Computer
Haider M. Al-Mashhadi; Hussain Jassim Fahad
Abstract
Electrical energy is one of the most important components of life today where different fields depend on it. The field of electrical energy distribution (electricity network), which transmits electrical energy from sources to consumers, is one of the most important areas that need to be developed and ...
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Electrical energy is one of the most important components of life today where different fields depend on it. The field of electrical energy distribution (electricity network), which transmits electrical energy from sources to consumers, is one of the most important areas that need to be developed and improved. In addition to analyzing electrical energy consumption, it needs to forecast consumption and determine consumer behavior in terms of consumption and how to balance supply and demand. The research aims to analyze weather data and find the relation between the weather factors and energy consumption in order to prepare data to use as a suitable data in machine learning model for future use. This model analyzes the building consumption rate for a particular area and takes into account the weather factors that affect electrical energy consumption, where (temperature, dew point, ultraviolet index) are selects based on the correlation confidence and then divided these factors into a set of categories using the K-Means algorithm to show the effect of each factors on the other.
BSc; Zaki Y. Abid; Thamir R. Saeed; Sameir A. Aziez
Volume 15, Issue 1 , April 2015, , Page 1-17
Abstract
Abstract – This paper presents a moving object tracker for monitoring system which can be used in a smart city. Kernel density estimation (KDE) algorithm has been used for representing a background model, while a minimum distance between the current image and the background has been used to extract ...
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Abstract – This paper presents a moving object tracker for monitoring system which can be used in a smart city. Kernel density estimation (KDE) algorithm has been used for representing a background model, while a minimum distance between the current image and the background has been used to extract the foreground. Also, morphological operations are carried out to remove the noise regions and to filter out ambiguous areas. The performance has been evaluated by determining the true, false, and miss detections of an object area. The optimal results have been obtained by adjusting the morphological operation sequence to be (close > thicken) combination by which the true-hits are 14 out of 16 while miss-number is 2 and zero false-hits, While, the percentage hit ratio was 87.5% (14 out of 16). Also, the salt noise introduction in video reduces the hit number from 14 to 11 when it increases from zero to 0.5 percent of the total frame pixels. The accepted absolute error ratio (in morphological properties of the matched object) is kept at 0.05 for all tests. The implementation has been built by using a combination of two platforms, ISE 14.6(2013) and Matlab(2013a) platforms, to avoid the size weakness of XC3S700A-FPGA board.
