Article | 01-September-2012
Journal-bearings play a significant role in industrial applications and the necessity of condition monitoring with nondestructive tests is increasing. This paper deals a proper fault detection technique based on power spectral density (PSD) of vibration signals in combination with K-Nearest Neighbor and Support Vector Machine (SVM). The frequency domain vibration signals of an internal combustion engine with three journal-bearing conditions were gained, corresponding to, (i) normal, (ii
International Journal on Smart Sensing and Intelligent Systems, Volume 5 , ISSUE 3, 685–700
Article | 01-September-2015
light sources and to select an estimation model of the CRI and the CCT using a low cost RGB sensor. The model estimation has been developed in this work and a separated algorithm for each source type has been built. The results show that using a K-Nearest Neighbor classifier, the error resulted less than $3.6%$. The error of the model estimation for the LED was 1.8%, for fluorescent light sources 0.09% and 1.2% for incandescent light sources.
J.-S. Botero V.,
F.-E. López G.,
J.-F. Vargas B.
International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 3, 1505–1524
Article | 01-March-2015
state components and time as its uncertain state component. Then, the space-time two-dimensional linear interpolation method is used to calculate the mean speed and subsequently the travel time. Finally, similar patterns are obtained using K Nearest Neighbor approach and predicted travel time is calculated by the Weighted Average method. The case study shows that the pattern matching method for travel time prediction based on microwave detection data produces sufficient accuracy, which solves the
International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 1, 658–676