Ting, Naim Suleyman

This paper introduces a new soft switched AC-DC boost converter with power factor correction (PFC). In the introduced converter, all devices are turned on and off under soft switching (SS). The main switch is turned on under zero voltage transition (ZVT) and turned off under zero current transition (ZCT). The main diode is turned on under zero voltage switching (ZVS) and turned off under zero current switching (ZCS). Meanwhile, there is not any current or voltage stress on the main devices. Besides, the auxiliary switch is turned on under ZCS and turned off under ZVS. The detailed theoretical analysis of the converter is presented, and also theoretical analysis is verified by a prototype with 100 kHz and 500 W. Also, the proposed converter has 99.8% power factor and 97.5% total efficiency at soft switching operation.

Şahin, Mustafa; Oğuz, Yüksel; Büyüktümtürk, Fuat

In this study, artificial neural networks were used to determine the intensity of brightness in interior spaces. The illumination elements to illuminate indoor spaces were considered, not individually, but as a system. So, during the planned maintenance periods of an illumination system, after its design and installation, simple brightness level measurements must be taken. For a threedimensional evaluation of the brightness level in indoor spaces in a speedy and accurate manner, the obtained brightness level measurement results and artificial neural network model were used. Upon estimation of the most suitable brightness level for indoor spaces by using the artificial neutral network model, the energy demands required by the illumination elements decreased. Consequently, in this study, with estimations of brightness levels, the extent to which the artificial neutral networks become successful was observed and more correct results have been obtained in terms of both economy and usage.

Şahin, Mustafa; Oğuz, Yüksel; Büyüktümtürk, Fuat

In this study, artificial neural networks were used to determine the intensity of brightness in interior spaces. The illumination elements to illuminate indoor spaces were considered, not individually, but as a system. So, during the planned maintenance periods of an illumination system, after its design and installation, simple brightness level measurements must be taken. For a threedimensional evaluation of the brightness level in indoor spaces in a speedy and accurate manner, the obtained brightness level measurement results and artificial neural network model were used. Upon estimation of the most suitable brightness level for indoor spaces by using the artificial neutral network model, the energy demands required by the illumination elements decreased. Consequently, in this study, with estimations of brightness levels, the extent to which the artificial neutral networks become successful was observed and more correct results have been obtained in terms of both economy and usage.