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  • In Jour Smart Sensing And Intelligent Systems

 

Article | 01-September-2012

AN APPROPRIATE PROCEDURE FOR DETECTION OF JOURNAL-BEARING FAULT USING POWER SPECTRAL DENSITY, K-NEAREST NEIGHBOR AND SUPPORT VECTOR MACHINE

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

A. Moosavian, H. Ahmadi, A. Tabatabaeefar, B. Sakhaei

International Journal on Smart Sensing and Intelligent Systems, Volume 5 , ISSUE 3, 685–700

Article | 01-September-2015

Classification of artificial light sources and estimation of Color Rendering Index using RGB sensors, K Nearest Neighbor and Radial Basis Function

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

TRAVEL TIME PREDICTION BASED ON PATTERN MATCHING METHOD

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

Jiandong Zhao, Feifei Xu, Wenhui Liu, Jigen Bai, Xiaoling Luo

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 1, 658–676

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