Sistemas de gestión de puentes : optimización de estrategias de mantenimiento : implementación en redes locales de carreteras

En la actualidad, y en consonancia con la tendencia de “sostenibilidad” extendida a todos los campos y parcelas de la ciencia, nos encontramos con un área de estudio basado en la problemática del inevitable deterioro de las estructuras existentes, y la gestión de las acciones a realizar para mantener las condiciones de servicio de los puentes y prolongar su vida útil. Tal y como se comienza a ver en las inversiones en los países avanzados, con una larga tradición en el desarrollo de sus infraestructuras, se muestra claramente el nuevo marco al que nos dirigimos. Las nuevas tendencias van encaminadas cada vez más a la conservación y mantenimiento, reduciéndose las partidas presupuestarias destinadas a nuevas actuaciones, debido a la completa vertebración territorial que se ha ido instaurando en estos países, entre los que España se encuentra. Este nutrido patrimonio de infraestructuras viarias, que cuentan a su vez con un importante número de estructuras, hacen necesarias las labores de gestión y mantenimiento de los puentes integrantes en las mismas. Bajo estas premisas, la tesis aborda el estado de desarrollo de la implementación de los sistemas de gestión de puentes, las tendencias actuales e identificación de campos por desarrollar, así como la aplicación específica a redes de carreteras de escasos recursos, más allá de la Red Estatal. Además de analizar las diversas metodologías de formación de inventarios, realización de inspecciones y evaluación del estado de puentes, se ha enfocado, como principal objetivo, el desarrollo de un sistema específico de predicción del deterioro y ayuda a la toma de decisiones. Este sistema, adicionalmente a la configuración tradicional de criterios de formación de bases de datos de estructuras e inspecciones, plantea, de forma justificada, la clasificación relativa al conjunto de la red gestionada, según su estado de condición. Eso permite, mediante técnicas de optimización, la correcta toma de decisiones a los técnicos encargados de la gestión de la red. Dentro de los diversos métodos de evaluación de la predicción de evolución del deterioro de cada puente, se plantea la utilización de un método bilineal simplificado envolvente del ajuste empírico realizado y de los modelos markovianos como la solución más efectiva para abordar el análisis de la predicción de la propagación del daño. Todo ello explotando la campaña experimenta realizada que, a partir de una serie de “fotografías técnicas” del estado de la red de puentes gestionados obtenidas mediante las inspecciones realizadas, es capaz de mejorar el proceso habitual de toma de decisiones. Toda la base teórica reflejada en el documento, se ve complementada mediante la implementación de un Sistema de Gestión de Puentes (SGP) específico, adaptado según las necesidades y limitaciones de la administración a la que se ha aplicado, en concreto, la Dirección General de Carreteras de la Junta de Comunidades de Castilla-La Mancha, para una muestra representativa del conjunto de puentes de la red de la provincia de Albacete, partiendo de una situación en la que no existe, actualmente, un sistema formal de gestión de puentes. Tras un meditado análisis del estado del arte dentro de los Capítulos 2 y 3, se plantea un modelo de predicción del deterioro dentro del Capítulo 4 “Modelo de Predicción del Deterioro”. De la misma manera, para la resolución del problema de optimización, se justifica la utilización de un novedoso sistema de optimización secuencial elegido dentro del Capítulo 5, los “Algoritmos Evolutivos”, en sus diferentes variantes, como la herramienta matemática más correcta para distribuir adecuadamente los recursos económicos dedicados a mantenimiento y conservación de los que esta administración pueda disponer en sus partidas de presupuesto a medio plazo. En el Capítulo 6, y en diversos Anexos al presente documento, se muestran los datos y resultados obtenidos de la aplicación específica desarrollada para la red local analizada, utilizando el modelo de deterioro y optimización secuencial, que garantiza la correcta asignación de los escasos recursos de los que disponen las redes autonómicas en España. Se plantea con especial interés la implantación de estos sistemas en la red secundaria española, debido a que reciben en los últimos tiempos una mayor responsabilidad de gestión, con recursos cada vez más limitados. Finalmente, en el Capítulo 7, se plantean una serie de conclusiones que nos hacen reflexionar de la necesidad de comenzar a pasar, en materia de gestión de infraestructuras, de los estudios teóricos y los congresos, hacia la aplicación y la práctica, con un planteamiento que nos debe llevar a cambios importantes en la forma de concebir la labor del ingeniero y las enseñanzas que se imparten en las escuelas. También se enumeran las aportaciones originales que plantea el documento frente al actual estado del arte. Se plantean, de la misma manera, las líneas de investigación en materia de Sistemas de Gestión de Puentes que pueden ayudar a refinar y mejorar los actuales sistemas utilizados. In line with the development of "sustainability" extended to all fields of science, we are faced with the inevitable and ongoing deterioration of existing structures, leading nowadays to the necessary management of maintaining the service conditions and life time extension of bridges. As per the increased amounts of money that can be observed being spent in the countries with an extensive and strong tradition in the development of their infrastructure, the trend can be clearly recognized. The new tendencies turn more and more towards conservation and maintenance, reducing programmed expenses for new construction activities, in line with the already wellestablished territorial structures, as is the case for Spain. This significant heritage of established road infrastructure, consequently containing a vast number of structures, imminently lead to necessary management and maintenance of the including bridges. Under these conditions, this thesis focusses on the status of the development of the management implementation for bridges, current trends, and identifying areas for further development. This also includes the specific application to road networks with limited resources, beyond the national highways. In addition to analyzing the various training methodologies, inventory inspections and condition assessments of bridges, the main objective has been the development of a specific methodology. This methodology, in addition to the traditional system of structure and inspection database training criteria, sustains the classification for the entire road network, according to their condition. This allows, through optimization techniques, for the correct decision making by the technical managers of the network. Among the various methods for assessing the evolution forecast of deterioration of each bridge, a simplified bilinear envelope adjustment made empirical method and Markov models as the most effective solution to address the analysis of predicting the spread of damage, arising from a "technical snapshot" obtained through inspections of the condition of the bridges included in the investigated network. All theoretical basis reflected in the document, is completed by implementing a specific Bridges Management System (BMS), adapted according to the needs and limitations of the authorities for which it has been applied, being in this case particularly the General Highways Directorate of the autonomous region of Castilla-La Mancha, for a representative sample of all bridges in the network in the province of Albacete, starting from a situation where there is currently no formal bridge management system. After an analysis of the state of the art in Chapters 2 and 3, a new deterioration prediction model is developed in Chapter 4, "Deterioration Prediction Model". In the same way, to solve the optimization problem is proposed the use of a singular system of sequential optimization elected under Chapter 5, the "Evolutionary Algorithms", the most suitable mathematical tool to adequately distribute the economic resources for maintenance and conservation for mid-term budget planning. In Chapter 6, and in the various appendices, data and results are presented of the developed application for the analyzed local network, from the optimization model, which guarantees the correct allocation of scarce resources at the disposal of authorities responsible for the regional networks in Spain. The implementation of these systems is witnessed with particular interest for the Spanish secondary network, because of the increasing management responsibility, with decreasing resources. Chapter 7 presents a series of conclusions that triggers to reconsider shifting from theoretical studies and conferences towards a practical implementation, considering how to properly conceive the engineering input and the related education. The original contributions of the document are also listed. In the same way, the research on the Bridges Management System can help evaluating and improving the used systematics.

Human activity recognition by inertial signals obtained from a smartphone

Human Activity Recognition (HAR) is an emerging research field with the aim to identify the actions carried out by a person given a set of observations and the surrounding environment. The wide growth in this research field inside the scientific community is mainly explained by the high number of applications that are arising in the last years. A great part of the most promising applications are related to the healthcare field, where it is possible to track the mobility of patients with motor dysfunction as also the physical activity in patients with cardiovascular risk. Until a few years ago, by using distinct kind of sensors, a patient follow-up was possible. However, far from being a long-term solution and with the smartphone irruption, that monitoring can be achieved in a non-invasive way by using the embedded smartphone’s sensors. For these reasons this Final Degree Project arises with the main target to evaluate new feature extraction techniques in order to carry out an activity and user recognition, and also an activity segmentation. The recognition is done thanks to the inertial signals integration obtained by two widespread sensors in the greater part of smartphones: accelerometer and gyroscope. In particular, six different activities are evaluated walking, walking-upstairs, walking-downstairs, sitting, standing and lying. Furthermore, a segmentation task is carried out taking into account the activities performed by thirty users. This can be done by using Hidden Markov Models and also a set of tools tested satisfactory in speech recognition: HTK (Hidden Markov Model Toolkit).

Identification of a human nuclear receptor defines a new signaling pathway for CYP3A induction

Nuclear receptors regulate metabolic pathways in response to changes in the environment by appropriate alterations in gene expression of key metabolic enzymes. Here, a computational search approach based on iteratively built hidden Markov models of nuclear receptors was used to identify a human nuclear receptor, termed hPAR, that is expressed in liver and intestines. hPAR was found to be efficiently activated by pregnanes and by clinically used drugs including rifampicin, an antibiotic known to selectively induce human but not murine CYP3A expression. The CYP3A drug-metabolizing enzymes are expressed in gut and liver in response to environmental chemicals and clinically used drugs. Interestingly, hPAR is not activated by pregnenolone 16α-carbonitrile, which is a potent inducer of murine CYP3A genes and an activator of the mouse receptor PXR.1. Furthermore, hPAR was found to bind to and trans-activate through a conserved regulatory sequence present in human but not murine CYP3A genes. These results provide evidence that hPAR and PXR.1 may represent orthologous genes from different species that have evolved to regulate overlapping target genes in response to pharmacologically distinct CYP3A activators, and have potential implications for the in vitro identification of drug interactions important to humans.

The InterPro database, an integrated documentation resource for protein families, domains and functional sites

Signature databases are vital tools for identifying distant relationships in novel sequences and hence for inferring protein function. InterPro is an integrated documentation resource for protein families, domains and functional sites, which amalgamates the efforts of the PROSITE, PRINTS, Pfam and ProDom database projects. Each InterPro entry includes a functional description, annotation, literature references and links back to the relevant member database(s). Release 2.0 of InterPro (October 2000) contains over 3000 entries, representing families, domains, repeats and sites of post-translational modification encoded by a total of 6804 different regular expressions, profiles, fingerprints and Hidden Markov Models. Each InterPro entry lists all the matches against SWISS-PROT and TrEMBL (more than 1 000 000 hits from 462 500 proteins in SWISS-PROT and TrEMBL). The database is accessible for text- and sequence-based searches at http://www.ebi.ac.uk/interpro/. Questions can be emailed to interhelp@ebi.ac.uk.

TIGRFAMs: a protein family resource for the functional identification of proteins

TIGRFAMs is a collection of protein families featuring curated multiple sequence alignments, hidden Markov models and associated information designed to support the automated functional identification of proteins by sequence homology. We introduce the term ‘equivalog’ to describe members of a set of homologous proteins that are conserved with respect to function since their last common ancestor. Related proteins are grouped into equivalog families where possible, and otherwise into protein families with other hierarchically defined homology types. TIGRFAMs currently contains over 800 protein families, available for searching or downloading at www.tigr.org/TIGRFAMs. Classification by equivalog family, where achievable, complements classification by orthology, superfamily, domain or motif. It provides the information best suited for automatic assignment of specific functions to proteins from large-scale genome sequencing projects.

Training and search methods for speech recognition.

Speech recognition involves three processes: extraction of acoustic indices from the speech signal, estimation of the probability that the observed index string was caused by a hypothesized utterance segment, and determination of the recognized utterance via a search among hypothesized alternatives. This paper is not concerned with the first process. Estimation of the probability of an index string involves a model of index production by any given utterance segment (e.g., a word). Hidden Markov models (HMMs) are used for this purpose [Makhoul, J. & Schwartz, R. (1995) Proc. Natl. Acad. Sci. USA 92, 9956-9963]. Their parameters are state transition probabilities and output probability distributions associated with the transitions. The Baum algorithm that obtains the values of these parameters from speech data via their successive reestimation will be described in this paper. The recognizer wishes to find the most probable utterance that could have caused the observed acoustic index string. That probability is the product of two factors: the probability that the utterance will produce the string and the probability that the speaker will wish to produce the utterance (the language model probability). Even if the vocabulary size is moderate, it is impossible to search for the utterance exhaustively. One practical algorithm is described [Viterbi, A. J. (1967) IEEE Trans. Inf. Theory IT-13, 260-267] that, given the index string, has a high likelihood of finding the most probable utterance.

Contexto e modularização em redes neurais recorrentes para aprendizagem de seqüências temporais

Este trabalho apresenta um sistema neural modular, que processa separadamente informações de contexto espacial e temporal, para a tarefa de reprodução de sequências temporais. Para o desenvolvimento do sistema neural foram considerados redes neurais recorrentes, modelos estocásticos, sistemas neurais modulares e processamento de informações de contexto. Em seguida, foram estudados três modelos com abordagens distintas para aprendizagem de seqüências temporais: uma rede neural parcialmente recorrente, um exemplo de sistema neural modular e um modelo estocástico utilizando a teoria de modelos markovianos escondidos. Com base nos estudos e modelos apresentados, esta pesquisa propõe um sistema formado por dois módulos sucessivos distintos. Uma rede de propagação direta (módulo estimador de contexto espacial) realiza o processamento de contexto espacial identificando a seqüência a ser reproduzida e fornecendo um protótipo do contexto para o segundo módulo. Este é formado por uma rede parcialmente recorrente (módulo de reprodução de sequências temporais) para aprender as informações de contexto temporal e reproduzir em suas saídas a seqüência identificada pelo módulo anterior. Para a finalidade mencionada, este mestrado utiliza a distribuição de Gibbs na saída do módulo para contexto espacial de forma que este forneça probabilidades de contexto espacial, indicando o grau de certeza do módulo e possibilitando a utilização de procedimentos especiais para os casos de dúvida. O sistema neural foi testado em conjuntos contendo trajetórias abertas, fechadas, e com diferentes situações de ambigüidade e complexidade. Duas situações distintas foram avaliadas: (a) capacidade do sistema em reproduzir trajetórias a partir de pontos iniciais treinados; e (b) capacidade de generalização do sistema reproduzindo trajetórias considerando pontos iniciais ou finais em situações não treinadas. A situação (b) é um problema de difícil ) solução em redes neurais devido à falta de contexto temporal, essencial na reprodução de seqüências. Foram realizados experimentos comparando o desempenho do sistema modular proposto com o de uma rede parcialmente recorrente operando sozinha e um sistema modular neural (TOTEM). Os resultados sugerem que o sistema proposto apresentou uma capacidade de generalização significamente melhor, sem que houvesse uma deterioração na capacidade de reproduzir seqüências treinadas. Esses resultados foram obtidos em sistema mais simples que o TOTEM.

Human Motion Analysis Based on Sequential Modeling of Radar Signal and Stereo Image Features

Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers. Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach.

Forecasting Elections with High Volatility

This article uses data from the social survey Allbus 1998 to introduce a method of forecasting elections in a context of electoral volatility. The approach models the processes of change in electoral behaviour, exploring patterns in order to model the volatility expressed by voters. The forecast is based on the matrix of transition probabilities, following the logic of Markov chains. The power of the matrix, and the use of the mover-stayer model, is debated for alternative forecasts. As an example of high volatility, the model uses data from the German general election of 1998. The unification of two German states in 1990 caused the incorporation of around 15 million new voters from East Germany who had limited familiarity and no direct experience of the political culture in West Germany. Under these circumstances, voters were expected to show high volatility.

Search of Method for Analyzing "Viability" of Innovative Projects

Questions of "viability" evaluation of innovation projects are considered in this article. As a method of evaluation Hidden Markov Models are used. Problem of determining model parameters, which reproduce test data with highest accuracy are solving. For training the model statistical data on the implementation of innovative projects are used. Baum-Welch algorithm is used as a training algorithm.

Modeling Depression Progression Dynamics from Electronic Health Record

Thesis (Master's)--University of Washington, 2016-06

Identification and analysis of chromodomain-containing proteins encoded in the mouse transcriptome

The chromodomain is 40-50 amino acids in length and is conserved in a wide range of chromatic and regulatory proteins involved in chromatin remodeling. Chromodomain-containing proteins can be classified into families based on their broader characteristics, in particular the presence of other types of domains, and which correlate with different subclasses of the chromodomains themselves. Hidden Markov model (HMM)-generated profiles of different subclasses of chromodomains were used here to identify sequences encoding chromodomain-containing proteins in the mouse transcriptome and genome. A total of 36 different loci encoding proteins containing chromodomains, including 17 novel loci, were identified. Six of these loci (including three apparent pseudogenes, a novel HP1 ortholog, and two novel Msl-3 transcription factor-like proteins) are not present in the human genome, whereas the human genome contains four loci (two CDY orthologs and two apparent CDY pseuclogenes) that are not present in mouse. A number of these loci exhibit alternative splicing to produce different isoforms, including 43 novel variants, some of which lack the chromodomain. The likely functions of these proteins are discussed in relation to the known functions of other chromodomain-containing proteins within the same family.

Wurst: a protein threading server with a structural scoring function, sequence profiles and optimized substitution matrices

Wurst is a protein threading program with an emphasis on high quality sequence to structure alignments (http://www.zbh.uni-hamburg.de/wurst). Submitted sequences are aligned to each of about 3000 templates with a conventional dynamic programming algorithm, but using a score function with sophisticated structure and sequence terms. The structure terms are a log-odds probability of sequence to structure fragment compatibility, obtained from a Bayesian classification procedure. A simplex optimization was used to optimize the sequence-based terms for the goal of alignment and model quality and to balance the sequence and structural contributions against each other. Both sequence and structural terms operate with sequence profiles.

Predictive vaccinology: Optimisation of predictions using support vector machine classifiers

Promiscuous human leukocyte antigen (HLA) binding peptides are ideal targets for vaccine development. Existing computational models for prediction of promiscuous peptides used hidden Markov models and artificial neural networks as prediction algorithms. We report a system based on support vector machines that outperforms previously published methods. Preliminary testing showed that it can predict peptides binding to HLA-A2 and -A3 super-type molecules with excellent accuracy, even for molecules where no binding data are currently available.

MULTIPRED: A computational system for prediction of promiscuous HLA binding peptides

MULTIPRED is a web-based computational system for the prediction of peptide binding to multiple molecules ( proteins) belonging to human leukocyte antigens (HLA) class I A2, A3 and class II DR supertypes. It uses hidden Markov models and artificial neural network methods as predictive engines. A novel data representation method enables MULTIPRED to predict peptides that promiscuously bind multiple HLA alleles within one HLA supertype. Extensive testing was performed for validation of the prediction models. Testing results show that MULTIPRED is both sensitive and specific and it has good predictive ability ( area under the receiver operating characteristic curve A(ROC) > 0.80). MULTIPRED can be used for the mapping of promiscuous T-cell epitopes as well as the regions of high concentration of these targets termed T-cell epitope hotspots. MULTIPRED is available at http:// antigen.i2r.a-star.edu.sg/ multipred/.