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001-es BibID:	BIBFORM009183
Első szerző:	Kalmár Tünde (gépészmérnök)
Cím:	Neural network predictor for thermal comfort conditions / Tünde Kalmár, Géza Husi, Sahin Yildirim, Ferenc Kalmár, Ikbal Eski
Dátum:	2009
ISSN:	1844-6043
Megjegyzések:	This paper is investigated thermal comfort conditions having surface and fresh air introduced directly in a room using neural network predictors. The investigation is divided into two steps. First step; experimental room system result is obtained the Building Physics Laboratory of the Department of Building Services, University of Debrecen. The room could be heated by radiator, on the wall, on the floor and on the ceiling heating system methods. During the operation of the heating system a series of measurements are derived, involving 20 students as subjects during education, in order to establish the effects of intermittency on thermal comfort feeling. The second step of the study; according to experimental results, some neural network predictors are used modelling the experimental room. Four types of ANNs are used to compare each other. From the results, it is noted that the proposed Radial Basis Neural Network gives the best results for analyzing thermal comfort conditions.
Tárgyszavak:	Műszaki tudományok Gépészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban thermal comfort surface heating neural networks
Megjelenés:	Journal of Computer Science and Control Systems. - 2 : 2 (2009), p. 97-102. -
További szerzők:	Yildirim, Şahin (1966-) (gépészmérnök) Eski, Ikbal Husi Géza (1962-) (gépészmérnök, mechatronikai mérnök, számítógépes tervezőmérnök) Kalmár Ferenc (1974-) (gépészmérnök)
Internet cím:	elektronikus változat elektronikus változat
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001-es BibID:	BIBFORM020529
Első szerző:	Yildirim, Şahin (gépészmérnök)
Cím:	Vibration analysis of food industries mixing systems for long life using neural networks / Yildirim, Sahin, Eski, Ikbal, Erkaya, Selcuk, Husi, Geza
Dátum:	2011
Megjegyzések:	Due to health problems on food industry, it is necessary to control exact mixing rate of some fruit juices. In this study; whole mixing systems with automation is investigated for different flow rates in the pipes. On the other hand, a robust analyzer is designed to predict real time vibrations on the system. Furthermore, from other investigations; neural networks have superior performance to predict such problems. For that reason, three types of neural networks are used to predict vibrations on different points of three tank mixing system. The results are improved that the proposed Radial Basis Neural Network (RBNN) has good performance at adapting vibration problems on mixing system. Finally, this type of neural network will be employed to analyze food industries automation systems.
ISBN:	978 1 4577 0838 1
Tárgyszavak:	Műszaki tudományok Gépészeti tudományok tanulmány, értekezés Vibrations Neural network
Megjelenés:	IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2011 : 3-7 July 2011 Budapest, Hungary. - p. 197-202. -
További szerzők:	Eski, Ikbal Erkaya, Selcuk Husi Géza (1962-) (gépészmérnök, mechatronikai mérnök, számítógépes tervezőmérnök)
Internet cím:	DOI Intézményi repozitóriumban (DEA) tárolt változat
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