The primary objective of the study is to evaluate safety performances of different exit ramps used in Florida and nationally. More specific, the research objectives include the following two parts: (1) to evaluate the impacts of different exit ramp types on safety performance for freeway diverge areas; and (2) to identify the different factors contributing to the crashes happening on the exit ramp sections. To achieve the research objectives, the research team investigated crash history at 424 sites throughout Florida. The study area includes two parts, the freeway diverge area and the exit ramp sections. For the freeway diverge areas, exit ramp types were defined based on the number of lanes used by vehicular traffic to exit freeways. Four exit ramp types were considered here including single-lane exit ramps (Type 1), sing-lane exit ramps without a taper (Type 2), two-lane exit ramps with an optional lane (Type 3), and two-lane exit ramps without an optional lane (Type 4). For the exit ramp sections, four ramp configurations, including diamond, out connection, free-flow loop and parclo loop, were considered. Cross-sectional comparisons were conducted to compare crash frequency, crash rate, crash severity and crash types between different exit ramp groups. Crash predictive models were also built to quantify the impacts of various contributing factors. On the freeway diverge areas, it shows that Type 1 exit ramp has the best safety performance in terms of the lowest crash frequency and crash rate. The crash prediction model shows that for one-lane exit ramp, replacing a Type 1 with a Type 2 will increase crash counts at freeway diverge areas by 15.57% while replacing a Type 3 with a Type 4 will increase crash counts by 10.80% for two-lane ramps. On the exit ramp sections, the out connection ramps appear to have the lowest average crash rate than the other three. The crash predictive model shows that replacing an out connection exit ramp with a diamond, free-flow, and parclo loop will increase crashes counts by 26.90%, 68.47% and 48.72% respectively. The results of this study will help transportation decision makers develop tailored technical guidelines governing the selection of the optimum design combinations on freeway diverge areas and exit ramp sections.

Die letzten zwanzig Jahre haben gezeigt, dass die Integration luftgestützter Lasertechnologien (Light Detection and Ranging; LiDAR) in die Erfassung von Waldressourcen dazu beitragen kann, die Genauigkeit von Schätzungen zu erhöhen. Um diese zu ermöglichen, müssen Feldaten mit LiDAR-Daten kombiniert werden. Diverse Techniken der Modellierung bieten die Möglichkeit, diese Verbindung statistisch zu beschreiben. Während die Wahl der Methode in der Regel nur geringen Einfluss auf Punktschätzer hat, liefert sie unterschiedliche Schätzungen der Genauigkeit. In der vorliegenden Studie wurde der Einfluss verschiedener Modellierungstechniken und Variablenauswahl auf die Genauigkeit von Schätzungen untersucht. Der Schwerpunkt der Arbeit liegt hierbei auf LiDAR Anwendungen im Rahmen von Waldinventuren. Die Methoden der Variablenauswahl, welche in dieser Studie berücksichtigt wurden, waren das Akaike Informationskriterium (AIC), das korrigierte Akaike Informationskriterium (AICc), und das bayesianische (oder Schwarz) Informationskriterium. Zudem wurden Variablen anhand der Konditionsnummer und des Varianzinflationsfaktors ausgewählt. Weitere Methoden, die in dieser Studie Berücksichtigung fanden, umfassen Ridge Regression, der least absolute shrinkage and selection operator (Lasso), und der Random Forest Algorithmus. Die Methoden der schrittweisen Variablenauswahl wurden sowohl im Rahmen der Modell-assistierten als auch der Modell-basierten Inferenz untersucht. Die übrigen Methoden wurden nur im Rahmen der Modell-assistierten Inferenz untersucht. In einer umfangreichen Simulationsstudie wurden die Einflüsse der Art der Modellierungsmethode und Art der Variablenauswahl auf die Genauigkeit der Schätzung von Populationsparametern (oberirdische Biomasse in Megagramm pro Hektar) ermittelt. Hierzu wurden fünf unterschiedliche Populationen genutzt. Drei künstliche Populationen wurden simuliert, zwei weitere basierten auf in Kanada und Norwegen erhobenen Waldinveturdaten. Canonical vine copulas wurden genutzt um synthetische Populationen aus diesen Waldinventurdaten zu generieren. Aus den Populationen wurden wiederholt einfache Zufallsstichproben gezogen und für jede Stichprobe wurden der Mittelwert und die Genauigkeit der Mittelwertschätzung geschäzt. Während für das Modell-basierte Verfahren nur ein Varianzschätzer untersucht wurde, wurden für den Modell-assistierten Ansatz drei unterschiedliche Schätzer untersucht. Die Ergebnisse der Simulationsstudie zeigten, dass das einfache Anwenden von schrittweisen Methoden zur Variablenauswahl generell zur Überschätzung der Genauigkeiten in LiDAR unterstützten Waldinventuren führt. Die verzerrte Schätzung der Genauigkeiten war vor allem für kleine Stichproben (n = 40 und n = 50) von Bedeutung. Für Stichproben von größerem Umfang (n = 400), war die Überschätzung der Genauigkeit vernachlässigbar. Gute Ergebnisse, im Hinblick auf Deckungsraten und empirischem Standardfehler, zeigten Ridge Regression, Lasso und der Random Forest Algorithmus. Aus den Ergebnissen dieser Studie kann abgeleitet werden, dass die zuletzt genannten Methoden in zukünftige LiDAR unterstützten Waldinventuren Berücksichtigung finden sollten.

Orientador: Anna Diva Plasencia Lotufo / Banca: Percival Bueno Araújo / Banca: Walmir de Freitas Filho / Resumo: Neste trabalho, dá-se ênfase à previsão de carga multinodal, também conhecida como previsão de carga por barramento. Para realizar esta demanda, há necessidade de dispor de uma técnica que proporcione a precisão desejada, seja confiável e de baixo tempo de processamento. O conhecimento prévio das cargas locais é de extrema importância para o planejamento e operação dos sistemas de energia elétrica. Para realizar a previsão de carga multinodal foram empregadas duas metodologias, uma que prevê as cargas individualmente e outra que utiliza as previsões dos fatores de participação e a previsão de carga global. O principal objetivo deste trabalho é elaborar um modelo de previsor de carga de curto prazo, genérico e que pode ser aplicado na previsão de carga multinodal. Para tanto, utilizou-se redes neurais de regressão generalizada (GRNN), cujas entradas são compostas de variáveis exógenas globais e de cargas locais, sem a necessidade da inclusão de variáveis exógenas locais. Ainda, projetou-se uma nova arquitetura de rede neural artificial, baseada na GRNN, além de propor um procedimento para a redução do número de entradas da GRNN e um filtro para o pré-processamento do banco de dados de treinamento. Os dados, para testar as metodologias e as redes neurais artificiais, são referentes a um subsistema de distribuição de energia elétrica da Nova Zelândia composto por nove subestações / Abstract: In this work, it is emphasized the multi-nodal load forecast, also known as bus load forecast. To perform this demand, there it is necessary a technique that is precise, trustable and has a short-time processing. The previous knowledge of the local loads is of extreme importance to the planning and operation of the electrical power and energy systems. To perform the multi-nodal load forecast is employed two different methodologies, one that forecast the loads individually and another that uses the participation factors forecasts and the global load forecast. The main objective of this work is to elaborate a generic model of a short-term load forecaster, which can be applied to the multi-nodal load forecast. For this, it was used general regression neural networks (GRNN), with inputs based on external global factors and local loads, without the need of external local factors. Still, it was developed a new architecture of an artificial neural network based on a GRNN and proposed a procedure to reduce the number of input variables of the GRNN and a filter for preprocessing the training data. The dataset, to test the methodologies and the artificial neural networks, refers to a New Zealand electrical distribution subsystem composed of nine substations / Mestre

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Made available in DSpace on 2014-06-11T19:22:32Z (GMT). No. of bitstreams: 0 Previous issue date: 2011-02-16Bitstream added on 2014-06-13T18:08:28Z : No. of bitstreams: 1 nosefilho_k_me_ilha.pdf: 1257365 bytes, checksum: f5d5c54cd646c650661ad9f32be4a6a4 (MD5) / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Neste trabalho, dá-se ênfase à previsão de carga multinodal, também conhecida como previsão de carga por barramento. Para realizar esta demanda, há necessidade de dispor de uma técnica que proporcione a precisão desejada, seja confiável e de baixo tempo de processamento. O conhecimento prévio das cargas locais é de extrema importância para o planejamento e operação dos sistemas de energia elétrica. Para realizar a previsão de carga multinodal foram empregadas duas metodologias, uma que prevê as cargas individualmente e outra que utiliza as previsões dos fatores de participação e a previsão de carga global. O principal objetivo deste trabalho é elaborar um modelo de previsor de carga de curto prazo, genérico e que pode ser aplicado na previsão de carga multinodal. Para tanto, utilizou-se redes neurais de regressão generalizada (GRNN), cujas entradas são compostas de variáveis exógenas globais e de cargas locais, sem a necessidade da inclusão de variáveis exógenas locais. Ainda, projetou-se uma nova arquitetura de rede neural artificial, baseada na GRNN, além de propor um procedimento para a redução do número de entradas da GRNN e um filtro para o pré-processamento do banco de dados de treinamento. Os dados, para testar as metodologias e as redes neurais artificiais, são referentes a um subsistema de distribuição de energia elétrica da Nova Zelândia composto por nove subestações / In this work, it is emphasized the multi-nodal load forecast, also known as bus load forecast. To perform this demand, there it is necessary a technique that is precise, trustable and has a short-time processing. The previous knowledge of the local loads is of extreme importance to the planning and operation of the electrical power and energy systems. To perform the multi-nodal load forecast is employed two different methodologies, one that forecast the loads individually and another that uses the participation factors forecasts and the global load forecast. The main objective of this work is to elaborate a generic model of a short-term load forecaster, which can be applied to the multi-nodal load forecast. For this, it was used general regression neural networks (GRNN), with inputs based on external global factors and local loads, without the need of external local factors. Still, it was developed a new architecture of an artificial neural network based on a GRNN and proposed a procedure to reduce the number of input variables of the GRNN and a filter for preprocessing the training data. The dataset, to test the methodologies and the artificial neural networks, refers to a New Zealand electrical distribution subsystem composed of nine substations

V této práci se zabýváme odhady populačního úhrnu s využitím pomoc- ných informací. V práci je popsán obecný regresní odhad a předpoklady, za kterých je splněna asymptotická normalita tohoto odhadu. Dále jsou zde po- psány kalibrační odhady a zmínka o jejich asymptotické ekvivalenci s obec- ným regresním odhadem. Odvozené závěry aplikujeme na data z RADIO- PROJEKTu a porovnáme je s výsledky získanými společnostmi, které tento projekt realizovali. Na závěr pomocí simulací porovnáme skutečné pravdě- podobnosti pokrytí interval· spolehlivosti pro populační úhrn spočítané na základě teorie uvedené v této práci a na základě metod společností realizu- jících RADIOPROJEKT. 1

The frequency estimation is a problem widely studied in many fields including electric power systems. Several methods have been proposed for this purpose, and most of them perform well when the signal is not distorted by harmonics or noises. This paper presents two new methods based on Artificial Neural Networks for frequency estimation. Both use Clarck s transform to generate a phasor that represent the system s signal. In the first methodology this phasor is normalized and feeds the Generalized Regression Neural Network, that ponders the values. At the end it s obtained a phasor where noisy and harmonics are attenuated. The neural network output is then used to calculate the electrical system frequency. Otherwise, the second methodology uses the Adaptive Linear Neural Network. This work tested also various methodologies of frequency estimation proposed in other knowledge fields such as radar, sonar, communications, biomedicine and aviation however with electrical power systems signals. These methods are: Lavopa (proposed by Lavopa et al. 2007), Quinn (proposed by Quinn, 1994), Jacobsen (proposed by Jacobsen e Kootsookos, 2007), Candan (proposed by Candan, 2011), Macleod (proposed by Macleod, 1998), Aboutanios (proposed by Aboutanios, 2004), Mulgrew (proposed by Aboutanios e Mulgrew, 2005), Ferreira (proposed by Ferreira 2001) e DPLL (proposed by Sithamparanathan, 2008). With the exception of DPLL the remaining methods are based on the Discrete Fourier Transform and seek the spectrum frequency peak to than find the fundamental frequency. The nine methodologies are compared with the proposed methods and with the commonly techniques used or studied for electric power systems. Tests include noisy signals, harmonics, sub-harmonics, frequency variations on step, ramp and sinusoidal, also variations on voltage and phase are considered. The tests also include a simulated signal where a load block is inserted and immediately after removed from the system. At the end a comparison is made between the techniques, been able to point each technique advantage and disadvantage trough the comparison identify the best methods to be applied on electrical power systems. / A estimação de frequência é um problema muito estudado em diversas áreas, dentre elas a dos sistemas elétricos de potência. Inúmeras metodologias foram propostas para esse fim, sendo que a maioria delas apresenta bom desempenho quando o sinal não está distorcido por componentes harmônicas ou ruídos. Este trabalho propõe duas novas metodologias fundamentadas em Redes Neurais Artificiais, de modo a estimar a frequência. Elas utilizam a transformada de Clarck para gerar um fasor que representa o sinal trifásico do sistema. Na primeira metodologia, esse fasor é normalizado e alimenta a Rede Neural de Regressão Generalizada, que faz a ponderação dos valores. Ao final, obtém-se um fasor em que ruídos e harmônicas são atenuados. A saída da rede neural é, então, utilizada para o cálculo da frequência do sistema elétrico. A segunda metodologia utiliza a Rede Neural Linear Adaptativa. Neste trabalho, também são testadas, para uso em sistemas elétricos de potência, diversas metodologias propostas em outras áreas de conhecimento, tais como radar, sonar, comunicação, biomedicina e aviação. São elas: Lavopa (proposta por Lavopa et al. 2007), Quinn (proposta por Quinn, 1994), Jacobsen (proposta por Jacobsen e Kootsookos, 2007), Candan (proposta por Candan, 2011), Macleod (proposta por Macleod, 1998), Aboutanios (proposta por Aboutanios, 2004), Mulgrew (proposta por Aboutanios e Mulgrew, 2005), Ferreira (proposta por Ferreira 2001) e DPLL (proposta por Sithamparanathan, 2008). Com exceção da DPLL, os demais métodos são fundamentados na transformada discreta de Fourier e buscam encontrar o pico do espectro de frequências, para, então, encontrar a frequência fundamental. As nove metodologias são comparadas juntamente com os métodos propostos e as técnicas já comumente usadas ou estudadas para sistemas elétricos. Os testes incluem sinais com ruídos, harmônicas, sub-harmônicas, variações de frequência em degrau, rampa e senoidal, variações de fase e tensão em degrau. Os testes ainda incluem um sinal provindo de simulação em que um bloco de carga é inserido e logo após retirado do sistema. Ao final é realizada uma comparação entre as técnicas, sendo possível identificar as vantagens e desvantagens de cada uma e, assim, indicar as melhores a serem usadas em sistemas elétricos de potência.

[ES] La Ley de Prevención de Riesgos Laborales de 8 de noviembre de 1995 (LPRL), en vigor desde el 10 de febrero de 1996, establece en su artículo 5: "tendrá por objeto la promoción de la mejora de las condiciones de trabajo dirigida a elevar el nivel de protección de la seguridad y la salud de los trabajadores en el trabajo." En esta Tesis se ha investigado la evolución de los índices de siniestralidad laboral y su relación con la evolución de diferentes variables explicativas relacionadas con el desarrollo normativo, el mercado de trabajo, la estructura productiva, las condiciones de empleo y las condiciones individuales, entre otras, para el caso de España y en el periodo 1995-2017, que abarca desde la promulgación de la LPRL hasta fechas recientes donde se disponía de los datos históricos necesarios. La investigación se ha centrado en los índices de salud más relevantes según su significado en términos de riesgo y/o sus componentes. El objetivo de la investigación ha sido el encontrar evidencias sobre relaciones causa-efecto entre índices y variables, a partir de las cuales extraer lecciones que facilitarán una mejor planificación de la acción preventiva. Para ello, se han propuesto varios modelos explicativos utilizando diferentes herramientas estadísticas, que han permitido formular de manera explícita y analizar la relación entre la evolución de los indicadores de salud ocupacional y la evolución de las principales variables explicativas. En términos generales puede concluirse que la implantación de dicha ley y normativa que la acompaña ha tenido un impacto positivo en las condiciones de trabajo y en consecuencia sobre el nivel de seguridad y salud de los trabajadores desde entonces y hasta la fecha. Sin embargo, se observan diferentes comportamientos cíclicos en la evolución de los indicadores, tales como los índices de incidencia, frecuencia y gravedad, que pone de manifiesto su dependencia de la naturaleza y comportamiento cíclico de algunas de las variables explicativas más importantes relacionadas con ciclos económicos, mercado de trabajo, estructura productiva, etc. Además, se observa como aspectos tales como la pertenencia a grupos de edad jóvenes o expertos, el nivel de estudios, determinadas categorías profesionales, y algunos sectores particulares tienen efectos significativos sobre los valores alcanzados por los índices de siniestralidad. En cambio, otros, como el trabajo a tiempo parcial o la contratación temporal no manifiestan tener tanta repercusión sobre los indicadores. / [CA] Partint de les dades corresponents als accidents ocorreguts en el període 1995-2017, es La Llei de Prevenció de Riscos Laborals de 8 de novembre de 1995 (*LPRL), en vigor des del 10 de febrer de 1996, estableix en el seu article 5: "tindrà per objecte la promoció de la millora de les condicions de treball dirigida a elevar el nivell de protecció de la seguretat i la salut dels treballadors en el treball." En aquesta Tesi s'ha investigat l'evolució dels índexs de sinistralitat laboral i la seua relació amb l'evolució de diferents variables explicatives relacionades amb el desenvolupament normatiu, el mercat de treball, l'estructura productiva, les condicions d'ocupació i les condicions individuals, entre altres, per al cas d'Espanya i en el període 1995-2017, que abasta des de la promulgació de la LPRL fins a dates recents on es disposava de les dades històriques necessàries. La investigació s'ha centrat en els índexs de salut més rellevants segons el seu significat en termes de risc i/o els seus components. L'objectiu de la investigació ha sigut el trobar evidències sobre relacions causa-efecte entre índexs i variables, a partir de les quals extraure lliçons que facilitaran una millor planificació de l'acció preventiva. Per a això, s'han proposat diversos models explicatius utilitzant diferents eines estadístiques, que han permés formular de manera explícita i analitzar la relació entre l'evolució dels indicadors de salut ocupacional i l'evolució de les principals variables explicatives. En termes generals pot concloure's que la implantació d'aquesta llei i normativa que l'acompanya ha tingut un impacte positiu en les condicions de treball i en conseqüència sobre el nivell de seguretat i salut dels treballadors des de llavors i fins hui. No obstant això, s'observen diferents comportaments cíclics en l'evolució dels indicadors, com ara els índexs d'incidència, freqüència i gravetat, que posa de manifest la seua dependència de la naturalesa i comportament cíclic d'algunes de les variables explicatives més importants relacionades amb cicles econòmics, mercat de treball, estructura productiva, etc. A més, s'observa com a aspectes com ara la pertinença a grups d'edat joves o experts, el nivell d'estudis, determinades categories professionals, i alguns sectors particulars tenen efectes significatius sobre els valors aconseguits pels índexs de sinistralitat. En canvi, uns altres, com el treball a temps parcial o la contractació temporal no manifesten tindre tanta repercussió sobre els indicadors. / [EN] The Occupational Risk Prevention Act of November 8, 1995 (ORPA), in force since February 10, 1996, establishes in its article 5: "will have as its objective the promotion of the improvement of working conditions aimed at raise the level of protection of the safety and health of workers at work. " This thesis has investigated the evolution of the occupational accident rates and their relationship with the evolution of different explanatory variables related to regulatory development, the labor market, the productive structure, employment conditions and individual conditions, among others, in the case of Spain and in the period 1995-2017, which ranges from the enactment of the LPRL to recent dates where the necessary historical data was available. Research has focused on the most relevant health indices according to their meaning in terms of risk and / or their components. The objective of the research has been to find evidence on cause-effect relationships between indices and variables, from which to extract lessons that will facilitate better planning of preventive action. To this end, several explanatory models have been proposed using different statistical tools, which have made it possible to explicitly formulate and analyze the relationship between the evolution of occupational health indicators and the evolution of the main explanatory variables. In general terms, it can be concluded that the implementation of said law and accompanying regulations has had a positive impact on working conditions and consequently on the level of health and safety of workers since then and to date. However, different cyclical behaviors are observed in the evolution of the indicators, such as incidence, frequency and severity indices, which highlights their dependence on the nature and cyclical behavior of some of the most important explanatory variables related to economic cycles, labor market, productive structure, etc. Furthermore, aspects such as belonging to young age groups or experts, educational level, certain professional categories, and some particular sectors are observed as having significant effects on the values reached by the accident rates. On the other hand, others, such as part-time work or temporary hiring, do not claim to have such an impact on the indicators. / Gallego Blasco, VS. (2021). Análisis de la incidencia de factores causales en la evolución de la siniestralidad laboral en España [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/168774 / TESIS