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International audience; This paper is an attempt to compare artificial neural networks and response surface methodology for modeling surface roughness and cutting force in terms of better coefficient of determination (R2), lower root mean square error (RMSE) and model predictive error (MPE). Models were developed based on three-level Box-Behnken design (BBD) of experiments with 15 experimental runs composed of three center points, conducted on Inconel 718 work material using coated carbide insert with cutting speed, feed rate and depth of cut as the process parameters under dry environment. Results show that the artificial neural network (ANN) compared with RSM is a better reliable and accurate approach for predicting and detecting the non-linearity of surface roughness and cutting force mathematical models in terms of correlation and errors. Indeed, the ANN prediction model provides a maximal benefit in terms of precision of 10.1% for cutting force (Fv) and 24.38% for surface roughness (Ra) compared with the RSM prediction model.

Political instability of several countries in the Middle East is overshadowing one of the biggest challenges of the upcoming century: Water - a natural resource that is easily taken for granted, but whose scarcity might lead to serious conflicts. This paper investigates an optimal Water Allocation of the Tigris and Euphrates Rivershed by introducing the WATER-Model. A series of scenarios are analyzed to examine the effects of different levels of cooperation for an optimal water allocation. Special emphasize is put on the effects of filling new Turkish reservoirs which can cause additional welfare losses if these actions are not done on a basin-wide coordinated basis. Modeling results show that Turkey is most efficient in its water usage. However, using the water for irrigation purposes in Turkey, instead of the Iraqi or Syrian domestic and industrial sector, decreases the overall welfare. Especially the Euphrates basin might thus encounter losses of up to 33% due to such strategic behaviour. The predicted water demand growth in the region is going to increase this water scarcity further. Minimum flow treaties between riparian countries, however, can help to increase the overall welfare and should therefore be fostered.

International audience; This paper is focused on the control of air-feed system of Polymer Electrolyte Membrane Fuel Cell (PEMFC). This system regulates the air entering in the cathode side of the fuel cell. The control objective is to maintain optimum net power output by regulating the oxygen excess ratio in its operating range, through the air compressor. This requires controllers with a fast response time in order to avoid oxygen starvation during load changes. The problem is addressed using a robust nonlinear second order sliding mode controller in cascaded structure. The controller is based on sub-optimal algorithm, which is known for its robustness under disturbances and uncertainties. The controller performance is validated through Hardware- In-Loop (HIL) simulation based on a commercial twin screw air compressor and a real time fuel cell emulation system. The simulation results show that the controller is robust and has a good transient performance under load variations and parametric uncertainties.

A recent theoretical literature highlighted the potential dangers of further increasing information disclosure by central banks. This paper gives a continuous empirical investigation of the existence of an optimal degree of transparency in the lines of van der Cruijsen et al. We test a quadratic relationship between central bank transparency and the inflation persistence by introducing some technical and economic modifications. Particularly, we used three new measures of transparency. An appropriate U shape test that was made through a Stata routine, recently developed by Lind and Mehlum, indicates a robust optimal intermediate degree of transparency, but its level is not. These results were obtained using a panel of 11 OECD central banks under the period 1999-2009. The estimations were run using a bias corrected LSDVC, a newly recent technique developed by Bruno for short dynamic panels with fixed effects, extended to accommodate unbalanced data.

International audience; This paper deals with the time-varying nonlinear analytical modeling of the electrodynamic loudspeaker. We propose a model which takes into account the variations of Small signal parameters. The six Small signal parameters ($R_{e}$, $L_{e}$, $Bl$, $R_{ms}$, $M_{ms}$, $C_{ms}$) depend on both time and input current. The electrodynamic loudspeaker is characterized by the electrical impedance which, precisely measured, allows us to construct polynomial functions for each Small signal parameter. By using this analytical model, we propose to compare two identical electrodynamic loudspeakers. One of them is supposed to be run in and the other one is not. The experimental methodology is based on a precise measurement. In all the paper, the time scale is assumed to be much longer than one period of the harmonic excitation.

A hybrid model that blends two non-linear data-driven models, i.e. an artificial neural network (ANN) and a moving block bootstrap (MBB), is proposed for modelling annual streamflows of rivers that exhibit complex dependence. In the proposed model, the annual streamflows are modelled initially using a radial basis function ANN model. The residuals extracted from the neural network model are resampled using the non-parametric resampling technique MBB to obtain innovations, which are then added back to the ANN-modelled flows to generate synthetic replicates. The model has been applied to three annual streamflow records with variable record length, selected from different geographic regions, namely Africa, USA and former USSR. The performance of the proposed ANN-based non-linear hybrid model has been compared with that of the linear parametric hybrid model. The results from the case studies indicate that the proposed ANN-based hybrid model (ANNHM) is able to reproduce the skewness present in the streamflows better compared to the linear parametric-based hybrid model (LPHM), owing to the effective capturing of the non-linearities. Moreover, the ANNHM, being a completely data-driven model, reproduces the features of the marginal distribution more closely than the LPHM, but offers less smoothing and no extrapolation value. It is observed that even though the preservation of the linear dependence structure by the ANNHM is inferior to the LPHM, the effective blending of the two non-linear models helps the ANNHM to predict the drought and the storage characteristics efficiently. Copyright � 2007 John Wiley & Sons, Ltd.

A nonlinear dynamic process can be described as a composition of several local affine models selected according to the process operating conditions. Such a compound system requires the identification of the local models from data. This work addresses a method for the identification and the optimal selection of the local affine models from a sequence of noisy measurements acquired from the process. The developed technique is applied to the identification of a simulated model.