Sviluppo di un sistema di climatizzazione automatico distribuito con l'utilizzo di tecniche di machine learning

Nella sede dell’azienda ospitante Alexide, si è ravvisata la mancanza di un sistema di controllo automatico da remoto dell’intero impianto di climatizzazione HVAC (Heating, Ventilation and Air Conditioning) utilizzato, e la soluzione migliore è risultata quella di attuare un processo di trasformazione della struttura in uno smart building. Ho quindi eseguito questa procedura di trasformazione digitale progettando e sviluppando un sistema distribuito in grado di gestire una serie di dati provenienti in tempo reale da sensori ambientali. L’architettura del sistema progettato è stata sviluppata in C# su ambiente dotNET, dove sono stati collezionati i dati necessari per il funzionamento del modello di predizione. Nella fattispecie sono stati utilizzati i dati provenienti dall’HVAC, da un sensore di temperatura interna dell'edificio e dal fotovoltaico installato nella struttura. La comunicazione tra il sistema distribuito e l’entità dell’HVAC avviene mediante il canale di comunicazione ModBus, mentre per quanto riguarda i dati della temperatura interna e del fotovoltaico questi vengono collezionati da sensori che inviano le informazioni sfruttando un canale di comunicazione che utilizza il protocollo MQTT, e lo stesso viene utilizzato come principale metodo di comunicazione all’interno del sistema, appoggiandosi ad un broker di messaggistica con modello publish/subscribe. L'automatizzazione del sistema è dovuta anche all'utilizzo di un modello di predizione con lo scopo di predire in maniera quanto più accurata possibile la temperatura interna all'edificio delle ore future. Per quanto riguarda il modello di predizione da me implementato e integrato nel sistema la scelta è stata quella di ispirarmi ad un modello ideato da Google nel 2014 ovvero il Sequence to Sequence. Il modello sviluppato si struttura come un encoder-decoder che utilizza le RNN, in particolare le reti LSTM.

QSAR modeling of antitubercular activity of diverse organic compounds

Tuberculosis (TB) is a worldwide infectious disease that has shown over time extremely high mortality levels. The urgent need to develop new antitubercular drugs is due to the increasing rate of appearance of multi-drug resistant strains to the commonly used drugs, and the longer durations of therapy and recovery, particularly in immuno-compromised patients. The major goal of the present study is the exploration of data from different families of compounds through the use of a variety of machine learning techniques so that robust QSAR-based models can be developed to further guide in the quest for new potent anti-TB compounds. Eight QSAR models were built using various types of descriptors (from ADRIANA.Code and Dragon software) with two publicly available structurally diverse data sets, including recent data deposited in PubChem. QSAR methodologies used Random Forests and Associative Neural Networks. Predictions for the external evaluation sets obtained accuracies in the range of 0.76-0.88 (for active/inactive classifications) and Q(2)=0.66-0.89 for regressions. Models developed in this study can be used to estimate the anti-TB activity of drug candidates at early stages of drug development (C) 2011 Elsevier B.V. All rights reserved.

Virtual power producers market strategies

This paper presents MASCEM - a multi-agent based electricity market simulator. MASCEM uses game theory, machine learning techniques, scenario analysis and optimisation techniques to model market agents and to provide them with decision-support. This paper mainly focus on the MASCEM ability to provide the means to model and simulate Virtual Power Producers (VPP). VPPs are represented as a coalition of agents, with specific characteristics and goals. The paper detail some of the most important aspects considered in VPP formation and in the aggregation of new producers and includes a case study.

MASCEM - an electricity market simulator providing coalition support for virtual power players

This paper presents MASCEM - a multi-agent based electricity market simulator. MASCEM uses game theory, machine learning techniques, scenario analysis and optimization techniques to model market agents and to provide them with decision-support. This paper mainly focus on the MASCEM ability to provide the means to model and simulate Virtual Power Players (VPP). VPPs are represented as a coalition of agents, with specific characteristics and goals. The paper details some of the most important aspects considered in VPP formation and in the aggregation of new producers and includes a case study based on real data.

Self-organization for scheduling in agile manufacturing

Agility refers to the manufacturing system ability to rapidly adapt to market and environmental changes in efficient and cost-effective ways. This paper addresses the development of self-organization methods to enhance the operations of a scheduling system, by integrating scheduling system, configuration and optimization into a single autonomic process requiring minimal manual intervention to increase productivity and effectiveness while minimizing complexity for users. We intend to conceptualize real manufacturing systems as interacting autonomous entities in order to build future Decision Support Systems (DSS) for Scheduling in agile manufacturing environments.

Collective intelligence on dynamic manufacturing scheduling optimization

Swarm Intelligence (SI) is a growing research field of Artificial Intelligence (AI). SI is the general term for several computational techniques which use ideas and get inspiration from the social behaviours of insects and of other animals. This paper presents hybridization and combination of different AI approaches, like Bio-Inspired Techniques (BIT), Multi-Agent systems (MAS) and Machine Learning Techniques (ML T). The resulting system is applied to the problem of jobs scheduling to machines on dynamic manufacturing environments.

Six thinking hats for electricity markets simulation: adapting meeting tools to agent decision

In almost all industrialized countries, the energy sector has suffered a severe restructuring that originated a greater complexity in market players’ interactions. The complexity that these changes brought made way for the creation of decision support tools that facilitate the study and understanding of these markets. MASCEM – “Multiagent Simulator for Competitive Electricity Markets” arose in this context providing a framework for evaluating new rules, new behaviour, and new participants in deregulated electricity markets. MASCEM uses game theory, machine learning techniques, scenario analysis and optimisation techniques to model market agents and to provide them with decision-support. ALBidS is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM it considers several different methodologies based on very distinct approaches. The Six Thinking Hats is a powerful technique used to look at decisions from different perspectives. This tool’s goal is to force the thinker to move outside his habitual thinking style. It was developed to be used mainly at meetings in order to “run better meetings, make faster decisions”. This dissertation presents a study about the applicability of the Six Thinking Hats technique in Decision Support Systems, particularly with the multiagent paradigm like the MASCEM simulator. As such this work’s proposal is of a new agent, a meta-learner based on STH technique that organizes several different ALBidS’ strategies and combines the distinct answers into a single one that, expectedly, out-performs any of them.

Algorithm for the Parkinson’s disease behavioural models characterization using a biosensor

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Analysis and visualization of energy use for university campus

The reduction of greenhouse gas emissions is one of the big global challenges for the next decades due to its severe impact on the atmosphere that leads to a change in the climate and other environmental factors. One of the main sources of greenhouse gas is energy consumption, therefore a number of initiatives and calls for awareness and sustainability in energy use are issued among different types of institutional and organizations. The European Council adopted in 2007 energy and climate change objectives for 20% improvement until 2020. All European countries are required to use energy with more efficiency. Several steps could be conducted for energy reduction: understanding the buildings behavior through time, revealing the factors that influence the consumption, applying the right measurement for reduction and sustainability, visualizing the hidden connection between our daily habits impacts on the natural world and promoting to more sustainable life. Researchers have suggested that feedback visualization can effectively encourage conservation with energy reduction rate of 18%. Furthermore, researchers have contributed to the identification process of a set of factors which are very likely to influence consumption. Such as occupancy level, occupants behavior, environmental conditions, building thermal envelope, climate zones, etc. Nowadays, the amount of energy consumption at the university campuses are huge and it needs great effort to meet the reduction requested by European Council as well as the cost reduction. Thus, the present study was performed on the university buildings as a use case to: a. Investigate the most dynamic influence factors on energy consumption in campus; b. Implement prediction model for electricity consumption using different techniques, such as the traditional regression way and the alternative machine learning techniques; and c. Assist energy management by providing a real time energy feedback and visualization in campus for more awareness and better decision making. This methodology is implemented to the use case of University Jaume I (UJI), located in Castellon, Spain.

Segmentação de imagens fetais com potencial para desenvolvimento de ferramentas de apoio ao diagnóstico

Programa Doutoral em Engenharia Eletrónica e de Computadores

Processament de música basat en el seu contingut

Projecte de recerca elaborat a partir d’una estada a la National University of Singapore Singapur, entre juliol i octubre del 2007. Donada l'explosió de la música a l'internet i la ràpida expansió de les col•leccions de música digital, un repte clau en l'àrea de la informació musical és el desenvolupament de sistemes de processament musical eficients i confiables. L'objectiu de la investigació proposada ha estat treballar en diferents aspectes de l'extracció, modelatge i processat del contingut musical. En particular, s’ha treballat en l'extracció, l'anàlisi i la manipulació de descriptors d'àudio de baix nivell, el modelatge de processos musicals, l'estudi i desenvolupament de tècniques d'aprenentatge automàtic per a processar àudio, i la identificació i extracció d'atributs musicals d'alt nivell. S’han revisat i millorat alguns components d'anàlisis d'àudio i revisat components per a l'extracció de descriptors inter-nota i intra-nota en enregistraments monofónics d'àudio. S’ha aplicat treball previ en Tempo a la formalització de diferents tasques musicals. Finalment, s’ha investigat el processat d'alt nivell de música basandonos en el seu contingut. Com exemple d'això, s’ha investigat com músics professionals expressen i comuniquen la seva interpretació del contingut musical i emocional de peces musicals, i hem usat aquesta informació per a identificar automàticament intèrprets. S’han estudiat les desviacions en paràmetres com to, temps, amplitud i timbre a nivell inter-nota i intra-nota.

Guiding students through competence-based educational material

Student guidance is an always desired characteristic in any educational system, butit represents special difficulty if it has to be deployed in an automated way to fulfilsuch needs in a computer supported educational tool. In this paper we explorepossible avenues relying on machine learning techniques, to be included in a nearfuture -in the form of a tutoring navigational tool- in a teleeducation platform -InterMediActor- currently under development. Since no data from that platform isavailable yet, the preliminary experiments presented in this paper are builtinterpreting every subject in the Telecommunications Degree at Universidad CarlosIII de Madrid as an aggregated macro-competence (following the methodologicalconsiderations in InterMediActor), such that marks achieved by students can beused as data for the models, to be replaced in a near future by real data directlymeasured inside InterMediActor. We evaluate the predictability of students qualifications, and we deploy a preventive early detection system -failure alert-, toidentify those students more prone to fail a certain subject such that correctivemeans can be deployed with sufficient anticipation.

Présentation personnalisée des informations environnementales

Nous présentons dans cette thèse notre travail dans le domaine de la visualisation. Nous nous sommes intéressés au problème de la génération des bulletins météorologiques. Étant donné une masse énorme d’information générée par Environnement Canada et un utilisateur, il faut lui générer une visualisation personnalisée qui répond à ses besoins et à ses préférences. Nous avons développé MeteoVis, un générateur de bulletin météorologique. Comme nous avons peu d’information sur le profil de l’utilisateur, nous nous sommes basés sur les utilisateurs similaires pour lui calculer ses besoins et ses préférences. Nous utilisons l'apprentissage non supervisé pour regrouper les utilisateurs similaires. Nous calculons le taux de similarité des profils utilisateurs dans le même cluster pour pondérer les besoins et les préférences. Nous avons mené, avec l’aide d'utilisateurs n’ayant aucun rapport avec le projet, des expériences d'évaluation et de comparaison de notre outil par rapport à celui utilisé actuellement par Environnement Canada. Les résultats de cette évaluation montrent que les visualisation générées par MeteoVis sont de loin meilleures que les bulletins actuels préparés par EC.

Automatic Image Annotation Using SURF Descriptors

In recent years there is an apparent shift in research from content based image retrieval (CBIR) to automatic image annotation in order to bridge the gap between low level features and high level semantics of images. Automatic Image Annotation (AIA) techniques facilitate extraction of high level semantic concepts from images by machine learning techniques. Many AIA techniques use feature analysis as the first step to identify the objects in the image. However, the high dimensional image features make the performance of the system worse. This paper describes and evaluates an automatic image annotation framework which uses SURF descriptors to select right number of features and right features for annotation. The proposed framework uses a hybrid approach in which k-means clustering is used in the training phase and fuzzy K-NN classification in the annotation phase. The performance of the system is evaluated using standard metrics.

Unsupervised genetic algorithm deployed for intrusion detection

This paper represents the first step in an on-going work for designing an unsupervised method based on genetic algorithm for intrusion detection. Its main role in a broader system is to notify of an unusual traffic and in that way provide the possibility of detecting unknown attacks. Most of the machine-learning techniques deployed for intrusion detection are supervised as these techniques are generally more accurate, but this implies the need of labeling the data for training and testing which is time-consuming and error-prone. Hence, our goal is to devise an anomaly detector which would be unsupervised, but at the same time robust and accurate. Genetic algorithms are robust and able to avoid getting stuck in local optima, unlike the rest of clustering techniques. The model is verified on KDD99 benchmark dataset, generating a solution competitive with the solutions of the state-of-the-art which demonstrates high possibilities of the proposed method.