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The complexity of engineered systems has increased remarkably to meet customer needs. In the continuously growing global market, it is essential for engineered systems to keep their productivities which can be achieved by higher reliability and availability. Integrated health management based on diagnostics and prognostics provides significant benefits, which includes increasing system safety and operational reliability, with a significant impact on the life-cycle costs, reducing operating costs and increasing revenues. Characteristics of complex systems such as nonlinearity, dynamicity, non-stationarity, and non-Gaussianity make diagnostics and prognostics more challenging tasks and decrease the application of classic reliability methods remarkably – as they cannot address the dynamic behaviour of these systems. This research has focused on detecting, predicting and isolating faults in engineered systems, using operational data with multifarious data characteristics. Complexities in the data, including non-Gaussianity and high nonlinearity, impose stringent challenges on fault analysis. To deal with these challenges, this research proposed an integrated data-driven methodology in which hidden Markov modelling (HMM) and Bayesian network (BN) were employed to detect, predict and isolate faults in a system. The fault detection and prediction were based on comparing and exploiting pattern similarity in the data via the loglikelihood values generated through HMM training. To identify the root cause of the faults, the probability values obtained from updating the BN were used which were based on the virtual evidence provided by HMM training and log-likelihood values. To set up a more accurate data-driven model – particularly BN structure – engineering analyses were employed in a structured way to explore the causal relationships in the system which is essential for reliability analysis of complex engineered systems. The automotive exhaust gas Aftertreatment system is a complex engineered system consisting of several subsystems working interdependently to meet emission legislations. The Aftertreatment system is a highly nonlinear, dynamic and non-stationary system. Consequently, it has multifarious data characteristics, where these characteristics raise the challenges of diagnostics and prognostics for this system, compared to some of the references systems, such as the Tennessee Eastman process or rolling bearings. The feasibility and effectiveness of the presented framework were discussed in conjunction with the application to a real-world case study of an exhaust gas Aftertreatment system which provided good validation of the methodology, proving feasibility to detect, predict, and isolate unidentified faults in dynamic processes.

Robotsystem har använts flitigt under de senaste årtiondena för att skapa automationslösningar i ett flertal områden. De flesta nuvarande automationslösningarna är begränsade av att uppgifterna de kan lösa måste vara repetitiva och förutsägbara. En av anledningarna till detta är att dagens robotsystem saknar förmåga att förstå och resonera om omvärlden. På grund av detta har forskare inom robotik och artificiell intelligens försökt att skapa intelligentare maskiner. Trots att stora framsteg har gjorts då det gäller att skapa robotar som kan fungera och interagera i en mänsklig miljö så finns det för nuvarande inget system som kommer i närheten av den mänskliga förmågan att resonera om omvärlden. För att förenkla problemet har vissa forskare föreslagit en alternativ lösning till helt självständiga robotar som verkar i mänskliga miljöer. Alternativet är att kombinera människors och maskiners förmågor. Exempelvis så kan en person verka på en avlägsen plats, som kanske inte är tillgänglig för personen i fråga på grund av olika orsaker, genom att använda fjärrstyrning. Vid fjärrstyrning skickar operatören kommandon till en robot som verkar som en förlängning av operatörens egen kropp. Segmentering och identifiering av rörelser skapade av en operatör kan användas för att tillhandahålla korrekt assistans vid fjärrstyrning eller samarbete mellan människa och maskin. Assistansen sker ofta inom ramen för virtuella fixturer där eftergivenheten hos fixturen kan justeras under exekveringen för att tillhandahålla ökad prestanda i form av ökad precision och minskad tid för att utföra uppgiften. Den här avhandlingen fokuserar på två aspekter av samarbete mellan människa och maskin. Klassificering av en operatörs rörelser till ett på förhand specificerat tillstånd under en manipuleringsuppgift och assistans under manipuleringsuppgiften baserat på virtuella fixturer. Den specifika tillämpningen som behandlas är manipuleringsuppgifter där en mänsklig operatör styr en robotmanipulator i ett fjärrstyrt eller samarbetande system. En metod för att följa förloppet av en uppgift medan den utförs genom att använda virtuella fixturer presenteras. Istället för att följa en på förhand specificerad plan så har operatören möjlighet att undvika oväntade hinder och avvika från modellen. För att möjliggöra detta estimeras kontinuerligt sannolikheten att operatören följer en viss trajektorie (deluppgift). Estimatet används sedan för att justera eftergivenheten hos den virtuella fixturen så att ett beslut om hur rörelsen ska fixeras kan tas medan uppgiften utförs. En flerlagers dold Markovmodell (eng. layered hidden Markov model) används för att modellera mänskliga färdigheter. En gestemklassificerare som klassificerar en operatörs rörelser till olika grundläggande handlingsprimitiver, eller gestemer, evalueras. Gestemklassificerarna används sedan i en flerlagers dold Markovmodell för att modellera en simulerad fjärrstyrd manipuleringsuppgift. Klassificeringsprestandan utvärderas med avseende på brus, antalet gestemer, typen på den dolda Markovmodellen och antalet tillgängliga träningssekvenser. Den flerlagers dolda Markovmodellen tillämpas sedan på data från en trajektorieföljningsuppgift i 2D och 3D med en robotmanipulator för att ge både kvalitativa och kvantitativa resultat. Resultaten tyder på att den flerlagers dolda Markovmodellen är väl lämpad för att modellera trajektorieföljningsuppgifter och att den flerlagers dolda Markovmodellen är robust med avseende på felklassificeringar i de underliggande gestemklassificerarna. / Robot systems have been used extensively during the last decades to provide automation solutions in a number of areas. The majority of the currently deployed automation systems are limited in that the tasks they can solve are required to be repetitive and predicable. One reason for this is the inability of today’s robot systems to understand and reason about the world. Therefore the robotics and artificial intelligence research communities have made significant research efforts to produce more intelligent machines. Although significant progress has been made towards achieving robots that can interact in a human environment there is currently no system that comes close to achieving the reasoning capabilities of humans. In order to reduce the complexity of the problem some researchers have proposed an alternative to creating fully autonomous robots capable of operating in human environments. The proposed alternative is to allow fusion of human and machine capabilities. For example, using teleoperation a human can operate at a remote site, which may not be accessible for the operator for a number of reasons, by issuing commands to a remote agent that will act as an extension of the operator’s body. Segmentation and recognition of operator generated motions can be used to provide appropriate assistance during task execution in teleoperative and human-machine collaborative settings. The assistance is usually provided in a virtual fixture framework where the level of compliance can be altered online in order to improve the performance in terms of execution time and overall precision. Acquiring, representing and modeling human skills are key research areas in teleoperation, programming-by-demonstration and human-machine collaborative settings. One of the common approaches is to divide the task that the operator is executing into several sub-tasks in order to provide manageable modeling. This thesis is focused on two aspects of human-machine collaborative systems. Classfication of an operator’s motion into a predefined state of a manipulation task and assistance during a manipulation task based on virtual fixtures. The particular applications considered consists of manipulation tasks where a human operator controls a robotic manipulator in a cooperative or teleoperative mode. A method for online task tracking using adaptive virtual fixtures is presented. Rather than executing a predefined plan, the operator has the ability to avoid unforeseen obstacles and deviate from the model. To allow this, the probability of following a certain trajectory sub-task) is estimated and used to automatically adjusts the compliance of a virtual fixture, thus providing an online decision of how to fixture the movement. A layered hidden Markov model is used to model human skills. A gestem classifier that classifies the operator’s motions into basic action-primitives, or gestemes, is evaluated. The gestem classifiers are then used in a layered hidden Markov model to model a simulated teleoperated task. The classification performance is evaluated with respect to noise, number of gestemes, type of the hidden Markov model and the available number of training sequences. The layered hidden Markov model is applied to data recorded during the execution of a trajectory-tracking task in 2D and 3D with a robotic manipulator in order to give qualitative as well as quantitative results for the proposed approach. The results indicate that the layered hidden Markov model is suitable for modeling teleoperative trajectory-tracking tasks and that the layered hidden Markov model is robust with respect to misclassifications in the underlying gestem classifiers. / QC 20101102

<p>This thesis details a collection of experiments performed on two condensed matter systems, Co-doped BaFe₂As₂ and URu₂Si₂. These two materials are related by their structural type (<em>ThCr₂Si₂</em>-type) serving as great examples of the diversity of material properties present in this family. They are also both superconducting materials and belong to the collection of strongly-correlated electron systems. The interest in studying the Ba(Fe,Co)₂As₂ group of materials is due to the high superconducting transition temperature in these (and related) materials, while the compound URu₂Si₂ was studied due to the presence of a poorly-understood 'hidden order' phase.</p> <p>Muon spin relaxation/rotation/resonance (µSR) was used to measure several single crystals of the series Ba(Fe_2-<em>x</em>Co<em>_x</em>)₂As₂ with Cobalt concentrations <em>x </em>= 0.038, 0.047, 0.061, 0.074, 0.107 and 0.114, and a single crystal of Sr(Fe_0.87Co_0.13)₂As₂. The two samples with the lowest doping, <em>x </em>= 0.038 and <em>x </em>= 0.047, showed strong c-axis magnetism occurring below the magnetic transition, T_SDW. The measurements suggest that the local magnetic field is increasingly disordered as the concentration of Co increases. These samples were shown to exhibit both superconductivity and magnetism, but that the entire sample contains non-zero local magnetic fields, meaning that superconductivity exists in or near regions of strong magnetic order.</p> <p>The remaining compounds (with <em>x </em>= 0.061, 0.074, 0.107, 0.114 and Sr(Fe_0.87Co_0.13)₂As₂) were measured with zero-field (ZF)-µSR and no magnetic ordering was found down to T = 1.65 K. An analytic Ginzburg-Landau model was used to fit the data and obtain absolute values for the penetration depth, λ. A model for the temperature dependence of the density of superconducting carriers, n_s ≈ λ², based on two <em>s</em>-wave gaps describes the data well. Below T_SC, a paramagnetic frequency shift was observed indicative of field-induced magnetism along the c crystallographic direction.</p> <p>Measurements of URu₂Si₂ under chemical and hydrostatic pressure have focused on measuring the spin correlations that are present in the hidden order phase. The chemical pressure that is induced by 5% Re doping perturbs, but does not destroy, the commensurate spin excitations. The spin gap that is present in the parent material is also present under this chemical doping. The hidden order phase survives at least halfway to the quantum critical point to ferromagnetism, but is weakened by the Re substitution.</p> <p>Under hydrostatic pressure of 10.1 kbar, URu₂Si₂ becomes antiferromagnetic, but the spin correlations are found to be qualitatively similar to those of the hidden order phase. The width in reciprocal space (Q-width) of the excitations and their gapped nature remains unchanged upon entering the antiferromagnetic phase. Quantitatively, there is an increase in the magnitude of the gap at Q = (1.4 0 0). This may be a result of the increase in the transition temperature preceding the entry to the antiferromagnetic phase.</p> <p>Due to the large difference in their properties, and hence the motivation for studying Ba(Fe_1-<em>x</em>Co<em>_x</em>)₂As₂ and URu₂Si₂, they will be introduced and presented separately. Chapter 1 will provide the necessary background material on Ba(Fe,Co)₂As₂, while Chapter 2 will provide the background for the work on URu₂Si₂. Chapter 3 will describe the experimental techniques that were used to study these systems.</p> <p>Original research results on Ba(Fe,Co)₂As₂ are presented in Chapter 4. This is mainly focused on µSR measurements of dopings that display superconductivity. Samples that did not order magnetically were measured in the mixed state to measure the vortex lattice to extract the various properties, including the superconducting pairing symmetry. Samples that did order magnetically were measured to analyze the amount of magnetic disorder and discover the extent of coexistence or phase separation between magnetism and superconductivity.</p> <p>Chapter 5 details the original research results on URu₂Si₂. This involved crystal growth of these compounds, and two neutron scattering experiments to measure the spin correlations while perturbing the hidden order state. The first experiment was done on a Re-doped crystal, URu_1.9Re_0.1Si₂. Doping with Re suppresses the hidden order, eventually leading to ferromagnetism at higher dopings. This work showed that the spin correlations are also suppressed, but not as quickly as the hidden order. The second experiment was on pure URu₂Si₂ under hydrostatic pressure. Applied pressure increases the hidden order transition, but eventually leads to antiferromagnetism, the phase in which the experiment was performed.</p> / Doctor of Philosophy (PhD)

<p>Spenser's design for the second book of The Faerie Queene involves hidden parallel and synnnetrical patterns, previously undetected, that have serious hermeneutic significance for the study of that poem and other literature of the Renaissance. My study is of form. The first chapter considers the structural approach to literature of the Renaissance and discusses my methodology. Chapter II reveals the simultaneous existence of a parallel and a symmetrical pattern of the stanzas of Book II as a whole. Chapters III and IV explore the simultaneous operation of five patterns--three parallel and two synnnetrical--for numerous pairs of cantos. Chapter V demonstrates the simultaneous existence of parallel and symmetrical patterns within each canto of Book II.</p> <p>What is presented is a demonstration of intricate construction along consistently predictable parallel and symmetrical lines. Such patterned composition has been detected previously in shorter Spenser poems, Epithalamion and "Aprill," in particular. My discoveries result from applying a method which, from shorter Spenser poems, one has an expectation will work. The method involves counting stanzas and dividing by two to determine the midpoint or arithmetical centre, then considering the stanzas in parallel and synnnetrical arrangements.</p> <p>Spenser creates, through the parallel and symmetrical placement of the episodes, characters, images, and themes of Book II, a microcosm of hidden analogies. The patterns are intricate and readily iii predictable. Spenser must have composed his poem according to such principles. While the ordinary reader may have experienced only the superficial sense of romance rambling that Spenser obviously intends to give, his more curious readers may have been aware of patterned composition along inevitable parallel and symmetrical lines and may have used presumption of patterning as a means of interpretation. They could have predicted a pattern and used the information of comparison, contrast, and reciprocal connnent to illuminate an image, character, or episode which they did not understand. For example, a reader who discovered the pairing of Belphoebe, a known type of Elizabeth, with Medina or Alma would get the hint that the latter are types of Elizabeth, though otherwise such a conclusion might seem only guesswork. The patterns provide a useful tool for criticism, suggesting and confirming interpretation.</p> <p>The patterns I have detected may not be the only ones awaiting discovery in The Faerie Queene. There may be some underlying principle involved that we don't see at the moment. Perhaps there is a set of mathematical ratios--some sort of mathematical formula for composition--involved in making the goodly framework of the poem. Pythagorean ratios and other symbolic proportions are now known to have been used in Renaissance architecture. Spenser speaks of Book II as a building and fills it with houses and temples as major symbols. He makes his book according to a "goodly frame." The mystique of arcane construction no doubt has Pythagorean, nee-Platonic, hermetic, and numerological significance.</p> / Doctor of Philosophy (PhD)

Hidden curriculum scholars have long since recognized the function of the visual in shaping the educational experiences of youth. Scholars have noted that the hidden curriculum of schooling has functioned as a primary socialization mechanism to reproduce capitalism, the state, gender, racial, and class-based inequalities. Today, urban high school spaces present both invisible and visible curricula that are shaped not only by the many images that comprise a school's visual culture, but also by the wider visual landscape. This is of particular import for working-class young Black women who are often framed and seen as social and economic problems within the discourse on urban schools/urban school failure. This discourse teaches. It is taught in and through the everyday visual texts, spaces, and places young Black women navigate to the point that the discourse linking Black femaleness, poverty, and failure becomes natural/normal. It is normalized to the point that it becomes "hidden in plain sight." The simultaneous transparency and invisibility of knowledge presents urban educators concerned about the Black girl and other youth of color with three intersecting problems. First, the educative role of the visual has been underexplored in the research literature on urban schools/urban schooling. Second, within the context of urban schools, we do not know enough about if and or how the educative role of the visual shapes young Black women's relationship with teaching and learning. Third, we do not know if or how the contentious relationship between visual learning inside and visual learning outside of school shapes young Black women's relationship with education as a formal institution and or a process. Given these three intersecting problems, this dissertation project centers on examining the educative impacts of place, visual culture, and design in an effort to fill the gap in the scholarship regarding this portion of the educational experiences of young Black women. Using visual ethnography and discourse analysis as primary methods, I engage a group of five primary student participants who attend a non-traditional, design-focused science and technology magnet school where they are one of the largest student cohorts. Einstein 2.0 is an instance of a progressive, non-normative, small learning community that is attentive to the power of the visual in shaping the teaching and learning experiences, especially for youth of color. In this way, it is a case that can help us better understand the challenges, opportunities, and complexities of harnessing the visual in the urban school context. In this study I argue that by creating a safe and emotionally engaging environment that rejects using punitive disciplinary frameworks and pseudo-factory/pseudo-prison design, Einstein's visual and school culture gave rise to an increased sense of emotional readiness for both producing and receiving knowledge that stands in sharp contrast to the more traditional ways urban schools often approach managing and controlling its student(s') body(ies). Given the increased role of the visual in shaping teaching and learning for youth in the 21st century urban context and the persistent link between young Black women and urban educational/societal failure, having the emotional readiness to deal with these challenges is crucial to their self-definitions (Collins, 2000) and internal motivation to reject and or exceed societal expectations. Using Einstein's approach to visual and organizational culture as a model, I make specific recommendations for educators tasked with or concerned about creating engaging school spaces for young Black women and other youth of color. These recommendations demand further attention to the ways that the visual, spatial, and emotional interact to contour the educational experiences and consumption practices of youth in urban America today. / Urban Education

The tremendous ongoing growth of wireless digital communications has raised spectrum shortage and security issues. In particular, the need for new spectrum is the main obstacle in continuing this growth. Recent studies on radio spectrum usage have shown that pre-allocation of spectrum bands to specific wireless communication applications leads to poor utilization of those allocated bands. Therefore, research into new techniques for efficient spectrum utilization is being aggressively pursued by academia, industry, and government. Such research efforts have given birth to two concepts: Cognitive Radio (CR) and Dynamic Spectrum Access (DSA) network. CR is believed to be the key enabling technology for DSA network implementation. CR based DSA (cDSA) networks utilizes white spectrum for its operational frequency bands. White spectrum is the set of frequency bands which are unoccupied temporarily by the users having first rights to the spectrum (called primary users). The main goal of cDSA networks is to access of white spectrum. For proper access, CR nodes must identify the right cDSA network and the absence of primary users before initiating radio transmission. To solve the cDSA network access problem, methods are proposed to design unique second-order cyclic features using Orthogonal Frequency Division Multiplexing (OFDM) pilots. By generating distinct OFDM pilot patterns and measuring spectral correlation characteristics of the cyclostationary OFDM signal, CR nodes can detect and uniquely identify cDSA networks. For this purpose, the second-order cyclic features of OFDM pilots are investigated analytically and through computer simulation. Based on analysis results, a general formula for estimating the dominant cycle frequencies is developed. This general formula is used extensively in cDSA network identification and OFDM signal detection, as well as pilot pattern estimation. CR spectrum awareness capability can be enhanced when it can classify the modulation type of incoming signals at low and varying signal-to-noise ratio. Signal classification allows CR to select a suitable demodulation process at the receiver and to establish a communication link. For this purpose, a threshold-based technique is proposed which utilizes cycle-frequency domain profile for signal detection and feature extraction. Hidden Markov Models (HMMs) are proposed for the signal classifier. The spectrum awareness capability of CR can be undermined by spoofing radio nodes. Automatic identification of malicious or malfunctioning radio signal transmitters is a major concern for CR information assurance. To minimize the threat from spoofing radio devices, radio signal fingerprinting using second-order cyclic features is proposed as an approach for Specific Emitter Identification (SEI). The feasibility of this approach is demonstrated through the identification of IEEE 802.11a/g OFDM signals from different Wireless Local Area Network (WLAN) card manufactures using HMMs. / Ph. D.

Radio frequency system optimization and troubleshooting remains one of the most challenging aspects of working in a cellular network. To stay competitive, cellular providers continually monitor the performance of their networks and use this information to determine where to improve or expand services. As a result, operators are saddled with the task of wading through overwhelmingly large amounts of data in order to trouble-shoot system problems. Part of the difficulty of this task is that for many complicated problems such as hand-off failure, clues about the cause of the failure are hidden deep within the statistics of underlying dynamic physical phenomena like fading, shadowing, and interference. In this research we propose that Hidden Markov Models (HMMs) be used as a method to infer signature statistics about the nature and sources of faults in a cellular system by fitting models to various time-series data measured throughout the network. By including HMMs in the network management tool, a provider can explore the statistical relationships between channel dynamics endemic to a cell and its resulting performance. This research effort also includes a new distance measure between a pair of HMMs that approximates the Kullback-Leibler divergence (KLD). Since there is no closed-form solution to calculate the KLD between the HMMs, the proposed analytical expression is very useful in classification and identification problems. A novel HMM based position location technique has been introduced that may be very useful for applications involving cognitive radios. / Ph. D.

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