Összesen 2 találat.


001-es BibID:BIBFORM076622
Első szerző:Aldabbas, Ashraf Khaled Abd Elkareem (informatikus)
Cím:Neural Network Estimation of Tourism Climatic Index (TCI) Based on Temperature-Humidity Index (THI)- Jordan Region Using Sensed Datasets / Ashraf AlDabbas, Zoltan Gal, Buchman Attila
ISSN:1844-9689 2343-8908
Megjegyzések:Jordan which is located in the heart of the world contains hundreds of historical and archaeological locations that have a supreme potential in enticing visitors. The impact of clime is important on many aspects of life such as the development of tourism and human health, tourists always wanted to choose the most convenient time and place that have appropriate weather circumstances. The goal of this study is to specify the preferable months (time) for tourism in Jordan regions. Neural network has been utilized to analyze several parameters of meteorologist (raining, temperature, speed of wind, moisture, sun radiation) by analyzing and specify tourism climatic index (TCI) and equiponderate it with THI index. The outcomes of this study shows that the finest time of the year to entice tourists is " April" which is categorized as to be "extraordinary" for visitors. TCI outcomes indicates that conditions are not convenient for tourism from July to August because of high temperature.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
TCI index
THI index
neural network
analysis of historical sensed data set
Megjelenés:Carpathian Journal of Electronic and Computer Engineering. - 11 : 2 (2018), p. 50-55. -
További szerzők:Gál Zoltán (1966-) (informatikus) Buchman Attila (1957-) (villamosmérnök)
Internet cím:Szerző által megadott URL
Intézményi repozitóriumban (DEA) tárolt változat


001-es BibID:BIBFORM062516
Első szerző:Gál Zoltán (informatikus)
Cím:On the statistical analysis of wireless sensor vs. wired data network traffics / Z. Gal, Gy. Terdik
Megjegyzések:Not only the infrastructure of Wireless Sensor Network (WSNs) and classical wired IP data networks are very different but the statistical characteristics of data flows transferred on these environments have technology specific features, too. Based on the dynamic evolution in the last years WSNs became important elements of the small physical sized network architectures and are included as basic components in the Internet of Things (IoT) new concept. The challenge to transmit packets on optimum wireless path with minimum energy consumption affects all layer (physical, data link, network, transport, application) services of the WSN protocol stack. Wireless IP data technologies like GSM/UMTS/WiFi/WiMAX are utilized with success in WAN/MAN networks in contrast with WSN, which is usable only for small distances and reduced transfer capacity of bytes. Because of the energy consumption minimization the channel access mechanism should be simple as much as possible. Classical IP traffics in LAN/WAN environment do not confront with consequences of the energy constraints. The MAC algorithms are much more sophisticated than for WSNs. The difference in the layer functions implies difference in the traffic characteristics of this two network types. In this paper WSN and IP WAN/MAN data flows are analyzed as time series. The sensor data flows were collected with TinyDB tools at the Intel Berkeley Research lab in 2004. The high speed IP data flows are available from public database of TIER links1. These significantly different types of data flows are investigated based on Lévy flights modeling. Long range dependence, self-similarity aspects of the inter-arrival time and the epoch ID time series are studied with sophisticated statistical analysis methods.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
Megjelenés:Carpathian Journal of Electronic and Computer Engineering 4 (2011), p. 41-48. -
További szerzők:Terdik György (1949-) (matematikus, informatikus)
Pályázati támogatás:TÁMOP-4.2.1./B-09/1/KONV-2010-0007/IK/IT
Internet cím:Szerző által megadott URL
Intézményi repozitóriumban (DEA) tárolt változat
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