New numerical procedure for the prediction of temperature development in early age concrete structures

A new numerical model for the prediction of temperature development in young concrete structures is briefly presented. With the pre-program. adiabatic hydration curves, which are used to determine the internal heat generation, are calculated. An artificial neural networks approach is used for this purpose. Adiabatic hydration curves, which were included in the learning set, were determined by our own experiments, using the adiabatic calorimeter which uses air as the coupling media. The main program is implemented in the finite element code. This program allows concrete structure designers and contractors to quantify and evaluate the effects of some concrete initial parameters on the adiabatic hydration curves and corresponding temperature development at an arbitrary point in the concrete element. Some examples are also presented and discussed. (C) 2009 Elsevier B.V. All rights reserve

The Payne: self-consistent ab initio fitting of stellar spectra

We present The Payne, a general method for the precise and simultaneous determination of numerous stellar labels from observed spectra, based on fitting physical spectral models. The Payne combines a number of important methodological aspects: it exploits the information from much of the available spectral range; it fits all labels (stellar parameters and element abundances) simultaneously; it uses spectral models, where the atmosphere structure and the radiative transport are consistently calculated to reflect the stellar labels. At its core The Payne has an approach to accurate and precise interpolation and prediction of the spectrum in high-dimensional label-space, which is flexible and robust, yet based on only a moderate number of ab initio models (O(1000) for 25 labels). With a simple neural-net-like functional form and a suitable choice of training labels, this interpolation yields a spectral flux prediction good to $10^{-3}$ rms across a wide range of $T_{\rm eff}$ and log g (including dwarfs and giants). We illustrate the power of this approach by applying it to the APOGEE DR14 data set, drawing on Kurucz models with recently improved line lists: without recalibration, we obtain physically sensible stellar parameters as well as 15 element abundances that appear to be more precise than the published APOGEE DR14 values. In short, The Payne is an approach that for the first time combines all these key ingredients, necessary for progress towards optimal modelling of survey spectra; and it leads to both precise and accurate estimates of stellar labels, based on physical models and without re-calibration. Both the codes and catalog are made publicly available online.Comment: 22 pages, 17 figures, 2 tables, ApJ (Accepted for publication- 2019 May 11

Developement of real time diagnostics and feedback algorithms for JET in view of the next step

Real time control of many plasma parameters will be an essential aspect in the development of reliable high performance operation of Next Step Tokamaks. The main prerequisites for any feedback scheme are the precise real-time determination of the quantities to be controlled, requiring top quality and highly reliable diagnostics, and the availability of robust control algorithms. A new set of real time diagnostics was recently implemented on JET to prove the feasibility of determining, with high accuracy and time resolution, the most important plasma quantities. With regard to feedback algorithms, new model–based controllers were developed to allow a more robust control of several plasma parameters. Both diagnostics and algorithms were successfully used in several experiments, ranging from H-mode plasmas to configuration with ITBs. Since elaboration of computationally heavy measurements is often required, significant attention was devoted to non-algorithmic methods like Digital or Cellular Neural/Nonlinear Networks. The real time hardware and software adopted architectures are also described with particular attention to their relevance to ITER.Comment: 12th International Congress on Plasma Physics, 25-29 October 2004, Nice (France

A genetic-algorithms based evolutionary computational neural network for modelling spatial interaction data

Building a feedforward computational neural network model (CNN) involves two distinct tasks: determination of the network topology and weight estimation. The specification of a problem adequate network topology is a key issue and the primary focus of this contribution. Up to now, this issue has been either completely neglected in spatial application domains, or tackled by search heuristics (see Fischer and Gopal 1994). With the view of modelling interactions over geographic space, this paper considers this problem as a global optimization problem and proposes a novel approach that embeds backpropagation learning into the evolutionary paradigm of genetic algorithms. This is accomplished by interweaving a genetic search for finding an optimal CNN topology with gradient-based backpropagation learning for determining the network parameters. Thus, the model builder will be relieved of the burden of identifying appropriate CNN-topologies that will allow a problem to be solved with simple, but powerful learning mechanisms, such as backpropagation of gradient descent errors. The approach has been applied to the family of three inputs, single hidden layer, single output feedforward CNN models using interregional telecommunication traffic data for Austria, to illustrate its performance and to evaluate its robustness.

Evolutionary design of nearest prototype classifiers

In pattern classification problems, many works have been carried out with the aim of designing good classifiers from different perspectives. These works achieve very good results in many domains. However, in general they are very dependent on some crucial parameters involved in the design. These parameters have to be found by a trial and error process or by some automatic methods, like heuristic search and genetic algorithms, that strongly decrease the performance of the method. For instance, in nearest prototype approaches, main parameters are the number of prototypes to use, the initial set, and a smoothing parameter. In this work, an evolutionary approach based on Nearest Prototype Classifier (ENPC) is introduced where no parameters are involved, thus overcoming all the problems that classical methods have in tuning and searching for the appropiate values. The algorithm is based on the evolution of a set of prototypes that can execute several operators in order to increase their quality in a local sense, and with a high classification accuracy emerging for the whole classifier. This new approach has been tested using four different classical domains, including such artificial distributions as spiral and uniform distibuted data sets, the Iris Data Set and an application domain about diabetes. In all the cases, the experiments show successfull results, not only in the classification accuracy, but also in the number and distribution of the prototypes achieved.Publicad