Conversa a Utopia

Joan Manuel del Pozo acaba de publicar la primera traducció catalana directa d’Utopia. Apareguda per primer cop en llatí el 1516, és l’obra més destacable de l’humanista anglès Tomas More, decapitat per protestar contra la pretensió d’Enric VIII d’envair un espai de poder que fins aleshores li havia estat aliè: l’espiritual. Utopia és una illa, i More la va imaginar com un paradís de tolerància religiosa, on no faltava la feina ni els recursos per a tothom. Amb el pretext de l’edició del llibre, Engega ha convidat el filòsof Joan Manuel del Pozo, el químic Miquel Costas i la doctora en Medicina Marta Aymerich, a mantenir una conversa gens utòpica, que us presentem a continuació

Inteligencia artificial para la detección y el diagnóstico de fallos

L' ús de tècniques de la intel·ligència artificial per a la detecció, la diagnòsi i control d' errors

Calidad de onda en el servicio eléctrico: causas y efectos de las perturbaciones que afectan la calidad de la electricidad

La qualitat d'ona en el servei elèctric

Monitorización y diagnostico de la calidad de onda : estrategias para la identificación y localización de faltas en sistemas de distribución eléctrica

Control de la qualitat de la Xarxa elèctrixa

An hybrid methodology for RL-based behavior coordination in a target following mission with an AUV

Proposes a behavior-based scheme for high-level control of autonomous underwater vehicles (AUVs). Two main characteristics can be highlighted in the control scheme. Behavior coordination is done through a hybrid methodology, which takes in advantages of the robustness and modularity in competitive approaches, as well as optimized trajectories

Colour image processing

In the context of the round table the following topics related to image colour processing will be discussed: historical point of view. Studies of Aguilonius, Gerritsen, Newton and Maxwell. CIE standard (Commission International de lpsilaEclaraige). Colour models. RGB, HIS, etc. Colour segmentation based on HSI model. Industrial applications. Summary and discussion. At the end, video images showing the robustness of colour in front of B/W images will be presented

Using appearance and context for outdoor scene object classification

We propose a probabilistic object classifier for outdoor scene analysis as a first step in solving the problem of scene context generation. The method begins with a top-down control, which uses the previously learned models (appearance and absolute location) to obtain an initial pixel-level classification. This information provides us the core of objects, which is used to acquire a more accurate object model. Therefore, their growing by specific active regions allows us to obtain an accurate recognition of known regions. Next, a stage of general segmentation provides the segmentation of unknown regions by a bottom-strategy. Finally, the last stage tries to perform a region fusion of known and unknown segmented objects. The result is both a segmentation of the image and a recognition of each segment as a given object class or as an unknown segmented object. Furthermore, experimental results are shown and evaluated to prove the validity of our proposal

Modeling and Classifying Breast Tissue Density in Mammograms

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal

Hybrid coordination of reinforcement learning-based behaviors for AUV control

This paper proposes a hybrid coordination method for behavior-based control architectures. The hybrid method takes advantages of the robustness and modularity in competitive approaches as well as optimized trajectories in cooperative ones. This paper shows the feasibility of applying this hybrid method with a 3D-navigation to an autonomous underwater vehicle (AUV). The behaviors are learnt online by means of reinforcement learning. A continuous Q-learning implemented with a feed-forward neural network is employed. Realistic simulations were carried out. The results obtained show the good performance of the hybrid method on behavior coordination as well as the convergence of the behaviors

Efficient learning of reactive robot behaviors with a Neural-Q_learning approach

The purpose of this paper is to propose a Neural-Q_learning approach designed for online learning of simple and reactive robot behaviors. In this approach, the Q_function is generalized by a multi-layer neural network allowing the use of continuous states and actions. The algorithm uses a database of the most recent learning samples to accelerate and guarantee the convergence. Each Neural-Q_learning function represents an independent, reactive and adaptive behavior which maps sensorial states to robot control actions. A group of these behaviors constitutes a reactive control scheme designed to fulfill simple missions. The paper centers on the description of the Neural-Q_learning based behaviors showing their performance with an underwater robot in a target following task. Real experiments demonstrate the convergence and stability of the learning system, pointing out its suitability for online robot learning. Advantages and limitations are discussed

A behavior-based scheme using reinforcement learning for autonomous underwater vehicles

This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs

A method to obtain the integrated contour image in colour scenes

This paper describes a method to achieve the most relevant contours of an image. The presented method proposes to integrate the information of the local contours from chromatic components such as H, S and I, taking into account the criteria of coherence of the local contour orientation values obtained from each of these components. The process is based on parametrizing pixel by pixel the local contours (magnitude and orientation values) from the H, S and I images. This process is carried out individually for each chromatic component. If the criterion of dispersion of the obtained orientation values is high, this chromatic component will lose relevance. A final processing integrates the extracted contours of the three chromatic components, generating the so-called integrated contours image

Evaluaci??n de la percepci??n de riesgo en la Educaci??n Vial : desarrollo de una escala dirigida a escolares de Educaci??n Primaria y Secundaria

Resumen de los autores. Res??menes en espa??ol e ingl??s

Implementaci??n de procedimientos gr??ficos y anal??ticos para la construcci??n de formas paralelas

Resumen tomado de la publicaci??n

Las Ciencias Sociales, Geograf??a e Historia y el desarrollo de competencias ciudadanas

Se analiza la propuesta que, sobre el enfoque curricular por competencias, ha realizado el gobierno de la provincia canadiense de Qu??bec. Un modelo que se propone como ejemplo al facilitar el cambio y la adaptaci??n del profesorado hacia el nuevo desarrollo curricular. En ??l, la ense??anza de las Ciencias Sociales de la Geograf??a y de la Historia se vincula a la Educaci??n para la Ciudadan??a. El alumnado ha de formar su pensamiento cr??tico y creativo y aplicarlo a la comprensi??n de su mundo y a la acci??n social. La ense??anza en el ??rea de Ciencias Sociales, Geograf??a e Historia ha de dotar a los alumnos y alumnas de instrumentos que les hagan ser m??s sabios y m??s aut??nomos y que les permitan escribir sus propios textos y desarrollar sus propios proyectos adem??s de convertirles en ciudadanos m??s comprometidos con su mundo y su futuro.