Disseny d’un programa que assisteix en la seqüenciació de peces de taller basats en mètodes heurístics

El GREP (grup de recerca de producte, procés i producció) de la UdG actualment disposa d’una eina informàtica desenvolupada en un PFC del 2003 que li permet fer la seqüenciació de la producció d’un taller mecànic amb un màxim de cinc productes, un nombre definit de possibles rutes de fabricació per a cada producte i tres màquines. Aquesta eina és molt resolutiva per a aquests casos, ja que estudia totes les possibilitats i les comprova una per una. No obstant aquest fet presenta una sèrie de limitacions com són el temps d’execució doncs al comprovar totes les seqüències té un elevat cost computacional i la rigidesa del sistema doncs no ens permet seqüenciar més productes ni més màquines. Per tal de donar solució a aquest problema es planteja generar una nova eina informàtica a partir de l’actual però que permeti seqüenciar més peces sense ocupar tanta memòria per així implementar-hi futures millores com el temps de preparació etc... Per a desenvolupar l’eina informàtica s’han utilitzat mètodes heurístics, concretament dos que són: algoritmes genètics i cerca TABU. Aquests mètodes destaquen perquè no busquen totes les combinacions possibles sinó que estudien una sèrie de combinacions i utilitzant mètodes de creuament i generació d’entorns busquen una solució

Multidimensional Adaptations for Open Learning Management Systems

Our work is focused on alleviating the workload for designers of adaptive courses on the complexity task of authoring adaptive learning designs adjusted to specific user characteristics and the user context. We propose an adaptation platform that consists in a set of intelligent agents where each agent carries out an independent adaptation task. The agents apply machine learning techniques to support the user modelling for the adaptation process

Scene Classification Using a Hybrid Generative/Discriminative Approach

We investigate whether dimensionality reduction using a latent generative model is beneficial for the task of weakly supervised scene classification. In detail, we are given a set of labeled images of scenes (for example, coast, forest, city, river, etc.), and our objective is to classify a new image into one of these categories. Our approach consists of first discovering latent ";topics"; using probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature here applied to a bag of visual words representation for each image, and subsequently, training a multiway classifier on the topic distribution vector for each image. We compare this approach to that of representing each image by a bag of visual words vector directly and training a multiway classifier on these vectors. To this end, we introduce a novel vocabulary using dense color SIFT descriptors and then investigate the classification performance under changes in the size of the visual vocabulary, the number of latent topics learned, and the type of discriminative classifier used (k-nearest neighbor or SVM). We achieve superior classification performance to recent publications that have used a bag of visual word representation, in all cases, using the authors' own data sets and testing protocols. We also investigate the gain in adding spatial information. We show applications to image retrieval with relevance feedback and to scene classification in videos

Semi-online neural-Q_leaming for real-time robot learning

Reinforcement learning (RL) is a very suitable technique for robot learning, as it can learn in unknown environments and in real-time computation. The main difficulties in adapting classic RL algorithms to robotic systems are the generalization problem and the correct observation of the Markovian state. This paper attempts to solve the generalization problem by proposing the semi-online neural-Q_learning algorithm (SONQL). The algorithm uses the classic Q_learning technique with two modifications. First, a neural network (NN) approximates the Q_function allowing the use of continuous states and actions. Second, a database of the most representative learning samples accelerates and stabilizes the convergence. The term semi-online is referred to the fact that the algorithm uses the current but also past learning samples. However, the algorithm is able to learn in real-time while the robot is interacting with the environment. The paper shows simulated results with the "mountain-car" benchmark and, also, real results with an underwater robot in a target following behavior

A concurrent region growing algorithm guided by circumscribed contours

Image segmentation of natural scenes constitutes a major problem in machine vision. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. This approach begins by detecting the main contours of the scene which are later used to guide a concurrent set of growing processes. A previous analysis of the seed pixels permits adjustment of the homogeneity criterion to the region's characteristics during the growing process. Since the high variability of regions representing outdoor scenes makes the classical homogeneity criteria useless, a new homogeneity criterion based on clustering analysis and convex hull construction is proposed. Experimental results have proven the reliability of the proposed approach

A new approach to segmentation based on fusing circumscribed contours, region growing and clustering

One of the major problems in machine vision is the segmentation of images of natural scenes. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. The main contours of the scene are detected and used to guide the posterior region growing process. The algorithm places a number of seeds at both sides of a contour allowing stating a set of concurrent growing processes. A previous analysis of the seeds permits to adjust the homogeneity criterion to the regions's characteristics. A new homogeneity criterion based on clustering analysis and convex hull construction is proposed

PRIM an Open Mobile Robot Platform. Motivation, Present and Future Trends

This paper is focused on the robot mobile platform PRIM (platform robot information multimedia). This robot has been made in order to cover two main needs of our group, on one hand the need for a full open mobile robotic platform that is very useful in fulfilling the teaching and research activity of our school community, and on the other hand with the idea of introducing an ethical product which would be useful as mobile multimedia information point as a service tool. This paper introduces exactly how the system is made up and explains just what the philosophy is behind this work. The navigation strategies and sensor fusion, where machine vision system is the most important one, are oriented towards goal achievement and are the key to the behaviour of the robot

A distributed environment for virtual and/or real experiments for underwater robots

This paper presents the distributed environment for virtual and/or real experiments for underwater robots (DEVRE). This environment is composed of a set of processes running on a local area network composed of three sites: 1) the onboard AUV computer; 2) a surface computer used as human-machine interface (HMI); and 3) a computer used for simulating the vehicle dynamics and representing the virtual world. The HMI can be transparently linked to the real sensors and actuators dealing with a real mission. It can also be linked with virtual sensors and virtual actuators, dealing with a virtual mission. The aim of DEVRE is to assist engineers during the software development and testing in the lab prior to real experiments

Embedding Emotional Context in Recommender Systems

Emotions are crucial for user's decision making in recommendation processes. We first introduce ambient recommender systems, which arise from the analysis of new trends on the exploitation of the emotional context in the next generation of recommender systems. We then explain some results of these new trends in real-world applications through the smart prediction assistant (SPA) platform in an intelligent learning guide with more than three million users. While most approaches to recommending have focused on algorithm performance. SPA makes recommendations to users on the basis of emotional information acquired in an incremental way. This article provides a cross-disciplinary perspective to achieve this goal in such recommender systems through a SPA platform. The methodology applied in SPA is the result of a bunch of technology transfer projects for large real-world rccommender systems

Estudi de la influència del lubricant en operacions de mecanització

Els processos de mecanitzat amb arrencament de ferritja consisteixen en arrencar contínuament petits fragments de material de la peça a la que es vol donar forma mitjançant les eines de tall. En treballar amb metalls, aquestes forces de tall són importants, i provoquen el desgast de les eines així com un escalfament important de l’eina i la peça. Per aquest motiu s’introdueixen els olis de tall, amb les principals funcions de refrigerar i lubricar. Tradicionalment, aquests olis de tall s’han subministrat a raig, quedant la zona de treball inundada. Els últims anys, degut a les millores tecnològiques i per intentar estalviar amb olis de tall, s’ha ideat un nou sistema anomenat mínima quantitat de lubricant, el qual subministra petites gotes d’oli, no soluble en aigua, dins un flux d’aire a pressió. Aquest sistema, a més, s’injecta prop de la zona de treball, on és estricament necessari. Aquest sistema redueix considerablement el consum d’oli de tall, i a més, augmenta l’eficiència de la lubricació. A part, elimina els posteriors tractaments per reciclar l’oli de tall un cop perd les seves propietats. Per aquest motiu, es pretén estudiar la utilització d’un mètode semblant al de la mínima quantitat de lubricant, subministrant el mateix oli de tall que s’utilitza convencionalment però polvoritzar per tal de convertir-lo en un aerosol

Desenvolupament d'una draga per al mostreig de sòls marins

A l'empresa PRAESENTIS S.L. se li proposà la construcció d'una draga per utilitzar-la en el mostreig de sòls marins en expedicions científiques a mar obert. Segons les especificacions del projecte, la draga resultant haurà de ser capaç de recollir mostres d'una capacitat d'entre 60 i 80 l. amb una profunditat de cavat de fins a 20 cm., i haurà de poder assolir profunditats de treball de fins a 500 m. per sota el nivell del mar. El tipus de sòl en el qual es treballarà és de tipus arenós. La draga projectada és un mecanisme basat en una pala bivalva activada per un cilindre hidràulic que utilitza com a bancada una estructura que a l'hora fa la funció de xassís

Classificació i reconeixement de vídeo

El principal objectiu d’aquest projecte és aconseguir classificar diferents vídeos d’esports segons la seva categoria. Els cercadors de text creen un vocabulari segons el significat de les diferents paraules per tal de poder identificar un document. En aquest projecte es va fer el mateix però mitjançant paraules visuals. Per exemple, es van intentar englobar com a una única paraula les diferents rodes que apareixien en els cotxes de rally. A partir de la freqüència amb què apareixien les paraules dels diferents grups dins d’una imatge vàrem crear histogrames de vocabulari que ens permetien tenir una descripció de la imatge. Per classificar un vídeo es van utilitzar els histogrames que descrivien els seus fotogrames. Com que cada histograma es podia considerar un vector de valors enters vàrem optar per utilitzar una màquina classificadora de vectors: una Support vector machine o SVM