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Aria Ghora Prabono,Bernardo Nugroho Yahya,Seok-Lyong Lee 대한산업공학회 2018 대한산업공학회 춘계학술대회논문집 Vol.2018 No.4
A machine learning-based approach to perform sensor event abstraction has been proposed to work with generic machine learning algorithm The proposed approach yields up to 91% abstraction accuracy with 90% f-measure The discovered process model from abstracted data yields up to 88% fitness with validated correctness
Aria Ghora Prabono,Bernardo Nugroho Yahya,Seok-Lyong Lee 한국경영과학회 2018 한국경영과학회 학술대회논문집 Vol.2018 No.04
A machine learning-based approach to perform sensor event abstraction has been proposed to work with generic machine learning algorithm The proposed approach yields up to 91% abstraction accuracy with 90% f-measure The discovered process model from abstracted data yields up to 88% fitness with validated correctness
Electric vehicle charging demand forecasting model based on big data technologies
Arias, M.B.,Bae, S. Applied Science Publishers 2016 APPLIED ENERGY Vol.183 No.-
<P>This paper presents a forecasting model to estimate electric vehicle charging demand based on big data technologies. Most previous studies have not considered real-world traffic distribution data and weather conditions in predicting the electric vehicle charging demand. In this paper, the historical traffic data and weather data of South Korea were used to formulate the forecasting model. The forecasting processes include a cluster analysis to classify traffic patterns, a relational analysis to identify influential factors, and a decision tree to establish classification criteria. The considered variables in this study were the charging starting time determined by the real-world traffic patterns and the initial state-of-charge of a battery. Example case studies for electric vehicle charging demand during weekdays and weekends in summer and winter were presented to show the different charging load profiles of electric vehicles in the residential and commercial sites. The presented forecasting model may allow power system engineers to anticipate electric vehicle charging demand based on historical traffic data and weather data. Therefore, the proposed electric vehicle charging demand model can be the foundation for the research on the impact of charging electric vehicles on the power system. (C) 2016 Elsevier Ltd. All rights reserved.</P>
Effects of Hybrid and Maturity on Maize Stover Ruminal Degradability in Cattle Fed Different Diets
Arias, S.,Di Marco, O.N.,Aello, M.S. Asian Australasian Association of Animal Productio 2003 Animal Bioscience Vol.16 No.11
The effect of maize hybrid (Suco and Dekalb 765, DK 765), maturity stage (milk, $R_3$ and 1/2 milk line, $R_5$) and animal diet (Diet 1: 70% lucerne hay+30% maize silage; Diet 2: 50% maize silage+20% sunflower meal+30% maize grain) on ruminal stover dry matter (DM) degradability was studied. Additionally, morphological and chemical plant composition was evaluated. Fodder samples ground at 2 mm were incubated in three Holstein steers (400 kg body weight) using the in situ technique. Ruminal degradation kinetics was studied and the effective degradability (ED) was estimated for an assumed kp of 5%/h. The in situ data was analyzed in a complete randomized block design with the animals as blocks. Significant interactions between hybrid${\times}$diet and maturity${\times}$diet on kinetic digestion parameters were detected. In Diet 1, hybrids did not differ in degradable fraction, kd or ED, although a minor difference (p<0.05) in the soluble fraction was found (25.5 and 23.2% for Suco and DK 765, respectively). In Diet 2, the DK 765 had greater degradable fraction (p<0.001) but smaller (p<0.01) kd than Suco, without differences in the soluble fraction or in ED. Anticipating the harvest increased ED of stover from 37.5% in $R_5$ to 44.6% in $R_3$ (average values across hybrids and diets) due to the increase (p<0.001) in the soluble fraction ($R_5$: 22.6%, $R_3$: 28.8%). It is concluded that hybrids had similar stover in situ DM degradability and that soluble fraction represent the main proportion of degradable substrates. Advancing the date of harvesting may not improve the in situ DM degradability of whole maize plant silage since the increase in stover quality is counteracted by the depression in the grain-to-stover ratio. The diet of the animal consuming silage might not improve stover utilization either.