• Journal of Information Technology Applications and Management
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• v.13 no.1
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• pp.77-94
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• 2006

Case-based Reasoning (CBR) is widely used in business and industry prediction. It is suitable to solve complex and unstructured business problems. Recently, the prediction accuracy of CBR has been enhanced by not only various machine learning algorithms such as genetic algorithms, relative weighting of Artificial Neural Network (ANN) input variable but also data mining technique such as feature selection, feature weighting, feature transformation, and instance selection As a result, CBR is even more widely used today in business area. In this study, we investigated the usefulness of the CBR method in forecasting success in implementing ERP systems. We used a CBR method based on the feature weighting technique to compare the performance of three different models : MDA (Multiple Discriminant Analysis), GECBR (GEneral CBR), FWCBR (CBR with Feature Weighting supported by Analytic Hierarchy Process). The study suggests that the FWCBR approach is a promising method for forecasting of successful ERP implementation in Small and Medium sized Enterprises.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.3
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• pp.162-170
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• 2014

Appearance-based subspace models such as eigenfaces have been widely recognized as one of the most successful approaches to face recognition and tracking. The success of eigenfaces mainly has its origins in the benefits offered by principal component analysis (PCA), the representational power of the underlying generative process for high-dimensional noisy facial image data. The sparse extension of PCA (SPCA) has recently received significant attention in the research community. SPCA functions by imposing sparseness constraints on the eigenvectors, a technique that has been shown to yield more robust solutions in many applications. However, when SPCA is applied to facial images, the time and space complexity of PCA learning becomes a critical issue (e.g., real-time tracking). In this paper, we propose a very fast and scalable greedy forward selection algorithm for SPCA. Unlike a recent semidefinite program-relaxation method that suffers from complex optimization, our approach can process several thousands of data dimensions in reasonable time with little accuracy loss. The effectiveness of our proposed method was demonstrated on real-world face recognition and tracking datasets.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.3
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• pp.209-215
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• 2014

Sequence tagging is the task of predicting frame-wise labels for a given input sequence and has important applications to diverse domains. Conventional methods such as maximum likelihood (ML) learning matches global features in empirical and model distributions, rather than local features, which directly translates into frame-wise prediction errors. Recent probabilistic sequence models such as conditional random fields (CRFs) have achieved great success in a variety of situations. In this paper, we introduce a novel discriminative CRF learning algorithm to minimize local feature mismatches. Unlike overall data fitting originating from global feature matching in ML learning, our approach reduces the total error over all frames in a sequence. We also provide an efficient gradient-based learning method via gradient forward-backward recursion, which requires the same computational complexity as ML learning. For several real-world sequence tagging problems, we empirically demonstrate that the proposed learning algorithm achieves significantly more accurate prediction performance than standard estimators.

• Proceedings of the KIEE Conference
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• 1998.07a
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• pp.146-148
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• 1998

For the shape design of electromagnetic devices using the FEM, the choice of design parameters influence to the success of the optimization process. If the design parameter distribution has a one to one corespondence with finite element model, we can encounter not only serious accuracy problem but also obtain a zigzag shape along the interface. The nodes between those design parameters can be parameterized by interpolating using one among many interpolation methods. The conventional parameterization of design parameters has a limit of application for shape, because design parameters and movable nodes are linearly intepolated. In this paper, using the B-spline curve that use to present any interfaces in computer graphics, the curvilinear parameterization between design parameters and node points is compared with the linear parameterization.

• Structural Engineering and Mechanics
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• v.55 no.1
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• pp.65-78
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• 2015

In recent years, implants have gained growing importance in all areas of medicine. The success of the treatment depends on many factors affecting the bone-implant, implant-abutment and abutment-prosthesis interfaces. In this paper, static and dynamic behaviors of the dental prosthesis are investigated. Three-dimensional finite element models of dental prosthesis were constructed. Dynamic loads in 5 sec applied on occlusal surface. Therefore, FEA was selected for use in this study to examine the effect of the static and dynamic loads on the stress distribution for an implant-supported fixed partial denture and supporting bone tissue.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05a
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• pp.211-214
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• 2003

Since the number of mobile Internet users has been increasing rapidly around the world, the mobile business which is a variety of applications of mobile Internet has gained attention among the related industry and academics. However, most researchers mainly focus on the issues concerning the trend, forecast, technoloies, and demographic characteristics of mobile Internet services. Further, only mobile Internet users have participated in surveys, excluding network operators and contents providers. The purpose of this research is to project the evolution of mobile business and identify its critical success factors. The results of this research are from the analysis of data collected not only from mobile Internet users but also from network operators and contents providers.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.4
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• pp.415-423
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• 2014

In recent decades, RFID (Radio Frequency IDentification) technology has been recognized as one of the most core competencies in implementing ubiquitous society. However, Korea has not seen good success in diffusion of RFID even though Korean government continues funding many projects to diffuse the technology in industries. Most previous researches overestimate the growth of Korean RFID market in contrary to real market situation. This study aims to analyze the Korean RFID market and find a reasonable forecasting model for it. Our experimental results show that Bass forecasting model provides the more realistic estimates than any other models and the analyses of forecasting error provide useful information for the better forecasting. We also observed that government policy plays a crucial role in the diffusion of RFID technology in Korea.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.6
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• pp.767-772
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• 2018

Recently, some meta-heuristic algorithms, such as GA(Genetic Algorithm) and GP(Genetic Programming), have been used to optimize CNN(Convolutional Neural Network). The CNN, which is one of the deep learning models, has seen much success in a variety of computer vision tasks. However, designing CNN architectures still requires expert knowledge and a lot of trial and error. In this paper, the recent attempts to automatically construct CNN architectures are investigated and analyzed. First, two GA based methods are summarized. One is the optimization of CNN structures with the number and size of filters, connection between consecutive layers, and activation functions of each layer. The other is an new encoding method to represent complex convolutional layers in a fixed-length binary string, Second, CGP(Cartesian Genetic Programming) based method is surveyed for CNN structure optimization with highly functional modules, such as convolutional blocks and tensor concatenation, as the node functions in CGP. The comparison for three approaches is analysed and the outlook for the potential next steps is suggested.

• Journal of the Korea Institute of Building Construction
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• v.11 no.5
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• pp.523-529
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• 2011

In a tall building construction with reinforced concrete structures, the selection of an appropriate formwork system is a crucial factor for the success of the project. Thus, selecting an appropriate formwork system affects the entire construction duration and cost, as well as subsequent construction activities. However, in practice, the selection of an appropriate formwork system has depended mainly on the intuitive and subjective opinion of working level employees with restricted experience. Therefore, in this study, a formwork system selection model using the Adaboost algorithm is proposed to support the selection of a formwork system that is suitable for the construction site conditions. To validate the applicability of the proposed model, the selection models Adaboost and ANN were both applied to actual case data of tall building construction in Korea. The Adaboost model showed slightly better accuracy than that of the ANN model. The Adaboost model can assist engineers to determine the appropriate formwork system at the inception of future projects.

• Knowledge Management Research
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• v.14 no.2
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• pp.1-24
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• 2013

With rapid changes in technology and global competition, the success of many companies has become progressively more dependent on their ability to bring unique products, services, and business models to market. Therefore, many companies have tried to use creativity to all over the management activities and the concept of creative management is emerging. Creative management is a new but rapidly growing research area. But there have been few studies on this topic. In this paper, we developed the framework for measuring Creative Management levels of a company. The contribution of this paper is the following. First, we suggested a systematic measurement tool for creative management level focused on the management capability, business process and outcomes. Secondly, the creative measurement system developed in this paper can support companies to implement creative management as a guideline for adopting creative management.