Találati lista | Tudóstér

001-es BibID:	BIBFORM027883
Első szerző:	Tisan, Alin
Cím:	Holistic modeling, design and optimal digital control of a combined renewable power system / Alin Tisan, Marcian Cirstea, Attila Buchman, Alberto Parera, Stefan Oniga, Danut Ilea
Dátum:	2010
Megjegyzések:	This paper deals with the holistic modeling approach of a combined Photovoltaic, Wind and Fuel Cell power system. The method is using the DK5 modeling / design environment from Mentor Graphics and is based on the new Handel-C programming language. The goal of the work carried out was to achieve an optimized holistic digital control system design, followed by its rapid prototyping into a single Field Programmable Gate Array (FPGA). This would also enable easy connection (by adapting the model/controller) to the grid. The holistic functional simulation of the system is performed in the same environment as its controller hardware implementation and timing analysis. The controller design was then downloaded in hardware onto a RC10 development board containing a Xilinx Spartan FPGA and was successfully tested experimentally. This approach enables the design and fast hardware implementation of efficient controllers for Distributed Energy Resource (DER) hybrid systems. 2010 IEEE.
Tárgyszavak:	Műszaki tudományok Villamosmérnöki tudományok konferenciacikk Control systems FPGA Modeling Power systems Renewable energy
Megjelenés:	Proceeding of the IEEE International Symposium on Industrial Electronics. - (2010), p. 2733-2738. -
További szerzők:	Cirstea, Marcian Buchman Attila (1957-) (villamosmérnök) Parera, Alberto Oniga István László (1960-) (villamosmérnök) Ilea, Danut
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Saját polcon:

001-es BibID:	BIBFORM020837
Első szerző:	Tisan, Alin
Cím:	Artificial olfaction system with hardware on-chip learning neural networks / Alin Tisan, Marcian Cirstea, Stefan Oniga, Attila Buchman
Dátum:	2010
Megjegyzések:	This paper presents a hardware implementation of a multilayer feed-forward neural network based on back propagation. The implementation is assumed to design and implement modules that emulate FF-BP functions with computing blocks of the predefined System Generator library and user defined blocks integrated in the System Generator library. The main application of the developed structure is an artificial olfactory system used to recognize the type of coffee presented in a test chamber. Data acquisition was achieved through the PC-MIO-16E-1 acquisition card and a virtual instrument, developed in Labview, for signal pre-processing and data logging into text files. The patterns presented (the type of coffee) have been recognized through neural networks. In order to select the RNA with the highest accuracy in recognising the coffee type, several different RNAs were simulated.
Tárgyszavak:	Műszaki tudományok Villamosmérnöki tudományok konferenciacikk folyóiratcikk Artificial olfaction On-chip learning Neural networks
Megjelenés:	12th Proceedings of the International Conference on Optimisation of Electrical and Electronic Equipment, OPTIM. - (2010), p. 884-889. -
További szerzők:	Cirstea, Marcian Oniga István László (1960-) (villamosmérnök) Buchman Attila (1957-) (villamosmérnök)
Internet cím:	Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Saját polcon: