Connected-X-out-of-(m; n):F システムの信頼度評価と最適設計に関する研究

首都大学東

Geostatistical Modeling of Built Environment Characteristics of Urban Moveability

Physical inactivity is considered as a major risk factor for non-communicable diseases. In particular, technological developments and environmental changes led to a decrease of physical activity in daily life. In the last two decades, the investigation of factors influencing physical activity more and more focused on characteristics of the built environment and identified opportunities as well as barriers for physical activity. The walkability concept was established to investigate the association between the built environment and physical activity. This research starts from a walkability index that combines urban measures for residential density, land use mix, and street connectivity. The walkability index was mainly used to investigate neighborhoods that support physical activity with regard to active transport of adults in everyday life. In this thesis, the walkability concept was expanded in order to assess walkability measures and recreational facilities that affect physical activity in European children. A new concept was introduced to capture opportunities for physical activity in the neighborhood of children, called moveability index

Determination of the main parameters affecting the performance of bridge falsework systems

Bridge falsework systems are one of the most common temporary structures used in the construction industry, namely to support the formwork during the construction, rehabilitation or retrofit works of concrete bridges and viaducts. This Thesis presents new results and research that improve the available knowledge about the structural behaviour, reliability, robustness and risk of these structures. The main, internal and external, hazards are identified and detailed, including the procedural, enabling and triggering hazards. The use of reduction factors to determine the values of the applied loads to design bridge falsework, and other temporary structures, is critically analysed and it is recommended not to use them, unless supported by specific site data. The importance of implementing effective quality control, inspection and communication measures to manage human errors during planning, designing and operation is highlighted. From the 192 tests carried out during the experimental campaign, consisting of five different tests using three different joint types, new results are obtained concerning bridge falsework components, namely the bending behaviour and resistance of spigot joints and forkhead joints (falsework to formwork interface) from which no published research was found. Existing joint models are evaluated and improved alternative models are developed. The results of numerical studies of a selected structural system are presented using a novel joint finite element and information gathered from the experimental tests. This new finite element has features that the available elements in ABAQUS® program do not have, specifically the capability of simulating an analytical modelling of the cyclic behaviour of joints with allowance for stiffness and resistance degradation and joint failure. The accuracy and precision of the developed numerical models improves the existing numerical results of full-scale tests of bridge falsework systems, in respect to structural behaviour and resistance. It is recommended that formwork should be explicitly modelled and modelling of spigot joints should follow the model presented in this Thesis. From a sensitivity analysis of the bridge falsework systems to modelling hypothesis, it is found that the most important joints are the beam-to-column joint, followed by the forkhead joint and the spigot joint, with variations of up to 70% between the resistance of the system when the joints are modelled as continuous or as pinned. A key contribution of the Thesis is to introduce a novel risk management methodology based on newly developed robustness and fragility indices. This new methodology is applicable, in principle, to all structural analyses not only those concerning bridge falsework systems. Based on advanced deterministic studies, the main parameters affecting the performance of bridge falsework are identified, analysed and discussed. These studies involved a comprehensive set of external and internal hazards: (i) applied external actions of different nature and (ii) structural configurations to design bridge falsework. It is found that differential ground settlements are a critical action and that stiffer systems are more sensitive. Also, it is highlighted by use of plenty examples that bracing is an essential design requirement. Advanced stochastic investigations are also carried out, in which the key random variables that control the stochastic behaviour of bridge falsework systems are identified, namely joint looseness and initial stiffness after looseness. Possible strategies to increase robustness and decrease fragility are discussed and based on an application example the cost-benefit of alternative solutions is investigated. It is concluded that implementing quality control and quality assurance procedures to bridge falsework elements is an extremely effective and efficient way of reducing existing risks. The information gathered in this Thesis can be used to develop more rational and reliable bridge falsework structures thus safer and more design efficient

Neuromodulated plasticity of the connectivity between the Prefrontal Cortex and the noradrenergic Locus Coeruleus

Incentive stimuli and environmental stressors are encoded at the level of the prefrontal cortex (PFC) circuits, which send their glutamatergic excitatory projections to several neuromodulatory regions, including the Locus Coeruleus (LC), the major source of noradrenaline (NA) for the entire forebrain. Despite the potential implications for NA-mediated regulation of action control and for the etiology of stress-related neuropsychiatric conditions, it remains to be established how LC neuronal activity is shaped by impinging PFC inputs (PFC\uf0e0LC) to affect behavior, and whether these inputs are modulated by in-vivo experience. By combining neurophysiological and optogenetic approaches together with behavioral paradigms in mice, we found that PFC \uf0e0LC stimulation supports learning and retrieval of contextual memory associations. Consistent with the occurrence of plasticity processes at LC synapses, long-lasting modulation of PFC\uf0e0LC projections relies on the endocannabinoid (eCB)-mediated signaling capacity, which is dynamically shaped by context adaptations and stress salience experiences. We also found that eCB-plasticity at PFC \u2192 LC synapses is regulated during the adolescence to adulthood transition. In summary, our results not only dissect the behavioral implications of neuromodulated plasticity at PFC inputs to the LC, but also unveil divergent synaptic substrates during postnatal development, which might be relevant to explain some of the different noradrenergic-mediated response in adolescents and adults. \u200

Advanced methods for earth observation data synergy for geophysical parameter retrieval

The first part of the thesis focuses on the analysis of relevant factors to estimate the response time between satellite-based and in-situ soil moisture (SM) using a Dynamic Time Warping (DTW). DTW was applied to the SMOS L4 SM, and was compared to in-situ root-zone SM in the REMEDHUS network in Western Spain. The method was customized to control the evolution of time lag during wetting and drying conditions. Climate factors in combination with crop growing seasons were studied to reveal SM-related processes. The heterogeneity of land use was analyzed using high-resolution images of NDVI from Sentinel-2 to provide information about the level of spatial representativity of SMOS data to each in-situ station. The comparison of long-term precipitation records and potential evapotranspiration allowed estimation of SM seasons describing different SM conditions depending on climate and soil properties. The second part of the thesis focuses on data-driven methods for sea ice segmentation and parameter retrieval. A Bayesian framework is employed to segment sets of multi-source satellite data. The Bayesian unsupervised learning algorithm allows to investigate the ‘hidden link’ between multiple data. The statistical properties are accounted for by a Gaussian Mixture Model, and the spatial interactions are reflected using Hidden Markov Random Fields. The algorithm segments spatial data into a number of classes, which are represented as a latent field in physical space and as clusters in feature space. In a first application, a two-step probabilistic approach based on Expectation-Maximization and the Bayesian segmentation algorithm was used to segment SAR images to discriminate surface water from sea ice types. Information on surface roughness is contained in the radar backscattering images which can be - in principle - used to detect melt ponds and to estimate high-resolution sea ice concentration (SIC). In a second study, the algorithm was applied to multi-incidence angle TB data from the SMOS L1C product to harness the its sensitivity to thin ice. The spatial patterns clearly discriminate well-determined areas of open water, old sea ice and a transition zone, which is sensitive to thin sea ice thickness (SIT) and SIC. In a third application, SMOS and the AMSR2 data are used to examine the joint effect of CIMR-like observations. The information contained in the low-frequency channels allows to reveal ranges of thin sea ice, and thicker ice can be determined from the relationship between the high-frequency channels and changing conditions as the sea ice ages. The proposed approach is suitable for merging large data sets and provides metrics for class analysis, and to make informed choices about integrating data from future missions into sea ice products. A regression neural network approach was investigated with the goal to infer SIT using TB data from the Flexible Microwave Payload 2 (FMPL-2) of the FSSCat mission. Two models - covering thin ice up to 0.6m and the full-range of SIT - were trained on Arctic data using ground truth data derived from the SMOS and Cryosat-2. This work demonstrates that moderate-cost CubeSat missions can provide valuable data for applications in Earth observation.La primera parte de la tesis se centra en el análisis de los factores relevantes para estimar el tiempo de respuesta entre la humedad del suelo (SM) basada en el satélite y la in-situ, utilizando una deformación temporal dinámica (DTW). El DTW se aplicó al SMOS L4 SM, y se comparó con la SM in-situ en la red REMEDHUS en el oeste de España. El método se adaptó para controlar la evolución del desfase temporal durante diferentes condiciones de humedad y secado. Se estudiaron los factores climáticos en combinación con los períodos de crecimiento de los cultivos para revelar los procesos relacionados con la SM. La heterogeneidad del uso del suelo se analizó utilizando imágenes de alta resolución de NDVI de Sentinel-2 para proporcionar información sobre el nivel de representatividad espacial de los datos de SMOS a cada estación in situ. La comparación de los patrones de precipitación a largo plazo y la evapotranspiración potencial permitió estimar las estaciones de SM que describen diferentes condiciones de SM en función del clima y las propiedades del suelo. La segunda parte de esta tesis se centra en métodos dirigidos por datos para la segmentación del hielo marino y la obtención de parámetros. Se emplea un método de inferencia bayesiano para segmentar conjuntos de datos satelitales de múltiples fuentes. El algoritmo de aprendizaje bayesiano no supervisado permite investigar el “vínculo oculto” entre múltiples datos. Las propiedades estadísticas se contabilizan mediante un modelo de mezcla gaussiana, y las interacciones espaciales se reflejan mediante campos aleatorios ocultos de Markov. El algoritmo segmenta los datos espaciales en una serie de clases, que se representan como un campo latente en el espacio físico y como clústeres en el espacio de las variables. En una primera aplicación, se utilizó un enfoque probabilístico de dos pasos basado en la maximización de expectativas y el algoritmo de segmentación bayesiano para segmentar imágenes SAR con el objetivo de discriminar el agua superficial de los tipos de hielo marino. La información sobre la rugosidad de la superficie está contenida en las imágenes de backscattering del radar, que puede utilizarse -en principio- para detectar estanques de deshielo y estimar la concentración de hielo marino (SIC) de alta resolución. En un segundo estudio, el algoritmo se aplicó a los datos TB de múltiples ángulos de incidencia del producto SMOS L1C para aprovechar su sensibilidad al hielo fino. Los patrones espaciales discriminan claramente áreas bien determinadas de aguas abiertas, hielo marino viejo y una zona de transición, que es sensible al espesor del hielo marino fino (SIT) y al SIC. En una tercera aplicación, se utilizan los datos de SMOS y de AMSR2 para examinar el efecto conjunto de las observaciones tipo CIMR. La información contenida en los canales de baja frecuencia permite revelar rangos de hielo marino delgado, y el hielo más grueso puede determinarse a partir de la relación entre los canales de alta frecuencia y las condiciones cambiantes a medida que el hielo marino envejece. El enfoque propuesto es adecuado para fusionar grandes conjuntos de datos y proporciona métricas para el análisis de clases, y para tomar decisiones informadas sobre la integración de datos de futuras misiones en los productos de hielo marino. Se investigó un enfoque de red neuronal de regresión con el objetivo de inferir el SIT utilizando datos de TB de la carga útil de microondas flexible 2 (FMPL-2) de la misión FSSCat. Se entrenaron dos modelos - que cubren el hielo fino hasta 0.6 m y el rango completo del SIT - con datos del Ártico utilizando datos de “ground truth” derivados del SMOS y del Cryosat-2. Este trabajo demuestra que las misiones CubeSat de coste moderado pueden proporcionar datos valiosos para aplicaciones de observación de la Tierra.Postprint (published version

Experimental and Data-driven Workflows for Microstructure-based Damage Prediction

Materialermüdung ist die häufigste Ursache für mechanisches Versagen. Die Degradationsmechanismen, welche die Lebensdauer von Bauteilen bei vergleichsweise ausgeprägten zyklischen Belastungen bestimmen, sind gut bekannt. Bei Belastungen im makroskopisch elastischen Bereich hingegen, der (sehr) hochzyklischen Ermüdung, bestimmen die innere Struktur eines Werkstoffs und die Wechselwirkung kristallografischer Defekte die Lebensdauer. Unter diesen Umständen sind die inneren Degradationsphänomene auf der mikroskopischen Skala weitgehend reversibel und führen nicht zur Bildung kritischer Schädigungen, die kontinuierlich wachsen können. Allerdings sind einige Kornensembles in polykristallinen Metallen, je nach den lokalen mikrostrukturellen Gegebenheiten, anfällig für Schädigungsinitiierung, Rissbildung und -wachstum und wirken daher als Schwachstellen. Daher weisen Bauteile, die solchen Belastungen ausgesetzt sind, oft eine ausgeprägte Lebensdauerstreuung auf. Die Tatsache, dass ein umfassendes mechanistisches Verständnis für diese Degradationsprozesse in verschiedenen Werkstoffen nicht vorliegt, hat zur Folge, dass die derzeitigen Modellierungsbemühungen die mittlere Lebensdauer und ihre Varianz in der Regel nur mit unbefriedigender Genauigkeit vorhersagen. Dies wiederum erschwert die Bauteilauslegung und macht die Nutzung von Sicherheitsfaktoren während des Dimensionierungsprozesses erforderlich. Abhilfe kann geschaffen werden, indem umfangreiche Daten zu Einflussfaktoren und deren Wirkung auf die Bildung initialer Ermüdungsschädigungen erhoben werden. Die Datenknappheit wirkt sich nach wie vor negativ auf Datenwissenschaftler und Modellierungsexperten aus, die versuchen, trotz geringer Stichprobengröße und unvollständigen Merkmalsräumen, mikrostrukturelle Abhängigkeiten abzuleiten, datengetriebene Vorhersagemodelle zu trainieren oder physikalische, regelbasierte Modelle zu parametrisieren. Die Tatsache, dass nur wenige kritische Schädigungen bezogen auf das gesamte Probenvolumen auftreten und die hochzyklische Ermüdung eine Vielzahl unterschiedlicher Abhängigkeiten aufweist, impliziert einige Anforderungen an die Datenerfassung und -verarbeitung. Am wichtigsten ist, dass die Messtechniken so empfindlich sind, dass nuancierte Schwankungen im Probenzustand erfasst werden können, dass die gesamte Routine effizient ist und dass die korrelative Mikroskopie räumliche Informationen aus verschiedenen Messungen miteinander verbindet. Das Hauptziel dieser Arbeit besteht darin, einen Workflow zu etablieren, der den Datenmangel behebt, so dass die zukünftige virtuelle Auslegung von Komponenten effizienter, zuverlässiger und nachhaltiger gestaltet werden kann. Zu diesem Zweck wird in dieser Arbeit ein kombinierter experimenteller und datenverarbeitender Workflow vorgeschlagen, um multimodale Datensätze zu Ermüdungsschädigungen zu erzeugen. Der Schwerpunkt liegt dabei auf dem Auftreten von lokalen Gleitbändern, der Rissinitiierung und dem Wachstum mikrostrukturell kurzer Risse. Der Workflow vereint die Ermüdungsprüfung von mesoskaligen Proben, um die Empfindlichkeit der Schädigungsdetektion zu erhöhen, die ergänzende Charakterisierung, die multimodale Registrierung und Datenfusion der heterogenen Daten, sowie die bildverarbeitungsbasierte Schädigungslokalisierung und -bewertung. Mesoskalige Biegeresonanzprüfung ermöglicht das Erreichen des hochzyklischen Ermüdungszustands in vergleichsweise kurzen Zeitspannen bei gleichzeitig verbessertem Auflösungsvermögen der Schädigungsentwicklung. Je nach Komplexität der einzelnen Bildverarbeitungsaufgaben und Datenverfügbarkeit werden entweder regelbasierte Bildverarbeitungsverfahren oder Repräsentationslernen gezielt eingesetzt. So sorgt beispielsweise die semantische Segmentierung von Schädigungsstellen dafür, dass wichtige Ermüdungsmerkmale aus mikroskopischen Abbildungen extrahiert werden können. Entlang des Workflows wird auf einen hohen Automatisierungsgrad Wert gelegt. Wann immer möglich, wurde die Generalisierbarkeit einzelner Workflow-Elemente untersucht. Dieser Workflow wird auf einen ferritischen Stahl (EN 1.4003) angewendet. Der resultierende Datensatz verknüpft unter anderem große verzerrungskorrigierte Mikrostrukturdaten mit der Schädigungslokalisierung und deren zyklischer Entwicklung. Im Zuge der Arbeit wird der Datensatz wird im Hinblick auf seinen Informationsgehalt untersucht, indem detaillierte, analytische Studien zur einzelnen Schädigungsbildung durchgeführt werden. Auf diese Weise konnten unter anderem neuartige, quantitative Erkenntnisse über mikrostrukturinduzierte plastische Verformungs- und Rissstopmechanismen gewonnen werden. Darüber hinaus werden aus dem Datensatz abgeleitete kornweise Merkmalsvektoren und binäre Schädigungskategorien verwendet, um einen Random-Forest-Klassifikator zu trainieren und dessen Vorhersagegüte zu bewerten. Der vorgeschlagene Workflow hat das Potenzial, die Grundlage für künftiges Data Mining und datengetriebene Modellierung mikrostrukturempfindlicher Ermüdung zu legen. Er erlaubt die effiziente Erhebung statistisch repräsentativer Datensätze mit gleichzeitig hohem Informationsgehalt und kann auf eine Vielzahl von Werkstoffen ausgeweitet werden

Automatic Extraction and Assessment of Entities from the Web

The search for information about entities, such as people or movies, plays an increasingly important role on the Web. This information is still scattered across many Web pages, making it more time consuming for a user to find all relevant information about an entity. This thesis describes techniques to extract entities and information about these entities from the Web, such as facts, opinions, questions and answers, interactive multimedia objects, and events. The findings of this thesis are that it is possible to create a large knowledge base automatically using a manually-crafted ontology. The precision of the extracted information was found to be between 75–90 % (facts and entities respectively) after using assessment algorithms. The algorithms from this thesis can be used to create such a knowledge base, which can be used in various research fields, such as question answering, named entity recognition, and information retrieval

Enhancing the corrosion resistance of API 5L X70 pipeline steel through thermomechanically controlled processing

Pipelines are widely used for transportation of oil and gas because they can carry large volume of these products at lower cost compared to rail cars and trucks. However, they are prone to environmentally assisted degradation. Different methods of optimizing pipeline steels such as micro-alloying, desulfurization, microstructure design, and inclusion morphology control have been used to improve their performance in different service environments. This study presents the use microstructure and texture control to improve the reliability of API 5L X70 pipeline steel. The properties of API 5L X70 steel was manipulated through thermomechanical processing. Pipeline steel plates were produced using different processing schedules. The final microstructure and texture of processed steels were determined. Also, electrochemical corrosion studies were performed on selected steels in hydrogen producing and non-hydrogen producing electrolyte solutions. In addition, samples from each steel was investigated for hydrogen induced cracking (HIC) with and without the application of tensile stress. Further annealing heat treatments were conducted on the steel with improved HIC behavior before assessing hydrogen embrittlement characteristics. All the tests were performed at room temperature. Generally, the lowest corrosion rate was measured in the hydrogen producing electrolyte due to the rapid formation of a corrosion protective film on the pipeline substrate. It was found that variations in the processing conditions affects corrosion and cracking behavior of steels. The least corrosion resistant steels experienced more intense surface deterioration after polarization. Subsequently, such steels were damaged by hydrogen attack. The steel with improved corrosion resistance displayed no visible cracking after probing in corrosive mediums and charging with hydrogen. Despite weak texture noticed in all the steels, (111) crystal planes showed better electrochemical corrosion resistance compared to (110) and (100). Moreover, microstructural features such as non-metallic inclusions/precipitates, grain characteristics, phase distribution, and local average misorientations were analyzed in relation crack initiation and propagation. Evidence suggests that cracking occurred mainly through deformed regions in the mid-thickness section. Early onset of crack formations was characterized by stepwise discontinuities. It was demonstrated that during in-situ tensile deformation and hydrogen charging, sudden failure takes place in the elastic zone. The obtained results indicate that cracks propagated through segregations of iron carbide around high angle grain boundaries. Also, multi-component inclusion particles comprising of angular (Ti-Nb) N precipitates, spherical Al-Mg-Ca oxides, and some traces of Mo-Mn-S influenced susceptibility to HIC. It was observed that hydrogen embrittlement susceptibility was lowered after annealing in two consecutive cycles compared to single annealing treatment. The double step heat treatment resulted in a dual-phase (ferrite and tempered martensite) microstructure with greater ductility than in original steels. Finally, the pipeline steel that was hot rolled (880 – 820 °C) and rapidly cooled (755 – 615 °C) at the rate of 25 °C/s has shown improved resistance to corrosion