• 한국산학기술학회:학술대회논문집
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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.

• 한국항해항만학회:학술대회논문집
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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.

• 한국정보통신학회:학술대회논문집
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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$^{-2}$ .

• 한국항해항만학회지
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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.

• 한국전기전자재료학회:학술대회논문집
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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.

• 한국수자원학회:학술대회논문집
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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.

• 정보처리학회논문지B
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• 제17B권4호
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• pp.309-316
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• 2010

본 연구에서는 얼굴 그리드 각도를 특징공간으로 하는 새로운 모델 기반 얼굴 표정 인식 방법을 제안한다. 제안 방식은 6가지 얼굴 대표 표정을 인식하기 위해 표정 그리드를 이용하여 그리드의 각 간선과 정점이 형성하는 각도를 기반으로 얼굴 특징 공간을 구성한다. 이 방법은 다른 표정 인식 알고리즘의 정확도를 낮추는 원인인 변환, 회전, 크기변화와 같은 어파인 변환에 강건한 특징을 보인다. 또한, 본 연구에서는 각도로 특징공간을 구성하고 이 공간 내에서 Wrapper 방식으로 특징 부분집합을 선택하는 과정을 설명한다. 선택한 특징들은 SVM, 3-NN 분류기를 이용해 분류하고 분류 결과는 2중 교차검증을 통해 검증하도록 한다. 본 연구가 제안한 방법에서는 94%의 표정 인식 결과를 보였으며 특히 특징 부분집합 선택 알고리즘을 적용한 결과 전체 특징을 이용한 경우보다 약 10%의 인식율 개선 효과를 보인다.

• 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.

• 한국수자원학회논문집
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• 제33권4호
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• pp.505-515
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• 2000

본 연구는 하천에서 호우의 발생에 따라 하천 유출수문곡선을 예측코자 블랙박스모형의 신경망이론을 적용하여 수문학적인 문제를 규명하고자 하였다. 이를 위해 신경망 이론 중 Levenverg-Marquardt 방법에 의한 오차역전파 알고리즘과 Radial Basis Function Network(RBFN)를 이용하여 IHP 대표유역인 보청청유역에 수문곡선을 적용하여 선행유출량 예측과 미학습 유역의 적용성을 검토하였다. 그 결과 복잡하고 비선형적인 수문계의 강우-유출 과정의 학습에 있어 RBFN은 은닉층에서 자율학습, 출력층에서 지도학습의 두 단계로 나누어 학습을 함으로서 BP 알고리즘보다 학습시간이 빠르게 나타났고, 선행유출량의 예측결과 여러 통계적 지표에서 RBFN이 BP 알고리즘보다 좋은 결과를 얻을 수 있었다. 미학습 유역의 적용성 검토에서도 BP알고리즘과 RBFN 모두 첨두치가 비교적 실측자료의 경향과 비슷한 경향으로 나타났다.