British Library Electronic Table of Contents (United Kingdom)

Timely detection of the pneumatic system problems is important in industry. Many techniques have been employed to solve this problem. In this paper, Genetic Algorithm (GA) based optimal configuration of neural networks is proposed for fault diagnostic of bottle filling systems. Back-propagation is used for neural networks algorithm. The back-propagation algorithm had six inputs and one output. A fitness function was designed to the minimize execution time of ANN model by keeping the number of hidden layer(s) and nodes as low as possible while the mean square error of estimated output error is minimized. The designed GA-ANN combination and the graphical user interface (GUI) eliminate the trial and error process for selection of the fastest and most accurate configuration. The performance of...

Energy Technology Data Exchange (ETDEWEB)

A novel approach is presented to extract relevant parameters associated with the energy loss of ejectiles from nuclear reactions obtained by digitizing the signals of a Bragg curve spectrometer. New and more powerful computational paradigms allow a more thorough pulse-shape analysis. This is fulfilled using a back-propagation artificial neural network as a pattern identifier. The known problem of over-training is discussed.

Energy Technology Data Exchange (ETDEWEB)

A novel way to extract relevant parameters associated with the outgoing ions from nuclear reactions, obtained by digitizing the signals provided by a Bragg curve spectrometer (BCS) is presented. This allowed the implementation of a more thorough pulse-shape analysis. Due to the complexity of this task, it was required to take advantage of new and more powerful computational paradigms. This was fulfilled using a back-propagation artificial neural network (ANN) as a pattern identifier. Over training of ANNs is a common problem during the training stage. In the performance of the ANN there is a compromise between its size and the size of the training set. Here, this effect will be illustrated in relation to the problem of Bragg Curve (BC) identification. (Author)

Energy Technology Data Exchange (ETDEWEB)

A back-propagation neural network technique is used at JET to extract plasma parameters like ion temperature, rotation velocities or spectral line intensities from charge exchange (CX) spectra. It is shown that in the case of the C VI CX spectra, neural networks can give a good estimation (better than +-20% accuracy) for the main plasma parameters (Ti, V{sub rot}). Since the neural network approach involves no iterations or initial guesses the speed with which a spectrum is processed is so high (0.2 ms/spectrum) that real time analysis will be achieved in the near future. 4 refs., 8 figs.

Science.gov (United States)

We propose a new algorithm for two-dimensional magnetotelluric (MT) inversion. Our algorithm is an MT inversion based on the steepest descent method, borrowed from the backpropagation technique of seismic inversion or reverse time migration, introduced in the middle 1980s by Lailly and Tarantola. The steepest descent direction can be calculated efficiently by using the symmetry of numerical Green's function derived from a mixed finite element method proposed by Nedelec for Maxwell's equation, without calculating the Jacobian matrix explicitly. We construct three different objective functions by taking the logarithm of the complex apparent resistivity as introduced in the recent waveform inversion algorithm by Shin and Min. These objective functions can be naturally separated into amplitude inversion, phase inversion and simultaneous inversion. We demonstrate our algorithm by showing three inversion results for synthetic data.

Energy Technology Data Exchange (ETDEWEB)

VSP data are usually acquired in order to obtain high-resolution images of complex structures in reservoirs and near boreholes. The authors present an elastic iterative migration scheme which has few limitations regarding the complexity of the geology, and where the macromodel for both P- and S-wave velocities is automatically improved and updated at each iteration. They avoid wavefield separation (up/down and P/S) and the simplifying assumptions of small dips underlying most such methods. The migration scheme is based on elastic inversion theory. The wavefield extrapolation is based on a high-order, coarse-grid, finite-difference solution to the elastic two-way wave equation. At each iteration, the macromodel is updated using a gradient method, in which the gradient is computed by correlation of forward-modelled fields with back-propagated residual fields. The first iteration of the migration scheme is equivalent to elastic reverse-time migration with an imaging ...

Science.gov (United States)

We present an image of the rupture propagation of the 2004 M6.0 Parkfield earthquake using records from a dense network of local strong motion stations. We back-propagate high-frequency waveforms in 3D with a method, similar to reverse time migration, to obtain an estimate of the distribution of radiated high-frequency seismic energy in space and time. The image is forced to be coherent at the known hypocenter location and the quake origin time by applying small static time shifts obtained using waveform cross-correlation. We observe that the Parkfield earthquake radiated a distinct secondary high-frequency phase, which is located about 12.5~km northwest of the hypocenter with an onset of seismic radiation about 5~s after the rupture initiation. The time history of the back-projection suggests a rupture velocity of 2.5~km/s between hypocenter and subevent. The back-projection result is confirmed by inversion of picked arrival times of the secondary event clearly ...