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Central European Geology
Authors:
Tomislav Malvić
,
Josipa Velić
,
Janina Horváth
, and
Marko Cvetković

Society of Petroleum Engineers Richardson . A. Bhatt 2002 Reservoir properties from well logs using neural networks

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Dramatic oods occurred in Central Europe in recent summers, Hungary having been seriously affected in its eastern part. Predictive approach based on modeling ood recurrence may be helpful in ood management. Summer oods are typically characterized by saturated catchment due to long-lasting heavy precipitation followed by a sudden extreme rainfall. In present work, an artificial neural network (ANN) models were evaluated for precipitation forecasting. A back propagation neural networks were trained with actual annual and monthly precipitation data from east Hungarian meteorological stations for a time period of 38 years. Predicted amounts are next-year-precipitation and summer precipitation in the next year. The ANN models provided a good with the actual data, and have shown a high feasibility in prediction of extreme precipitation.

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International Review of Applied Sciences and Engineering
Authors:
Ammar Al-Jodah
,
Saad Jabbar Abbas
,
Alaq F. Hasan
,
Amjad J. Humaidi
,
Abdulkareem Sh. Mahdi Al-Obaidi
,
Arif A. AL-Qassar
, and
Raaed F. Hassan

design based on Convolutional Neural Network Trajectory Tracking (CNNTT) controller to control mobile robot to find optimal path in the presence of obstacles using hybrid swarm optimization. In [ 15 ], Al-Araji et al. proposed an adaptive nonlinear

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parameters to determine the best value for each variable and to improve the foam quality. All of them are mean to achieve the rapid development of high-quality cold asphalt design [ 10 ]. The neural network model stands out as a distinctive approach in the

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Pollack Periodica
Authors:
Ádám Pintér
,
Balázs Schmuck
, and
Sándor Szénási

S. , Bakó L. Visual trajectory control of a mobile robot using FPGA implemented neural network , Pollack Periodica , Vol. 4 , No. 3 , 2009 , pp. 4 – 142 . [4

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Abstract  

An electronic nose utilising an array of six-bulk acoustic wave polymer coated Piezoelectric Quartz (PZQ) sensors has been developed. The nose was presented with 346 samples of fresh edible oil headspace volatiles, generated at 45°C. Extra virgin olive (EVO), Non-virgin olive oil (OI) and Sunflower oil (SFO), were used over a period of 30 days. The sensor responses were then analysed producing an architecture for the Radial Basis Function Artificial Neural Network (RBF). It was found that the RBF results were excellent, giving classifications of above 99% for the vegetable oil test samples.

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temperature test. Fan et al. [ 10 ] performed laboratory tests on eight square sections to study the resistance of stainless-steel box columns under high temperature. Neural network is a new method that has been applied for elaborating heat data, but it never

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the mobile phone can be detected by the built-in NFC reader as it is show in [ 5 ]. It also uses neural network for cleaning the acceleration sensor data, but not for step classification and detection. The most popular and still special infrastructure

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Central European Geology
Authors:
Ana Brcković
,
Monika Kovačević
,
Marko Cvetković
,
Iva Kolenković Močilac
,
David Rukavina
, and
Bruno Saftić

, artificial neural networks (ANNs) were used, as they had previously proved successful in similar tasks of handling well log and seismic data ( Bhatt 2002 ; Cvetković et al. 2009 , Malvić et al. 2010 , Bagheripour et al. 2013 ). Geologic

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Omondi A. R., Rajapakse J. C. FPGA implementations of neural networks , Springer, 2006. Ladányi G., Jenei I. Analysis of plastic peridynamic material with RBF meshless method

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