University College of Engineering

About: University College of Engineering is a based out in . It is known for research contribution in the topics: Diesel fuel & Diesel engine. The organization has 6988 authors who have published 6695 publications receiving 81179 citations.

A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem

Abstract: In a general flow-shop situation, where all the jobs must pass through all the machines in the same order, certain heuristic algorithms propose that the jobs with higher total process time should be given higher priority than the jobs with less total process time. Based on this premise, a simple algorithm is presented in this paper, which produces very good sequences in comparison with existing heuristics. The results of the proposed algorithm have been compared with the results from 15 other algorithms in an independent study by Park [13], who shows that the proposed algorithm performs especially well on large flow-shop problems in both the static and dynamic sequencing environments.

Walking Patterns of Normal Men

Abstract: A simple and inexpensive photographic method has been developed whereby many kinematic components of the walking act in the sagittal, frontal, and transverse planes can be measured and related temporally. A factorial design was used to study the displacement patterns of sixty normal men who ranged i

Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations

Abstract: Data on the concentrations of seven environmental pollutants (CH"4, NMHC, CO, CO"2, NO, NO"2 and SO"2) and meteorological variables (wind speed and direction, air temperature, relative humidity and solar radiation) were employed to predict the concentration of ozone in the atmosphere using both multiple linear and principal component regression methods Separate analyses were carried out for day light and night time periods For both periods the pollutants were highly correlated, but were all negatively correlated with ozone Multiple regression analysis was used to fit the ozone data using the pollutant and meteorological variables as predictors A variable selection method based on high loadings of varimax rotated principal components was used to obtain subsets of the predictor variables to be included in the regression model of the logarithm of the ozone data It was found that while high temperature and high solar energy tended to increase the day time ozone concentrations, the pollutants NO and SO"2 being emitted to the atmosphere were being depleted Night time ozone concentrations were influenced predominantly by the nitrogen oxides (NO+NO"2), with the meteorological variables playing no significant role However, the model did not predict the night time ozone concentrations as accurately as it did for the day time This could be due to other factors that were not explicitly considered in this study