Ontology for Data Representation in the Production of Cotton Fiber in Brazil

An important feature in computer systems developed for the agricultural sector is to satisfy the heterogeneity of data generated in different processes. Most problems related with this heterogeneity arise from the lack of standard for different computing solutions proposed. An efficient solution for that is to create a single standard for data exchange. The study on the actual process involved in cotton production was based on a research developed by the Brazilian Agricultural Research Corporation (EMBRAPA) that reports all phases as a result of the compilation of several theoretical and practical researches related to cotton crop. The proposition of a standard starts with the identification of the most important classes of data involved in the process, and includes an ontology that is the systematization of concepts related to the production of cotton fiber and results in a set of classes, relations, functions and instances. The results are used as a reference for the development of computational tools, transforming implicit knowledge into applications that support the knowledge described. This research is based on data from the Midwest of Brazil. The choice of the cotton process as a study case comes from the fact that Brazil is one of the major players and there are several improvements required for system integration in this segment.

Materiali Compositi a Matrice Cementizia per i Rinforzi Strutturali

L’utilizzo degli FRP (Fiber Reinforced Polymer) nel campo dell’ingegneria civile riguarda essenzialmente il settore del restauro delle strutture degradate o danneggiate e quello dell’adeguamento statico delle strutture edificate in zona sismica; in questi settori è evidente la difficoltà operativa alla quale si va in contro se si volessero utilizzare tecniche di intervento che sfruttano materiali tradizionali. I motivi per cui è opportuno intervenire con sistemi compositi fibrosi sono: • l’estrema leggerezza del rinforzo, da cui ne deriva un incremento pressoché nullo delle masse sismiche ed allo stesso tempo un considerevole aumento della duttilità strutturale; • messa in opera senza l’ausilio di particolari attrezzature da un numero limitato di operatori, da cui un minore costo della mano d’opera; • posizionamento in tempi brevi e spesso senza interrompere l’esercizio della struttura. Il parametro principale che definisce le caratteristiche di un rinforzo fibroso non è la resistenza a trazione, che risulta essere ben al di sopra dei tassi di lavoro cui sono soggette le fibre, bensì il modulo elastico, di fatti, più tale valore è elevato maggiore sarà il contributo irrigidente che il rinforzo potrà fornire all’elemento strutturale sul quale è applicato. Generalmente per il rinforzo di strutture in c.a. si preferiscono fibre sia con resistenza a trazione medio-alta (>2000 MPa) che con modulo elastico medio-alto (E=170-250 GPa), mentre per il recupero degli edifici in muratura o con struttura in legno si scelgono fibre con modulo di elasticità più basso (E≤80 GPa) tipo quelle aramidiche che meglio si accordano con la rigidezza propria del supporto rinforzato. In questo contesto, ormai ampliamente ben disposto nei confronti dei compositi, si affacciano ora nuove generazioni di rinforzi. A gli ormai “classici” FRP, realizzati con fibre di carbonio o fibre di vetro accoppiate a matrici organiche (resine epossidiche), si affiancano gli FRCM (Fiber Reinforced Cementitious Matrix), i TRM (Textile Reinforced Mortars) e gli SRG (Steel Reinforced Grout) che sfruttano sia le eccezionali proprietà di fibre di nuova concezione come quelle in PBO (Poliparafenilenbenzobisoxazolo), sia un materiale come l’acciaio, che, per quanto comune nel campo dell’edilizia, viene caratterizzato da lavorazioni innovative che ne migliorano le prestazioni meccaniche. Tutte queste nuove tipologie di compositi, nonostante siano state annoverate con nomenclature così differenti, sono però accomunate dell’elemento che ne permette il funzionamento e l’adesione al supporto: la matrice cementizia

Longitudinal bar buckling behavior

Reinforced concrete columns might fail because of buckling of the longitudinal reinforcing bar when exposed to earthquake motions. Depending on the hoop stiffness and the length-over-diameter ratio, the instability can be local (in between two subsequent hoops) or global (the buckling length comprises several hoop spacings). To get insight into the topic, an extensive literary research of 19 existing models has been carried out including different approaches and assumptions which yield different results. Finite element fiberanalysis was carried out to study the local buckling behavior with varying length-over-diameter and initial imperfection-over-diameter ratios. The comparison of the analytical results with some experimental results shows good agreement before the post buckling behavior undergoes large deformation. Furthermore, different global buckling analysis cases were run considering the influence of different parameters; for certain hoop stiffnesses and length-over-diameter ratios local buckling was encountered. A parametric study yields an adimensional critical stress in function of a stiffness ratio characterized by the reinforcement configuration. Colonne in cemento armato possono collassare per via dell’instabilità dell’armatura longitudinale se sottoposte all’azione di un sisma. In funzione della rigidezza dei ferri trasversali e del rapporto lunghezza d’inflessione-diametro, l’instabilità può essere locale (fra due staffe adiacenti) o globale (la lunghezza d’instabilità comprende alcune staffe). Per introdurre alla materia, è proposta un’esauriente ricerca bibliografica di 19 modelli esistenti che include approcci e ipotesi differenti che portano a risultati distinti. Tramite un’analisi a fibre e elementi finiti si è studiata l’instabilità locale con vari rapporti lunghezza d’inflessione-diametro e imperfezione iniziale-diametro. Il confronto dei risultati analitici con quelli sperimentali mostra una buona coincidenza fino al raggiungimento di grandi spostamenti. Inoltre, il caso d’instabilità globale è stato simulato valutando l’influenza di vari parametri; per certe configurazioni di rigidezza delle staffe e lunghezza d’inflessione-diametro si hanno ottenuto casi di instabilità locale. Uno studio parametrico ha permesso di ottenere un carico critico adimensionale in funzione del rapporto di rigidezza dato dalle caratteristiche dell’armatura.

Bonded structures for enhanced damage tolerant pressurized fuselages

Adhesive bonding provides solutions to realize cost effective and low weight aircraft fuselage structures, in particular where the Damage Tolerance (DT) is the design criterion. Bonded structures that combine Metal Laminates (MLs) and eventually Selective Reinforcements can guarantee slow crack propagation, crack arrest and large damage capability. To optimize the design exploiting the benefit of bonded structures incorporating selective reinforcement requires reliable analysis tools. The effect of bonded doublers / selective reinforcements is very difficult to be predicted numerically or analytically due to the complexity of the underlying mechanisms and failures modes acting. Reliable predictions of crack growth and residual strength can only be based on sound empirical and phenomenological considerations strictly related to the specific structural concept. Large flat stiffened panels that combine MLs and selective reinforcements have been tested with the purpose of investigating solutions applicable to pressurized fuselages. The large test campaign (for a total of 35 stiffened panels) has quantitatively investigated the role of the different metallic skin concepts (monolithic vs. MLs) of the aluminum, titanium and glass-fiber reinforcements, of the stringers material and cross sections and of the geometry and location of doublers / selective reinforcements. Bonded doublers and selective reinforcements confirmed to be outstanding tools to improve the DT properties of structural elements with a minor weight increase. However the choice of proper materials for the skin and the stringers must be not underestimated since they play an important role as well. A fuselage structural concept has been developed to exploit the benefit of a metal laminate design concept in terms of high Fatigue and Damage Tolerance (F&DT) performances. The structure used laminated skin (0.8mm thick), bonded stringers, two different splicing solutions and selective reinforcements (glass prepreg embedded in the laminate) under the circumferential frames. To validate the design concept a curved panel was manufactured and tested under loading conditions representative of a single aisle fuselage: cyclic internal pressurization plus longitudinal loads. The geometry of the panel, design and loading conditions were tailored for the requirements of the upper front fuselage. The curved panel has been fatigue tested for 60 000 cycles before the introduction of artificial damages (cracks in longitudinal and circumferential directions). The crack growth of the artificial damages has been investigated for about 85 000 cycles. At the end a residual strength test has been performed with a “2 bay over broken frame” longitudinal crack. The reparability of this innovative concept has been taken into account during design and demonstrated with the use of an external riveted repair. The F&DT curved panel test has confirmed that a long fatigue life and high damage tolerance can be achieved with a hybrid metal laminate low weight configuration. The superior fatigue life from metal laminates and the high damage tolerance characteristics provided by integrated selective reinforcements are the key concepts that provided the excellent performances. The weight comparison between the innovative bonded concept and a conventional monolithic riveted design solution showed a significant potential weight saving but the weight advantages shall be traded off with the additional costs.

Aktionenlernen mit Selbstorganisierenden Karten und Reinforcement Learning

Die vorliegende Arbeit beschäftigt sich mit der Entwicklung eines Funktionsapproximators und dessen Verwendung in Verfahren zum Lernen von diskreten und kontinuierlichen Aktionen: 1. Ein allgemeiner Funktionsapproximator – Locally Weighted Interpolating Growing Neural Gas (LWIGNG) – wird auf Basis eines Wachsenden Neuralen Gases (GNG) entwickelt. Die topologische Nachbarschaft in der Neuronenstruktur wird verwendet, um zwischen benachbarten Neuronen zu interpolieren und durch lokale Gewichtung die Approximation zu berechnen. Die Leistungsfähigkeit des Ansatzes, insbesondere in Hinsicht auf sich verändernde Zielfunktionen und sich verändernde Eingabeverteilungen, wird in verschiedenen Experimenten unter Beweis gestellt. 2. Zum Lernen diskreter Aktionen wird das LWIGNG-Verfahren mit Q-Learning zur Q-LWIGNG-Methode verbunden. Dafür muss der zugrunde liegende GNG-Algorithmus abgeändert werden, da die Eingabedaten beim Aktionenlernen eine bestimmte Reihenfolge haben. Q-LWIGNG erzielt sehr gute Ergebnisse beim Stabbalance- und beim Mountain-Car-Problem und gute Ergebnisse beim Acrobot-Problem. 3. Zum Lernen kontinuierlicher Aktionen wird ein REINFORCE-Algorithmus mit LWIGNG zur ReinforceGNG-Methode verbunden. Dabei wird eine Actor-Critic-Architektur eingesetzt, um aus zeitverzögerten Belohnungen zu lernen. LWIGNG approximiert sowohl die Zustands-Wertefunktion als auch die Politik, die in Form von situationsabhängigen Parametern einer Normalverteilung repräsentiert wird. ReinforceGNG wird erfolgreich zum Lernen von Bewegungen für einen simulierten 2-rädrigen Roboter eingesetzt, der einen rollenden Ball unter bestimmten Bedingungen abfangen soll.

Studio dell’effetto di cicli termici su materiali CFRP (Carbon Fiber Reinforced Polymer) attraverso prove di caratterizzazione statica

L’intento di questa tesi è fornire un andamento di alcune proprietà dei materiali compositi in fibra di carbonio, CFRP, utilizzati soprattutto nell’ambito aeronautico e navale, esposti quindi a condizioni ambientali specifiche di variazione ciclica della temperatura. Lo studio è effettuato sulle prove di caratterizzazione statica, di compressione, flessione in tre punti e taglio interlaminare, che generano risultati sulla resistenza delle fibre e della matrice e sul modulo elastico a compressione e trazione del composito.

Fiber beam-columns models with flexure-shear interaction for nonlinear analysis of reinforced concrete structures

The aim of this study was to develop a model capable to capture the different contributions which characterize the nonlinear behaviour of reinforced concrete structures. In particular, especially for non slender structures, the contribution to the nonlinear deformation due to bending may be not sufficient to determine the structural response. Two different models characterized by a fibre beam-column element are here proposed. These models can reproduce the flexure-shear interaction in the nonlinear range, with the purpose to improve the analysis in shear-critical structures. The first element discussed is based on flexibility formulation which is associated with the Modified Compression Field Theory as material constitutive law. The other model described in this thesis is based on a three-field variational formulation which is associated with a 3D generalized plastic-damage model as constitutive relationship. The first model proposed in this thesis was developed trying to combine a fibre beamcolumn element based on the flexibility formulation with the MCFT theory as constitutive relationship. The flexibility formulation, in fact, seems to be particularly effective for analysis in the nonlinear field. Just the coupling between the fibre element to model the structure and the shear panel to model the individual fibres allows to describe the nonlinear response associated to flexure and shear, and especially their interaction in the nonlinear field. The model was implemented in an original matlab® computer code, for describing the response of generic structures. The simulations carried out allowed to verify the field of working of the model. Comparisons with available experimental results related to reinforced concrete shears wall were performed in order to validate the model. These results are characterized by the peculiarity of distinguishing the different contributions due to flexure and shear separately. The presented simulations were carried out, in particular, for monotonic loading. The model was tested also through numerical comparisons with other computer programs. Finally it was applied for performing a numerical study on the influence of the nonlinear shear response for non slender reinforced concrete (RC) members. Another approach to the problem has been studied during a period of research at the University of California Berkeley. The beam formulation follows the assumptions of the Timoshenko shear beam theory for the displacement field, and uses a three-field variational formulation in the derivation of the element response. A generalized plasticity model is implemented for structural steel and a 3D plastic-damage model is used for the simulation of concrete. The transverse normal stress is used to satisfy the transverse equilibrium equations of at each control section, this criterion is also used for the condensation of degrees of freedom from the 3D constitutive material to a beam element. In this thesis is presented the beam formulation and the constitutive relationships, different analysis and comparisons are still carrying out between the two model presented.

Fiber-Optic Technologies for Wireline and Wireless In-building Networks

This doctoral dissertation aims to establish fiber-optic technologies overcoming the limiting issues of data communications in indoor environments. Specific applications are broadband mobile distribution in different in-building scenarios and high-speed digital transmission over short-range wired optical systems. Two key enabling technologies are considered: Radio over Fiber (RoF) techniques over standard silica fibers for distributed antenna systems (DAS) and plastic optical fibers (POFs) for short-range communications. Hence, the objectives and achievements of this thesis are related to the application of RoF and POF technologies in different in-building scenarios. On one hand, a theoretical and experimental analysis combined with demonstration activities has been performed on cost-effective RoF systems. An extensive modeling on modal noise impact both on linear and non-linear characteristics of RoF link over silica multimode fiber has been performed to achieve link design rules for an optimum choice of the transmitter, receiver and launching technique. A successful transmission of Long Term Evolution (LTE) mobile signals on the resulting optimized RoF system over silica multimode fiber employing a Fabry-Perot LD, central launch technique and a photodiode with a built-in ball lens was demonstrated up to 525m with performances well compliant with standard requirements. On the other hand, digital signal processing techniques to overcome the bandwidth limitation of POF have been investigated. An uncoded net bit-rate of 5.15Gbit/s was obtained on a 50m long POF link employing an eye-safe transmitter, a silicon photodiode, and DMT modulation with bit and power loading algorithm. With the insertion of 3x2N quadrature amplitude modulation constellation formats, an uncoded net-bit-rate of 5.4Gbit/s was obtained on a 50 m long POF link employing an eye-safe transmitter and a silicon avalanche photodiode. Moreover, simultaneous transmission of baseband 2Gbit/s with DMT and 200Mbit/s with an ultra-wideband radio signal has been validated over a 50m long POF link.

Reinforcement Lernen mit Regularisierungsnetzwerken

Die Arbeit behandelt das Problem der Skalierbarkeit von Reinforcement Lernen auf hochdimensionale und komplexe Aufgabenstellungen. Unter Reinforcement Lernen versteht man dabei eine auf approximativem Dynamischen Programmieren basierende Klasse von Lernverfahren, die speziell Anwendung in der Künstlichen Intelligenz findet und zur autonomen Steuerung simulierter Agenten oder realer Hardwareroboter in dynamischen und unwägbaren Umwelten genutzt werden kann. Dazu wird mittels Regression aus Stichproben eine Funktion bestimmt, die die Lösung einer "Optimalitätsgleichung" (Bellman) ist und aus der sich näherungsweise optimale Entscheidungen ableiten lassen. Eine große Hürde stellt dabei die Dimensionalität des Zustandsraums dar, die häufig hoch und daher traditionellen gitterbasierten Approximationsverfahren wenig zugänglich ist. Das Ziel dieser Arbeit ist es, Reinforcement Lernen durch nichtparametrisierte Funktionsapproximation (genauer, Regularisierungsnetze) auf -- im Prinzip beliebig -- hochdimensionale Probleme anwendbar zu machen. Regularisierungsnetze sind eine Verallgemeinerung von gewöhnlichen Basisfunktionsnetzen, die die gesuchte Lösung durch die Daten parametrisieren, wodurch die explizite Wahl von Knoten/Basisfunktionen entfällt und so bei hochdimensionalen Eingaben der "Fluch der Dimension" umgangen werden kann. Gleichzeitig sind Regularisierungsnetze aber auch lineare Approximatoren, die technisch einfach handhabbar sind und für die die bestehenden Konvergenzaussagen von Reinforcement Lernen Gültigkeit behalten (anders als etwa bei Feed-Forward Neuronalen Netzen). Allen diesen theoretischen Vorteilen gegenüber steht allerdings ein sehr praktisches Problem: der Rechenaufwand bei der Verwendung von Regularisierungsnetzen skaliert von Natur aus wie O(n**3), wobei n die Anzahl der Daten ist. Das ist besonders deswegen problematisch, weil bei Reinforcement Lernen der Lernprozeß online erfolgt -- die Stichproben werden von einem Agenten/Roboter erzeugt, während er mit der Umwelt interagiert. Anpassungen an der Lösung müssen daher sofort und mit wenig Rechenaufwand vorgenommen werden. Der Beitrag dieser Arbeit gliedert sich daher in zwei Teile: Im ersten Teil der Arbeit formulieren wir für Regularisierungsnetze einen effizienten Lernalgorithmus zum Lösen allgemeiner Regressionsaufgaben, der speziell auf die Anforderungen von Online-Lernen zugeschnitten ist. Unser Ansatz basiert auf der Vorgehensweise von Recursive Least-Squares, kann aber mit konstantem Zeitaufwand nicht nur neue Daten sondern auch neue Basisfunktionen in das bestehende Modell einfügen. Ermöglicht wird das durch die "Subset of Regressors" Approximation, wodurch der Kern durch eine stark reduzierte Auswahl von Trainingsdaten approximiert wird, und einer gierigen Auswahlwahlprozedur, die diese Basiselemente direkt aus dem Datenstrom zur Laufzeit selektiert. Im zweiten Teil übertragen wir diesen Algorithmus auf approximative Politik-Evaluation mittels Least-Squares basiertem Temporal-Difference Lernen, und integrieren diesen Baustein in ein Gesamtsystem zum autonomen Lernen von optimalem Verhalten. Insgesamt entwickeln wir ein in hohem Maße dateneffizientes Verfahren, das insbesondere für Lernprobleme aus der Robotik mit kontinuierlichen und hochdimensionalen Zustandsräumen sowie stochastischen Zustandsübergängen geeignet ist. Dabei sind wir nicht auf ein Modell der Umwelt angewiesen, arbeiten weitestgehend unabhängig von der Dimension des Zustandsraums, erzielen Konvergenz bereits mit relativ wenigen Agent-Umwelt Interaktionen, und können dank des effizienten Online-Algorithmus auch im Kontext zeitkritischer Echtzeitanwendungen operieren. Wir demonstrieren die Leistungsfähigkeit unseres Ansatzes anhand von zwei realistischen und komplexen Anwendungsbeispielen: dem Problem RoboCup-Keepaway, sowie der Steuerung eines (simulierten) Oktopus-Tentakels.

Study of the mechanisms of shear transfer in fiber-reinforced concrete elements

matlab functions for the validation of push-off tests results

Utilizzo industriale di cfrp (carbon fiber reinforced polymers) nanomodificati con nanofibre prodotte per elettrospinning

Attraverso questo studio sono state indagate le proprietà di compositi laminati in fibra di carbonio (CFRP) nano-modificati con nanofibre in Nylon 6.6, in termini di resistenza al danneggiamento da impatti a bassa velocità (con caratterizzazione Drop Weight at Low Velocity) e di smorzamento della vibrazione (con caratterizzazione a damping). Sono stati indagate due configurazioni di nanorinforzo differenti, confrontate con le prestazioni di provini vergini laminati tradizionalmente. Sono infine state operate delle analisi grafiche delle micrografie di campioni sezionati per trarre conclusioni di carattere tecnologico.

Development and applications of hollow fiber flow field-flow fractionation in the bioanalytical field. Studies of aggregation phenomena in complex protein samples

Recent advances in the fast growing area of therapeutic/diagnostic proteins and antibodies - novel and highly specific drugs - as well as the progress in the field of functional proteomics regarding the correlation between the aggregation of damaged proteins and (immuno) senescence or aging-related pathologies, underline the need for adequate analytical methods for the detection, separation, characterization and quantification of protein aggregates, regardless of the their origin or formation mechanism. Hollow fiber flow field-flow fractionation (HF5), the miniaturized version of FlowFFF and integral part of the Eclipse DUALTEC FFF separation system, was the focus of this research; this flow-based separation technique proved to be uniquely suited for the hydrodynamic size-based separation of proteins and protein aggregates in a very broad size and molecular weight (MW) range, often present at trace levels. HF5 has shown to be (a) highly selective in terms of protein diffusion coefficients, (b) versatile in terms of bio-compatible carrier solution choice, (c) able to preserve the biophysical properties/molecular conformation of the proteins/protein aggregates and (d) able to discriminate between different types of protein aggregates. Thanks to the miniaturization advantages and the online coupling with highly sensitive detection techniques (UV/Vis, intrinsic fluorescence and multi-angle light scattering), HF5 had very low detection/quantification limits for protein aggregates. Compared to size-exclusion chromatography (SEC), HF5 demonstrated superior selectivity and potential as orthogonal analytical method in the extended characterization assays, often required by therapeutic protein formulations. In addition, the developed HF5 methods have proven to be rapid, highly selective, sensitive and repeatable. HF5 was ideally suitable as first dimension of separation of aging-related protein aggregates from whole cell lysates (proteome pre-fractionation method) and, by HF5-(UV)-MALS online coupling, important biophysical information on the fractionated proteins and protein aggregates was gathered: size (rms radius and hydrodynamic radius), absolute MW and conformation.