Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants

The prediction of electrical power produced in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power production can vary depending on environmental variables, such as temperature, pressure, and humidity. Thus, the business problem is how to predict the power production as a function of these environmental conditions, in order to maximize the profit. The research community has solved this problem by applying Machine Learning techniques, and has managed to reduce the computational and time costs in comparison with the traditional thermodynamical analysis. Until now, this challenge has been tackled from a batch learning perspective, in which data is assumed to be at rest, and where models do not continuously integrate new information into already constructed models. We present an approach closer to the Big Data and Internet of Things paradigms, in which data are continuously arriving and where models learn incrementally, achieving significant enhancements in terms of data processing (time, memory and computational costs), and obtaining competitive performances. This work compares and examines the hourly electrical power prediction of several streaming regressors, and discusses about the best technique in terms of time processing and predictive performance to be applied on this streaming scenario.This work has been partially supported by the EU project iDev40. This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 783163. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Austria, Germany, Belgium, Italy, Spain, Romania. It has also been supported by the Basque Government (Spain) through the project VIRTUAL (KK-2018/00096), and by Ministerio de Economía y Competitividad of Spain (Grant Ref. TIN2017-85887-C2-2-P)

Analyzing social media discourse - an approach using semi-supervised learning

The ability to handle large amounts of unstructured information, to optimize strategic business opportunities, and to identify fundamental lessons among competitors through benchmarking, are essential skills of every business sector. Currently, there are dozens of social media analytics’ applications aiming at providing organizations with informed decision making tools. However, these applications rely on providing quantitative information, rather than qualitative information that is relevant and intelligible for managers. In order to address these aspects, we propose a semi-supervised learning procedure that discovers and compiles information taken from online social media, organizing it in a scheme that can be strategically relevant. We illustrate our procedure using a case study where we collected and analysed the social media discourse of 43 organizations operating on the Higher Public Polytechnic Education Sector. During the analysis we created an “editorial model” that character izes the posts in the area. We describe in detail the training and the execution of an ensemble of classifying algorithms. In this study we focus on the techniques used to increase the accuracy and stability of the classifiers.info:eu-repo/semantics/publishedVersio

Data analytics for mobile traffic in 5G networks using machine learning techniques

This thesis collects the research works I pursued as Ph.D. candidate at the Universitat Politecnica de Catalunya (UPC). Most of the work has been accomplished at the Mobile Network Department Centre Tecnologic de Telecomunicacions de Catalunya (CTTC). The main topic of my research is the study of mobile network traffic through the analysis of operative networks dataset using machine learning techniques. Understanding first the actual network deployments is fundamental for next-generation network (5G) for improving the performance and Quality of Service (QoS) of the users. The work starts from the collection of a novel type of dataset, using an over-the-air monitoring tool, that allows to extract the control information from the radio-link channel, without harming the users’ identities. The subsequent analysis comprehends a statistical characterization of the traffic and the derivation of prediction models for the network traffic. A wide group of algorithms are implemented and compared, in order to identify the highest performances. Moreover, the thesis addresses a set of applications in the context mobile networks that are prerogatives in the future mobile networks. This includes the detection of urban anomalies, the user classification based on the demanded network services, the design of a proactive wake-up scheme for efficient-energy devices.Esta tesis recoge los trabajos de investigación que realicé como Ph.D. candidato a la Universitat Politecnica de Catalunya (UPC). La mayor parte del trabajo se ha realizado en el Centro Tecnológico de Telecomunicaciones de Catalunya (CTTC) del Departamento de Redes Móviles. El tema principal de mi investigación es el estudio del tráfico de la red móvil a través del análisis del conjunto de datos de redes operativas utilizando técnicas de aprendizaje automático. Comprender primero las implementaciones de red reales es fundamental para la red de próxima generación (5G) para mejorar el rendimiento y la calidad de servicio (QoS) de los usuarios. El trabajo comienza con la recopilación de un nuevo tipo de conjunto de datos, utilizando una herramienta de monitoreo por aire, que permite extraer la información de control del canal de radioenlace, sin dañar las identidades de los usuarios. El análisis posterior comprende una caracterización estadística del tráfico y la derivación de modelos de predicción para el tráfico de red. Se implementa y compara un amplio grupo de algoritmos para identificar los rendimientos más altos. Además, la tesis aborda un conjunto de aplicaciones en el contexto de redes móviles que son prerrogativas en las redes móviles futuras. Esto incluye la detección de anomalías urbanas, la clasificación de usuarios basada en los servicios de red demandados, el diseño de un esquema de activación proactiva para dispositivos de energía eficiente.Postprint (published version

Recommendation of songs through deep learning techniques

The present project exposes the investigation and implementation of song recommender systems based on collaborative filtering (CF) and deep learning based techniques. These song recommender systems will automatically create personalized lists of songs depending on the tastes of each user. Recommender systems have become nowadays a very popular and important field of study in machine learning because of the evolution of music industry. To develop the recommender systems, the Million Song Dataset will be used. This dataset will be analyzed thoroughly to conclude if it is valid for recommendation tasks. If these results are valid, a subset of this dataset will be taken to input the recommender system model. First, a Collaborative Filtering recommender will be developed, having as input the number of times each user has listened to a particular song (implicit feedback). This recommender will be trained, validated and tested to be aware of its performance. Consequently, an artist classifier having as a model a convolutional neural network (CNN) and as input a song audio signal will be developed. This is done in order to have a prepared neural network in order to implement deep content-based technique in future steps. The inputs of the CNN will be MFCC of the songs audio signals. Different procedures to extract the MFCC and will be done and compared based on the CNN results. Different CNN architectures will be studied as well. Finally, an approach of a hybrid recommender system (called novelty detection in this project) will be made. This hybrid recommender system will combine collaborative filtering and deep learning based techniques. As a result, a system able to recommend popular and unpopular songs will be obtained (thanks to deep learning based technique).El presente proyecto expone la investigación e implementación de un sistema de recomendación de canciones, basado en las técnicas de filtrado colaborativo y deep learning. Este sistema de recomendación de canciones creará de forma automática listas de canciones personalizadas en función de los gustos de cada usuario. Actualmente, los sistemas de recomendación son bastante famosos y se han convertido en un campo de estudio muy importante en aprendizaje automático, debido a la evolución de la industria de la música. Para desarrollar el sistema de recomendación se ha utilizado el conjunto de datos Million Song Dataset. Este conjunto de datos será analizado minuciosamente para concluir en su es válido o no para desarrollar el recomendador. Si resulta ser válido, un subconjunto de datos de este conjunto de datos será la entrada del model del sistema de recomendación. Primero, un recomendador basado en filtrado colaborativo será desarrollado, teniendo como entrada el número de veces que cada usuario ha escuchado cierta canción (feedback implícito). Este recomendador será entrenado, validado y probado para ser conscientes de su funcionamiento. Posteriormente se desarollará un clasificador de artistas que tendrá como modelo una red convolucional, y como entrada la señal de audio de una canción. Esto se hará para tener una red neuronal preparada para implementar la técnica deep content-based en un futuro. Las entradas de la red convolucional serán los coeficientes de Mel (MFCC) de la señal de audio de las canciones. Se realizarán y comparán diferentes procedimientos para extraer estos coeficientes be done and compared based on the CNN results. También se estudiarán diferentes arquitecturas de la red convolucional. Finalmente, se realizará un acercamiento a un sistema de recomendación (llamado en este proyecto: novelty detection). Este sistema de recomendación híbrido combinará las tecnicas de filtrado colaborativo y deep learning. Como resultado se tendrá un sistema capaz de recomendar canciones populares y no populares (gracias a la técnica deep learning based).Ingeniería de Sistemas Audiovisuale

Quadri-dimensional approach for data analytics in mobile networks

The telecommunication market is growing at a very fast pace with the evolution of new technologies to support high speed throughput and the availability of a wide range of services and applications in the mobile networks. This has led to a need for communication service providers (CSPs) to shift their focus from network elements monitoring towards services monitoring and subscribers’ satisfaction by introducing the service quality management (SQM) and the customer experience management (CEM) that require fast responses to reduce the time to find and solve network problems, to ensure efficiency and proactive maintenance, to improve the quality of service (QoS) and the quality of experience (QoE) of the subscribers. While both the SQM and the CEM demand multiple information from different interfaces, managing multiple data sources adds an extra layer of complexity with the collection of data. While several studies and researches have been conducted for data analytics in mobile networks, most of them did not consider analytics based on the four dimensions involved in the mobile networks environment which are the subscriber, the handset, the service and the network element with multiple interface correlation. The main objective of this research was to develop mobile network analytics models applied to the 3G packet-switched domain by analysing data from the radio network with the Iub interface and the core network with the Gn interface to provide a fast root cause analysis (RCA) approach considering the four dimensions involved in the mobile networks. This was achieved by using the latest computer engineering advancements which are Big Data platforms and data mining techniques through machine learning algorithms.Electrical and Mining EngineeringM. Tech. (Electrical Engineering