A Solution of the Figure-ground Problem for Biological Vision

A neural network model of 3-D visual perception and figure-ground separation by visual cortex is introduced. The theory provides a unified explanation of how a 2-D image may generate a 3-D percept; how figures pop-out from cluttered backgrounds; how spatially sparse disparity cues can generate continuous surface representations at different perceived depths; how representations of occluded regions can be completed and recognized without usually being seen; how occluded regions can sometimes be seen during percepts of transparency; how high spatial frequency parts of an image may appear closer than low spatial frequency parts; how sharp targets are detected better against a figure and blurred targets are detector better against a background; how low spatial frequency parts of an image may be fused while high spatial frequency parts are rivalrous; how sparse blue cones can generate vivid blue surface percepts; how 3-D neon color spreading, visual phantoms, and tissue contrast percepts are generated; how conjunctions of color-and-depth may rapidly pop-out during visual search. These explanations arise derived from an ecological analysis of how monocularly viewed parts of an image inherit the appropriate depth from contiguous binocularly viewed parts, as during DaVinci stereopsis. The model predicts the functional role and ordering of multiple interactions within and between the two parvocellular processing streams that join LGN to prestriate area V4. Interactions from cells representing larger scales and disparities to cells representing smaller scales and disparities are of particular importance.

Low-cost Process monitoring for polymer extrusion

Polymer extrusion is regarded as an energy-intensive production process, and the real-time monitoring of both energy consumption and melt quality has become necessary to meet new carbon regulations and survive in the highly competitive plastics market. The use of a power meter is a simple and easy way to monitor energy, but the cost can sometimes be high. On the other hand, viscosity is regarded as one of the key indicators of melt quality in the polymer extrusion process. Unfortunately, viscosity cannot be measured directly using current sensory technology. The employment of on-line, in-line or off-line rheometers is sometimes useful, but these instruments either involve signal delay or cause flow restrictions to the extrusion process, which is obviously not suitable for real-time monitoring and control in practice. In this paper, simple and accurate real-time energy monitoring methods are developed. This is achieved by looking inside the controller, and using control variables to calculate the power consumption. For viscosity monitoring, a ‘soft-sensor’ approach based on an RBF neural network model is developed. The model is obtained through a two-stage selection and differential evolution, enabling compact and accurate solutions for viscosity monitoring. The proposed monitoring methods were tested and validated on a Killion KTS-100 extruder, and the experimental results show high accuracy compared with traditional monitoring approaches.

Advanced Modelling and Optimization of Infared Oven in Injection Stretch Blow-moulding for Energy Saving

In the production process of polyethylene terephthalate (PET) bottles, the initial temperature of preforms plays a central role on the final thickness, intensity and other structural properties of the bottles. Also, the difference between inside and outside temperature profiles could make a significant impact on the final product quality. The preforms are preheated by infrared heating oven system which is often an open loop system and relies heavily on trial and error approach to adjust the lamp power settings. In this paper, a radial basis function (RBF) neural network model, optimized by a two-stage selection (TSS) algorithm combined with partial swarm optimization (PSO), is developed to model the nonlinear relations between the lamp power settings and the output temperature profile of PET bottles. Then an improved PSO method for lamp setting adjustment using the above model is presented. Simulation results based on experimental data confirm the effectiveness of the modelling and optimization method.

Development and Evaluation of Blind Identification Techniques for Nonlinear Systems

Identification and Control of Non‐linear dynamical systems are challenging problems to the control engineers.The topic is equally relevant in communication,weather prediction ,bio medical systems and even in social systems,where nonlinearity is an integral part of the system behavior.Most of the real world systems are nonlinear in nature and wide applications are there for nonlinear system identification/modeling.The basic approach in analyzing the nonlinear systems is to build a model from known behavior manifest in the form of system output.The problem of modeling boils down to computing a suitably parameterized model,representing the process.The parameters of the model are adjusted to optimize a performanace function,based on error between the given process output and identified process/model output.While the linear system identification is well established with many classical approaches,most of those methods cannot be directly applied for nonlinear system identification.The problem becomes more complex if the system is completely unknown but only the output time series is available.Blind recognition problem is the direct consequence of such a situation.The thesis concentrates on such problems.Capability of Artificial Neural Networks to approximate many nonlinear input-output maps makes it predominantly suitable for building a function for the identification of nonlinear systems,where only the time series is available.The literature is rich with a variety of algorithms to train the Neural Network model.A comprehensive study of the computation of the model parameters,using the different algorithms and the comparison among them to choose the best technique is still a demanding requirement from practical system designers,which is not available in a concise form in the literature.The thesis is thus an attempt to develop and evaluate some of the well known algorithms and propose some new techniques,in the context of Blind recognition of nonlinear systems.It also attempts to establish the relative merits and demerits of the different approaches.comprehensiveness is achieved in utilizing the benefits of well known evaluation techniques from statistics. The study concludes by providing the results of implementation of the currently available and modified versions and newly introduced techniques for nonlinear blind system modeling followed by a comparison of their performance.It is expected that,such comprehensive study and the comparison process can be of great relevance in many fields including chemical,electrical,biological,financial and weather data analysis.Further the results reported would be of immense help for practical system designers and analysts in selecting the most appropriate method based on the goodness of the model for the particular context.

Los conceptos de salud financiera, riesgo y epidemiología en los estados financieros de las compañías del sector de extracción de petróleo crudo y gas natural en Colombia

El presente proyecto tiene como objeto identificar cuáles son los conceptos de salud, enfermedad, epidemiología y riesgo aplicables a las empresas del sector de extracción de petróleo y gas natural en Colombia. Dado, el bajo nivel de predicción de los análisis financieros tradicionales y su insuficiencia, en términos de inversión y toma de decisiones a largo plazo, además de no considerar variables como el riesgo y las expectativas de futuro, surge la necesidad de abordar diferentes perspectivas y modelos integradores. Esta apreciación es pertinente dentro del sector de extracción de petróleo y gas natural, debido a la creciente inversión extranjera que ha reportado, US$2.862 millones en el 2010, cifra mayor a diez veces su valor en el año 2003. Así pues, se podrían desarrollar modelos multi-dimensional, con base en los conceptos de salud financiera, epidemiológicos y estadísticos. El termino de salud y su adopción en el sector empresarial, resulta útil y mantiene una coherencia conceptual, evidenciando una presencia de diferentes subsistemas o factores interactuantes e interconectados. Es necesario mencionar también, que un modelo multidimensional (multi-stage) debe tener en cuenta el riesgo y el análisis epidemiológico ha demostrado ser útil al momento de determinarlo e integrarlo en el sistema junto a otros conceptos, como la razón de riesgo y riesgo relativo. Esto se analizará mediante un estudio teórico-conceptual, que complementa un estudio previo, para contribuir al proyecto de finanzas corporativas de la línea de investigación en Gerencia.

Modelling and inverting complex-valued Wiener systems

We develop a complex-valued (CV) B-spline neural network approach for efficient identification and inversion of CV Wiener systems. The CV nonlinear static function in the Wiener system is represented using the tensor product of two univariate B-spline neural networks. With the aid of a least squares parameter initialisation, the Gauss-Newton algorithm effectively estimates the model parameters that include the CV linear dynamic model coefficients and B-spline neural network weights. The identification algorithm naturally incorporates the efficient De Boor algorithm with both the B-spline curve and first order derivative recursions. An accurate inverse of the CV Wiener system is then obtained, in which the inverse of the CV nonlinear static function of the Wiener system is calculated efficiently using the Gaussian-Newton algorithm based on the estimated B-spline neural network model, with the aid of the De Boor recursions. The effectiveness of our approach for identification and inversion of CV Wiener systems is demonstrated using the application of digital predistorter design for high power amplifiers with memory

Nonlinear equalization of Hammerstein OFDM systems

A practical orthogonal frequency-division multiplexing (OFDM) system can generally be modelled by the Hammerstein system that includes the nonlinear distortion effects of the high power amplifier (HPA) at transmitter. In this contribution, we advocate a novel nonlinear equalization scheme for OFDM Hammerstein systems. We model the nonlinear HPA, which represents the static nonlinearity of the OFDM Hammerstein channel, by a B-spline neural network, and we develop a highly effective alternating least squares algorithm for estimating the parameters of the OFDM Hammerstein channel, including channel impulse response coefficients and the parameters of the B-spline model. Moreover, we also use another B-spline neural network to model the inversion of the HPA’s nonlinearity, and the parameters of this inverting B-spline model can easily be estimated using the standard least squares algorithm based on the pseudo training data obtained as a byproduct of the Hammerstein channel identification. Equalization of the OFDM Hammerstein channel can then be accomplished by the usual one-tap linear equalization as well as the inverse B-spline neural network model obtained. The effectiveness of our nonlinear equalization scheme for OFDM Hammerstein channels is demonstrated by simulation results.

The prediction of damage condition in regards to damage factor influence of light structures on expansive soils in Victoria, Australia

This paper proposes a neural network model using genetic algorithm for a model for the prediction of the damage condition of existing light structures founded in expansive soils in Victoria, Australia. It also accounts for both individual effects and interactive effects of the damage factors influencing the deterioration of light structures. A Neural Network Model was chosen because it can deal with 'noisy' data while a Genetic Algorithm was chosen because it does not get `trapped' in local optimum like other gradient descent methods. The results obtained were promising and indicate that a Neural Network Model trained using a Genetic Algorithm has the ability to develop an interactive relationship and a Predicted Damage Conditions Model.

Integrating simulated annealing and delta technique for constructing optimal prediction intervals

This paper aims at developing a new criterion for quantitative assessment of prediction intervals. The proposed criterion is developed based on both key measures related to quality of prediction intervals: length and coverage probability. This criterion is applied as a cost function for optimizing prediction intervals constructed using delta technique for neural network model. Optimization seeks out to minimize length of prediction intervals without compromising their coverage probability. Simulated Annealing method is employed for readjusting neural network parameters for minimization of the new cost function. To further ameliorate search efficiency of the optimization method, parameters of the network trained using weight decay method are considered as the initial set in Simulated Annealing algorithm. Implementation of the proposed method for a real world case study shows length and coverage probability of constructed prediction intervals are better than those constructed using traditional techniques.

The ranking of factors influencing the behaviour of light structures on expansive soils in Victoria, Australia

In this paper a Neural Network Model was used to develop a ranking of the potential damage influences for light structures on expansive soils in Victoria. These influences include geology, Thornthwaite moisture index, vegetation covers, construction foundation type, construction wall type, geographical region and age of building when first inspected. Approximately 400 cases of damage to light structures in Victoria, Australia were considered in this study. Feedforward Backpropagation was adopted to train the data. The ranking of importance was estimated using connection weight approach and then compared to results calculated from sensitivity analysis. From the analysis, the ranking of importance for potential damage factor was noted.

Intelligent traffic light control of isolated intersections using machine learning methods

Traffic congestion is one of the major problems in modern cities. This study applies machine learning methods to determine green times in order to minimize in an isolated intersection. Q-learning and neural networks are applied here to set signal light times and minimize total delays. It is assumed that an intersection behaves in a similar fashion to an intelligent agent learning how to set green times in each cycle based on traffic information. Here, a comparison between Q-learning and neural network is presented. In Q-learning, considering continuous green time requires a large state space, making the learning process practically impossible. In contrast to Q-learning methods, the neural network model can easily set the appropriate green time to fit the traffic demand. The performance of the proposed neural network is compared with two traditional alternatives for controlling traffic lights. Simulation results indicate that the application of the proposed method greatly reduces the total delay in the network compared to the alternative methods.

Damage identification scheme based on compressive sensing

Civil infrastructures are critical to every nation, due to their substantial investment, long service period, and enormous negative impacts after failure. However, they inevitably deteriorate during their service lives. Therefore, methods capable of assessing conditions and identifying damage in a structure timely and accurately have drawn increasing attention. Recently, compressive sensing (CS), a significant breakthrough in signal processing, has been proposed to capture and represent compressible signals at a rate significantly below the traditional Nyquist rate. Due to its sound theoretical background and notable influence, this methodology has been successfully applied in many research areas. In order to explore its application in structural damage identification, a new CS-based damage identification scheme is proposed in this paper, by regarding damage identification problems as pattern classification problems. The time domain structural responses are transferred to the frequency domain as sparse representation, and then the numerical simulated data under various damage scenarios will be used to train a feature matrix as input information. This matrix can be used for damage identification through an optimization process. This will be one of the first few applications of this advanced technique to structural engineering areas. In order to demonstrate its effectiveness, numerical simulation results on a complex pipe soil interaction model are used to train the parameters and then to identify the simulated pipe degradation damage and free-spanning damage. To further demonstrate the method, vibration tests of a steel pipe laid on the ground are carried out. The measured acceleration time histories are used for damage identification. Both numerical and experimental verification results confirm that the proposed damage identification scheme will be a promising tool for structural health monitoring.

Aplicação de redes neurais na classificação de rentabilidade futura de empresas

Este trabalho tem por motivação evidenciar a eficiência de redes neurais na classificação de rentabilidade futura de empresas, e desta forma, prover suporte para o desenvolvimento de sistemas de apoio a tomada de decisão de investimentos. Para serem comparados com o modelo de redes neurais, foram escolhidos o modelo clássico de regressão linear múltipla, como referência mínima, e o de regressão logística ordenada, como marca comparativa de desempenho (benchmark). Neste texto, extraímos dados financeiros e contábeis das 1000 melhores empresas listadas, anualmente, entre 1996 e 2006, na publicação Melhores e Maiores – Exame (Editora Abril). Os três modelos foram construídos tendo como base as informações das empresas entre 1996 e 2005. Dadas as informações de 2005 para estimar a classificação das empresas em 2006, os resultados dos três modelos foram comparados com as classificações observadas em 2006, e o modelo de redes neurais gerou o melhor resultado.

Modelagem e medições de ondas de rádio para predição de perda de propagação em ambientes urbanos

In this dissertation new models of propagation path loss predictions are proposed by from techniques of optimization recent and measures of power levels for the urban and suburban areas of Natal, city of Brazilian northeast. These new proposed models are: (i) a statistical model that was implemented based in the addition of second-order statistics for the power and the altimetry of the relief in model of linear losses; (ii) a artificial neural networks model used the training of the algorithm backpropagation, in order to get the equation of propagation losses; (iii) a model based on the technique of the random walker, that considers the random of the absorption and the chaos of the environment and than its unknown parameters for the equation of propagation losses are determined through of a neural network. The digitalization of the relief for the urban and suburban areas of Natal were carried through of the development of specific computational programs and had been used available maps in the Statistics and Geography Brazilian Institute. The validations of the proposed propagation models had been carried through comparisons with measures and propagation classic models, and numerical good agreements were observed. These new considered models could be applied to any urban and suburban scenes with characteristic similar architectural to the city of Natal

Degradação fotoquímica de hidrocarbonetos da gasolina em efluentes aquosos

Photo-oxidation processes of toxic organic compounds have been widely studied. This work seeks the application of the photo-Fenton process for the degradation of hydrocarbons in water. The gasoline found in the refinery, without additives and alcohol, was used as the model pollutant. The effects of the concentration of the following substances have been properly evaluated: hydrogen peroxide (100-200 mM), iron ions (0.5-1 mM) and sodium chloride (200 2000 ppm). The experiments were accomplished in reactor with UV lamp and in a falling film solar reactor. The photo-oxidation process was monitored by measurements of the absorption spectra, total organic carbon (TOC) and chemical oxygen demand (COD). Experimental results demonstrated that the photo-Fenton process is feasible for the treatment of wastewaters containing aliphatic hydrocarbons, inclusive in the presence of salts. These conditions are similar to the water produced by the petroleum fields, generated in the extraction and production of petroleum. A neural network model of process correlated well the observed data for the photooxidation process of hydrocarbons