Popular media, literature, and theory suggests that technology has induced a newly evolved, posthuman and postmodern (or "post-Enlightenment") cyborg consciousness. I suggest, as an alternative reading to the notion that we are evolving towards a disembodied posthuman state which will revolutionise what it means to be human, that the literature of cyborgs incorporates and reinscribes traditional narratives about human identity. This project analyses representative tropes of the cyborg in contemporary discourse from an explicitly historical perspective. Although dualisms such as mind/matter or soul/body are recognised in current theorising of the cyborg, little has been written about the historical relationship of mechanism and humanity in the ongoing discussion of cyborg mind/body ontology. The cyborg in much of our literature throughout a wide range of genres is represented by the exaggerated and horrifying effacement of human embodiment to embellish an underlying concern about the consequences to the human spirit when we can be reproduced by technological means. This thesis argues that much of the discourse about the novelty of the "postmodern" human-machine, however, is not unprecedented. Cyborg literature re-presents themes and concerns regarding the man-machine of the seventeenth and eighteenth centuries, and continues to reflect a religious debate about the spirit within the material body. Beginning with current notions of the supposed obsolescence of the body, this thesis explores how the contemporary cyborg functions as a device to reflect traditional (frequently Christian) values. Drawing on eighteenth-century medical philosophy and the satirical literary responses to mechanist definitions of body and soul, I demonstrate literary connections between medical and literary metaphors of the Enlightenment man-machine and the postmodern cyborg in popular media, fiction, and theory. The debate surrounding eighteenth-century materialism, primarily metaphorical and analogical in its representation of the body's mechanisms, contributed directly to current notions of figurative disembodiment and the status of the human soul in contemporary literature. I conclude that the cyborg as a figure of literature does not indicate a revolutionary change in social consciousness but repeatedly is a device used to affirm traditional religious concepts of human reproduction, individual free will, spirit and body, and life after death.

In data analysis, data points are usually analyzed based on their relations to other points (e.g., distance or inner product). This kind of relation can be analyzed on the manifold of the data set. Manifold learning is an approach to understand such relations. Various manifold learning methods have been developed and their effectiveness has been demonstrated in many real-world problems in pattern recognition and signal processing. However, most existing manifold learning algorithms only consider one manifold based on one dissimilarity matrix. In practice, multiple measurements may be available, and could be utilized. In pattern recognition systems, data integration has been an important consideration for improved accuracy given multiple measurements. Some data integration algorithms have been proposed to address this issue. These integration algorithms mostly use statistical information from the data set such as uncertainty of each data source, but they do not use the structural information (i.e., the geometric relations between data points). Such a structure is naturally described by a manifold. Even though manifold learning and data integration have been successfully used for data analysis, they have not been considered in a single integrated framework. When we have multiple measurements generated from the same data set and mapped onto different manifolds, those measurements can be integrated using the structural information on these multiple manifolds. Furthermore, we can better understand the structure of the data set by combining multiple measurements in each manifold using data integration techniques. In this dissertation, I present a new concept, manifold integration, a data integration method using the structure of data expressed in multiple manifolds. In order to achieve manifold integration, I formulated the manifold integration concept, and derived three manifold integration algorithms. Experimental results showed the algorithms' effectiveness in classification and dimension reduction. Moreover, for manifold integration, I showed that there are good theoretical and neuroscientific applications. I expect the manifold integration approach to serve as an effective framework for analyzing multimodal data sets on multiple manifolds. Also, I expect that my research on manifold integration will catalyze both manifold learning and data integration research.

The central government and most of local governments in Taiwan have adopted the e-mail services to provide citizens for requesting services or expressing their opinions through Internet. Traditionally, these requests/opinions need to be manually classified into appropriate departments for service rendering. However, due to the ever-increasing number of requests/opinions received, the manual classification approach is time consuming and becomes impractical. Therefore, in this study, we attempt to apply text categorization techniques for constructing automatically a classification mechanism in order to establish an efficient e-government service portal. The purpose of this thesis is to investigate effectiveness of different text categorization methods in supporting automatic classification of service requests/opinions emails sent to Mayor¡¦s mailbox. Specifically, in each phase of text categorization learning, we adopt and evaluate two methods commonly employed in prior research. In the feature selection phase, both the maximal x2¡@statistic method and the weighted average x2¡@statistic method of x2¡@statistic are evaluated. We consider the Binary and TFxIDF representation schemes in the document representation phase. Finally, we adopt the decision tree induction technique and the support vector machines (SVM) technique for inducing a text categorization model for our target e-government application. Our empirical evaluation results show that the text categorization method that employs the maximal x2 statistic method for feature selection, the Binary representation scheme, and the support vector machines as the underlying induction algorithm can reach an accuracy rate of 77.28% and an recall and precision rates of more than 77%. Such satisfactory classification effectiveness suggests that the text categorization approach can be employed to establish an effective and intelligent e-government service portal.

Recent research has demonstrated the use of electrical signature analysis (ESA), that is, the use of induction motor currents and voltages, for early detection of motor faults in the form of embedded algorithms. In the event of multiple motors energized by a common voltage bus, the cost of installing and maintaining fault monitoring and detection devices on each motor may be avoided, by using bus level aggregate electrical measurements to assess the health of the entire population of motors. In this research an approach for detecting commonly encountered induction motor mechanical faults from bus level aggregate electrical measurements is investigated. A mechanical fault indicator is computed processing the raw electrical measurements through a series of signal processing algorithms. Inference of an incipient fault is made by the percentage relative change of the fault indicator from the “healthy” baseline, thus defining a Fault Indicator Change (FIC). To investigate the posed research problem, healthy and faulty motors with broken rotor bar faults are simulated using a detailed transient motor model. The FIC based on aggregate electrical measurements is studied through simulations of different motor banks containing the same faulty motor. The degradation in the FIC when using aggregate measurements, as compared to using individual motor measurements, is investigated. For a given motor bank configuration, the variation in FIC with increasing number of faulty motors is also studied. In addition to simulation studies experimental results from a two-motor setup are analyzed. The FIC and degradation in the FIC in the case of load eccentricity fault, and a combination of shaft looseness and bearing damage is studied through staged fault experiments in the laboratory setup. In this research, the viability of using bus level aggregate electrical measurements for detecting incipient faults in motors energized by a common voltage bus is demonstrated. The proposed approach is limited in that as the power rating fraction of faulty motors to healthy motors in a given configuration decreases, it becomes far more difficult to detect the presence of incipient faults at very early stages.

Coordinate measuring machines are widely used to generate data points from an actual surface. The generated measurement data must be analyzed to yield critical geometric deviations of the measured part according to the requirements specified by the designer. However, ANSI standards do not specify the methods that should be used to evaluate the tolerances. The coordinate measuring machines employ different verification algorithms which may yield different results. Functional requirements or assembly conditions on a manufactured part are normally translated into geometric constraints to which the part must conform. Minimum zone evaluation technique is used when the measured data is regarded as an exact copy of the actual surface and the tolerance zone is represented as geometric constraints on the data. In the present study, a new zone-fitting algorithm is proposed. The algorithm evaluates the minimum zone that encompasses the set of measured points from the actual surface. The search for the rigid body transformation that places the set of points in the zone is modeled as a nonlinear optimization problem. The algorithm is employed to find the form tolerance of 2-D (line, circle) as well as 3-D geometries (cylinder). It is also used to propose an inspection methodology for turbine blades. By constraining the transformation parameters, the proposed methodology determines whether the points measured at the 2-D cross-sections fit in the corresponding tolerance zones simultaneously.

Dual head placement machines are commonly used in industry for placing components on circuit cards with great speed and accuracy. This thesis evaluates a novel approach for prescribing process plans for circuit card assembly on dual head placement machines. Process planning involves assigning component types to heads and to feeder slots associated with each head and prescribing appropriate sequences of picking, placing and nozzle-changing steps. The approach decomposes these decisions into four inter-related problems: P1, P2, P3 and P4. This thesis reviews this approach; presents a new heuristic to address P1; a method to facilitate P2 and P3 solutions; a method to control nozzle changes in P4; tests approaches to P1, P2, P3 and P4; and presents a thorough analysis of computational results to evaluate the efficacy of the approach which aims to balance workloads on machine heads to maximize assembly line throughput.

With the advent of Distributed Memory Machines (DMMs) numerous work have been undertaken to ease the work of a programmer these systems. Data parallel languages like Fortran D, Vienna Fortran, High Performance Fortran and C+ allow the user to specify data distribution across processor with some directives, and the compiler for these language use the directives to compile the programme in to an SPMD code. There are number of old program which are still in use and rewriting them in to new data parallel languages is a costly effort. Most of the work on these parallelizing compilers concentrate on efficient data communication between the processors.With the advancement in technology, data communication time is also decreasing.This allows bigger programs to execute in the same time span.The resources of a DMM being finite puts a limit on the size of the problem that can be run. Improving the memory usage for a problem will hence allow us run bigger size problems. Further, as communication speed increases, the overhead caused by house-keeping computations like global index to local index transformation, and owner processor computation will degrade the performance of the resultant code. Hence a uniform and efficient method for these computations also becomes a necessity. We have implemented parallelizing parts of a compiler using the SUIF compiler system, which accepts programs written in Fortran77 with directives to the compiler as comments. The output of the compiler is an SPMD C program, with embedded PVM calls for message communication between the processors. We have also proposed algorithms to improve data communications,and minimizing memory usage in the output code. A uniform method for performing owner processor computations and global-to-local transformations has also been implemented.

Wie erledigen zelluläre Maschinen ihre Aufgaben? Wie funktionieren sie? Zur Beantwortung dieser Fragen werden in unserer wissenschaftlichen Arbeitsgruppe bionanotechnologische Methoden entwickelt, die es ermöglichen, diese nur wenige Nanometer großen Maschinen bei der Ausübung ihrer Arbeiten zu beobachten. Gleichzeitig detektieren diese Methoden molekulare Wechselwirkungsmechanismen, die die einzelnen Maschinen der Zelle steuern. Erste Beispiele zeigen den Einfluss pharmazeutischer Wirkstoffe zur Regulierung der Proteinfunktion im molekularen Detail. Hierdurch werden völlig neue Möglichkeiten geschaffen, um molekulare Schalter zellulärer Maschinen zu finden und zu betätigen. / How do cellular machines function to fulfil their specific tasks so efficiently? How are they regulated? We apply and develop bio-nanotechnological approaches to directly observe these nanoscale cellular machines while they are at work. At the same time, we can gain insights into the working schedule of a cell, and can detect molecular mechanisms which drive and direct single cellular machines. With this unique possibility to reveal the switching mechanisms of the cellular machines, we could apply these switches to purposefully direct and regulate cellular processes. First examples follow the actions of pharmacological compounds on the function of a cellular machine at molecular resolution, and provide hitherto unexpected perspectives to develop and optimise such compounds to precisely target the function of cellular processes.

<p>This licentiate thesis presents a software architecture for inductive labeled dependency parsing of unrestricted natural language text, which achieves a strict modularization of parsing algorithm, feature model and learning method such that these parameters can be varied independently. The architecture is based on the theoretical framework of inductive dependency parsing by Nivre \citeyear{nivre06c} and has been realized in MaltParser, a system that supports several parsing algorithms and learning methods, for which complex feature models can be defined in a special description language. Special attention is given in this thesis to learning methods based on support vector machines (SVM).</p><p>The implementation is validated in three sets of experiments using data from three languages (Chinese, English and Swedish). First, we check if the implementation realizes the underlying architecture. The experiments show that the MaltParser system outperforms the baseline and satisfies the basic constraints of well-formedness. Furthermore, the experiments show that it is possible to vary parsing algorithm, feature model and learning method independently. Secondly, we focus on the special properties of the SVM interface. It is possible to reduce the learning and parsing time without sacrificing accuracy by dividing the training data into smaller sets, according to the part-of-speech of the next token in the current parser configuration. Thirdly, the last set of experiments present a broad empirical study that compares SVM to memory-based learning (MBL) with five different feature models, where all combinations have gone through parameter optimization for both learning methods. The study shows that SVM outperforms MBL for more complex and lexicalized feature models with respect to parsing accuracy. There are also indications that SVM, with a splitting strategy, can achieve faster parsing than MBL. The parsing accuracy achieved is the highest reported for the Swedish data set and very close to the state of the art for Chinese and English.</p> / <p>Denna licentiatavhandling presenterar en mjukvaruarkitektur för</p><p>datadriven dependensparsning, dvs. för att automatiskt skapa en</p><p>syntaktisk analys i form av dependensgrafer för meningar i texter</p><p>på naturligt språk. Arkitekturen bygger på idén att man ska kunna variera parsningsalgoritm, särdragsmodell och inlärningsmetod oberoende av varandra. Till grund för denna arkitektur har vi använt det teoretiska ramverket för induktiv dependensparsning presenterat av Nivre \citeyear{nivre06c}. Arkitekturen har realiserats i programvaran MaltParser, där det är möjligt att definiera komplexa särdragsmodeller i ett speciellt beskrivningsspråk. I denna avhandling kommer vi att lägga extra tyngd vid att beskriva hur vi har integrerat inlärningsmetoden supportvektor-maskiner (SVM).</p><p>MaltParser valideras med tre experimentserier, där data från tre språk används (kinesiska, engelska och svenska). I den första experimentserien kontrolleras om implementationen realiserar den underliggande arkitekturen. Experimenten visar att MaltParser utklassar en trivial metod för dependensparsning (\emph{eng}. baseline) och de grundläggande kraven på välformade dependensgrafer uppfylls. Dessutom visar experimenten att det är möjligt att variera parsningsalgoritm, särdragsmodell och inlärningsmetod oberoende av varandra. Den andra experimentserien fokuserar på de speciella egenskaperna för SVM-gränssnittet. Experimenten visar att det är möjligt att reducera inlärnings- och parsningstiden utan att förlora i parsningskorrekthet genom att dela upp träningsdata enligt ordklasstaggen för nästa ord i nuvarande parsningskonfiguration. Den tredje och sista experimentserien presenterar en empirisk undersökning som jämför SVM med minnesbaserad inlärning (MBL). Studien använder sig av fem särdragsmodeller, där alla kombinationer av språk, inlärningsmetod och särdragsmodell</p><p>har genomgått omfattande parameteroptimering. Experimenten visar att SVM överträffar MBL för mer komplexa och lexikaliserade särdragsmodeller med avseende på parsningskorrekthet. Det finns även vissa indikationer på att SVM, med en uppdelningsstrategi, kan parsa en text snabbare än MBL. För svenska kan vi rapportera den högsta parsningskorrektheten hittills och för kinesiska och engelska är resultaten nära de bästa som har rapporterats.</p>

Thèse doctorat : Génie électrique : Lille 1 : 2003. / N° d'ordre (Lille 1) : 3421. Résumé en français et en anglais. Bibliogr. p. 187-190.