Soft computing models to identify typical meteorological days

Soft computing models are capable of identifying patterns that can characterize a ‘typical day’ in terms of its meteorological conditions. This multidisciplinary study examines data on six meteorological parameters gathered in a Spanish city. Data on these and other variables were collected for over 6 months, in 2007, from a pollution measurement station that forms part of a network of similar stations in the Spanish Autonomous Region of Castile–Leon. A comparison of the meteorological data allows relationships to be established between the meteorological variables and the days of the year. One of the main contributions of this study is the selection of appropriate data processing techniques, in order to identify typical days by analysing meteorological variables and aerosol pollutants. Two case studies are analysed in an attempt to identify a typical day in summer and in autumn

Técnicas inteligentes para el análisis de condiciones medioambientales

[ES] Como es bien sabido, la calidad del aire es un tema importante y preocupante en la actualidad que afecta no solamente a la salud humana sino a otros muchos aspectos como el cambio climático o la supervivencia de la biosfera. En los últimos años, numerosas entidades públicas se han ido adaptando a las restrictivas medidas de contaminación ambiental impuestas por las diversas normativas europeas, siendo España uno de los países obligados a cumplir estas normativas. Tanto en España como en otros países existen diversas redes de monitorización de la calidad del aire y de adquisición de valores meteorológicos de una forma continua. Estas redes de estaciones de medida no sólo están presentes en las grandes ciudades sino también en zonas periféricas, polígonos industriales y en zonas donde la preservación de la naturaleza es fundamental. Además, están sometidas a constantes procesos de reordenación para mejorar su función. En la presente Tesis Doctoral se han aplicado diversas técnicas inteligentes (Soft Computing más específicamente) a conjuntos de datos públicos con información meteorológica y/o de calidad del aire. Las técnicas aplicadas llevan a cabo fundamentalmente dos tareas: reducción de la dimensionalidad y agrupamiento (clustering). Estas se han aplicado de forma aislada y de forma combinada para mejorar los resultados obtenidos en el análisis de la información medioambiental. Las técnicas de reducción de la dimensionalidad aplicadas son: Principal Component Analysis (PCA) como técnica aplicada en primer lugar para obtener una primera aproximación a la estructura del conjunto de datos, Locally Linear Embedding (LLE) como técnica no lineal local, Maximum Likelihood Hebbian Learning (MLHL) y Cooperative Maximum Likelihood Hebbian Learning (CMLHL) como modelos neuronales que implementan Exploratory Projection Pursuit, Curvilinear Component Analysis (CCA) como modelo no lineal que intenta preservar la distancia entre los puntos en la salida, Multidimensional Scalling (MDS) como técnica global no lineal basada en la matriz de distancias, Isometric Mapping (ISOMAP) como técnica derivada de MDS y los Self-Organizing Maps (SOM), un importante modelo neuronal que implementa aprendizaje competitivo. Las técnicas de agrupamiento aplicadas han sido por una lado particionales: k-means como primer método a aplicar en agrupamiento y que busca la asignación de muestras a grupos aplicando métricas de distancia, SOM k-means que utiliza los algoritmos de SOM para la actualización de los pesos, k-medoids como técnica derivada de k-means y que asigna el centroide de cada grupo a uno de los puntos del mismo y fuzzy c-means, técnica que aplica lógica difusa para tareas de agrupamiento. Por otro lado, también se ha empleado el método aglomerativo jerárquico en el que se van formando los grupos de forma ascendente, junto con diversos métodos de evaluación de agrupamiento que sirven para determinar el posible número de grupos existentes en un conjunto de datos y dendrogramas para obtener una representación gráfica de la agrupación de los datos en forma de árbol. Los casos de estudio han sido cuidadosamente seleccionados y se extienden desde el ámbito local, regional hasta el nacional. Por otra parte, también se ha dado importancia a los periodos de tiempo seleccionados. En alguno de los estudios se analizan periodos de tiempo tan cortos como un día para el análisis de la meteorología/calidad del aire en un breve periodo de tiempo en un lugar determinado, mientras que en otros se emplean ventanas temporales próximas a una década y en los puntos más representativos climatológicamente en España. Partiendo de uno o más conjuntos de datos públicos con la información más completa posible acerca de las condiciones medioambientales (meteorológica, de calidad del aire o ambas), pero siempre analizando variables determinantes en la caracterización de las condiciones medioambientales, el objetivo es extraer la información fundamental almacenada en los conjuntos de datos mediante las técnicas inteligentes. De esta forma es posible analizar las condiciones medioambientales en los casos de estudio seleccionados. En cada uno de los casos de estudio se hace un análisis de la situación meteorológica o de calidad del aire en las localizaciones y periodos seleccionados, buscando semejanzas y diferencias en las muestras de datos analizadas y haciendo énfasis en aquellas situaciones anómalas detectadas y tratando de dar explicación a las mismas. También se hace un análisis comparativo de los resultados obtenidos con las distintas técnicas empleadas, planteando las ventajas e inconvenientes del uso de cada uno de ellas en cada caso de estudio. Las técnicas de reducción de la dimensionalidad resultan de gran utilidad para analizar gráficamente conjuntos de datos multidimensionales, encontrar relaciones en los datos y detectar situaciones anómalas. De manera complementaria, las técnicas de agrupamiento revelan la estructura de un conjunto de datos asignando las muestras de datos a los distintos grupos en función de las medidas de distancias y similitud aplicadas. Esto resulta de gran utilidad en el presente trabajo para entender las semejanzas y diferencias en la meteorología y/o calidad del aire de los distintos puntos seleccionados en cada caso de estudio. [EN] It is well known that air quality is an important and worrying issue nowadays, affecting not only human health but also many other aspects such as climate change or the survival of the biosphere. In recent years, many public institutions have been adapted to the restrictive normative about environmental pollution imposed by European regulations, being Spain one of the countries that must comply with these regulations. Both in Spain and in other countries there are various air-quality networks and stations for the continuous acquisition of meteorological parameters. These networks are not only present in big cities, but also in peripheral and industrial areas, as well as in places where the preservation of nature is fundamental key issue. Furthermore, they are constantly rearranged to improve their function. In present PhD Thesis, different intelligent techniques (more specifically, Soft Computing techniques) have been applied to publicly available databases with air quality and/or meteorological information. The applied techniques perform two fundamental tasks: dimensionality reduction and clustering. They have been applied in isolation and in conjunction in order to improve the results in the analysis of environmental conditions. The applied dimensionality reductions techniques are: Principal Component Analysis (PCA) as the technique firstly applied to obtain an approximation to the dataset structure, Locally Linear Embedding (LLE) as a non-linear local technique, Maximum Likelihood Hebbian Learning (MLHL) and Cooperative Maximum Likelihood Hebbian Learning (CMLHL) as neural models which implement Exploratory Projection Pursuit, Curvilinear Component Analysis (CCA) as a non-linear technique which tries to preserve the interpoint distance in the output space, Multidimensional Scalling (MDS) as a non-linear global technique operating with the distance matrix, Isometric Mapping (ISOMAP) as a technique derived from MDS and Self-Organizing Maps (SOM), as a competitive learning neural model. The applied clustering techniques are, on the one hand partitional techniques: k-means as the clustering technique firstly applied, which assigns samples to groups using distance metrics, SOM k-means which use the SOM algorithm for the weight updating process, k-medoids as a k-means derived technique which assigns the centroid of each cluster to one of the belonging samples, and fuzzy c-means as a fuzzy-logic based technique for grouping samples. On the other hand, hierarchical agglomerative techniques have also been applied (where groups are formed in an ascending way) together with different clustering evaluation indexes, used to determine the possible number of existing groups in a dataset, and finally dendrograms for a tree-form graphical representation of clustering. Case studies have been carefully selected and range from local, regional to national contexts. Similarly, the selected periods of time have also been a priority. In some of the studies, the analyzed period of time is one day long, considered for the analysis of meteorological / air quality in a short time interval in a certain place, while in other cases, long periods of time (close to a decade), are used to analyze some of the most climatological representative places in Spain. From one or more public datasets comprising all the information about environmental conditions (weather, air quality, or both), but always analyzing key variables in the characterization of environmental conditions, the goal is to extract the meaningfully information in the datasets by applying intelligent techniques. This leads to an analysis of the environmental conditions in the selected case studies. In each case study, an analysis of the weather or air quality conditions is carried out in the selected places and periods of time, searching for similarities and differences in the analyzed data samples, emphasizing those detected anomalous situations and trying to give an explanation to these phenomena’s. A comparative analysis of the results obtained with the different techniques applied is also performed, considering the advantages and disadvantages of using each of them in each case study Dimensionality reduction techniques are useful for graphically analyzing high-dimensional data sets, find relationships in datasets and detect anomalous situations. Complementarily, clustering techniques reveal the structure of datasets by assigning the data samples to different clusters depending on the applied distance and similarity measures. This is useful in present work to understand the similarities and differences in the meteorological and / or air quality conditions of the different locations selected in each case study

Role playing games and emotions in dispute resolution environments

Electronic contracting, mostly through software agents, led to an impressive growth in electronic transactions, but also in the number of disputes arising out of these transactions. Paper-based courts are however unable to efficiently deal with this increase in disputes. On the other hand, current Online Dispute Resolution methodologies are impersonal and cold, leaving aside important information such as the disputants’ body language and emotions. In that sense, in this paper we propose the creation of environments for dispute resolution that can complement the existing tools with important context information. This, we believe, will lead to dispute resolution tools that will more efficiently achieve mutually satisfactory outcomes.The work described in this paper is included in TIARAC - Telematics and Artificial Intelligence in Alternative Conflict Resolution Project (PTDC/JUR/71354/2006), which is a research project supported by FCT (Science & Technology Foundation), Portugal

Using clustering techniques for intelligent camera-based user interfaces

The area of Human-Machine Interface is growing fast due to its high importance in all technological systems. The basic idea behind designing human-machine interfaces is to enrich the communication with the technology in a natural and easy way. Gesture interfaces are a good example of transparent interfaces. Such interfaces must identify properly the action the user wants to perform, so the proper gesture recognition is of the highest importance. However, most of the systems based on gesture recognition use complex methods requiring high-resource devices. In this work, we propose to model gestures capturing their temporal properties, which significantly reduce storage requirements, and use clustering techniques, namely self-organizing maps and unsupervised genetic algorithm, for their classification. We further propose to train a certain number of algorithms with different parameters and combine their decision using majority voting in order to decrease the false positive rate. The main advantage of the approach is its simplicity, which enables the implementation using devices with limited resources, and therefore low cost. The testing results demonstrate its high potential

Enriching conflict resolution environments with the provision of context information

It is a common affair to settle disputes out of courts nowadays, through negotiation, mediation or any other mean. This has also been implemented over telecommunication means under the so-called Online Dispute Resolution methods. However, this new technology-supported approach is impersonal and cold, leaving aside important issues such as the disputants’ body language, stress level or emotional response while being based on forms, e-mails or chat rooms. To overcome this shortcoming in this paper it is proposed the creation of intelligent environments for conflict resolution that can complement the existing tools with important knowledge about the context of interaction. This will allow decisionmakers to take better framed decisions based not only on figures but also on important contextual information, similarly to what happens when parties communicate in the physical presence of each other.This work is part-funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-028980 (PTDC/EEI-SII/1386/2012) and PEst-OE/ EEI/UI0752/2011. The work of Davide Carneiro is also supported by a doctoral grant by FCT (SFRH/BD/64890/2009).info:eu-repo/semantics/publishedVersio

Privacy and data protection towards elderly healthcare

Developed societies are registering a dramatic change in terms of population evolution, being the most important fact the high tendency in the ageing of the whole population. An alarming fact is that the birth-rate is dropping very fast, inverting the ageing pyramid that used to have a higher incidence on the young population, now having a higher incidence in the older population. In the quest to provide answers to some problems the elderly population has, applications and projects arise from the Ambient Assisted Living area, providing services that help the user in his daily life, providing the needed help and trying to be the less invasive as possible. The fact is that these systems operate optimally by using information about the user, assisting him accordingly to his preferences. The data gathered for such events is highly personal and sensitive. Being this data escalating several stages until it finally is ready to be inserted in the system. This can cause a loss of privacy and data protection. In this document we present an Ambient Assisted Living project towards assistance to an elderly population and the problems and possible solutions in the legal area towards loss of privacy, data protection and personal information.(undefined

Soft computing models to analyze atmospheric pollution issues

Multidisciplinary research into statistical and soft computing models is detailed that analyses data on emissions of atmospheric pollution in urban areas. The research analyses the impact on atmospheric pollution of an extended bank holiday weekend in Spain. Levels of atmospheric pollution are classified in relation to the days of the week, seeking to differentiate between working days and non-working days by taking account of such aspects as industrial activity and traffic levels. The case study is based on data collected by a station at the city of Burgos, which forms part of the pollution measurement station network within the Spanish Autonomous Region of Castile-Leon

A bio-inspired robust controller for a refinery plant process

This research presents a novel bio-inspired knowledge method, based on gain scheduling, for the calculation of Proportional-Integral-Derivative controller parameters that will prevent system instability. The aim is to prevent a transition to control system instability due to undesirable controller parameters that may be introduced manually by an operator. Each significant operation point in the system is identified first. Then, a solid stability structure is calculated, using transfer functions, in order to program a bio-inspired model by using an artificial neural network. The novel method is empirically verified under working conditions in a real refinery plant process