@article{(Open Science Index):https://publications.waset.org/pdf/10013262,
	  title     = {Optimization of Electromagnetic Interference Measurement by Convolutional Neural Network},
	  author    = {Hussam Elias and  Ninovic Perez and  Holger Hirsch},
	  country	= {},
	  institution	= {},
	  abstract     = {With ever-increasing use of equipment, device or more generally any electrical or electronic system, the chance of Electromagnetic incompatibility incidents has considerably increased which demands more attention to ensure the possible risks of these technologies. Therefore, complying with certain Electromagnetic compatibility (EMC) rules and not overtaking an acceptable level of radiated emissions are utmost importance for the diffusion of electronic products. In this paper, developed measure tool and a convolutional neural network were used to propose a method to reduce the required time to carry out the final measurement phase of Electromagnetic interference (EMI) measurement according to the norm EN 55032 by predicting the radiated emission and determining the height of the antenna that meets the maximum radiation value.},
	    journal   = {International Journal of Electronics and Communication Engineering},
	  volume    = {17},
	  number    = {10},
	  year      = {2023},
	  pages     = {225 - 232},
	  ee        = {https://publications.waset.org/pdf/10013262},
	  url   	= {https://publications.waset.org/vol/202},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 202, 2023},
	}