Comparison of Statistical and Semantic Similarity Techniques for Paraphrase Identification

This paper compares statistical technique of paraphrase identification to semantic technique of paraphrase identification. The statistical techniques used for comparison are word set and word-order based methods where as the semantic technique used is the WordNet similarity matrix method described by Stevenson and Fernando in [3].

Video Object Tracking And Analysis For Computer Assisted Surgery

Pedicle screw insertion technique has made revolution in the surgical treatment of spinal fractures and spinal disorders. Although X- ray fluoroscopy based navigation is popular, there is risk of prolonged exposure to X- ray radiation. Systems that have lower radiation risk are generally quite expensive. The position and orientation of the drill is clinically very important in pedicle screw fixation. In this paper, the position and orientation of the marker on the drill is determined using pattern recognition based methods, using geometric features, obtained from the input video sequence taken from CCD camera. A search is then performed on the video frames after preprocessing, to obtain the exact position and orientation of the drill. An animated graphics, showing the instantaneous position and orientation of the drill is then overlaid on the processed video for real time drill control and navigation

Development of Directionally Adaptive Techniques for Single Image Super-Resolution

Super Resolution problem is an inverse problem and refers to the process of producing a High resolution (HR) image, making use of one or more Low Resolution (LR) observations. It includes up sampling the image, thereby, increasing the maximum spatial frequency and removing degradations that arise during the image capture namely aliasing and blurring. The work presented in this thesis is based on learning based single image super-resolution. In learning based super-resolution algorithms, a training set or database of available HR images are used to construct the HR image of an image captured using a LR camera. In the training set, images are stored as patches or coefficients of feature representations like wavelet transform, DCT, etc. Single frame image super-resolution can be used in applications where database of HR images are available. The advantage of this method is that by skilfully creating a database of suitable training images, one can improve the quality of the super-resolved image. A new super resolution method based on wavelet transform is developed and it is better than conventional wavelet transform based methods and standard interpolation methods. Super-resolution techniques based on skewed anisotropic transform called directionlet transform are developed to convert a low resolution image which is of small size into a high resolution image of large size. Super-resolution algorithm not only increases the size, but also reduces the degradations occurred during the process of capturing image. This method outperforms the standard interpolation methods and the wavelet methods, both visually and in terms of SNR values. Artifacts like aliasing and ringing effects are also eliminated in this method. The super-resolution methods are implemented using, both critically sampled and over sampled directionlets. The conventional directionlet transform is computationally complex. Hence lifting scheme is used for implementation of directionlets. The new single image super-resolution method based on lifting scheme reduces computational complexity and thereby reduces computation time. The quality of the super resolved image depends on the type of wavelet basis used. A study is conducted to find the effect of different wavelets on the single image super-resolution method. Finally this new method implemented on grey images is extended to colour images and noisy images

Neuartige Verfahren für die Überwachung und Diagnose von aktiv magnetgelagerten rotierenden Maschinen

Mit aktiven Magnetlagern ist es möglich, rotierende Körper durch magnetische Felder berührungsfrei zu lagern. Systembedingt sind bei aktiv magnetgelagerten Maschinen wesentliche Signale ohne zusätzlichen Aufwand an Messtechnik für Diagnoseaufgaben verfügbar. In der Arbeit wird ein Konzept entwickelt, das durch Verwendung der systeminhärenten Signale eine Diagnose magnetgelagerter rotierender Maschinen ermöglicht und somit neben einer kontinuierlichen Anlagenüberwachung eine schnelle Bewertung des Anlagenzustandes gestattet. Fehler können rechtzeitig und ursächlich in Art und Größe erkannt und entsprechende Gegenmaßnahmen eingeleitet werden. Anhand der erfassten Signale geschieht die Gewinnung von Merkmalen mit signal- und modellgestützten Verfahren. Für den Magnetlagerregelkreis erfolgen Untersuchungen zum Einsatz modellgestützter Parameteridentifikationsverfahren, deren Verwendbarkeit wird bei der Diagnose am Regler und Leistungsverstärker nachgewiesen. Unter Nutzung von Simulationsmodellen sowie durch Experimente an Versuchsständen werden die Merkmalsverläufe im normalen Referenzzustand und bei auftretenden Fehlern aufgenommen und die Ergebnisse in einer Wissensbasis abgelegt. Diese dient als Grundlage zur Festlegung von Grenzwerten und Regeln für die Überwachung des Systems und zur Erstellung wissensbasierter Diagnosemodelle. Bei der Überwachung werden die Merkmalsausprägungen auf das Überschreiten von Grenzwerten überprüft, Informationen über erkannte Fehler und Betriebszustände gebildet sowie gegebenenfalls Alarmmeldungen ausgegeben. Sich langsam anbahnende Fehler können durch die Berechnung der Merkmalstrends mit Hilfe der Regressionsanalyse erkannt werden. Über die bisher bei aktiven Magnetlagern übliche Überwachung von Grenzwerten hinaus erfolgt bei der Fehlerdiagnose eine Verknüpfung der extrahierten Merkmale zur Identifizierung und Lokalisierung auftretender Fehler. Die Diagnose geschieht mittels regelbasierter Fuzzy-Logik, dies gestattet die Einbeziehung von linguistischen Aussagen in Form von Expertenwissen sowie die Berücksichtigung von Unbestimmtheiten und ermöglicht damit eine Diagnose komplexer Systeme. Für Aktor-, Sensor- und Reglerfehler im Magnetlagerregelkreis sowie Fehler durch externe Kräfte und Unwuchten werden Diagnosemodelle erstellt und verifiziert. Es erfolgt der Nachweis, dass das entwickelte Diagnosekonzept mit beherrschbarem Rechenaufwand korrekte Diagnoseaussagen liefert. Durch Kaskadierung von Fuzzy-Logik-Modulen wird die Transparenz des Regelwerks gewahrt und die Abarbeitung der Regeln optimiert. Endresultat ist ein neuartiges hybrides Diagnosekonzept, welches signal- und modellgestützte Verfahren der Merkmalsgewinnung mit wissensbasierten Methoden der Fehlerdiagnose kombiniert. Das entwickelte Diagnosekonzept ist für die Anpassung an unterschiedliche Anforderungen und Anwendungen bei rotierenden Maschinen konzipiert.

Verfahren zur echtzeitfähigen Simulation dynamischer Emissionsverläufe ausgewählter Schadstoffe von Ottomotoren

Im Rahmen dieser Arbeit werden Modellbildungsverfahren zur echtzeitfähigen Simulation wichtiger Schadstoffkomponenten im Abgasstrom von Verbrennungsmotoren vorgestellt. Es wird ein ganzheitlicher Entwicklungsablauf dargestellt, dessen einzelne Schritte, beginnend bei der Ver-suchsplanung über die Erstellung einer geeigneten Modellstruktur bis hin zur Modellvalidierung, detailliert beschrieben werden. Diese Methoden werden zur Nachbildung der dynamischen Emissi-onsverläufe relevanter Schadstoffe des Ottomotors angewendet. Die abgeleiteten Emissionsmodelle dienen zusammen mit einer Gesamtmotorsimulation zur Optimierung von Betriebstrategien in Hybridfahrzeugen. Im ersten Abschnitt der Arbeit wird eine systematische Vorgehensweise zur Planung und Erstellung von komplexen, dynamischen und echtzeitfähigen Modellstrukturen aufgezeigt. Es beginnt mit einer physikalisch motivierten Strukturierung, die eine geeignete Unterteilung eines Prozessmodells in einzelne überschaubare Elemente vorsieht. Diese Teilmodelle werden dann, jeweils ausgehend von einem möglichst einfachen nominalen Modellkern, schrittweise erweitert und ermöglichen zum Abschluss eine robuste Nachbildung auch komplexen, dynamischen Verhaltens bei hinreichender Genauigkeit. Da einige Teilmodelle als neuronale Netze realisiert werden, wurde eigens ein Verfah-ren zur sogenannten diskreten evidenten Interpolation (DEI) entwickelt, das beim Training einge-setzt, und bei minimaler Messdatenanzahl ein plausibles, also evidentes Verhalten experimenteller Modelle sicherstellen kann. Zum Abgleich der einzelnen Teilmodelle wurden statistische Versuchs-pläne erstellt, die sowohl mit klassischen DoE-Methoden als auch mittels einer iterativen Versuchs-planung (iDoE ) generiert wurden. Im zweiten Teil der Arbeit werden, nach Ermittlung der wichtigsten Einflussparameter, die Model-strukturen zur Nachbildung dynamischer Emissionsverläufe ausgewählter Abgaskomponenten vor-gestellt, wie unverbrannte Kohlenwasserstoffe (HC), Stickstoffmonoxid (NO) sowie Kohlenmono-xid (CO). Die vorgestellten Simulationsmodelle bilden die Schadstoffkonzentrationen eines Ver-brennungsmotors im Kaltstart sowie in der anschließenden Warmlaufphase in Echtzeit nach. Im Vergleich zur obligatorischen Nachbildung des stationären Verhaltens wird hier auch das dynami-sche Verhalten des Verbrennungsmotors in transienten Betriebsphasen ausreichend korrekt darge-stellt. Eine konsequente Anwendung der im ersten Teil der Arbeit vorgestellten Methodik erlaubt, trotz einer Vielzahl von Prozesseinflussgrößen, auch hier eine hohe Simulationsqualität und Ro-bustheit. Die Modelle der Schadstoffemissionen, eingebettet in das dynamische Gesamtmodell eines Ver-brennungsmotors, werden zur Ableitung einer optimalen Betriebsstrategie im Hybridfahrzeug ein-gesetzt. Zur Lösung solcher Optimierungsaufgaben bieten sich modellbasierte Verfahren in beson-derer Weise an, wobei insbesondere unter Verwendung dynamischer als auch kaltstartfähiger Mo-delle und der damit verbundenen Realitätsnähe eine hohe Ausgabequalität erreicht werden kann.

Meshing highly regular structures: The case of super carbon nanotubes of arbitrary order

Mesh generation is an important step inmany numerical methods.We present the “HierarchicalGraphMeshing” (HGM)method as a novel approach to mesh generation, based on algebraic graph theory.The HGM method can be used to systematically construct configurations exhibiting multiple hierarchies and complex symmetry characteristics. The hierarchical description of structures provided by the HGM method can be exploited to increase the efficiency of multiscale and multigrid methods. In this paper, the HGMmethod is employed for the systematic construction of super carbon nanotubes of arbitrary order, which present a pertinent example of structurally and geometrically complex, yet highly regular, structures. The HGMalgorithm is computationally efficient and exhibits good scaling characteristics. In particular, it scales linearly for super carbon nanotube structures and is working much faster than geometry-based methods employing neighborhood search algorithms. Its modular character makes it conducive to automatization. For the generation of a mesh, the information about the geometry of the structure in a given configuration is added in a way that relates geometric symmetries to structural symmetries. The intrinsically hierarchic description of the resulting mesh greatly reduces the effort of determining mesh hierarchies for multigrid and multiscale applications and helps to exploit symmetry-related methods in the mechanical analysis of complex structures.

Alignment by Maximization of Mutual Information

A new information-theoretic approach is presented for finding the pose of an object in an image. The technique does not require information about the surface properties of the object, besides its shape, and is robust with respect to variations of illumination. In our derivation, few assumptions are made about the nature of the imaging process. As a result the algorithms are quite general and can foreseeably be used in a wide variety of imaging situations. Experiments are presented that demonstrate the approach registering magnetic resonance (MR) images with computed tomography (CT) images, aligning a complex 3D object model to real scenes including clutter and occlusion, tracking a human head in a video sequence and aligning a view-based 2D object model to real images. The method is based on a formulation of the mutual information between the model and the image called EMMA. As applied here the technique is intensity-based, rather than feature-based. It works well in domains where edge or gradient-magnitude based methods have difficulty, yet it is more robust than traditional correlation. Additionally, it has an efficient implementation that is based on stochastic approximation. Finally, we will describe a number of additional real-world applications that can be solved efficiently and reliably using EMMA. EMMA can be used in machine learning to find maximally informative projections of high-dimensional data. EMMA can also be used to detect and correct corruption in magnetic resonance images (MRI).

Localización de faltas en sistemas de distribución de energía eléctrica usando métodos basados en el modelo y métodos basados en el conocimiento

La calidad de energía eléctrica incluye la calidad del suministro y la calidad de la atención al cliente. La calidad del suministro a su vez se considera que la conforman dos partes, la forma de onda y la continuidad. En esta tesis se aborda la continuidad del suministro a través de la localización de faltas. Este problema se encuentra relativamente resuelto en los sistemas de transmisión, donde por las características homogéneas de la línea, la medición en ambos terminales y la disponibilidad de diversos equipos, se puede localizar el sitio de falta con una precisión relativamente alta. En sistemas de distribución, sin embargo, la localización de faltas es un problema complejo y aún no resuelto. La complejidad es debida principalmente a la presencia de conductores no homogéneos, cargas intermedias, derivaciones laterales y desbalances en el sistema y la carga. Además, normalmente, en estos sistemas sólo se cuenta con medidas en la subestación, y un modelo simplificado del circuito. Los principales esfuerzos en la localización han estado orientados al desarrollo de métodos que utilicen el fundamental de la tensión y de la corriente en la subestación, para estimar la reactancia hasta la falta. Como la obtención de la reactancia permite cuantificar la distancia al sitio de falta a partir del uso del modelo, el Método se considera Basado en el Modelo (MBM). Sin embargo, algunas de sus desventajas están asociadas a la necesidad de un buen modelo del sistema y a la posibilidad de localizar varios sitios donde puede haber ocurrido la falta, esto es, se puede presentar múltiple estimación del sitio de falta. Como aporte, en esta tesis se presenta un análisis y prueba comparativa entre varios de los MBM frecuentemente referenciados. Adicionalmente se complementa la solución con métodos que utilizan otro tipo de información, como la obtenida de las bases históricas de faltas con registros de tensión y corriente medidos en la subestación (no se limita solamente al fundamental). Como herramienta de extracción de información de estos registros, se utilizan y prueban dos técnicas de clasificación (LAMDA y SVM). Éstas relacionan las características obtenidas de la señal, con la zona bajo falta y se denominan en este documento como Métodos de Clasificación Basados en el Conocimiento (MCBC). La información que usan los MCBC se obtiene de los registros de tensión y de corriente medidos en la subestación de distribución, antes, durante y después de la falta. Los registros se procesan para obtener los siguientes descriptores: a) la magnitud de la variación de tensión ( dV ), b) la variación de la magnitud de corriente ( dI ), c) la variación de la potencia ( dS ), d) la reactancia de falta ( Xf ), e) la frecuencia del transitorio ( f ), y f) el valor propio máximo de la matriz de correlación de corrientes (Sv), cada uno de los cuales ha sido seleccionado por facilitar la localización de la falta. A partir de estos descriptores, se proponen diferentes conjuntos de entrenamiento y validación de los MCBC, y mediante una metodología que muestra la posibilidad de hallar relaciones entre estos conjuntos y las zonas en las cuales se presenta la falta, se seleccionan los de mejor comportamiento. Los resultados de aplicación, demuestran que con la combinación de los MCBC con los MBM, se puede reducir el problema de la múltiple estimación del sitio de falta. El MCBC determina la zona de falta, mientras que el MBM encuentra la distancia desde el punto de medida hasta la falta, la integración en un esquema híbrido toma las mejores características de cada método. En este documento, lo que se conoce como híbrido es la combinación de los MBM y los MCBC, de una forma complementaria. Finalmente y para comprobar los aportes de esta tesis, se propone y prueba un esquema de integración híbrida para localización de faltas en dos sistemas de distribución diferentes. Tanto los métodos que usan los parámetros del sistema y se fundamentan en la estimación de la impedancia (MBM), como aquellos que usan como información los descriptores y se fundamentan en técnicas de clasificación (MCBC), muestran su validez para resolver el problema de localización de faltas. Ambas metodologías propuestas tienen ventajas y desventajas, pero según la teoría de integración de métodos presentada, se alcanza una alta complementariedad, que permite la formulación de híbridos que mejoran los resultados, reduciendo o evitando el problema de la múltiple estimación de la falta.

Problems with determining the particle size distribution of chalk soil and some of their implications

Particle size distribution (psd) is one of the most important features of the soil because it affects many of its other properties, and it determines how soil should be managed. To understand the properties of chalk soil, psd analyses should be based on the original material (including carbonates), and not just the acid-resistant fraction. Laser-based methods rather than traditional sedimentation methods are being used increasingly to determine particle size to reduce the cost of analysis. We give an overview of both approaches and the problems associated with them for analyzing the psd of chalk soil. In particular, we show that it is not appropriate to use the widely adopted 8 pm boundary between the clay and silt size fractions for samples determined by laser to estimate proportions of these size fractions that are equivalent to those based on sedimentation. We present data from field and national-scale surveys of soil derived from chalk in England. Results from both types of survey showed that laser methods tend to over-estimate the clay-size fraction compared to sedimentation for the 8 mu m clay/silt boundary, and we suggest reasons for this. For soil derived from chalk, either the sedimentation methods need to be modified or it would be more appropriate to use a 4 pm threshold as an interim solution for laser methods. Correlations between the proportions of sand- and clay-sized fractions, and other properties such as organic matter and volumetric water content, were the opposite of what one would expect for soil dominated by silicate minerals. For water content, this appeared to be due to the predominance of porous, chalk fragments in the sand-sized fraction rather than quartz grains, and the abundance of fine (<2 mu m) calcite crystals rather than phyllosilicates in the clay-sized fraction. This was confirmed by scanning electron microscope (SEM) analyses. "Of all the rocks with which 1 am acquainted, there is none whose formation seems to tax the ingenuity of theorists so severely, as the chalk, in whatever respect we may think fit to consider it". Thomas Allan, FRS Edinburgh 1823, Transactions of the Royal Society of Edinburgh. (C) 2009 Natural Environment Research Council (NERC) Published by Elsevier B.V. All rights reserved.

Sensitivity analysis of a catchment-scale nitrogen model

There are now considerable expectations that semi-distributed models are useful tools for supporting catchment water quality management. However, insufficient attention has been given to evaluating the uncertainties inherent to this type of model, especially those associated with the spatial disaggregation of the catchment. The Integrated Nitrogen in Catchments model (INCA) is subjected to an extensive regionalised sensitivity analysis in application to the River Kennet, part of the groundwater-dominated upper Thames catchment, UK The main results are: (1) model output was generally insensitive to land-phase parameters, very sensitive to groundwater parameters, including initial conditions, and significantly sensitive to in-river parameters; (2) INCA was able to produce good fits simultaneously to the available flow, nitrate and ammonium in-river data sets; (3) representing parameters as heterogeneous over the catchment (206 calibrated parameters) rather than homogeneous (24 calibrated parameters) produced a significant improvement in fit to nitrate but no significant improvement to flow and caused a deterioration in ammonium performance; (4) the analysis indicated that calibrating the flow-related parameters first, then calibrating the remaining parameters (as opposed to calibrating all parameters together) was not a sensible strategy in this case; (5) even the parameters to which the model output was most sensitive suffered from high uncertainty due to spatial inconsistencies in the estimated optimum values, parameter equifinality and the sampling error associated with the calibration method; (6) soil and groundwater nutrient and flow data are needed to reduce. uncertainty in initial conditions, residence times and nitrogen transformation parameters, and long-term historic data are needed so that key responses to changes in land-use management can be assimilated. The results indicate the general, difficulty of reconciling the questions which catchment nutrient models are expected to answer with typically limited data sets and limited knowledge about suitable model structures. The results demonstrate the importance of analysing semi-distributed model uncertainties prior to model application, and illustrate the value and limitations of using Monte Carlo-based methods for doing so. (c) 2005 Elsevier B.V. All rights reserved.

Identifying appropriate construction procurement strategies

The origins of the various procurement methods in construction are considered, followed by a discussion of the circumstances for which each procurement system is best suited. The likely future development sin management-based methods are discussed in the context of current research, showing that co-ordination of trade and specialist contractors , more flexible contracting, constructive conflict management and a potential polarization of views about project management in the industry are likely.

Prediction of global and local model quality in CASP8 using the ModFOLD server

The development of effective methods for predicting the quality of three-dimensional (3D) models is fundamentally important for the success of tertiary structure (TS) prediction strategies. Since CASP7, the Quality Assessment (QA) category has existed to gauge the ability of various model quality assessment programs (MQAPs) at predicting the relative quality of individual 3D models. For the CASP8 experiment, automated predictions were submitted in the QA category using two methods from the ModFOLD server-ModFOLD version 1.1 and ModFOLDclust. ModFOLD version 1.1 is a single-model machine learning based method, which was used for automated predictions of global model quality (QMODE1). ModFOLDclust is a simple clustering based method, which was used for automated predictions of both global and local quality (QMODE2). In addition, manual predictions of model quality were made using ModFOLD version 2.0-an experimental method that combines the scores from ModFOLDclust and ModFOLD v1.1. Predictions from the ModFOLDclust method were the most successful of the three in terms of the global model quality, whilst the ModFOLD v1.1 method was comparable in performance to other single-model based methods. In addition, the ModFOLDclust method performed well at predicting the per-residue, or local, model quality scores. Predictions of the per-residue errors in our own 3D models, selected using the ModFOLD v2.0 method, were also the most accurate compared with those from other methods. All of the MQAPs described are publicly accessible via the ModFOLD server at: http://www.reading.ac.uk/bioinf/ModFOLD/. The methods are also freely available to download from: http://www.reading.ac.uk/bioinf/downloads/.

Aligning sequences to structures

Most newly sequenced proteins are likely to adopt a similar structure to one which has already been experimentally determined. For this reason, the most successful approaches to protein structure prediction have been template-based methods. Such prediction methods attempt to identify and model the folds of unknown structures by aligning the target sequences to a set of representative template structures within a fold library. In this chapter, I discuss the development of template-based approaches to fold prediction, from the traditional techniques to the recent state-of-the-art methods. I also discuss the recent development of structural annotation databases, which contain models built by aligning the sequences from entire proteomes against known structures. Finally, I run through a practical step-by-step guide for aligning target sequences to known structures and contemplate the future direction of template-based structure prediction.

Benchmarking consensus model quality assessment for protein fold recognition

Background: Selecting the highest quality 3D model of a protein structure from a number of alternatives remains an important challenge in the field of structural bioinformatics. Many Model Quality Assessment Programs (MQAPs) have been developed which adopt various strategies in order to tackle this problem, ranging from the so called "true" MQAPs capable of producing a single energy score based on a single model, to methods which rely on structural comparisons of multiple models or additional information from meta-servers. However, it is clear that no current method can separate the highest accuracy models from the lowest consistently. In this paper, a number of the top performing MQAP methods are benchmarked in the context of the potential value that they add to protein fold recognition. Two novel methods are also described: ModSSEA, which based on the alignment of predicted secondary structure elements and ModFOLD which combines several true MQAP methods using an artificial neural network. Results: The ModSSEA method is found to be an effective model quality assessment program for ranking multiple models from many servers, however further accuracy can be gained by using the consensus approach of ModFOLD. The ModFOLD method is shown to significantly outperform the true MQAPs tested and is competitive with methods which make use of clustering or additional information from multiple servers. Several of the true MQAPs are also shown to add value to most individual fold recognition servers by improving model selection, when applied as a post filter in order to re-rank models. Conclusion: MQAPs should be benchmarked appropriately for the practical context in which they are intended to be used. Clustering based methods are the top performing MQAPs where many models are available from many servers; however, they often do not add value to individual fold recognition servers when limited models are available. Conversely, the true MQAP methods tested can often be used as effective post filters for re-ranking few models from individual fold recognition servers and further improvements can be achieved using a consensus of these methods.

Recent developments in genetic data analysis: what can they tell us about human demographic history?

Over the last decade, a number of new methods of population genetic analysis based on likelihood have been introduced. This review describes and explains the general statistical techniques that have recently been used, and discusses the underlying population genetic models. Experimental papers that use these methods to infer human demographic and phylogeographic history are reviewed. It appears that the use of likelihood has hitherto had little impact in the field of human population genetics, which is still primarily driven by more traditional approaches. However, with the current uncertainty about the effects of natural selection, population structure and ascertainment of single-nucleotide polymorphism markers, it is suggested that likelihood-based methods may have a greater impact in the future.