• Proceedings of the KAIS Fall Conference
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• 한국산학기술학회 2003년도 Proceeding
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• pp.178-184
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• 2003

Case-based reasoning (CBR) often shows significant promise for improving effectiveness of complex and unstructured decision making. Consequently, it has been applied to various problem-solving areas including manufacturing, finance and marketing. However, the design of appropriate case indexing and retrieval mechanisms to improve the performance of CBR is still challenging issue. Most of previous studies to improve the effectiveness for CBR have focused on the similarity function or optimization of case features and their weights. However, according to some of prior researches, finding the optimal k parameter for k-nearest neighbor (k-NN) is also crucial to improve the performance of CBR system. Nonetheless, there have been few attempts which have tried to optimize the number of neighbors, especially using artificial intelligence (AI) techniques. In this study, we introduce a genetic algorithm (GA) to optimize the number of neighbors to combine. This study applies the new model to the real-world case provided by an online shopping mall in Korea. Experimental results show that a GA-optimized k-NN approach outperforms other AI techniques for purchasing behavior forecasting.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권3호
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• pp.185-191
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• 2008

In this paper, experimental studies of a neural network (NN) control technique for non-model based position control of the x-y table robot are presented. Decentralized neural networks are used to control each axis of the x-y table robot separately. For an each neural network compensator, an inverse control technique is used. The neural network control technique called the reference compensation technique (RCT) is conceptually different from the existing neural controllers in that the NN controller compensates for uncertainties in the dynamical system by modifying desired trajectories. The back-propagation learning algorithm is developed in a real time DSP board for on-line learning. Practical real time position control experiments are conducted on the x-y table robot. Experimental results of using neural networks show more excellent position tracking than that of when PD controllers are used only.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 한국항해항만학회 2005년도 춘계학술대회 논문집
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• pp.457-463
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• 2005

The use of the internet to facilitate commerce among companies promises vast benefits. Lots of e-marketplaces are building for several industries such as chemistry, airplane, and automobile industries. This study proposed new B2B EC business model for the shipping industry which concerns relatively massive fixed assets to be fully utilized. To be successful the proposed model gives participants to support useful information. To do this the expert system is constructed as the hybrid prediction system of neural network (NN) and memory based reasoning (MBR) with self-organizing map (SOM) and knowledge augmentaton technique using qualitative reasoning (QR). The expert system supports participants useful information coping with dynamic market environment. with this transportation companies are induced to participate in the proposed e-marketplace and helped for exchanges easily. Also participants would utilize their assets fully through B2B exchanges.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국해양정보통신학회 2002년도 추계종합학술대회
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• pp.575-578
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• 2002

In this paper, we discuss carrier trapping model and trap characteristics of nitrided oxide thin film. Based on the experimental results, the carrier trapping model for system having multi-traps is proposed and is fitted with experimental data in order to determine trap parameter of nitride oxide and O2 annealed nitrided oxide. As a results of curve fitting, the heavy nitridation of oxide introduces three kinds of traps with capture cross section $\sigma$n1=1.48$\times$10$^{-17}$ $\textrm{cm}^2$, $\sigma$n2=1.51$\times$10$^{-19}$ $\textrm{cm}^2$, $\sigma$p=1.53$\times$10$^{-18}$ $\textrm{cm}^2$ and corresponding trap densities Nnl=2.66$\times$10$^{12}$ Cm$^{-2}$ , Nn2=1.32$\times$10$^{12}$ Cm$^{-2}$ , Np=8.35$\times$10$^{12}$ Cm$^{-2}$ .

• Journal of Navigation and Port Research
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• 제29권4호
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• pp.349-355
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• 2005

The use of the internet to facilitate commerce among companies promises vast benefits. Lots of e-marketplaces are building for several industries such as chemistry, airplane, and automobile industries. This study provides the new B2B EC business model for the shipping industry which concerns relatively massive fixed assets to be fully utilized. To be successful the proposed model gives participants useful information. To do this the expert system is constructed with the hybrid prediction system of neural network (NN) and memory based reasoning (MBR) with self-organizing map (SOM) and knowledge augmentation technique using qualitative reasoning (QR). The expert system supports participants useful information coping with dynamic market environment. with this shipping companies are induced to participate in the proposed e-marketplace and helped for exchanges easily. Also participants would utilize their assets fully through B2B exchanges.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 한국전기전자재료학회 2005년도 하계학술대회 논문집 Vol.6
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• pp.221-222
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• 2005

In this paper, application of NN (Neural Network) as a method of pattern discrimination of PD(partial discharge) which occurs at the stator coil of traction motor was studied. For PD data acquisition, three defective models are manufactured such as internal discharge model, slot discharge model and surface discharge model. PD data for recognition were acquired from PD detector and DAQ board which is able to analysis the PD signal and perform the pattern discrimination. Statistical distributions and parameters are calculated to discriminate PD sources. And also these statistical distribution parameters are applied to classify PD sources by BP and has good recognition rate on the discharge sources.

• Proceedings of the Korea Water Resources Association Conference
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• 한국수자원학회 2018년도 학술발표회
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• pp.157-157
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• 2018

A reliable and accurate downscaling model which can provide climate change information, obtained from global climate models (GCMs), at finer resolution has been always of great interest to researchers. In order to achieve this model, linear methods widely have been studied in the past decades. However, nonlinear methods also can be potentially beneficial to solve downscaling problem. Therefore, this study explored the applicability of some nonlinear machine learning techniques such as neural network (NN), extreme learning machine (ELM), and ELM autoencoder (ELM-AE) as well as a linear method, least absolute shrinkage and selection operator (LASSO), to build a reliable temperature downscaling model. ELM is an efficient learning algorithm for generalized single layer feed-forward neural networks (SLFNs). Its excellent training speed and good generalization capability make ELM an efficient solution for SLFNs compared to traditional time-consuming learning methods like back propagation (BP). However, due to its shallow architecture, ELM may not capture all of nonlinear relationships between input features. To address this issue, ELM-AE was tested in the current study for temperature downscaling.

• The KIPS Transactions:PartB
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• 제17B권4호
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• pp.309-316
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• 2010

This paper introduces a new model based method for facial expression recognition that uses facial grid angles as feature space. In order to be able to recognize the six main facial expression, proposed method uses a grid approach and therefore it establishes a new feature space based on the angles that each gird's edge and vertex form. The way taken in the paper is robust against several affine transformations such as translation, rotation, and scaling which in other approaches are considered very harmful in the overall accuracy of a facial expression recognition algorithm. Also, this paper demonstrates the process that the feature space is created using angles and how a selection process of feature subset within this space is applied with Wrapper approach. Selected features are classified by SVM, 3-NN classifier and classification results are validated with two-tier cross validation. Proposed method shows 94% classification result and feature selection algorithm improves results by up to 10% over the full set of feature.

• Biomolecules & Therapeutics
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• 제19권2호
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• pp.224-230
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• 2011

In order to develop silk peptide (SP) preparations possessing cognition-enhancing effect, several candidates were screened through in vitro assays, and their effectiveness was investigated in facilitated brain aging model rats. Incubation of brain acetyl-cholinesterase with SP-PN (1-1,000 ${\mu}g/ml$) led to inhibition of the enzyme activity up to 35%, in contrast to a negligible effect of SP-NN. The expression of choline acetyltransferase (ChAT) mRNA of neural stem cells expressing ChAT gene (F3.ChAT) was increased by 24-hour treatment with 10 and 100 ${\mu}g/ml$ SP-NN (1.35 and 2.20 folds) and SP-PN (2.40 and 1.34 folds). Four-week subcutaneous injections with D-galactose (150 mg/kg) increased activated hippocampal astrocytes to 1.7 folds (a marker of brain injury and aging), decreased acetylcholine concentration in cerebrospinal fluid by 45-50%, and thereby impaired learning and memory function in passive avoidance and water-maze performances. Oral treatment with SP preparations (50 or 300 mg/kg) for 5 weeks from 1 week prior to D-galactose injection exerted recovering activities on acetylcholine depletion and brain injury/aging as well as cognitive deficit induced by D-galactose. The results indicate that SP preparations restore cognitive functions of facilitated brain aging model rats by increasing the release of acetylcholine, in addition to neuroprotective activity.

• Journal of Korea Water Resources Association
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• 제33권4호
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• pp.505-515
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• 2000

THe purpose of this study is to forecast of runoff hydrographs according to rainfall event in a stream. The neural network theory as a hydrologic blackbox model is used to solve hydrological problems. The Back-Propagation(BP) algorithm by the Levenberg-Marquardt(LM) techniques and Radial Basis Function(RBF) network in Neural Network(NN) models are used. Runoff hydrograph is forecasted in Bocheongstream basin which is a IHP the representative basin. The possibility of a simulation for runoff hydrographs about unlearned stations is considered. The results show that NN models are performed to effective learning for rainfall-runoff process of hydrologic system which involves a complexity and nonliner relationships. The RBF networks consist of 2 learning steps. The first step is an unsupervised learning in hidden layer and the next step is a supervised learning in output layer. Therefore, the RBF networks could provide rather time saved in the learning step than the BP algorithm. The peak discharge both BP algorithm and RBF network model in the estimation of an unlearned are a is trended to observed values.