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PM<SUB>10</SUB> 농도 예측을 위한 머신러닝 기반 결측치 처리의 실증적 분석
이주현(Juhyun Lee),이윤관(Younkwan Lee),홍유진(Yoojin Hong),전문구(Moongu Jeon) 대한전자공학회 2019 대한전자공학회 학술대회 Vol.2019 No.6
The data including meteorology and air pollutants data for forecasting PM10 concentration recorded by monitoring stations contained various number of missing values due to measurement device defects or inability to measure by natural disasters. Therefore, it is very important to reasonably fill these missing values to improve the accuracy of PM10 concentration prediction. In this paper, we discuss a variety of machine learning based methods including Linear and Non-linear techniques to handle missing data. We use 5 methods to deal with missing values, and for each case creates datasets containing 10 types of weather information collected in Seoul area for predicting PM10, and we compare the PM10 concentration prediction performance using the datasets based on the Long Short-Term Memory Neural Network. Experiments show that the Non-linear imputation methods achieved significantly improved performance in PM10 concentration prediction compare to the linear imputation methods.
손문구(MoonGu Son),이관행(Kwan H Lee) (사)한국CDE학회 2016 한국 CAD/CAM 학회 학술발표회 논문집 Vol.2016 No.동계
Deformation lamps is technique to adding moving effect on static object by projecting luminance pattern unlike conventional projection mapping technique that modify the appearance of object. It is necessary that deformed image sequence shows movement. However these deformed image sequences made by manual or video that has movement effect. In this paper, using motion texture creates movement effect from target object’s still image. This technique has given the dynamic effect of the static object causing the interest of the audience and can be applied to advertising, interior design, art, and exhibition.
Seokhyoung Lee,Moongu Jeon,Shin, V. IEEE 2012 IEEE transactions on industrial electronics Vol.59 No.11
<P>A new distributed fusion filtering algorithm for linear multiple time-delayed systems is proposed. The multisensory distributed fusion filter is formed by the summation of local Kalman filters having time delays (LKFTDs) in both the system and measurement models. The proposed distributed filter has a parallel structure that enables processing of multisensory measurements; thereby, it is more reliable than the centralized version if some sensors turn faulty. The key contribution of this paper is the derivation of recursive error cross-covariance equations between the LKFTDs to compute the optimal matrix fusion weights. In the particular case of multisensory dynamic systems having time delays in only the measurement model, the obtained results coincide with the previous work of Sun. The high accuracy and efficiency of the proposed distributed filter are then demonstrated through its implementation on a vehicle suspension system.</P>