Estudio de la aplicación de sistemas basados en el conocimiento a la operación de una planta de tratamiento de residuos sólidos urbanos por valorización energética

Una de las actuaciones posibles para la gestión de los residuos sólidos urbanos es la valorización energética, es decir la incineración con recuperación de energía. Sin embargo es muy importante controlar adecuadamente el proceso de incineración para evitar en lo posible la liberación de sustancias contaminantes a la atmósfera que puedan ocasionar problemas de contaminación industrial.Conseguir que tanto el proceso de incineración como el tratamiento de los gases se realice en condiciones óptimas presupone tener un buen conocimiento de las dependencias entre las variables de proceso. Se precisan métodos adecuados de medida de las variables más importantes y tratar los valores medidos con modelos adecuados para transformarlos en magnitudes de mando. Un modelo clásico para el control parece poco prometedor en este caso debido a la complejidad de los procesos, la falta de descripción cuantitativa y la necesidad de hacer los cálculos en tiempo real. Esto sólo se puede conseguir con la ayuda de las modernas técnicas de proceso de datos y métodos informáticos, tales como el empleo de técnicas de simulación, modelos matemáticos, sistemas basados en el conocimiento e interfases inteligentes. En [Ono, 1989] se describe un sistema de control basado en la lógica difusa aplicado al campo de la incineración de residuos urbanos. En el centro de investigación FZK de Karslruhe se están desarrollando aplicaciones que combinan la lógica difusa con las redes neuronales [Jaeschke, Keller, 1994] para el control de la planta piloto de incineración de residuos TAMARA. En esta tesis se plantea la aplicación de un método de adquisición de conocimiento para el control de sistemas complejos inspirado en el comportamiento humano. Cuando nos encontramos ante una situación desconocida al principio no sabemos como actuar, salvo por la extrapolación de experiencias anteriores que puedan ser útiles. Aplicando procedimientos de prueba y error, refuerzo de hipótesis, etc., vamos adquiriendo y refinando el conocimiento, y elaborando un modelo mental. Podemos diseñar un método análogo, que pueda ser implementado en un sistema informático, mediante el empleo de técnicas de Inteligencia Artificial.Así, en un proceso complejo muchas veces disponemos de un conjunto de datos del proceso que a priori no nos dan información suficientemente estructurada para que nos sea útil. Para la adquisición de conocimiento pasamos por una serie de etapas: - Hacemos una primera selección de cuales son las variables que nos interesa conocer. - Estado del sistema. En primer lugar podemos empezar por aplicar técnicas de clasificación (aprendizaje no supervisado) para agrupar los datos y obtener una representación del estado de la planta. Es posible establecer una clasificación, pero normalmente casi todos los datos están en una sola clase, que corresponde a la operación normal. Hecho esto y para refinar el conocimiento utilizamos métodos estadísticos clásicos para buscar correlaciones entre variables (análisis de componentes principales) y así poder simplificar y reducir la lista de variables. - Análisis de las señales. Para analizar y clasificar las señales (por ejemplo la temperatura del horno) es posible utilizar métodos capaces de describir mejor el comportamiento no lineal del sistema, como las redes neuronales. Otro paso más consiste en establecer relaciones causales entre las variables. Para ello nos sirven de ayuda los modelos analíticos - Como resultado final del proceso se pasa al diseño del sistema basado en el conocimiento. El objetivo principal es aplicar el método al caso concreto del control de una planta de tratamiento de residuos sólidos urbanos por valorización energética. En primer lugar, en el capítulo 2 Los residuos sólidos urbanos, se trata el problema global de la gestión de los residuos, dando una visión general de las diferentes alternativas existentes, y de la situación nacional e internacional en la actualidad. Se analiza con mayor detalle la problemática de la incineración de los residuos, poniendo especial interés en aquellas características de los residuos que tienen mayor importancia de cara al proceso de combustión.En el capítulo 3, Descripción del proceso, se hace una descripción general del proceso de incineración y de los distintos elementos de una planta incineradora: desde la recepción y almacenamiento de los residuos, pasando por los distintos tipos de hornos y las exigencias de los códigos de buena práctica de combustión, el sistema de aire de combustión y el sistema de humos. Se presentan también los distintos sistemas de depuración de los gases de combustión, y finalmente el sistema de evacuación de cenizas y escorias.El capítulo 4, La planta de tratamiento de residuos sólidos urbanos de Girona, describe los principales sistemas de la planta incineradora de Girona: la alimentación de residuos, el tipo de horno, el sistema de recuperación de energía, y el sistema de depuración de los gases de combustión Se describe también el sistema de control, la operación, los datos de funcionamiento de la planta, la instrumentación y las variables que son de interés para el control del proceso de combustión.En el capítulo 5, Técnicas utilizadas, se proporciona una visión global de los sistemas basados en el conocimiento y de los sistemas expertos. Se explican las diferentes técnicas utilizadas: redes neuronales, sistemas de clasificación, modelos cualitativos, y sistemas expertos, ilustradas con algunos ejemplos de aplicación.Con respecto a los sistemas basados en el conocimiento se analizan en primer lugar las condiciones para su aplicabilidad, y las formas de representación del conocimiento. A continuación se describen las distintas formas de razonamiento: redes neuronales, sistemas expertos y lógica difusa, y se realiza una comparación entre ellas. Se presenta una aplicación de las redes neuronales al análisis de series temporales de temperatura.Se trata también la problemática del análisis de los datos de operación mediante técnicas estadísticas y el empleo de técnicas de clasificación. Otro apartado está dedicado a los distintos tipos de modelos, incluyendo una discusión de los modelos cualitativos.Se describe el sistema de diseño asistido por ordenador para el diseño de sistemas de supervisión CASSD que se utiliza en esta tesis, y las herramientas de análisis para obtener información cualitativa del comportamiento del proceso: Abstractores y ALCMEN. Se incluye un ejemplo de aplicación de estas técnicas para hallar las relaciones entre la temperatura y las acciones del operador. Finalmente se analizan las principales características de los sistemas expertos en general, y del sistema experto CEES 2.0 que también forma parte del sistema CASSD que se ha utilizado.El capítulo 6, Resultados, muestra los resultados obtenidos mediante la aplicación de las diferentes técnicas, redes neuronales, clasificación, el desarrollo de la modelización del proceso de combustión, y la generación de reglas. Dentro del apartado de análisis de datos se emplea una red neuronal para la clasificación de una señal de temperatura. También se describe la utilización del método LINNEO+ para la clasificación de los estados de operación de la planta.En el apartado dedicado a la modelización se desarrolla un modelo de combustión que sirve de base para analizar el comportamiento del horno en régimen estacionario y dinámico. Se define un parámetro, la superficie de llama, relacionado con la extensión del fuego en la parrilla. Mediante un modelo linealizado se analiza la respuesta dinámica del proceso de incineración. Luego se pasa a la definición de relaciones cualitativas entre las variables que se utilizan en la elaboración de un modelo cualitativo. A continuación se desarrolla un nuevo modelo cualitativo, tomando como base el modelo dinámico analítico.Finalmente se aborda el desarrollo de la base de conocimiento del sistema experto, mediante la generación de reglas En el capítulo 7, Sistema de control de una planta incineradora, se analizan los objetivos de un sistema de control de una planta incineradora, su diseño e implementación. Se describen los objetivos básicos del sistema de control de la combustión, su configuración y la implementación en Matlab/Simulink utilizando las distintas herramientas que se han desarrollado en el capítulo anterior.Por último para mostrar como pueden aplicarse los distintos métodos desarrollados en esta tesis se construye un sistema experto para mantener constante la temperatura del horno actuando sobre la alimentación de residuos.Finalmente en el capítulo Conclusiones, se presentan las conclusiones y resultados de esta tesis.

Historical and current machine intelligence

In this article, we provide an initial insight into the study of MI and what it means for a machine to be intelligent. We discuss how MI has progressed to date and consider future scenarios in a realistic and logical way as much as possible. To do this, we unravel one of the major stumbling blocks to the study of MI, which is the field that has become widely known as "artificial intelligence"

Artificial keys for botanical identification using a multilayer perceptron neural network (MLP)

In this paper, practical generation of identification keys for biological taxa using a multilayer perceptron neural network is described. Unlike conventional expert systems, this method does not require an expert for key generation, but is merely based on recordings of observed character states. Like a human taxonomist, its judgement is based on experience, and it is therefore capable of generalized identification of taxa. An initial study involving identification of three species of Iris with greater than 90% confidence is presented here. In addition, the horticulturally significant genus Lithops (Aizoaceae/Mesembryanthemaceae), popular with enthusiasts of succulent plants, is used as a more practical example, because of the difficulty of generation of a conventional key to species, and the existence of a relatively recent monograph. It is demonstrated that such an Artificial Neural Network Key (ANNKEY) can identify more than half (52.9%) of the species in this genus, after training with representative data, even though data for one character is completely missing.

Deception-detection and machine intelligence in practical Turing tests

Deception-detection is the crux of Turing’s experiment to examine machine thinking conveyed through a capacity to respond with sustained and satisfactory answers to unrestricted questions put by a human interrogator. However, in 60 years to the month since the publication of Computing Machinery and Intelligence little agreement exists for a canonical format for Turing’s textual game of imitation, deception and machine intelligence. This research raises from the trapped mine of philosophical claims, counter-claims and rebuttals Turing’s own distinct five minutes question-answer imitation game, which he envisioned practicalised in two different ways: a) A two-participant, interrogator-witness viva voce, b) A three-participant, comparison of a machine with a human both questioned simultaneously by a human interrogator. Using Loebner’s 18th Prize for Artificial Intelligence contest, and Colby et al.’s 1972 transcript analysis paradigm, this research practicalised Turing’s imitation game with over 400 human participants and 13 machines across three original experiments. Results show that, at the current state of technology, a deception rate of 8.33% was achieved by machines in 60 human-machine simultaneous comparison tests. Results also show more than 1 in 3 Reviewers succumbed to hidden interlocutor misidentification after reading transcripts from experiment 2. Deception-detection is essential to uncover the increasing number of malfeasant programmes, such as CyberLover, developed to steal identity and financially defraud users in chatrooms across the Internet. Practicalising Turing’s two tests can assist in understanding natural dialogue and mitigate the risk from cybercrime.

Resting tremor classification and detection in Parkinson's disease patients

Parkinson is a neurodegenerative disease, in which tremor is the main symptom. This paper investigates the use of different classification methods to identify tremors experienced by Parkinsonian patients.Some previous research has focussed tremor analysis on external body signals (e.g., electromyography, accelerometer signals, etc.). Our advantage is that we have access to sub-cortical data, which facilitates the applicability of the obtained results into real medical devices since we are dealing with brain signals directly. Local field potentials (LFP) were recorded in the subthalamic nucleus of 7 Parkinsonian patients through the implanted electrodes of a deep brain stimulation (DBS) device prior to its internalization. Measured LFP signals were preprocessed by means of splinting, down sampling, filtering, normalization and rec-tification. Then, feature extraction was conducted through a multi-level decomposition via a wavelettrans form. Finally, artificial intelligence techniques were applied to feature selection, clustering of tremor types, and tremor detection.The key contribution of this paper is to present initial results which indicate, to a high degree of certainty, that there appear to be two distinct subgroups of patients within the group-1 of patients according to the Consensus Statement of the Movement Disorder Society on Tremor. Such results may well lead to different resultant treatments for the patients involved, depending on how their tremor has been classified. Moreover, we propose a new approach for demand driven stimulation, in which tremor detection is also based on the subtype of tremor the patient has. Applying this knowledge to the tremor detection problem, it can be concluded that the results improve when patient clustering is applied prior to detection.

Towards expressive rule induction on IP network event streams

In order to gain insights into events and issues that may cause errors and outages in parts of IP networks, intelligent methods that capture and express causal relationships online (in real-time) are needed. Whereas generalised rule induction has been explored for non-streaming data applications, its application and adaptation on streaming data is mostly undeveloped or based on periodic and ad-hoc training with batch algorithms. Some association rule mining approaches for streaming data do exist, however, they can only express binary causal relationships. This paper presents the ongoing work on Online Generalised Rule Induction (OGRI) in order to create expressive and adaptive rule sets real-time that can be applied to a broad range of applications, including network telemetry data streams.

Multiprobabilistic prediction in early medical diagnoses

This paper describes the methodology of providing multiprobability predictions for proteomic mass spectrometry data. The methodology is based on a newly developed machine learning framework called Venn machines. Is allows to output a valid probability interval. The methodology is designed for mass spectrometry data. For demonstrative purposes, we applied this methodology to MALDI-TOF data sets in order to predict the diagnosis of heart disease and early diagnoses of ovarian cancer and breast cancer. The experiments showed that probability intervals are narrow, that is, the output of the multiprobability predictor is similar to a single probability distribution. In addition, probability intervals produced for heart disease and ovarian cancer data were more accurate than the output of corresponding probability predictor. When Venn machines were forced to make point predictions, the accuracy of such predictions is for the most data better than the accuracy of the underlying algorithm that outputs single probability distribution of a label. Application of this methodology to MALDI-TOF data sets empirically demonstrates the validity. The accuracy of the proposed method on ovarian cancer data rises from 66.7 % 11 months in advance of the moment of diagnosis to up to 90.2 % at the moment of diagnosis. The same approach has been applied to heart disease data without time dependency, although the achieved accuracy was not as high (up to 69.9 %). The methodology allowed us to confirm mass spectrometry peaks previously identified as carrying statistically significant information for discrimination between controls and cases.

Efficiency issues of evolutionary k-means

One of the top ten most influential data mining algorithms, k-means, is known for being simple and scalable. However, it is sensitive to initialization of prototypes and requires that the number of clusters be specified in advance. This paper shows that evolutionary techniques conceived to guide the application of k-means can be more computationally efficient than systematic (i.e., repetitive) approaches that try to get around the above-mentioned drawbacks by repeatedly running the algorithm from different configurations for the number of clusters and initial positions of prototypes. To do so, a modified version of a (k-means based) fast evolutionary algorithm for clustering is employed. Theoretical complexity analyses for the systematic and evolutionary algorithms under interest are provided. Computational experiments and statistical analyses of the results are presented for artificial and text mining data sets. (C) 2010 Elsevier B.V. All rights reserved.

Selecting salient objects in real scenes: An oscillatory correlation model

Attention is a critical mechanism for visual scene analysis. By means of attention, it is possible to break down the analysis of a complex scene to the analysis of its parts through a selection process. Empirical studies demonstrate that attentional selection is conducted on visual objects as a whole. We present a neurocomputational model of object-based selection in the framework of oscillatory correlation. By segmenting an input scene and integrating the segments with their conspicuity obtained from a saliency map, the model selects salient objects rather than salient locations. The proposed system is composed of three modules: a saliency map providing saliency values of image locations, image segmentation for breaking the input scene into a set of objects, and object selection which allows one of the objects of the scene to be selected at a time. This object selection system has been applied to real gray-level and color images and the simulation results show the effectiveness of the system. (C) 2010 Elsevier Ltd. All rights reserved.

Partitions selection strategy for set of clustering solutions

Clustering is a difficult task: there is no single cluster definition and the data can have more than one underlying structure. Pareto-based multi-objective genetic algorithms (e.g., MOCK Multi-Objective Clustering with automatic K-determination and MOCLE-Multi-Objective Clustering Ensemble) were proposed to tackle these problems. However, the output of such algorithms can often contains a high number of partitions, becoming difficult for an expert to manually analyze all of them. In order to deal with this problem, we present two selection strategies, which are based on the corrected Rand, to choose a subset of solutions. To test them, they are applied to the set of solutions produced by MOCK and MOCLE in the context of several datasets. The study was also extended to select a reduced set of partitions from the initial population of MOCLE. These analysis show that both versions of selection strategy proposed are very effective. They can significantly reduce the number of solutions and, at the same time, keep the quality and the diversity of the partitions in the original set of solutions. (C) 2010 Elsevier B.V. All rights reserved.

An investigation into feature construction to assist word sense disambiguation

Identifying the correct sense of a word in context is crucial for many tasks in natural language processing (machine translation is an example). State-of-the art methods for Word Sense Disambiguation (WSD) build models using hand-crafted features that usually capturing shallow linguistic information. Complex background knowledge, such as semantic relationships, are typically either not used, or used in specialised manner, due to the limitations of the feature-based modelling techniques used. On the other hand, empirical results from the use of Inductive Logic Programming (ILP) systems have repeatedly shown that they can use diverse sources of background knowledge when constructing models. In this paper, we investigate whether this ability of ILP systems could be used to improve the predictive accuracy of models for WSD. Specifically, we examine the use of a general-purpose ILP system as a method to construct a set of features using semantic, syntactic and lexical information. This feature-set is then used by a common modelling technique in the field (a support vector machine) to construct a classifier for predicting the sense of a word. In our investigation we examine one-shot and incremental approaches to feature-set construction applied to monolingual and bilingual WSD tasks. The monolingual tasks use 32 verbs and 85 verbs and nouns (in English) from the SENSEVAL-3 and SemEval-2007 benchmarks; while the bilingual WSD task consists of 7 highly ambiguous verbs in translating from English to Portuguese. The results are encouraging: the ILP-assisted models show substantial improvements over those that simply use shallow features. In addition, incremental feature-set construction appears to identify smaller and better sets of features. Taken together, the results suggest that the use of ILP with diverse sources of background knowledge provide a way for making substantial progress in the field of WSD.

Generating Segmented Quality Meshes from Images

Techniques devoted to generating triangular meshes from intensity images either take as input a segmented image or generate a mesh without distinguishing individual structures contained in the image. These facts may cause difficulties in using such techniques in some applications, such as numerical simulations. In this work we reformulate a previously developed technique for mesh generation from intensity images called Imesh. This reformulation makes Imesh more versatile due to an unified framework that allows an easy change of refinement metric, rendering it effective for constructing meshes for applications with varied requirements, such as numerical simulation and image modeling. Furthermore, a deeper study about the point insertion problem and the development of geometrical criterion for segmentation is also reported in this paper. Meshes with theoretical guarantee of quality can also be obtained for each individual image structure as a post-processing step, a characteristic not usually found in other methods. The tests demonstrate the flexibility and the effectiveness of the approach.

A hybrid case adaptation approach for case-based reasoning

Case-Based Reasoning is a methodology for problem solving based on past experiences. This methodology tries to solve a new problem by retrieving and adapting previously known solutions of similar problems. However, retrieved solutions, in general, require adaptations in order to be applied to new contexts. One of the major challenges in Case-Based Reasoning is the development of an efficient methodology for case adaptation. The most widely used form of adaptation employs hand coded adaptation rules, which demands a significant knowledge acquisition and engineering effort. An alternative to overcome the difficulties associated with the acquisition of knowledge for case adaptation has been the use of hybrid approaches and automatic learning algorithms for the acquisition of the knowledge used for the adaptation. We investigate the use of hybrid approaches for case adaptation employing Machine Learning algorithms. The approaches investigated how to automatically learn adaptation knowledge from a case base and apply it to adapt retrieved solutions. In order to verify the potential of the proposed approaches, they are experimentally compared with individual Machine Learning techniques. The results obtained indicate the potential of these approaches as an efficient approach for acquiring case adaptation knowledge. They show that the combination of Instance-Based Learning and Inductive Learning paradigms and the use of a data set of adaptation patterns yield adaptations of the retrieved solutions with high predictive accuracy.

Poly-bagging predictors for classification modelling for credit scoring

Credit scoring modelling comprises one of the leading formal tools for supporting the granting of credit. Its core objective consists of the generation of a score by means of which potential clients can be listed in the order of the probability of default. A critical factor is whether a credit scoring model is accurate enough in order to provide correct classification of the client as a good or bad payer. In this context the concept of bootstraping aggregating (bagging) arises. The basic idea is to generate multiple classifiers by obtaining the predicted values from the fitted models to several replicated datasets and then combining them into a single predictive classification in order to improve the classification accuracy. In this paper we propose a new bagging-type variant procedure, which we call poly-bagging, consisting of combining predictors over a succession of resamplings. The study is derived by credit scoring modelling. The proposed poly-bagging procedure was applied to some different artificial datasets and to a real granting of credit dataset up to three successions of resamplings. We observed better classification accuracy for the two-bagged and the three-bagged models for all considered setups. These results lead to a strong indication that the poly-bagging approach may promote improvement on the modelling performance measures, while keeping a flexible and straightforward bagging-type structure easy to implement. (C) 2011 Elsevier Ltd. All rights reserved.