management
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• 2005.10a
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• pp.1100-1105
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• 2005

To compete with large construction companies whose dominance in market share is deepening recently, the needs for improving field production system of mid-size construction companies have increased rapidly. The development of an adequate model that focuses on the organizational size and work process of the mid-size construction companies are needed to increase productivity through the informatization in the construction industry[0]. The authors of this study used case studies on the problems of the mid-size construction companies in procuring work and presented a method to solve such problems so that the managers in head office of those mid-size construction companies can effecitively deal with multiple sites[0].

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.456-469
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• 2022

This research was conducted to investigate the structural relationship social support of college students has with their occupational values, career decision, and quality of life. Subjects of study were art and design college students in Seoul. Social support, occupational values, career decision, and quality of life were measured as criteria. SPSS and AMOS were used to test structural model and the relationship among variables. Results of this study are as follows. First, effects of social support on internal and external occupational values were studied. Second, effects of social support on quality of life-satisfaction and happiness-were studied. Third, effects of occupational values on career decision standards were studied. External occupational values had a statistically significant correlation with career decision, while internal occupational values were found to have no statistically significant effect on career decision. Based on the results, it can be inferred that colleges which need to support the careers and employment of students must support students with informational and instrumental support. Design students must be provided with proper career coaching and environment improvement, as well as development of programs suitable for various talents and aptitudes, so that they can shape appropriate occupational values.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.390-395
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• 2004

A semiconductor manufacturing process is the complicate and dynamic process, and consists of many sub-processes. An etching process is the most important process in the semiconductor fabrication. In this paper, the decision support system based upon data mining and knowledge discovery is an important factor to improve the productivity and yield. The proposed decision support system consists of a neural network model and an inference system based on fuzzy logic Firstly, the product results are predicted by the neural network model constructed by the product patterns that represent the quality of the etching process. And the product patters are classified by expert's knowledge. Finally, the product conditions are estimated by the fuzzy inference system using the rules extracted from the classified patterns. Prediction of product qualities can be linked to each input and process variables. We employ data mining and intelligent techniques to find the best condition of the etching process. The proposed decision support system is efficient and easy to be implemented for the process management based upon expert's knowledge.

• Spatial Information Research
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• v.17 no.3
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• pp.251-259
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• 2009

As a result of the current trend towards promoting conservation of the ecosystem, there have been various studies conducted to determine ways to establish an ecological network. The development of analytical methods and an environmental database of GIS has made the creation of this network more efficient. This study focuses on the development of an urban spatial decision support system based on 'Landscape Ecology Theory'. The spatial decision support system suggested in this study consists of four stages. First, landscape patch for the core areas, which are major structures of the ecological network, was determined using the GIS overlay method. Second, a forest habitat was investigated to determine connectivity assessment. Using the gravity model, connectivity assessment at the habitat forest was conducted to select the needed connecting area. Third, the most suitable corridor routes for the eco-network were presented using the least-cost path analysis. Finally, a brief investigation was conducted to determine the conflict areas between the study result and landuse. The results of this study can be applied to urban green network planning. Moreover, the method developed in this study can be utilized to control urban sprawl, promote biodiversity.

• Korean Journal of Construction Engineering and Management
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• v.12 no.5
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• pp.55-62
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• 2011

Recently, construction work has diversified and become larger. So, a systematic measurement and management measures are required. In this study, USN technology which is one of the most important network technologies was selected. Based on elements derived from comparison of each element of standard was calculated according to the importance of the elements. The importance of the decision to support the proposed model is explained by integrating the importance of each criteria and decision-support model by considering the situation and creating a matrix of considerations for the construction of earthwork. The results of this study show that USN technology in the context of judging criteria can be selected for the earthwork.

• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1136-1146
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• 2005

Fingerprint classification is useful in an automated fingerprint identification system (AFIS) to reduce the matching time by categorizing fingerprints. Based on Henry system that classifies fingerprints into S classes, various techniques such as neural networks and support vector machines (SVMs) have been widely used to classify fingerprints. Especially, SVMs of high classification performance have been actively investigated. Since the SVM is binary classifier, we propose a novel classifier-combination model, multiple decision templates (MuDTs), to classily fingerprints. The method extracts several clusters of different characteristics from samples of a class and constructs a suitable combination model to overcome the restriction of the single model, which may be subject to the ambiguous images. With the experimental results of the proposed on the FingerCodes extracted from NIST Database4 for the five-class and four-class problems, we have achieved a classification accuracy of $90.4\%\;and\;94.9\%\;with\;1.8\%$ rejection, respectively.

• Journal of Information Technology and Architecture
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• v.9 no.1
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• pp.43-55
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• 2012

In recent years, outsourcing of information systems, including decision support systems has become a key method for managing the system portfolio of a corporation. Since the outsourced DSSs provide their own models and solvers, which may be created on the basis of different modeling practices and system platforms, the decision maker wishing to solve business problems using the outsourced DSSs frequently faces a difficulty in selecting and/or applying appropriate models and solvers to the problems on hand. This paper proposes a DSS outsourcing architecture that enables a user to discover and execute appropriate models and solvers, even though the user is not knowledgeable enough about all the details of the models and solvers. Specifically, this paper adopts a Web services approach to integrate the heterogeneous models and solvers by encapsulating individual models and solvers as Web services and hiding all system specific implementation details from the users.

• Knowledge Management Research
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• v.10 no.3
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• pp.17-33
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• 2009

The emergence of the Internet shopping agent enabled consumers to enjoy Internet shopping more easily and quickly. Especially, the role of Internet shopping agent is becoming more important following the information overload trend on the Internet in that the consumers can promptly obtain information about a certain product and its price among countless items on the Internet. As a result, consumers can now enjoy shopping more easily, compared to the offline shopping which requires a lot of efforts in comparing the products and purchasing them. Since the Internet shopping agents collect extensive information about the products' price, delivery period, detailed characteristics, etc., and present a comparison table containing the information to the consumers, the consumers can shop more quickly at lower price using such shopping agents. However, it has not been sufficiently studied about how the various functions of shopping agents actually support consumers' purchase decision making procedure in everyday life, and if they do, in which stages they play a supporting role in consumers' purchase decision making system. Therefore, this study conducts an empirical analysis on the role of the Internet shopping agents in the purchase decision making process of the consumers, considering the Internet shopping agent as a decision making supporting system. Moreover, it analyzes how the effects of the Internet shopping agent differs according to the consumers' knowledge level about the products.

• Journal of Biomedical Engineering Research
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• v.43 no.2
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• pp.102-108
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• 2022

Purpose: To propose a deep learning-based clinical decision support technique for laryngeal disease on epiglottis, tongue and vocal cords. Materials and Methods: A total of 873 laryngeal endoscopic images were acquired from the PACS database of Pusan N ational University Yangsan Hospital. and VGG16 model was applied with transfer learning and fine-tuning. Results: The values of precision, recall, accuracy and F1-score for test dataset were 0.94, 0.97, 0.95 and 0.95 for epiglottis images, 0.91, 1.00, 0.95 and 0.95 for tongue images, and 0.90, 0.64, 0.73 and 0.75 for vocal cord images, respectively. Conclusion: Experimental results demonstrated that the proposed model have a potential as a tool for decision-supporting of otolaryngologist during manual inspection of laryngeal endoscopic images.

• Journal of the Korea Institute of Building Construction
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• v.11 no.1
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• pp.91-99
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• 2011

Bayesian network is a form of probabilistic graphical model. It incorporates human reasoning to deal with sparse data availability and to determine the probabilities of uncertain cases. In this research, bayesian network is adopted to model the problem of construction project cost. General information, time, cost, and material, the four main factors dominating the characteristic of construction costs, are incorporated into the model. This research presents verify a model that were conducted to illustrate the functionality and application of a decision support system for predicting the costs. The Markov Chain Monte Carlo (MCMC) method is applied to estimate parameter distributions. Furthermore, it is shown that not all the parameters are normally distributed. In addition, cost estimates based on the Gibbs output is performed. It can enhance the decision the decision-making process.