By C. A. Brebbia, G. M. Carlomagno
As laptop types turn into extra trustworthy and ready to signify extra practical difficulties, unique info is resulting in the improvement of applicable new sorts of experiments. Experimental measurements are conditioned to the necessities of the computational types. consequently it is necessary that scientists engaged on experiments converse with researchers constructing desktop codes, in addition to these conducting measurements on prototypes. The orderly and innovative concurrent improvement of a majority of these fields is vital for the growth of engineering sciences. CMEM 2009 is the Fourteenth foreign convention during this good proven sequence on Computational tools and Experimental Measurements. those winning conferences supply a different discussion board for the overview of the most recent paintings at the interplay among computational tools and experimental measurements. the themes contain: Computational and Experimental equipment; Experimental and Computational research; Direct, oblique and In-Situ Measurements; Detection and sign Processing; info Processing; Fluid stream; warmth move and Thermal procedures; fabric Characterisation; Structural and pressure research; business functions; woodland Fires.
Read or Download Computational Methods and Experimental Measurements XIV (WIT Transactions on Modelling and Simulation) (Wit Transactions on Modeling and Simulation; Fourteenth ... Methods and Experimental Measurements) PDF
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Extra resources for Computational Methods and Experimental Measurements XIV (WIT Transactions on Modelling and Simulation) (Wit Transactions on Modeling and Simulation; Fourteenth ... Methods and Experimental Measurements)
Tamura, M. Tateishi, Capabilities of a four-layered feedforward neural network: four layers versus three”, IEEE Transactions on Neural Nets, 8(2) (1997) 251-255.
005). However after testing with two different vehicles, the conclusion is that on one hand the structure of the ANN model is suitable. On the other hand each vehicle has its specific model parameters. This paper shows that it is difficult to develop a universal model that predicts the gear based on the RPM and speed of any car. Keywords: feed-forward artificial neural networks, gear predictor, manual transmission. 1 Introduction 0B The drivetrain system of the automobile engine consists of the following parts: engine, transmission, drive shaft, differential, and driven wheels.
However, due to low moisture content during the studied period there is not any freezing cycle. As we can see on Figure 3, overhygroscopic moisture content is reached only in summer month when the temperature is above zero. 000000 1325 1375 1425 1475 1525 1575 1625 Time [days] Figure 3: Hygrothermal performance of concrete, simple concrete wall (CR), 2 mm under the surface. In concrete modified by fly ash, the overhygroscopic moisture content is reached once per a reference year, but as in the previous case this happens in summer month so there are not any possibilities of creation of freezing cycles.