Blancos blandos enfatizados para clasificación máquina

El modo más habitual de entrenar máquinas de clasificación es minimizar mediante búsqueda analítica una función de coste que depende de los valores de blancos y de salidas. Ello impone abandonar las salidas duras, no derivables. Además, el carácter discreto de los blancos no permite obtener buenos diseños considerando salidas lineales. De lo anterior surge la conveniencia de emplear las clásicas activaciones sigmoidales; ahora bien, su presencia no puede considerarse “natural” para cualesquiera problemas. Por otra parte, la ponderación de los errores entre blanco y salida de la máquina es una técnica bien conocida que permite conceder más atención a aquellos ejemplos que resulten más importantes para un buen aprendizaje. Esa importancia típicamente es función creciente del correspondiente error y de la proximidad a la frontera de decisión de las muestras, aunque en forma dependiente del problema y no conocida “a priori”. Lo dicho conduce a concebir la posibilidad de construir y aplicar blancos blandos enfatizados (“Emphasized Soft Targets”, ESTs): valores modificados de los blancos, en principio distintos para distintos ejemplos, establecidos según la relevancia de cada ejemplo para el aprendizaje. Con ello, cabe la posibilidad de prescindir de la activación, aprovechando el carácter “continuo” de los ESTs, al tiempo que el énfasis facilita obtener diseños de buenas prestaciones. Debe resaltarse que la supresión de la activación permite utilizar para clasificación formulaciones que son propias de la estimación, como es el caso del modelado directo de las muestras mediante mezcla de gaussianas (“Gaussian Mixture Models”, GMM) y, sobre todo, de los llamados procesos gaussianos (“Gaussian Processes”, GP), versión generalizada del filtro de Wiener y que presenta numerosas ventajas de manejo e interpretacin frente a métodos alternativos de regresión no lineal. La presente Tesis explora la utilización de ESTs para resolver problemas de clasificación, considerando tanto esquemas tradicionales -perceptrones multicapa (“Multi-Layer Perceptrons”, MLPs) entre los discriminativos, GMMs entre los generativos- como los ya mencionados GPs. Se presenta y aplica una poderosa forma de ESTs, consistente en una combinación convexa local del blanco original y la salida de un clasificador auxiliar o guía, siendo el parámetro funcional de combinación dependiente del error y la proximidad a la frontera de cada muestra tratada por la guía. Los resultados obtenidos indican que estos ESTs permiten frecuentemente alcanzar mejores (y muy altas) prestaciones, si bien a cambio de un sensible inconveniente de carga computacional debido a la necesidad de determinar mediante validación cruzada (“Cross Validation”, CV) los valores de los parámetros de la forma de los ESTs. Versiones simplificadas llevan a situaciones intermedias. Una sostenida reflexión sobre el desarrollo del trabajo y los resultados de éste condujo a determinar una semejanza funcional inmediata entre ponderaciones de errores y ESTs, así como a una interpretación del papel de las activaciones desde la perspectiva de regulación de la atención que se dedica a las diferentes muestras. Ello abre la posibilidad de recurrir a conversiones de ponderaciones a ESTs y de ESTs a ponderaciones en una serie de situaciones en que cabe esperar ventajas -mejora de prestaciones o simplificación de arquitecturas-, así como de disponer de orientación para elegir formas de las activaciones. Tales posibilidades se examinan y discuten en el Capítulo 6, y las más atractivas se incluyen como sugerencia de líneas futuras, junto con otras y la revisión de las aportaciones de la Tesis, en el último capítulo. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------The most frequent training of classification machines consists on minimizing, by means of an analytical search, a cost function which depends on targets and output values. It does not allow the use of hard outputs, that are not derivable. Furthermore, the discrete character of the classification targets does not permit to get good designs with linear outputs. The importance of introducing the classical sigmoidal activations emerges from the above facts; but it is clear that these activations cannot be considered as “natural” for all the classification problems. On the other hand, weighting output errors is a well known technique that serves to pay more attention to those examples that are more relevant for a good learning. This relevance is usually related with the corresponding error and with the proximity to the decision border of each sample, although in a previously unknown and problem dependent manner. The previous facts suggest the possibility of constructing and applying Emphasized Soft Targets (ESTs): Modified values for the targets, basically different for different examples, and defined according to the relevance of each labeled sample for the learning process. In this way, it is possible to avoid the presence of the nonlinear activation, because the ESTs are “continuous”, and, simultaneously, the effect of the emphasis helps to obtain a good performance. We remark that suppressing the nonlinear activation permits to develop classifier designs that are based on regression models, such as the direct form of Gaussian Mixture Models (GMMs), and, mainly, the so-called Gaussian Processes (GPs), a generalized version of the famous Wiener filter, which offers many working and interpretation advantages in comparison with alternative nonlinear regression methods. This Thesis explores the use of ESTs to solve classification problems, considering both traditional machines -Multi-Layer Perceptrons (MLPs) among the discriminative family, GMMs among the generative schemes- and GPs. A powerful form of ESTs is introduced and applied; it consists on a local convex combination of the original target and the output of an auxiliary classifier, or “guide”. The functional combination parameter depends on each sample’s error and its proximity to the border according to the auxiliary classifier. A lot of experimental results support the hypothesis of that ESTs frequently allow to get a better (and very high) performance, although paying a significant increase of the training computational effort, due to the need of carrying out Cross Validation (CV) processes to establish the values of ESTs parameters. Simplified versions of the proposed EST forms offer intermediate levels of compromise. Thinking all the time on the work being developed and its results led to find an immediate functional similarity between error weighting and ESTs, as well as to an interpretation of the role of nonlinear activations from the perspective of controlling the degree of attention to different examples. This opens the way to converting sample weighting methods to ESTs and viceversa in a series of situations that promise advantages when doing so (better performance or even simpler architectures). Also, the said perspective on the role of the nonlinear activations gives a guide to select their forms. All these possibilities are considered and discussed in Chapter 6, and the most promising of them are included as suggestions of new research lines, along with other opportunities and a resume of contributions, in the final chapter

Process control performance evaluation in the case of variable set-point with experimental applications

The purpose of this paper is to contribute with some refinements to recent methods of analysis of control loop performance, based on the well-established principle of Internal Model Control (IMC). Lower limits for the absolute value of the integral of control error (IAE) and the total variation of control action (TV) are assumed as reference values for a control considered good or at least acceptable. The overall performance index assumes as a benchmark a controller tuned according to rules of S(implified)IMC technique and is appropriately defined with respect to the lower limits of the two metrics IAE and TV. This allows the assessment of control loop performance, that is, the validity of tuning for PID-type controllers in response to different types of reference change. In fact, one can assess performance in the case of set-point changes as steps, ramps, or generic varying trends over time. In order to demonstrate the validity of the refined technique, several examples of simulation, case studies on a pilot plant, and real industrial data are presented

Secondary metabolism responses in two Pisum sativum L. cultivars cultivated under Fe deficiency conditions

The present study was carried out to investigate the Fe deficiency effect on the secondary metabolism  responses in two Pisum sativum cultivars characterized by different tolerance to Fe deficiency. Previous study  investigating the physiological responses to Fe deficiency in these two pea cultivars showed that Kelvedon was  more tolerant than Lincoln. Both cultivars were grown in the absence or presence of Fe with the addition of bicarbonate for twelve days. Higher concentrations of phenols and flavonoids were observed in Fe-deficient  tissues of both cultivars; however, the increase was greater in the tolerant cultivar than in the susceptible  one. The activity of shikimate pathway enzymes tested was more enhanced in the tolerant cultivar. In  addition, lipid peroxidation and H2O2 concentrations were more increased in the susceptible cultivar when  compared with the tolerant one. Peroxidase activity was increased in the tolerant cultivar grown under  bicarbonate supply, while a considerable diminution was observed in the susceptible one, suggesting the  involvement of this antioxidant enzyme in the tolerance of pea to Fe deficiency. The lignifying peroxidases  activity was more decreased in Lincoln than in Kelvedon, especially in the presence of bicarbonate. Our data  suggest that the tolerance of Kelvedon was related to its ability to modulate the phenolic metabolism pathway and to enhance the antioxidant potentials.Key words: Iron deficiency, bicarbonate, phenolic metabolism, antioxidative enzymes, Pisum sativum

Cognitive Control Systems in Steel Processing Lines for Minimised Energy Consumption and Higher Product Quality (Cognitive Control) : Final Report

The aim of Cognitive Control was to create cognitive automation systems with the capabilities automatic control performance monitoring (CPM), self-detection and automatic diagnosis of faults (sensors, actuators, controller) and self-adaptation in control system environments to optimise the product quality and minimise energy consumption in steel during the whole life cycle. In this project several software tools for online Control Performance Monitoring (CPM), monitoring energy efficiency, diagnosis of poor performance root-causes and control re-tuning for univariable and multivariable, linear and nonlinear processes were developed. The software tools were Graphical User Interface (GUI) that provided interface to access process data. The implemented methodologies were subsequently published as conference and journal papers. The methods were tested at hot strip mills, annealing furnaces and galvanizing lines

A dynamic model and performance analysis of a stepped rotary flow control valve

The hydraulic independent metering (IM) is an advanced actuator driving technique that allows the implementation of advanced control algorithms or methods. The main concept of IM is to control hydraulic actuators ports, which are the meterin and meter-out, separately. In this paper, a novel stepped rotary type valve has been developed for embedding in hydraulic independent metering systems, instead of conventional types such as poppet and spool. The insertion leads to developing different and novel control techniques, which require a software in loop and hardware in loop simulation of the proposed system. The paper explores the dynamic representation of this valve and defines its own performance limitations. This includes the development of a linear model comprising its two main sub-parts which are the stepper motor and the rotary orifice. Consequently, the linear timeinvariant methods are used to explore the performance of the valve by considering the effect of different parameters namely the pressure drop, friction coefficient, damping coefficient and bristle coefficient