A Multi-agent architecture for optimizing energy consumption using comfort agreements

[ES]Desde 1980 el consumo de energía global ha crecido más del doble y se prevé que la tendencia siga creciendo de forma continua. Del total de energía consumida en la Unión Europea, los edificios representan el 25%. La Unión Europea, a través de Horizon 2020, está apostando fuerte en el desarrollo de proyectos que impulsen una renovación energética mediante la renovación de los servicios energéticos en los hogares y el desarrollo de nuevos hábitos en los consumidores. El desarrollo tecnológico ha producido grandes avances en el campo de la campo de la computación y la electrónica. Esto ha permitido el desarrollo de técnicas de procesamiento y análisis de grandes volúmenes de datos y el desarrollo de sensores y dispositivos IoT de altas prestaciones. Estos avances han sido incluidos en los nuevos edificios desarrollando el concepto de edificios inteligentes proveyendo de una mayor seguridad, confort o ahorro económico. Aunque todavía es posible desarrollar nuevos enfoques centrados de forma más específica al usuario y adaptada al entorno para obtener una mayor reducción económica sin reducir el confort del usuario. La presente tesis doctoral define una arquitectura cuyo objetivo se focaliza en proporcionar una optimización energética, independiente de las características del edificio en el cual sea desplegada, mediante la negociación entre todos los usuarios implicados para el acuerdo común de las preferencias de confort que satisfagan el rango de confort de todos los usuarios a la vez que se produce la optimización energética deseada. Sobre la arquitectura presentada, se ha construido una plataforma de captura de datos del entorno, obtención de información de fuentes externa y de los propios usuarios. La plataforma realiza continuamente análisis de los datos recopilados de forma que estos datos se conviertan en información útil para el sistema y tomar decisiones que permitan reducir el consumo energético. Además, la arquitectura integra técnicas de computación social que faculta mantener las preferencias de los usuarios en términos de temperatura e iluminación, siendo un problema es doble, optimizar el consumo energético y mantener las preferencias que se han fijado la negociación. Como resultado, se obtiene una arquitectura dinámica y auto-adaptativa, capaz de lograr una optimización energetica en edificios manteniendo el confort de los usuarios

Recommender systems based on hybrid models

[EN]Recommender Systems (RSs) play a very important role in web navigation, ensuring that the users easily find the information they are looking for. Today’s social networks contain a large amount of information and it is necessary that they employ mechanism that will guide users to the information they are interested in. However, to be able to recommend content according to user preferences, it is necessary to analyse their profiles and determine their preferences. The present study presents the work related to different recommender systems focused on two different hybrid models. Both of them are using a Case-Based Reasoning (CBR) system combined with the training of an Artificial Intelligence (AI) algorithm. First, some information is analyzed and trained with an AI algorithm in order to determine relevant patters hidden on the information. Then, the CBR system extends the system using a series of metrics and similar past cases to decide whether the recommendation is likely to be recommended to a user. Finally, the last step on the CBR is to propose recommendations to the final user, whose job is to validate or reject the proposal feeding the cases database

Analysis of sentiments on the onset of Covid-19 using Machine Learning Techniques

The novel coronavirus (Covid-19) pandemic has struck the whole world and is one of the most striking topics on social media platforms. Sentiment outbreak on social media enduring various thoughts, opinions, and emotions about the Covid-19 disease, expressing views they are feeling presently. Analyzing sentiments helps to yield better results. Gathering data from different blogging sites like Facebook, Twitter, Weibo, YouTube, Instagram, etc., and Twitter is the largest repository. Videos, text, and audio were also collected from repositories. Sentiment analysis uses opinion mining to acquire the sentiments of its users and categorizes them accordingly as positive, negative, and neutral. Analytical and machine learning classification is implemented to 3586 tweets collected in different time frames. In this paper, sentiment analysis was performed on tweets accumulated during the Covid-19 pandemic, Coronavirus disease. Tweets are collected from the Twitter database using Hydrator a web-based application. Data-preprocessing removes all the noise, outliers from the raw data. With Natural Language Toolkit (NLTK), text classification for sentiment analysis and calculate the score subjective polarity, counts, and sentiment distribution. N-gram is used in textual mining -and Natural Language Processing for a continuous sequence of words in a text or document applying uni-gram, bi-gram, and tri-gram for statistical computation. Term frequency and Inverse document frequency (TF-IDF) is a feature extraction technique that converts textual data into numeric form. Vectorize data feed to our model to obtain insights from linguistic data. Linear SVC, MultinomialNB, GBM, and Random Forest classifier with Tfidf classification model applied to our proposed model. Linear Support Vector classification performs better than the other two classifiers. Results depict that RF performs better./

A multi-agent system for the classification of gender and age from images

[EN] The automatic classification of human images on the basis of age range and gender can be used in audiovisual content adaptation for Smart TVs or marquee advertising. Knowledge about users is used by publishing agencies and departments regulating TV content; on the basis of this information (age, gender) they are able to provide content that suits the interests of users. To this end, the creation of a highly precise image pattern recognition system is necessary, this may be one of the greatest challenges faced by computer technology in the last decades. These recognition systems must apply different pattern recognition techniques, in order to distinct gender and age in the images. In this work, we propose a multi-agent system that integrates different techniques for the acquisition, preprocessing and processing of images for the classification of age and gender. The system has been tested in an office building. Thanks to the use of a multi-agent system which allows to apply different workflows simultaneously, the performance of different methods could be compared (each flow with a different configuration). Experimental results have confirmed that a good preprocessing stage is necessary if we want the classification methods to perform well (Fisherfaces, Eigenfaces, Local Binary Patterns, Multilayer perceptron). The Fisherfaces method has proved to be more effective than MLP and the training time was shorter. In terms of the classification of age, Fisherfaces offers the best results in comparison to the rest of the system’s classifiers. The use of filters has allowed to reduce dimensionality, as a result the workload was reduced, a great advantage in a system that performs classification in real time

Blockchain-based architecture for the control of logistics activities: Pharmaceutical utilities case study

Logistics services involve a wide range of transport operations between distributors and clients. Currently, the large number of intermediaries are a challenge for this sector, as it makes all the processes more complicated. To face that problem, we propose a system that uses smart contracts to remove intermediaries and speed up logistics activities. Our new model combines smart contracts and a multi-agent system in a single platform to improve the current logistics system by increasing organization, security and getting rid of several human intermediaries to automate its processes, making distribution times significantly faster. Also, with this kind of approach, it is possible to apply penalties to parties that do not comply with the terms of using this platform.This work was developed as part of ‘Virtual Ledger Technologies DLT/Blockchain y Cripto-IOT sobre organizaciones virtuales de agentes ligeros y su aplicación en la eficiencia en el transporte de última milla’, ID SA267P18, project cofinanced by Junta Castilla y León, Consejería de Educación and FEDER funds. Also, the research work carried out by Yeray Mezquita is supported by the pre-doctoral fellowship from the University of Salamanca and Banco Santander

Detection of Cattle Using Drones and Convolutional Neural Networks

[EN] Multirotor drones have been one of the most important technological advances of the last decade. Their mechanics are simple compared to other types of drones and their possibilities in flight are greater. For example, they can take-off vertically. Their capabilities have therefore brought progress to many professional activities. Moreover, advances in computing and telecommunications have also broadened the range of activities in which drones may be used. Currently, artificial intelligence and information analysis are the main areas of research in the field of computing. The case study presented in this article employed artificial intelligence techniques in the analysis of information captured by drones. More specifically, the camera installed in the drone took images which were later analyzed using Convolutional Neural Networks (CNNs) to identify the objects captured in the images. In this research, a CNN was trained to detect cattle, however the same training process could be followed to develop a CNN for the detection of any other object. This article describes the design of the platform for real-time analysis of information and its performance in the detection of cattle

An adjective selection personality assessment method using gradient boosting machine learning

Goldberg’s 100 Unipolar Markers remains one of the most popular ways to measure personality traits, in particular, the Big Five. An important reduction was later preformed by Saucier, using a sub-set of 40 markers. Both assessments are performed by presenting a set of markers, or adjectives, to the subject, requesting him to quantify each marker using a 9-point rating scale. Consequently, the goal of this study is to conduct experiments and propose a shorter alternative where the subject is only required to identify which adjectives describe him the most. Hence, a web platform was developed for data collection, requesting subjects to rate each adjective and select those describing him the most. Based on a Gradient Boosting approach, two distinct Machine Learning architectures were conceived, tuned and evaluated. The first makes use of regressors to provide an exact score of the Big Five while the second uses classifiers to provide a binned output. As input, both receive the one-hot encoded selection of adjectives. Both architectures performed well. The first is able to quantify the Big Five with an approximate error of 5 units of measure, while the second shows a micro-averaged f1-score of 83%. Since all adjectives are used to compute all traits, models are able to harness inter-trait relationships, being possible to further reduce the set of adjectives by removing those that have smaller importance.This work has been supported by FCT - Fundação para a Ciência e a Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. It was also partially supported by a Portuguese doctoral grant, SFRH/BD/130125/2017, issued by FCT in Portugal

Prosumers Flexibility as Support for Ancillary Services in Low Voltage Level

The prosumers flexibility procurement has increased due to the current penetration of distributed and variable renewable energy sources. The prosumers flexibility is often able to quickly adjust the power consumption, making it well suited as a primary and secondary reserve for ancillary services. In the era of smart grids, the role of the aggregator has been increasingly exploited and considered as a player that can facilitate small prosumers' participation in electricity markets. This paper proposes an approach based on the use of prosumers flexibility by an aggregator to support ancillary services at a low voltage level. An asymmetric pool market approach is considered for flexibility negotiation between prosumers and the local marker operator (aggregator). From the achieved results it is possible to conclude that the use of flexibility can bring technical and economic benefits for network operators

Analysis of sentiments on the onset of Covid-19 using Machine Learning Techniques

The novel coronavirus (Covid-19) pandemic has struck the whole world and is one of the most striking topics on social media platforms. Sentiment outbreak on social media enduring various thoughts, opinions, and emotions about the Covid-19 disease, expressing views they are feeling presently. Analyzing sentiments helps to yield better results. Gathering data from different blogging sites like Facebook, Twitter, Weibo, YouTube, Instagram, etc., and Twitter is the largest repository. Videos, text, and audio were also collected from repositories. Sentiment analysis uses opinion mining to acquire the sentiments of its users and categorizes them accordingly as positive, negative, and neutral. Analytical and machine learning classification is implemented to 3586 tweets collected in different time frames.  In this paper, sentiment analysis was performed on tweets accumulated during the Covid-19 pandemic, Coronavirus disease. Tweets are collected from the Twitter database using Hydrator a web-based application. Data-preprocessing removes all the noise, outliers from the raw data. With Natural Language Toolkit (NLTK), text classification for sentiment analysis and calculate the score subjective polarity, counts, and sentiment distribution. N-gram is used in textual mining -and Natural Language Processing for a continuous sequence of words in a text or document applying uni-gram, bi-gram, and tri-gram for statistical computation. Term frequency and Inverse document frequency (TF-IDF) is a feature extraction technique that converts textual data into numeric form. Vectorize data feed to our model to obtain insights from linguistic data. Linear SVC, MultinomialNB, GBM, and Random Forest classifier with Tfidf classification model applied to our proposed model. Linear Support Vector classification performs better than the other two classifiers. Results depict that RF performs better

Stress compensation by gap monolayers for stacked InAs/GaAs quantum dots solar cells

In this work we report the stacking of 10 and 50 InAs quantum dots layers using 2 monolayers of GaP for stress compensation and a stack period of 18 nm on GaAs (001) substrates. Very good structural and optical quality is found in both samples. Vertical alignment of the dots is observed by transmission electron microscopy suggesting the existence of residual stress around them. Photocurrent measurements show light absorption up to 1.2 μm in the nanostructures together with a reduction in the blue response of the device. As a result of the phosphorus incorporation in the barriers, a very high thermal activation energy (431 meV) has also been obtained for the quantum dot emission