• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2005.11a
• /
• pp.571-578
• /
• 2005

Corporate credit rating analysis has drawn a lot of research interests in previous studies, and recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper applies support vector machines (SVMs) to the corporate credit rating problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, the researcher uses a grid-search technique using 5-fold cross-validation to find out the optimal parameter values of kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM, the researcher compares its performance with those of multiple discriminant analysis (MDA), case-based reasoning (CBR), and three-layer fully connected back-propagation neural networks (BPNs). The experiment results show that SVM outperforms the other methods.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2007.11a
• /
• pp.489-498
• /
• 2007

For better predictions and classifications in customer recommendation, this study proposes an integrative model that efficiently combines the currently-in-use statistical and artificial intelligence models. In particular, by integrating the models such as Association Rule, Frequency Matrix, and Rule Induction, this study suggests an integrative prediction model. The data set for the tests is collected from a convenience store G, which is the number one in its brand in S. Korea. This data set contains sales information on customer transactions from September 1, 2005 to December 7, 2005. About 1,000 transactions are selected for a specific item. Using this data set, it suggests an integrated model predicting whether a customer buys or not buys a specific product for target marketing strategy. The performance of integrated model is compared with that of other models. The results from the experiments show that the performance of integrated model is superior to that of all other models such as Association Rule, Frequency Matrix, and Rule Induction.

• Journal of the Korean Society of Propulsion Engineers
• /
• v.23 no.4
• /
• pp.98-103
• /
• 2019

Integrity prediction of the operation equipment of naval vessels is essential to maintain the efficiency of the operation performance in urgent situations. Recently, the integrated condition assessment system(ICAS) was introduced and maintained to improve operational performance. This technology is related with ICAS, and it must be localized through extensive research. In this paper, we present the results of applying the data-driven model to the predictability methods of diesel generators, which are naval vessel operation equipment.

• Journal of computational fluids engineering
• /
• v.11 no.3 s.34
• /
• pp.59-63
• /
• 2006

Accurate prediction of a supersonic missile base drag continues to defy even well-rounded CFD codes. In an effort to address the accuracy and predictability of the base drags, the influence of grid system and competitive turbulence models on the base drag is analyzed. Characteristics of some turbulence models is reviewed through incompressible turbulent flow over a flat plate, and performance for the base drag prediction of several turbulence models such as Baldwin-Loman(B-L), Spalart-Allmaras(S-A), k-$\varepsilon$, k-$\omega$ model is assessed. When compressibility correction is injected into the S-A model, prediction accuracy of the base drag is enhanced. The NSWC wind tunnel test data are utilized for comparison of CFD and semi-empirical codes on the accuracy of base drag predictability: they are about equal, but CFD tends to perform better. It is also found that, as angle of attack of a missile with control fins increases, even the best CFD analysis tool we have lacks the accuracy needed for the base drag prediction.

• ETRI Journal
• /
• v.28 no.4
• /
• pp.425-434
• /
• 2006

We propose a fast macroblock mode prediction and decision algorithm based on contextual information for Pslices in the H.264/AVC video standard, in which the mode prediction part is composed of intra and inter modes. There are nine $4{\times}4$ and four $16{\times}16$ modes in the intra mode prediction, and seven block types exist for the best coding gain based on rate-distortion optimization. This scheme gives rise to exhaustive computations (search) in the coding procedure. To overcome this problem, a fast inter mode prediction scheme is applied that uses contextual mode information for P-slices. We verify the performance of the proposed scheme through a comparative analysis of experimental results. The suggested mode search procedure increased more than 57% in speed compared to a full mode search and more than 20% compared to the other methods.

• IEIE Transactions on Smart Processing and Computing
• /
• v.5 no.2
• /
• pp.143-151
• /
• 2016

This paper presents new prediction methods to improve compression performance of the so-called near-lossless RGB-domain image coder, which is designed to effectively decrease the memory bandwidth of a system-on-chip (SoC) for image processing. First, variable block size (VBS)-based intra prediction is employed to eliminate spatial redundancy for the green (G) component of an input image on a pixel-line basis. Second, inter-color prediction (ICP) using spectral correlation is performed to predict the R and B components from the previously reconstructed G-component image. Experimental results show that the proposed algorithm improves coding efficiency by up to 30% compared with an existing algorithm for natural images, and improves coding efficiency with low computational cost by about 50% for computer graphics (CG) images.

• Proceedings of the IEEK Conference
• /
• 2009.05a
• /
• pp.163-165
• /
• 2009

Automatic Vision Inspection(AVI) systems automatically detect defect features and measure their sizes via camera vision. It is important to predict the performance of an AVI to meet customer's specification in advance. In this paper, we propose a statistical method for prediction of false alarm rate regarding inconsistency of defect size measuremet process. We only need are a simple experimental trial for repeated defect size measurement test. The statistical features from the experiement are utilized in the prediction process. Therefore, the proposed method is swift and easy to implement and use. The experiment shows a close prediction compared to manual inspection results.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.27 no.2
• /
• pp.10-16
• /
• 2004

Data mining technique is the exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules. This paper uses a data mining technique for the prediction of defect types in manufacturing Process. The Purpose of this Paper is to model the recognition of defect type Patterns and Prediction of each defect type before it occurs in manufacturing process. The proposed model consists of data handling, defect type analysis, and defect type prediction stages. The performance measurement shows that it is higher in prediction accuracy than logistic regression model.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
• /
• v.10 no.2
• /
• pp.74-79
• /
• 2011

The goal of this research is to reduce dynamic and static power consumption for a low power cache system. The proposed cache can achieve a low power consumption by using a drowsy and a way prediction mechanism. For reducing the static power, the drowsy technique is used at 4-way set associative cache. And for reducing the dynamic energy, one among four ways is selectively accessed on the basis of information in the Way-Line Prediction Unit (WLPU). This prediction mechanism does not introduce any additional delay though prediction misses are occurred. The WLPU can effectively reduce the performance overhead of the conventional drowsy caching by waking only a drowsy cache line and one way in advance. Our results show that the proposed cache can reduce the power consumption by about 40% compared with the 4-way drowsy cache.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.28 no.1
• /
• pp.49-54
• /
• 2005

Recently, the reliability are applied for many industrial products, and many products are required to guarantee in quality and in performance. The purpose of this paper is to present some of reliability prediction methodologies using failure rate database for machinery parts that are applicable to machine tools. VDI Turret, which is core component of the NC Lathe, was chosen as the target of the reliability prediction. The results of reliability prediction has shown the failure rate, MTBF(Mean Time Between Failure), and reliability of the VDI Turret. It is expected that proposed methodologies will be applicable to prediction of reliability for other components of machine tools.