Designing labeled graph classifiers by exploiting the R\'enyi entropy of the dissimilarity representation

Representing patterns as labeled graphs is becoming increasingly common in the broad field of computational intelligence. Accordingly, a wide repertoire of pattern recognition tools, such as classifiers and knowledge discovery procedures, are nowadays available and tested for various datasets of labeled graphs. However, the design of effective learning procedures operating in the space of labeled graphs is still a challenging problem, especially from the computational complexity viewpoint. In this paper, we present a major improvement of a general-purpose classifier for graphs, which is conceived on an interplay between dissimilarity representation, clustering, information-theoretic techniques, and evolutionary optimization algorithms. The improvement focuses on a specific key subroutine devised to compress the input data. We prove different theorems which are fundamental to the setting of the parameters controlling such a compression operation. We demonstrate the effectiveness of the resulting classifier by benchmarking the developed variants on well-known datasets of labeled graphs, considering as distinct performance indicators the classification accuracy, computing time, and parsimony in terms of structural complexity of the synthesized classification models. The results show state-of-the-art standards in terms of test set accuracy and a considerable speed-up for what concerns the computing time.Comment: Revised versio

An Appearance-Based Tracking Algorithm for Aerial Search and Rescue Purposes

The automation of the Wilderness Search and Rescue (WiSAR) task aims for high levels of understanding of various scenery. In addition, working in unfriendly and complex environments may cause a time delay in the operation and consequently put human lives at stake. In order to address this problem, Unmanned Aerial Vehicles (UAVs), which provide potential support to the conventional methods, are used. These vehicles are provided with reliable human detection and tracking algorithms; in order to be able to find and track the bodies of the victims in complex environments, and a robust control system to maintain safe distances from the detected bodies. In this paper, a human detection based on the color and depth data captured from onboard sensors is proposed. Moreover, the proposal of computing data association from the skeleton pose and a visual appearance measurement allows the tracking of multiple people with invariance to the scale, translation and rotation of the point of view with respect to the target objects. The system has been validated with real and simulation experiments, and the obtained results show the ability to track multiple individuals even after long-term disappearances. Furthermore, the simulations present the robustness of the implemented reactive control system as a promising tool for assisting the pilot to perform approaching maneuvers in a safe and smooth manner.This research is supported by Madrid Community project SEGVAUTO 4.0 P2018/EMT-4362) and by the Spanish Government CICYT projects (TRA2015-63708-R and TRA2016-78886-C3-1-R), and Ministerio de Educación, Cultura y Deporte para la Formación de Profesorado Universitario (FPU14/02143). Also, we gratefully acknowledge the support of the NVIDIA Corporation with the donation of the GPUs used for this research

New methods for discovering local behaviour in mixed databases

Clustering techniques are widely used. There are many applications where it is desired to find automatically groups or hidden information in the data set. Finding a model of the system based in the integration of several local models is placed among other applications. Local model could have many structures; however, a linear structure is the most common one, due to its simplicity. This work aims at finding improvements in several fields, but all them will be applied to this finding of a set of local models in a database. On the one hand, a way of codifying the categorical information into numerical values has been designed, in order to apply a numerical algorithm to the whole data set. On the other hand, a cost index has been developed, which will be optimized globally, to find the parameters of the local clusters that best define the output of the process. Each of the techniques has been applied to several experiments and results show the improvements over the actual techniques.Barceló Rico, F. (2009). New methods for discovering local behaviour in mixed databases. http://hdl.handle.net/10251/12739Archivo delegad

Implicit personalization in driving assistance: State-of-the-art and open issues

In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With the consideration of driving preferences and driver characteristics, these systems become more acceptable and trustworthy. This article presents a survey on recent advances in implicit personalized driving assistance. We classify the collection of work into three main categories: 1) personalized Safe Driving Systems (SDS), 2) personalized Driver Monitoring Systems (DMS), and 3) personalized In-vehicle Information Systems (IVIS). For each category, we provide a comprehensive review of current applications and related techniques along with the discussion of industry status, benefits of personalization, application prospects, and future focal points. Both relevant driving datasets and open issues about personalized driving assistance are discussed to facilitate future research. By creating an organized categorization of the field, we hope that this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the application of these techniques within the driving automation community</h2

Applications of fuzzy counterpropagation neural networks to non-linear function approximation and background noise elimination

An adaptive filter which can operate in an unknown environment by performing a learning mechanism that is suitable for the speech enhancement process. This research develops a novel ANN model which incorporates the fuzzy set approach and which can perform a non-linear function approximation. The model is used as the basic structure of an adaptive filter. The learning capability of ANN is expected to be able to reduce the development time and cost of the designing adaptive filters based on fuzzy set approach. A combination of both techniques may result in a learnable system that can tackle the vagueness problem of a changing environment where the adaptive filter operates. This proposed model is called Fuzzy Counterpropagation Network (Fuzzy CPN). It has fast learning capability and self-growing structure. This model is applied to non-linear function approximation, chaotic time series prediction and background noise elimination

Bootstrap–CURE: A novel clustering approach for sensor data: an application to 3D printing industry

The agenda of Industry 4.0 highlights smart manufacturing by making machines smart enough to make data-driven decisions. Large-scale 3D printers, being one of the important pillars in Industry 4.0, are equipped with smart sensors to continuously monitor print processes and make automated decisions. One of the biggest challenges in decision autonomy is to consume data quickly along the process and extract knowledge from the printer, suitable for improving the printing process. This paper presents the innovative unsupervised learning approach, bootstrap–CURE, to decode the sensor patterns and operation modes of 3D printers by analyzing multivariate sensor data. An automatic technique to detect the suitable number of clusters using the dendrogram is developed. The proposed methodology is scalable and significantly reduces computational cost as compared to classical CURE. A distinct combination of the 3D printer’s sensors is found, and its impact on the printing process is also discussed. A real application is presented to illustrate the performance and usefulness of the proposal. In addition, a new state of the art for sensor data analysis is presented.This work was supported in part by KEMLG-at-IDEAI (UPC) under Grant SGR-2017-574 from the Catalan government.Peer ReviewedPostprint (published version

ISBIS 2016: Meeting on Statistics in Business and Industry

This Book includes the abstracts of the talks presented at the 2016 International Symposium on Business and Industrial Statistics, held at Barcelona, June 8-10, 2016, hosted at the Universitat Politècnica de Catalunya - Barcelona TECH, by the Department of Statistics and Operations Research. The location of the meeting was at ETSEIB Building (Escola Tecnica Superior d'Enginyeria Industrial) at Avda Diagonal 647. The meeting organizers celebrated the continued success of ISBIS and ENBIS society, and the meeting draw together the international community of statisticians, both academics and industry professionals, who share the goal of making statistics the foundation for decision making in business and related applications. The Scientific Program Committee was constituted by: David Banks, Duke University Amílcar Oliveira, DCeT - Universidade Aberta and CEAUL Teresa A. Oliveira, DCeT - Universidade Aberta and CEAUL Nalini Ravishankar, University of Connecticut Xavier Tort Martorell, Universitat Politécnica de Catalunya, Barcelona TECH Martina Vandebroek, KU Leuven Vincenzo Esposito Vinzi, ESSEC Business Schoo

Assessment and Redesign of the Synoptic Water Quality Monitoring Network in the Great Smoky Mountains National Park

The purpose of this study was to assess and redesign an existing 83-site synoptic water quality monitoring network in the Great Smoky Mountains National Park. The study involved a spatial analysis of water quality data (pH, ANC, conductivity, chloride, nitrate, sulfate, sodium, and potassium), watershed characteristics (geology, morphology, and vegetation), and collocated site information to determine which sites were redundant and a temporal analysis to determine the effectiveness of the current sampling frequency to detect long-term trends. The spatial analysis employed a simulated annealing algorithm using the variable costs of the network and the results of multivariate data techniques to identify an optimized subset of the existing sampling sites based on a maximization of benefits. A second simulated annealing algorithm was created to identify optimum user-defined monitoring networks of n sites and to validate the results of the first simulated annealing program. The first simulated annealing program identified an optimized network consisting of 67 of the existing 83 sampling sites. The second simulated annealing algorithm bracketed the same 67 sites and also provided a basis for an ordered discontinuation of sampling sites by identifying the best ten-site monitoring network through the best 70-site monitoring network. The temporal analysis employed the “effective” sample method, Sen\u27s slope estimator, Mann-Kendall test for trend, and a boxplot analysis to determine the effectiveness and the power of the current sampling frequency to detect long-term trends. The results showed that the current sampling frequency of four samples per year presents a low statistical power for short historical records. However, increasing the v sampling frequency to more than 12 samples per year creates serial dependence between samples. By combining the results of the spatial and temporal analyses a new network is proposed by dividing the network into primary, secondary, and tertiary sites with sampling frequencies of six and 12 samples per year. Seventeen new sites are also proposed to collect additional data above 3000 feet MSL because the existing number of sampling sites is not proportional to park area in certain elevation ranges

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining

Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions