3D-tuoteyhdistelmämallien ja mittakuvien tuottaminen PDM-järjestelmästä

Tämä diplomityö on selvitystyö mittakuvien ja kolmiulotteisten CAD-mallien tuottamisesta. Mittakuvat ja 3D-CAD-mallit halutaan Neles-tuotemerkin omaaville tuotteille. Olennaisena osana työssä on tuotetiedonhallintajärjestelmä AtonPDM, koska mittakuvia ja malleja toivottaisiin hallittavan AtonPDM-järjestelmällä. Työ tehdään Metso Automationin (MA) Flow Control (FC) –liiketoimintalinjalle. Nykyiset mittakuvat aiheuttavat ongelmia sekä MA:ssa että asiakkaille. MA:ssa mittakuvien tekeminen kestää kauemmin kuin asiakas toivoisi. Nykyisen mittakuvaohjelmiston riittämättömät ominaisuudet aiheuttavat lisätyötä mittakuvien valmistuksessa. Asiakkaille mittakuvien viivästyminen on suunnittelua hidastava tekijä. Mittakuvissa olevat virheet ja puutteet vaikeuttavat asiakkaan suunnittelutyötä ja saattavat päästä läpi tarkatuksien myös loppusuunnitelmiin, jolloin seurauksena voi olla rahallisia menetyksiä. Tämän päivän putkistosuunnittelu hoidetaan suurimmaksi osaksi 3D-CADohjelmistoilla. Suunnittelua helpotetaan ja nopeutetaan valmiilla komponenttien 3Dmalleilla, joihin on liitetty tuotetietoa. Työssä on haastateltu MA:n henkilökuntaa, CAD-järjestelmien toimittajia ja asiakkaita. Haastattelut ovat työn tärkein tiedonlähde. Teoriatietoa on selvitetty kirjoista, lehdistä ja internetistä. Teoriaosiossa käydään läpi tuotetiedonhallinta (Product Data Management, PDM), tietokanta ja parametrinen mallintaminen. Työn lopputuloksena on pyritty saamaan kuvaus siitä miten tuotetaan 3D-CAD-mallit ja mittakuvat Neles-tuotteista käyttäen tiedonlähteenä AtonPDM:ää ja millaisia 3Dmallien ja mittakuvien tulee olla. Malleja ja mittakuvia tuottavaan järjestelmään on etsitty ratkaisuja CAD-ohjelmistoista. Ratkaisuja on verrattu keskenään ominaisuuksien, joustavuuden ja AtonPDM yhteensopivuuden perusteella.

Features extraction based on the Discrete Hartley Transform for closed contour

In this paper the authors propose a new closed contour descriptor that could be seen as a Feature Extractor of closed contours based on the Discrete Hartley Transform (DHT), its main characteristic is that uses only half of the coefficients required by Elliptical Fourier Descriptors (EFD) to obtain a contour approximation with similar error measure. The proposed closed contour descriptor provides an excellent capability of information compression useful for a great number of AI applications. Moreover it can provide scale, position and rotation invariance, and last but not least it has the advantage that both the parameterization and the reconstructed shape from the compressed set can be computed very efficiently by the fast Discrete Hartley Transform (DHT) algorithm. This Feature Extractor could be useful when the application claims for reversible features and when the user needs and easy measure of the quality for a given level of compression, scalable from low to very high quality.

Biomedical Event Extraction with Machine Learning

Biomedical natural language processing (BioNLP) is a subfield of natural language processing, an area of computational linguistics concerned with developing programs that work with natural language: written texts and speech. Biomedical relation extraction concerns the detection of semantic relations such as protein-protein interactions (PPI) from scientific texts. The aim is to enhance information retrieval by detecting relations between concepts, not just individual concepts as with a keyword search. In recent years, events have been proposed as a more detailed alternative for simple pairwise PPI relations. Events provide a systematic, structural representation for annotating the content of natural language texts. Events are characterized by annotated trigger words, directed and typed arguments and the ability to nest other events. For example, the sentence “Protein A causes protein B to bind protein C” can be annotated with the nested event structure CAUSE(A, BIND(B, C)). Converted to such formal representations, the information of natural language texts can be used by computational applications. Biomedical event annotations were introduced by the BioInfer and GENIA corpora, and event extraction was popularized by the BioNLP'09 Shared Task on Event Extraction. In this thesis we present a method for automated event extraction, implemented as the Turku Event Extraction System (TEES). A unified graph format is defined for representing event annotations and the problem of extracting complex event structures is decomposed into a number of independent classification tasks. These classification tasks are solved using SVM and RLS classifiers, utilizing rich feature representations built from full dependency parsing. Building on earlier work on pairwise relation extraction and using a generalized graph representation, the resulting TEES system is capable of detecting binary relations as well as complex event structures. We show that this event extraction system has good performance, reaching the first place in the BioNLP'09 Shared Task on Event Extraction. Subsequently, TEES has achieved several first ranks in the BioNLP'11 and BioNLP'13 Shared Tasks, as well as shown competitive performance in the binary relation Drug-Drug Interaction Extraction 2011 and 2013 shared tasks. The Turku Event Extraction System is published as a freely available open-source project, documenting the research in detail as well as making the method available for practical applications. In particular, in this thesis we describe the application of the event extraction method to PubMed-scale text mining, showing how the developed approach not only shows good performance, but is generalizable and applicable to large-scale real-world text mining projects. Finally, we discuss related literature, summarize the contributions of the work and present some thoughts on future directions for biomedical event extraction. This thesis includes and builds on six original research publications. The first of these introduces the analysis of dependency parses that leads to development of TEES. The entries in the three BioNLP Shared Tasks, as well as in the DDIExtraction 2011 task are covered in four publications, and the sixth one demonstrates the application of the system to PubMed-scale text mining.

Physical and chemical characterization of the pulp of different varieties of avocado targeting oil extraction potential

The aim of this study was to evaluate the physicochemical properties of avocado pulp of four different varieties (Avocado, Guatemala, Dickinson, and Butter pear) and to identify which has the greatest potential for oil extraction. Fresh avocado pulp was characterized by moisture, protein, fat, ash, carbohydrates and energy contents were determined. The carotenoids and chlorophyll contents were determined by the organic solvent extraction method. The results showed significant differences in the composition of the fruit when varieties are compared. However, the striking feature in all varieties is high lipid content; Avocado and Dickinson are the most suitable varieties for oil extraction, taking into account moisture content and the levels of lipids in the pulp. Moreover, it could be said that the variety Dickinson is the most affected by the parameters evaluated in terms of overall quality. Chlorophyll and carotenoids, fat-soluble pigments, showed a negative correlation with respect to lipids since it could be related to its function in the fruit. The varieties Avocado and Dickinson are an alternative to oil extraction having great commercial potential to be exploited thus avoiding waste and increasing farmers’ income.

Applying 3D modelling technology to traditional craftwork : rapid prototyping in artisanal jewellery making and its impact on the perceived value of jewellery

Depuis la révolution industrielle, l’évolution de la technologie bouleverse le monde de la fabrication. Aujourd'hui, de nouvelles technologies telles que le prototypage rapide font une percée dans des domaines comme celui de la fabrication de bijoux, appartenant jadis à l'artisanat et en bouscule les traditions par l'introduction de méthodes plus rapides et plus faciles. Cette recherche vise à répondre aux deux questions suivantes : - ‘En quoi le prototypage rapide influence-t-il la pratique de fabrication de bijoux?’ - ‘En quoi influence-t-il de potentiels acheteurs dans leur appréciation du bijou?’ L' approche consiste en une collecte de données faite au cours de trois entretiens avec différents bijoutiers et une rencontre de deux groupes de discussion composés de consommateurs potentiels. Les résultats ont révélé l’utilité du prototypage rapide pour surmonter un certain nombre d'obstacles inhérents au fait-main, tel que dans sa géométrie, sa commercialisation, et sa finesse de détails. Cependant, il se crée une distance entre la main du bijoutier et l'objet, changeant ainsi la nature de la pratique. Cette technologie est perçue comme un moyen moins authentique car la machine rappelle la production de masse et la possibilité de reproduction en série détruit la notion d’unicité du bijou, en réduisant ainsi sa charge émotionnelle. Cette recherche propose une meilleure compréhension de l'utilisation du prototypage rapide et de ses conséquences dans la fabrication de bijoux. Peut-être ouvrira-t-elle la voie à une recherche visant un meilleur mariage entre cette technique et les méthodes traditionnelles.

Optimierung der laserinduzierten Plasmaspektroskopie an Metallen durch Femtosekunden-Doppelpulse für die Entwicklung eines hochauflösenden 3D-Rasterabbildungsverfahrens

Die laserinduzierte Plasmaspektroskopie (LIPS) ist eine spektrochemische Elementanalyse zur Bestimmung der atomaren Zusammensetzung einer beliebigen Probe. Für die Analyse ist keine spezielle Probenpräparation nötig und kann unter atmosphärischen Bedingungen an Proben in jedem Aggregatzustand durchgeführt werden. Femtosekunden Laserpulse bieten die Vorteile einer präzisen Ablation mit geringem thermischen Schaden sowie einer hohen Reproduzierbarkeit. Damit ist fs-LIPS ein vielversprechendes Werkzeug für die Mikroanalyse technischer Proben, insbesondere zur Untersuchung ihres Ermüdungsverhaltens. Dabei ist interessant, wie sich die initiierten Mikrorisse innerhalb der materialspezifschen Struktur ausbreiten. In der vorliegenden Arbeit sollte daher ein schnelles und einfach zu handhabendes 3D-Rasterabbildungsverfahren zur Untersuchung der Rissausbreitung in TiAl, einer neuen Legierungsklasse, entwickelt werden. Dazu wurde fs-LIPS (30 fs, 785 nm) mit einem modifizierten Mikroskopaufbau (Objektiv: 50x/NA 0.5) kombiniert, welcher eine präzise, automatisierte Probenpositionierung ermöglicht. Spektrochemische Sensitivität und räumliches Auflösungsvermögen wurden in energieabhängigen Einzel- und Multipulsexperimenten untersucht. 10 Laserpulse pro Position mit einer Pulsenergie von je 100 nJ führten in TiAl zum bestmöglichen Kompromiss aus hohem S/N-Verhältnis von 10:1 und kleinen Lochstrukturen mit inneren Durchmessern von 1.4 µm. Die für das Verfahren entscheidende laterale Auflösung, dem minimalen Lochabstand bei konstantem LIPS-Signal, beträgt mit den obigen Parametern 2 µm und ist die bislang höchste bekannte Auflösung einer auf fs-LIPS basierenden Mikro-/Mapping-Analyse im Fernfeld. Fs-LIPS Scans von Teststrukturen sowie Mikrorissen in TiAl demonstrieren eine spektrochemische Sensitivität von 3 %. Scans in Tiefenrichtung erzielen mit denselben Parametern eine axiale Auflösung von 1 µm. Um die spektrochemische Sensitivität von fs-LIPS zu erhöhen und ein besseres Verständnis für die physikalischen Prozesse während der Laserablation zu erhalten, wurde in Pump-Probe-Experimenten untersucht, in wieweit fs-Doppelpulse den laserinduzierten Abtrag sowie die Plasmaemission beeinflussen. Dazu wurden in einem Mach-Zehnder-Interferometer Pulsabstände von 100 fs bis 2 ns realisiert, Gesamtenergie und Intensitätsverhältnis beider Pulse variiert sowie der Einfluss der Materialparameter untersucht. Sowohl das LIPS-Signal als auch die Lochstrukturen zeigen eine Abhängigkeit von der Verzögerungszeit. Diese wurden in vier verschiedene Regimes eingeteilt und den physikalischen Prozessen während der Laserablation zugeordnet: Die Thermalisierung des Elektronensystems für Pulsabstände unter 1 ps, Schmelzprozesse zwischen 1 und 10 ps, der Beginn des Abtrags nach mehreren 10 ps und die Expansion der Plasmawolke nach über 100 ps. Dabei wird das LIPS-Signal effizient verstärkt und bei 800 ps maximal. Die Lochdurchmesser ändern sich als Funktion des Pulsabstands wenig im Vergleich zur Tiefe. Die gesamte Abtragsrate variiert um maximal 50 %, während sich das LIPS-Signal vervielfacht: Für Ti und TiAl typischerweise um das Dreifache, für Al um das 10-fache. Die gemessenen Transienten zeigen eine hohe Reproduzierbarkeit, jedoch kaum eine Energie- bzw. materialspezifische Abhängigkeit. Mit diesen Ergebnissen wurde eine gezielte Optimierung der DP-LIPS-Parameter an Al durchgeführt: Bei einem Pulsabstand von 800 ps und einer Gesamtenergie von 65 nJ (vierfach über der Ablationsschwelle) wurde eine 40-fache Signalerhöhung bei geringerem Rauschen erzielt. Die Lochdurchmesser vergrößerten sich dabei um 44 % auf (650±150) nm, die Lochtiefe um das Doppelte auf (100±15) nm. Damit war es möglich, die spektrochemische Sensitivität von fs-LIPS zu erhöhen und gleichzeitig die hohe räumliche Auflösung aufrecht zu erhalten.

A Statistical Image-Based Shape Model for Visual Hull Reconstruction and 3D Structure Inference

We present a statistical image-based shape + structure model for Bayesian visual hull reconstruction and 3D structure inference. The 3D shape of a class of objects is represented by sets of contours from silhouette views simultaneously observed from multiple calibrated cameras. Bayesian reconstructions of new shapes are then estimated using a prior density constructed with a mixture model and probabilistic principal components analysis. We show how the use of a class-specific prior in a visual hull reconstruction can reduce the effect of segmentation errors from the silhouette extraction process. The proposed method is applied to a data set of pedestrian images, and improvements in the approximate 3D models under various noise conditions are shown. We further augment the shape model to incorporate structural features of interest; unknown structural parameters for a novel set of contours are then inferred via the Bayesian reconstruction process. Model matching and parameter inference are done entirely in the image domain and require no explicit 3D construction. Our shape model enables accurate estimation of structure despite segmentation errors or missing views in the input silhouettes, and works even with only a single input view. Using a data set of thousands of pedestrian images generated from a synthetic model, we can accurately infer the 3D locations of 19 joints on the body based on observed silhouette contours from real images.

View-Based Strategies for 3D Object Recognition

A persistent issue of debate in the area of 3D object recognition concerns the nature of the experientially acquired object models in the primate visual system. One prominent proposal in this regard has expounded the use of object centered models, such as representations of the objects' 3D structures in a coordinate frame independent of the viewing parameters [Marr and Nishihara, 1978]. In contrast to this is another proposal which suggests that the viewing parameters encountered during the learning phase might be inextricably linked to subsequent performance on a recognition task [Tarr and Pinker, 1989; Poggio and Edelman, 1990]. The 'object model', according to this idea, is simply a collection of the sample views encountered during training. Given that object centered recognition strategies have the attractive feature of leading to viewpoint independence, they have garnered much of the research effort in the field of computational vision. Furthermore, since human recognition performance seems remarkably robust in the face of imaging variations [Ellis et al., 1989], it has often been implicitly assumed that the visual system employs an object centered strategy. In the present study we examine this assumption more closely. Our experimental results with a class of novel 3D structures strongly suggest the use of a view-based strategy by the human visual system even when it has the opportunity of constructing and using object-centered models. In fact, for our chosen class of objects, the results seem to support a stronger claim: 3D object recognition is 2D view-based.

Capaware: plataforma de desarrollo de software libre para aplicacines geográficas 3D multicapa

En este trabajo se presenta Capaware, una plataforma de software libre para el desarrollo de aplicaciones geográficas 3D multicapa, que surge a partir de la iniciativa del Instituto Tecnológico de Canarias en colaboración con la Universidad de Las Palmas de Gran Canaria. Este entorno simplifica la creación de aplicaciones 3D sobre territorios geográficos extensos, disponiendo de una herramienta muy visual que aporta un nuevo punto de vista muy importante para una toma de decisiones eficaz. Capaware proporciona una interfaz fácil de usar y muy flexible que simplifica el desarrollo de nuevas aplicaciones, permitiéndonos crear rápidamente entornos virtuales con múltiples capas de información sobre el terreno. Con las capacidades clásicas de un Sistema de Información Geográfica (SIG), Capaware permite actualmente la carga de capas WMS sobre entornos 3D, añadir objetos 3D sobre el terreno, y visualizar elementos dinámicos, ofreciendo una nueva perspectiva de la información analizada. Así mismo, podemos administrar las capas de recursos y elementos que se pueden representar sobre la zona geográfica en cuestión. (...)

Online Robust 3D Mapping Using Structure from Motion Cues

This paper presents a complete solution for creating accurate 3D textured models from monocular video sequences. The methods are developed within the framework of sequential structure from motion, where a 3D model of the environment is maintained and updated as new visual information becomes available. The camera position is recovered by directly associating the 3D scene model with local image observations. Compared to standard structure from motion techniques, this approach decreases the error accumulation while increasing the robustness to scene occlusions and feature association failures. The obtained 3D information is used to generate high quality, composite visual maps of the scene (mosaics). The visual maps are used to create texture-mapped, realistic views of the scene

A neurofuzzy network knowledge extraction and extended gram-Schmidt algorithm for model subspace decomposition

This paper introduces a new neurofuzzy model construction and parameter estimation algorithm from observed finite data sets, based on a Takagi and Sugeno (T-S) inference mechanism and a new extended Gram-Schmidt orthogonal decomposition algorithm, for the modeling of a priori unknown dynamical systems in the form of a set of fuzzy rules. The first contribution of the paper is the introduction of a one to one mapping between a fuzzy rule-base and a model matrix feature subspace using the T-S inference mechanism. This link enables the numerical properties associated with a rule-based matrix subspace, the relationships amongst these matrix subspaces, and the correlation between the output vector and a rule-base matrix subspace, to be investigated and extracted as rule-based knowledge to enhance model transparency. The matrix subspace spanned by a fuzzy rule is initially derived as the input regression matrix multiplied by a weighting matrix that consists of the corresponding fuzzy membership functions over the training data set. Model transparency is explored by the derivation of an equivalence between an A-optimality experimental design criterion of the weighting matrix and the average model output sensitivity to the fuzzy rule, so that rule-bases can be effectively measured by their identifiability via the A-optimality experimental design criterion. The A-optimality experimental design criterion of the weighting matrices of fuzzy rules is used to construct an initial model rule-base. An extended Gram-Schmidt algorithm is then developed to estimate the parameter vector for each rule. This new algorithm decomposes the model rule-bases via an orthogonal subspace decomposition approach, so as to enhance model transparency with the capability of interpreting the derived rule-base energy level. This new approach is computationally simpler than the conventional Gram-Schmidt algorithm for resolving high dimensional regression problems, whereby it is computationally desirable to decompose complex models into a few submodels rather than a single model with large number of input variables and the associated curse of dimensionality problem. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and D-optimality

A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a priori unknown dynamical systems from observed finite data sets in the form of a set of fuzzy rules. Based on a Takagi-Sugeno (T-S) inference mechanism a one to one mapping between a fuzzy rule base and a model matrix feature subspace is established. This link enables rule based knowledge to be extracted from matrix subspace to enhance model transparency. In order to achieve maximized model robustness and sparsity, a new robust extended Gram-Schmidt (G-S) method has been introduced via two effective and complementary approaches of regularization and D-optimality experimental design. Model rule bases are decomposed into orthogonal subspaces, so as to enhance model transparency with the capability of interpreting the derived rule base energy level. A locally regularized orthogonal least squares algorithm, combined with a D-optimality used for subspace based rule selection, has been extended for fuzzy rule regularization and subspace based information extraction. By using a weighting for the D-optimality cost function, the entire model construction procedure becomes automatic. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

Learning enhanced 3D models for vehicle tracking

This paper presents an enhanced hypothesis verification strategy for 3D object recognition. A new learning methodology is presented which integrates the traditional dichotomic object-centred and appearance-based representations in computer vision giving improved hypothesis verification under iconic matching. The "appearance" of a 3D object is learnt using an eigenspace representation obtained as it is tracked through a scene. The feature representation implicitly models the background and the objects observed enabling the segmentation of the objects from the background. The method is shown to enhance model-based tracking, particularly in the presence of clutter and occlusion, and to provide a basis for identification. The unified approach is discussed in the context of the traffic surveillance domain. The approach is demonstrated on real-world image sequences and compared to previous (edge-based) iconic evaluation techniques.

Advanced feature selection methods in multinominal dementia classification from structural MRI data

Recent studies showed that features extracted from brain MRIs can well discriminate Alzheimer’s disease from Mild Cognitive Impairment. This study provides an algorithm that sequentially applies advanced feature selection methods for findings the best subset of features in terms of binary classification accuracy. The classifiers that provided the highest accuracies, have been then used for solving a multi-class problem by the one-versus-one strategy. Although several approaches based on Regions of Interest (ROIs) extraction exist, the prediction power of features has not yet investigated by comparing filter and wrapper techniques. The findings of this work suggest that (i) the IntraCranial Volume (ICV) normalization can lead to overfitting and worst the accuracy prediction of test set and (ii) the combined use of a Random Forest-based filter with a Support Vector Machines-based wrapper, improves accuracy of binary classification.

3D printing of biocompatible supramolecular polymers and their composites

A series of polymers capable of self-assembling into infinite networks via supramolecular interactions have been designed, synthesized, and characterized for use in 3D printing applications. The biocompatible polymers and their composites with silica nanoparticles were successfully utilized to deposit both simple cubic structures, as well as a more complex twisted pyramidal feature. The polymers were found to be not toxic to a chondrogenic cell line, according to ISO 10993-5 and 10993-12 standard tests and the cells attached to the supramolecular polymers as demonstrated by confocal microscopy. Silica nanoparticles were then dispersed within the polymer matrix, yielding a composite material which was optimized for inkjet printing. The hybrid material showed promise in preliminary tests to facilitate the 3D deposition of a more complex structure.