• Proceedings of the Korea Information Processing Society Conference
• /
• 2005.05a
• /
• pp.589-592
• /
• 2005

웹을 통한 정보량의 폭발적인 증가로 인하여, 사용자에게 적합한 정보만을 제공할 수 있는 개인화 기술에 관심이 증가하고 있다. 정보를 선별하고 추천하는 대표적인 개인화 기술로서 Contentbased Filtering(CBF) 기법과 Collaborative Filtering(CF) 기법이 널리 사용되고 있다. 본 논문에서는 위에서 언급한 CBF 기법과 CF 기법을 혼합하여, 사용자 선호도를 보다 정확하게 반영할 수 있는 새로운 모델을 제시한다. 또한, Demographic Filtering 기법과 전문가의 추천을 고려한 Fusion Model 을 제시한다. 그리고 사용자 선호 모델을 실시간으로 반영하기 위한 업데이트 방법을 Exponential Smoothing 기법을 사용하여 구성하였다.

• KIEE International Transactions on Power Engineering
• /
• v.4A no.3
• /
• pp.159-166
• /
• 2004

Forecasting prices in electricity markets is critical for consumers and producers in planning their operations and managing their price risk. We utilize the generalized autoregressive conditionally heteroskedastic (GARCH) method to forecast the electricity prices in two regions of New York： New York City and Central New York State. We contrast the one-day forecasts of the GARCH against techniques such as dynamic regression, transfer function models, and exponential smoothing. We also examine the effect on our forecasting of omitting some of the extreme values in the electricity prices. We show that accounting for the extreme values and the heteroskedactic variance in the electricity price time-series can significantly improve the accuracy of the forecasting. Additionally, we document the higher volatility in New York City electricity prices. Differences in volatility between regions are important in the pricing of electricity options and for analyzing market performance.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.12 no.1
• /
• pp.163-172
• /
• 1988

An off-line Geometric Adaptive Control Scheme is applied to the milling machine to identify its guideway errors. In the milling process, the workpiece fixed on the bed travels along the guideway while the tool and spindle system is fixed onto the machine. The scheme is based on the exponential smoothing of post-process measurements of relative machining errors due to the tool, workpiece and bed deflections. The guideway error identification system consists of a gap sensor, a, not necessarily accurate, straightedge, and the numerical control unit. Without a priori knowledge of the variations of the cutting parameters, the time-varying parameters are also estimated by an exponentially weighted recursive least squares method. Experimental results show that the guideway error is well identified within the range of RMS values of geometric error changes between machining passes disregarding the machining conditions.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 2008.10a
• /
• pp.225-228
• /
• 2008

The electricity supply and demand to be stable to a system link increase of the variance power supply and operation are requested in jeju Island electricity system. A short-term Load forecasting which uses the characteristic of the Load is essential consequently. We use the interrelationship of the electricity Load and change of a summertime temperature and data refining in the paper. We presented a short-term Load forecasting algorithm of jeju Island and used the correlation coefficient to the criteria of the refining. We used each temperature area data to be refined and forecasted a short-term Load to an exponential smoothing method.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.54 no.6
• /
• pp.259-266
• /
• 2005

Short-term load forecasting algorithm using neural networks and the sensitivity of temperatures in the summer season is proposed. In recent 10 years, many researchers have focused on artificial neural network approach for the load forecasting. In order to improve the accuracy of the load forecasting, input parameters of neural networks are investigated for three training cases of previous 7-days, 14-days, and 30-days. As the result of the investigation, the training case of previous 7-days is selected in the proposed algorithm. Test results show that the proposed algorithm improves the accuracy of the load forecasting.

• Proceedings of the KSME Conference
• /
• 2001.06b
• /
• pp.700-705
• /
• 2001

The paper presents a seam tracking controller of high speed rotating arc sensor developed by microprocessor based system. The seam tracking algorithm is based on the average current value at each interval region of four phase points on one rotating cycle. To remove the noise effect for the measured current, the area during one rotating cycle is separated into four regions of front, rear, left and right. The average values at each region are calculated, using the regional current values and a low pass filter incorporating the moving average and exponential smoothing methods is adopted.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2003.11c
• /
• pp.1699-1702
• /
• 2003

최근에 인터넷을 통한 전자경매가 보편화되면서 경매 물품의 가격 결정에 대한 관심이 증가하고 있다. 또한, 경매물품의 낙찰가를 판매자가 결정하거나 정보 검색이론의 사례 유사도에 기초하여 생성하는 에이전트가 연구되고 있다. 그러나, 이것은 경매 물품에 대한 최근의 변화 요인을 반영하지 못하고, 상품 추천에서 사용하는 사례 유사도를 가격 결정에 적용하여 잘못된 가격이 생성되는 경우가 많다. 본 논문에서는 이러한 문제점을 해결하고자 시계열 예측에서 사용하는 지수평활법을 이용하여 최근의 경매자료로부터 경매 등륵 물품의 낙찰 예정가를 자동으로 생성하는 시스템을 제안한다. 성능 실험 결과, 본 시스템을 사용할 경우에 경매 물품의 실제 낙찰가와 차이를 줄여 낙찰률을 높이고, 경매 물품의 객관적인 가격형성이 가능함을 보인다. 또한 기존의 사례 유사도를 이용한 낙찰 예정가 생성 방식과의 성능 비교를 통하여 새로운 방법의 효율성을 나타낸다.

• Proceedings of the KIEE Conference
• /
• 1996.11a
• /
• pp.447-449
• /
• 1996

This paper describes an algorithm for short term load forecasting using the Kohonen neural network. Single layer Kohonen neural network presents a lot of advantageous features for practical application. It takes less training time compared to other networks such as BP network, and moreover, its self organized feature can amend the distorted data. The originality of proposed approach is to use a Kohonen map toclassify data representing load patterns and to use directly the information stored in the weight vectors of the Kohonen map to pridict the load. Proposed method was tested with KEPCO hourly record(1993-1995) show better forecasting results compared with conventional exponential smoothing method.

• The Korean Journal of Financial Studies
• /
• v.3 no.2
• /
• pp.267-279
• /
• 1996

The prediction of future demand is a vital task in managing business operations. To this end, traditional approaches often focused on statistical techniques such as exponential smoothing and moving average. The need for better accuracy has led to nonlinear techniques such as neural networks and case based reasoning. In addition, experimental design techniques such as orthogonal arrays may be used to assist in the formulation of an effective methodology. This paper investigates a multistrategy approach involving neural nets, case based reasoning, and orthogonal arrays. Neural nets and case based reasoning are employed both separately and in combination, while orthoarrays are used to determine the best architecture for each approach. The comparative evaluation is performed in the context of an application relating to the prediction of Treasury notes.

• Journal of Power System Engineering
• /
• v.6 no.4
• /
• pp.43-48
• /
• 2002

This paper presents a seam tracking controller of high speed rotating arc sensor developed by microprocessor based system. The seam tracking algorithm is based on the average current value at each interval region of four phase points on one rotating cycle. To remove the noise effect for the measured current, the area during one rotating cycle is separated into four regions of front, rear, left and right. The average values at each region are calculated, using the regional current values and a low pass filter incorporating the moving average and exponential smoothing methods is adopted. The effectiveness is proven through the experimental results for several kinds of welding condition.