Structural combination of neural network models

Forecasts combinations normally use point forecasts that were obtained from different models or sources ([1], [2], [3]). This paper explores the incorporation of internal structure parameters of feed-forward neural network (NN) models as an approach to combine their forecasts via ensembles. First, the generated NN models that could be part of the ensembles are subject to a clustering algorithm that uses the structure parameters and, from each of the clusters obtained, a small set of models is selected and their forecasts are combined in a two-stage procedure. Secondly, in an alternative and simpler implementation, a subset of the generated NN models is selected by using several reference points in the model structure parameter space. The choice of the reference points is optimised through a genetic algorithm and the models selected are averaged. Hourly electricity demand time series is used to assess multi-step ahead forecasting performance for up to a 12 hours horizon. Results are compared against several statistical benchmarks, the average of the individual forecasts and the best models in the ensembles. Results show that the clusterbased (CB) structural combinations do better than the genetic algorithm (GA) structural combinations in outperforming the average forecast, which is the traditional point forecast from an ensemble

An experimental study of clogging fault diagnosis in heat exchangers based on vibration signals

The water-circulating heat exchangers employed in petrochemical industrials have attracted great attentions in condition monitoring and fault diagnosis. In this paper, an approach based on vibration signals is proposed. By the proposed method, vibration signals are collected for different conditions through various high-precision wireless sensors mounted on the surface of the heat exchanger. Furthermore, by analyzing the characteristics of the vibration signals, a database of fault patterns is established, which therefore provides a scheme for conditional monitoring of the heat exchanger. An experimental platform is set up to evaluate the feasibility and effectiveness of the proposed approach, and support vector machine based on dimensionless parameters is developed for fault classification. The results have shown that the proposed method is efficient and has achieved a high accuracy for benchmarking vibration signals under both normal and faulty conditions

Indicators of Economic Crises : A Data-Driven Clustering Approach

The determination of reliable early-warning indicators of economic crises is a hot topic in economic sciences. Pinning down recurring patterns or combinations of macroeconomic indicators is indispensable for adequate policy adjustments to prevent a looming crisis. We investigate the ability of several macroeconomic variables telling crisis countries apart from non-crisis economies. We introduce a selfcalibrated clustering-algorithm, which accounts for both similarity and dissimilarity in macroeconomic fundamentals across countries. Furthermore, imposing a desired community structure, we allow the data to decide by itself, which combination of indicators would have most accurately foreseen the exogeneously defined network topology. We quantitatively evaluate the degree of matching between the data-generated clustering and the desired community-structure.info:eu-repo/semantics/publishedVersio

Text-based Sentiment Analysis and Music Emotion Recognition

Nowadays, with the expansion of social media, large amounts of user-generated texts like tweets, blog posts or product reviews are shared online. Sentiment polarity analysis of such texts has become highly attractive and is utilized in recommender systems, market predictions, business intelligence and more. We also witness deep learning techniques becoming top performers on those types of tasks. There are however several problems that need to be solved for efficient use of deep neural networks on text mining and text polarity analysis. First of all, deep neural networks are data hungry. They need to be fed with datasets that are big in size, cleaned and preprocessed as well as properly labeled. Second, the modern natural language processing concept of word embeddings as a dense and distributed text feature representation solves sparsity and dimensionality problems of the traditional bag-of-words model. Still, there are various uncertainties regarding the use of word vectors: should they be generated from the same dataset that is used to train the model or it is better to source them from big and popular collections that work as generic text feature representations? Third, it is not easy for practitioners to find a simple and highly effective deep learning setup for various document lengths and types. Recurrent neural networks are weak with longer texts and optimal convolution-pooling combinations are not easily conceived. It is thus convenient to have generic neural network architectures that are effective and can adapt to various texts, encapsulating much of design complexity. This thesis addresses the above problems to provide methodological and practical insights for utilizing neural networks on sentiment analysis of texts and achieving state of the art results. Regarding the first problem, the effectiveness of various crowdsourcing alternatives is explored and two medium-sized and emotion-labeled song datasets are created utilizing social tags. One of the research interests of Telecom Italia was the exploration of relations between music emotional stimulation and driving style. Consequently, a context-aware music recommender system that aims to enhance driving comfort and safety was also designed. To address the second problem, a series of experiments with large text collections of various contents and domains were conducted. Word embeddings of different parameters were exercised and results revealed that their quality is influenced (mostly but not only) by the size of texts they were created from. When working with small text datasets, it is thus important to source word features from popular and generic word embedding collections. Regarding the third problem, a series of experiments involving convolutional and max-pooling neural layers were conducted. Various patterns relating text properties and network parameters with optimal classification accuracy were observed. Combining convolutions of words, bigrams, and trigrams with regional max-pooling layers in a couple of stacks produced the best results. The derived architecture achieves competitive performance on sentiment polarity analysis of movie, business and product reviews. Given that labeled data are becoming the bottleneck of the current deep learning systems, a future research direction could be the exploration of various data programming possibilities for constructing even bigger labeled datasets. Investigation of feature-level or decision-level ensemble techniques in the context of deep neural networks could also be fruitful. Different feature types do usually represent complementary characteristics of data. Combining word embedding and traditional text features or utilizing recurrent networks on document splits and then aggregating the predictions could further increase prediction accuracy of such models

Computer-assisted diagnosis of wireless-capsule endoscopic images using neural network based techniques

Computerised processing of medical images can ease the search of the representative features in the images. The endoscopic images possess rich information expressed by texture. In this paper schemes have been developed to extract texture features from the texture spectra in the chromatic and achromatic domains for a selected region of interest from each colour component histogram of images acquired by the new M2A Swallowable Capsule. The implementation of advanced learning-based schemes and the concept of fusion of multiple classifiers have been also adopted in this paper. The preliminary test results support the feasibility of the proposed methodology

Smart Energy and Intelligent Transportation Systems

With the Internet of Things and various information and communication technologies, a city can manage its assets in a smarter way, constituting the urban development vision of a smart city. This facilitates a more efficient use of physical infrastructure and encourages citizen participation. Smart energy and smart mobility are among the key aspects of the smart city, in which the electric vehicle (EV) is believed to take a key role. EVs are powered by various energy sources or the electricity grid. With proper scheduling, a large fleet of EVs can be charged from charging stations and parking infrastructures. Although the battery capacity of a single EV is small, an aggregation of EVs can perform as a significant power source or load, constituting a vehicle-to-grid (V2G) system. Besides acquiring energy from the grid, in V2G, EVs can also support the grid by providing various demand response and auxiliary services. Thanks to this, we can reduce our reliance on fossil fuels and utilize the renewable energy more effectively. This Special Issue “Smart Energy and Intelligent Transportation Systems” addresses existing knowledge gaps and advances smart energy and mobility. It consists of five peer-reviewed papers that cover a range of subjects and applications related to smart energy and transportation

CES-513 Stages for Developing Control Systems using EMG and EEG Signals: A survey

Bio-signals such as EMG (Electromyography), EEG (Electroencephalography), EOG (Electrooculogram), ECG (Electrocardiogram) have been deployed recently to develop control systems for improving the quality of life of disabled and elderly people. This technical report aims to review the current deployment of these state of the art control systems and explain some challenge issues. In particular, the stages for developing EMG and EEG based control systems are categorized, namely data acquisition, data segmentation, feature extraction, classification, and controller. Some related Bio-control applications are outlined. Finally a brief conclusion is summarized.

Proposal of an adaptive infotainment system depending on driving scenario complexity

Tesi en modalitat Doctorat industrialPla de Doctorat industrial de la Generalitat de CatalunyaThe PhD research project is framed within the plan of industrial doctorates of the “Generalitat de Catalunya”. During the investigation, most of the work was carried out at the facilities of the vehicle manufacturer SEAT, specifically at the information and entertainment (infotainment) department. In the same way, there was a continuous cooperation with the telematics department of the UPC. The main objective of the project consisted in the design and validation of an adaptive infotainment system dependent on the driving complexity. The system was created with the purpose of increasing driver’ experience while guaranteeing a proper level of road safety. Given the increasing number of application and services available in current infotainment systems, it becomes necessary to devise a system capable of balancing these two counterparts. The most relevant parameters that can be used for balancing these metrics while driving are: type of services offered, interfaces available for interacting with the services, the complexity of driving and the profile of the driver. The present study can be divided into two main development phases, each phase had as outcome a real physical block that came to be part of the final system. The final system was integrated in a vehicle and validated in real driving conditions. The first phase consisted in the creation of a model capable of estimating the driving complexity based on a set of variables related to driving. The model was built by employing machine learning methods and the dataset necessary to create it was collected from several driving routes carried out by different participants. This phase allowed to create a model capable of estimating, with a satisfactory accuracy, the complexity of the road using easily extractable variables in any modern vehicle. This approach simplify the implementation of this algorithm in current vehicles. The second phase consisted in the classification of a set of principles that allow the design of the adaptive infotainment system based on the complexity of the road. These principles are defined based on previous researches undertaken in the field of usability and user experience of graphical interfaces. According to these of principles, a real adaptive infotainment system with the most commonly used functionalities; navigation, radio and media was designed and integrated in a real vehicle. The developed system was able to adapt the presentation of the content according to the estimation of the driving complexity given by the block developed in phase one. The adaptive system was validated in real driving scenarios by several participants and results showed a high level of acceptance and satisfaction towards this adaptive infotainment. As a starting point for future research, a proof of concept was carried out to integrate new interfaces into a vehicle. The interface used as reference was a Head Mounted screen that offered redundant information in relation to the instrument cluster. Tests with participants served to understand how users perceive the introduction of new technologies and how objective benefits could be blurred by initial biases.El proyecto de investigación de doctorado se enmarca dentro del plan de doctorados industriales de la Generalitat de Catalunya. Durante la investigación, la mayor parte del trabajo se llevó a cabo en las instalaciones del fabricante de vehículos SEAT, específicamente en el departamento de información y entretenimiento (infotainment). Del mismo modo, hubo una cooperación continua con el departamento de telemática de la UPC. El objetivo principal del proyecto consistió en el diseño y la validación de un sistema de información y entretenimiento adaptativo que se ajustaba de acuerdo a la complejidad de la conducción. El sistema fue creado con el propósito de aumentar la experiencia del conductor y garantizar un nivel adecuado en la seguridad vial. El proyecto surge dado el número creciente de aplicaciones y servicios disponibles en los sistemas actuales de información y entretenimiento; es por ello que se hace necesario contar con un sistema capaz de equilibrar estas dos contrapartes. Los parámetros más relevantes que se pueden usar para equilibrar estas métricas durante la conducción son: el tipo de servicios ofrecidos, las interfaces disponibles para interactuar con los servicios, la complejidad de la conducción y el perfil del conductor. El presente estudio se puede dividir en dos fases principales de desarrollo, cada fase tuvo como resultado un componente que se convirtió en parte del sistema final. El sistema final fue integrado en un vehículo y validado en condiciones reales de conducción. La primera fase consistió en la creación de un modelo capaz de estimar la complejidad de la conducción en base a un conjunto de variables relacionadas con la conducción. El modelo se construyó empleando "Machine Learning Methods" y el conjunto de datos necesario para crearlo se recopiló a partir de varias rutas de conducción realizadas por diferentes participantes. Esta fase permitió crear un modelo capaz de estimar, con una precisión satisfactoria, la complejidad de la carretera utilizando variables fácilmente extraíbles en cualquier vehículo moderno. Este enfoque simplifica la implementación de este algoritmo en los vehículos actuales. La segunda fase consistió en la clasificación de un conjunto de principios que permiten el diseño del sistema de información y entretenimiento adaptativo basado en la complejidad de la carretera. Estos principios se definen en base a investigaciones anteriores realizadas en el campo de usabilidad y experiencia del usuario con interfaces gráficas. De acuerdo con estos principios, un sistema de entretenimiento y entretenimiento real integrando las funcionalidades más utilizadas; navegación, radio y audio fue diseñado e integrado en un vehículo real. El sistema desarrollado pudo adaptar la presentación del contenido según la estimación de la complejidad de conducción dada por el bloque desarrollado en la primera fase. El sistema adaptativo fue validado en escenarios de conducción reales por varios participantes y los resultados mostraron un alto nivel de aceptación y satisfacción hacia este entretenimiento informativo adaptativo. Como punto de partida para futuras investigaciones, se llevó a cabo una prueba de concepto para integrar nuevas interfaces en un vehículo. La interfaz utilizada como referencia era una pantalla a la altura de los ojos (Head Mounted Display) que ofrecía información redundante en relación con el grupo de instrumentos. Las pruebas con los participantes sirvieron para comprender cómo perciben los usuarios la introducción de nuevas tecnologías y cómo los sesgos iniciales podrían difuminar los beneficios.Postprint (published version