Developments in Hyper Real-Time Simulation of Transient Heat-Flow in Buildings

This paper reports on the latest results in the development of a new approach for simulating the thermal behavior of buildings that overcomes the limitations of conventional heat-transfer simulation methods such as FDM and FEM. The proposed technique uses a coarse-grain approach to model development whereby each element represents a complete building component such as a wall, internal space, or floor. The thermal behavior of each coarse-grain element is captured using empirical modeling techniques such as artificial neural networks (ANNs). The main advantages of the approach compared to conventional simulation methods are: (a) simplified model construction for the end-user; (b) simplified model reconfiguration; (c) significantly faster simulation runs (orders of magnitude faster for two and three-dimensional models); and (d) potentially more accurate results. The paper demonstrates the viability of the approach through a number of experiments with a model of a composite wall. The approach is shown to be able to sustain highly accurate longterm simulation runs, if the coarse-grain modeling elements are implemented as ANNs. In contrast, an implementation of the coarse-grain elements using a linear model is shown to function inaccurately and erratically. The paper concludes with an identification of on-going work and future areas for development of the technique

Medical image tomography: A statistically tailored neural network approach

In medical computed tomography (CT) the tomographic images are reconstructed from planar information collected 180∘ to 360∘ around the patient. In clinical applications, the reconstructions are typically produced using a filtered backprojection algorithm. Filtered backprojection methods have limitations that create a high percentage of statistical uncertainty in the reconstructed images. Many techniques have been developed which produce better reconstructions, but they tend to be computationally expensive, and thus, impractical for clinical use;Artificial neural networks (ANN) have been shown to be adept at learning and then simulating complex functional relationships. For medical tomography, a neural network can be trained to produce a reconstructed medical image given the planar data as input. Once trained an ANN can produce an accurate reconstruction very quickly;A backpropagation ANN with statistically derived activation functions has been developed to improve the trainability and generalization ability of a network to produce accurate reconstructions. The tailored activation functions are derived from the estimated probability density functions (p.d.f.s) of the ANN training data set. A set of sigmoid derivative functions are fitted to the p.d.f.s and then integrated to produce the ANN activation functions, which are also estimates of the cumulative distribution functions (c.d.f.s) of the training data. The statistically tailored activation functions and their derivatives are substituted for the logistic function and its derivative that are typically used in backpropagation ANNs;A set of geometric images was derived for training an ANN for cardiac SPECT image reconstruction. The planar projections for the geometric images were simulated using the Monte Carlo method to produce sixty-four 64-quadrant planar views taken 180 about each image. A 4096 x 629 x 4096 architecture ANN was simulated on the MasPar MP-2, a massively parallel single-instruction multiple-data (SIMD) computer. The ANN was trained on the set of geometric tomographic images. Trained on the geometric images, the ANN was able to generalize the input-to-output function of the planar data-to-tomogram and accurately reconstruct actual cardiac SPECT images

A Neural Network Linking Process

A novel method of integrating multiple neural networks into one large network via a process referred to as a neural network linking process is proposed. Neural networks are commonly trained to solve a specific problem for an encapsulated problem domain. A single network can undertake simple classification or generalisation problems. Dividing them into sub-problems, which in turn are solved by a sub-network, can disentangle more complicated classification or generalisation problems. A controller generally combines sub-network results. A controller can be, for instance, a gating network, voting system or a mathematical combiner. In each case, every sub-network is used as a separate unit and is not interconnected to any other sub-network. However, with the linking process a novel method for linking trained sub-networks into one large network by maintaining the knowledge of each individual sub-network is introduced. Furthermore, the linked network will utilize a stimulus process in order to distinguish the type of sub-problem to be solved, by largely retaining the accuracy of the sub-network, as well as being one step closer to the biological reality

Modelo de un sistema para la selección automática en dominios complejos con una estrategia cooperativa, de conjuntos de entrenamiento y arquitecturas ideales de redes de neuronas artificiales ulilizando alogaritmos genéticos

[Resumen] Esta tesis estudia el desarrollo de un modelo de sistema evolutivo y distribuido para la automatización y optimización de la construcción de RR,NN.AA. que se apliquen a dominios complejos. Los trabajos de investigación se centran en cuatro áreas: * Identificar, de forma automática, configuraciones óptimas de capas, elementos de proceso (EP) y parámetros de los EP, evitando el proceso de prueba y error que, actualmente, realiza el diseñador de la red. * Diseñar un método para la selección de un conjunto de entrenamiento óptimo a partir de series temporales, complementando así los métodos existentes para la discriminación de variables de entrada. * Se propone un método alternativo para el proceso de entrenamiento de RR.NN.AA. ya que, al aplicarlas a problemas complejos, como la predicción en el dominio temporal, los métodos de gradiente presentan problemas de mínimos locales. * Por último, se pretende armonizar, con una aproximación cooperativa, las distintas fases de desarrollo de la RNA: diseño del conjunto de entrenamiento, ajuste de los parámetros de la arquitectura y proceso de entrenamiento