Aeronautical engineering: A continuing bibliography with indexes (supplement 247)

This bibliography lists 437 reports, articles, and other documents introduced into the NASA scientific and technical information system in December, 1989. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

The Public Service Media and Public Service Internet Manifesto

This book presents the collectively authored Public Service Media and Public Service Internet Manifesto and accompanying materials.The Internet and the media landscape are broken. The dominant commercial Internet platforms endanger democracy. They have created a communications landscape overwhelmed by surveillance, advertising, fake news, hate speech, conspiracy theories, and algorithmic politics. Commercial Internet platforms have harmed citizens, users, everyday life, and society. Democracy and digital democracy require Public Service Media. A democracy-enhancing Internet requires Public Service Media becoming Public Service Internet platforms – an Internet of the public, by the public, and for the public; an Internet that advances instead of threatens democracy and the public sphere. The Public Service Internet is based on Internet platforms operated by a variety of Public Service Media, taking the public service remit into the digital age. The Public Service Internet provides opportunities for public debate, participation, and the advancement of social cohesion. Accompanying the Manifesto are materials that informed its creation: Christian Fuchs’ report of the results of the Public Service Media/Internet Survey, the written version of Graham Murdock’s online talk on public service media today, and a summary of an ecomitee.com discussion of the Manifesto’s foundations

High Dimensional Covariance Estimation for Spatio-Temporal Processes

High dimensional time series and array-valued data are ubiquitous in signal processing, machine learning, and science. Due to the additional (temporal) direction, the total dimensionality of the data is often extremely high, requiring large numbers of training examples to learn the distribution using unstructured techniques. However, due to difficulties in sampling, small population sizes, and/or rapid system changes in time, it is often the case that very few relevant training samples are available, necessitating the imposition of structure on the data if learning is to be done. The mean and covariance are useful tools to describe high dimensional distributions because (via the Gaussian likelihood function) they are a data-efficient way to describe a general multivariate distribution, and allow for simple inference, prediction, and regression via classical techniques. In this work, we develop various forms of multidimensional covariance structure that explicitly exploit the array structure of the data, in a way analogous to the widely used low rank modeling of the mean. This allows dramatic reductions in the number of training samples required, in some cases to a single training sample. Covariance models of this form have been increasing in interest recently, and statistical performance bounds for high dimensional estimation in sample-starved scenarios are of great relevance. This thesis focuses on the high-dimensional covariance estimation problem, exploiting spatio-temporal structure to reduce sample complexity. Contributions are made in the following areas: (1) development of a variety of rich Kronecker product-based covariance models allowing the exploitation of spatio-temporal and other structure with applications to sample-starved real data problems, (2) strong performance bounds for high-dimensional estimation of covariances under each model, and (3) a strongly adaptive online method for estimating changing optimal low-dimensional metrics (inverse covariances) for high-dimensional data from a series of similarity labels.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137082/1/greenewk_1.pd

Development of Hierarchical Optimization-based Models for Multiscale Damage Detection

In recent years, health monitoring of structure and infrastructure systems has become a valuable source of information for evaluating structural integrity, durability and reliability throughout the lifecycle of structures as well as ensuring optimal maintenance planning and operation. Important advances in sensor and computer technologies made possible to process a large amount of data, to extract the characteristic features of the signals, and to link those to the current structural conditions. In general, the process of data feature extraction relates to solving an inverse problem, in either a data-driven or a model-based type setting. This dissertation explores state-of-the-art hierarchical optimization-based computational algorithms for solving multiscale model-based inverse problems such as system identification and damage detection. The basic idea is to apply optimization tools to quantify an established model or system, characterized by a set of unknown governing parameters, via minimizing the discrepancy between the predicted system response and the measured data. We herein propose hierarchical optimization algorithms such as the improved artificial bee colony algorithms integrated with local search operators to accomplish this task. In this dissertation, developments in multiscale damage detection are presented in two parts. In the first part, efficient hybrid bee algorithms in both serial and parallel schemes are proposed for time domain input-output and output-only identification of macro-scale linear/nonlinear systems such as buildings and bridges. Solution updating strategies of the artificial bee colony algorithm are improved for faster convergence, meanwhile, the simplex method and gradient-based optimization techniques are employed as local search operators for accurate solution tuning. In the case of output-only measurements, both system parameters and the time history of input excitations can be simultaneously identified using a modified Newmark integration scheme. The synergy between the proposed method and Bayesian inference are proposed to quantify uncertainties of a system. Numerical and experimental applications are investigated and presented for macro-scale system identification, finite element model updating and damage detection. In the second part, a framework combining the eXtended Finite Element Method (XFEM) and the proposed optimization algorithms is investigated, for nondestructive detection of multiple flaws/defects embedded in meso-scale systems such as critical structural components like plates. The measurements are either static strains or displacements. The number of flaws as well as their locations and sizes can be identified. XFEM with circular and/or elliptical void enrichments is employed to solve the forward problem and alleviates the costly re-meshing along with the update of flaw boundaries in the identification process. Numerical investigations are presented to validate the proposed method in application to detection of multiple flaws and damage regions. Overall, the proposed multiscale methodologies show a great potential in assessing the structural integrity of building and bridge systems, critical structural components, etc., leading to a smart structure and infrastructure management system

Key Performance Monitoring and Diagnosis in Industrial Automation Processes

With ever increasing global competition, monitoring and diagnosis methods based on key performance indicator (KPI) are increasingly receiving attention in the process industry. Primarily due to the scale and complexity of modern automation processes, application of signal processing and model-based monitoring methods is too costly and time-consuming. On the other hand, due to the availability of cheap measurement and storage systems, a large amount of process and KPI data is obtained. As a result, developing data-driven KPI monitoring methods has become an area of great interest in both academics and industry. Therefore, this thesis is focused on the data-driven design of systematic KPI monitoring and diagnosis systems for industrial automation processes. Depending on the relationship between the low-level process variables and the high-level KPIs, industrial processes can be classified into three groups: 1. Static processes (SPs) are those described by algebraic equations; 2. Lumped-parameter processes (LPPs) are those described by ordinary differential equations; and 3. Distributed-parameter processes (DPPs) are those described by partial differential equations. For each of these groups of processes, analytical redundancy plays a very important role when developing efficient process monitoring tools. For SPs, multivariate-statistics-based methods have been used. However, their applicability is restricted by high mathematical complexity, high design costs and low diagnostic performance. For this reason, an alternative improved method has been proposed in this thesis. For LPPs, complex model-based methods have been implemented. Therefore, to reduce the design costs required for monitoring LPPs, efficient Subspace identification based approaches are presented. Finally, since there are very few available model-based methods for DPPs, this thesis presents novel approaches for KPI monitoring in DPPs. For all these methods, the design procedures are based on the process I/O data and do not require advanced mathematical knowledge. After performance degradation has been detected, it is important to identify the root causes to prevent further losses. In industrial processes, performance degradation is more often caused by multiplicative faults. In this work, a new data-driven multiplicative fault diagnosis approach is proposed. This approach aims at assisting the maintenance personnel by narrowing down the investigation scope. As a result, overall equipment effectiveness (OEE) can be significantly improved. To show the effectiveness of the proposed approaches, case studies on the Tennessee Eastman benchmark process, the continuous stirred tank heater benchmark and the simulated drying section of a paper machine have been performed. The proposed methods worked successfully with these processes.Key Performance Überwachung und Diagnose in industriellen Automatisierungsprozessen Im Rahmen einer stetigen Zunahme des globalen Wettbewerbs erhalten Key Performance Indikator (KPI) basierte Überwachungs- und Diagnosetechniken zunehmend Aufmerksamkeit in der Prozessindustrie. Vor allem vor dem Hintergrund von Umfang und Komplexität moderner Automatisierungsprozesse ist die Anwendung von Signalverarbeitung und modellbasierten Überwachungstechniken zu teuer und zu zeitaufwendig. Andererseits ist häufig auf Grund der Verfügbarkeit von günstigen Mess- und Speichersystemen, eine große Menge von Prozess- und KPI-Daten vorhanden. Daher ist die Entwicklung von datenbasierten Verfahren ein Forschungsfeld, welches sowohl im akademischen als auch im industriellen Bereich mit großem Interesse verfolgt wird. Dementsprechend liegt der Fokus der vorliegenden Arbeit auf einem systematischen und datenbasierten Entwurf von KPI-Überwachungs- und -Diagnosesystemen für industrielle Automatisierungsprozesse. Anhand der Beziehung zwischen den low-level Prozessgrößen und den high-level KPIs können industrielle Prozesse in drei Gruppen eingeteilt werden: 1. Statische Prozesse (SP) sind Prozesse, die sich durch algebraische Gleichungen beschrieben lassen; 2. Konzentrierte-Parameter Prozesse (KPP) sind Prozesse, welche durch gewöhnliche Differentialgleichungen beschrieben werden; und 3. Verteilte-Parameter Prozesse (VPP) sind Prozesse, welche durch partielle Differentialgleichungen beschrieben werden. Für jede dieser Gruppen spielt das Konzept der analytischen Redundanz eine sehr wichtige Rolle bei der Entwicklung von effizienten Prozessüberwachungs-Tools. Für SP, sind multivariate statistische Verfahren verwendet worden. Allerdings ist deren Anwendbarkeit durch hohe mathematische Komplexität, einen hohen Entwurfsaufwand und eine geringen Diagnoseleistung beschränkt. Aus diesem Grund wird ein alternatives, verbessertes Verfahren in dieser Arbeit vorgeschlagen. Für KPP, sind komplexe modellbasierte Methoden implementiert worden. Um die Entwicklungskosten für die Überwachung der KPP zu reduzieren, wird eine effiziente Methode, basierend auf Subspace-Identifikation, vorgestellt. Da es nur sehr wenige modellbasierte Methoden für VPP gibt, präsentiert diese Arbeit schließlich neue Verfahren für die KPI- Überwachung in VPP. Alle vorgestellten Verfahren basieren auf den Prozess E/A Daten und erfordern daher keine tiefergehenden mathematischen Kenntnisse über den Prozess. Nach erfolgreicher Erkennung des Leistungsabfalls eines KPI, ist es in einem nächsten Schritt erforderlich die Ursache zu identifizieren, um weitere ökonomische Verluste zu verhindern. In industriellen Prozessen wird ein Leistungsabfall häufig durch multiplikative Fehler verursacht. In dieser Arbeit wird ein neues datenbasiertes, multiplikatives Fehlerdiagnoseverfahren vorgeschlagen. Dieses Verfahren soll der Unterstützung des Wartungspersonals dienen, indem eine Eingrenzung der Problemursache vorgenommen wird. Als Ergebnis kann somit die OEE (Overall Equipment Effectiveness) deutlich verbessert werden. Um die Wirksamkeit der vorgeschlagenen Verfahren zu demonstrieren, wurden verschiedene Fallstudien an Hand des „Tennessee Eastman“ Benchmark, des „continuous stirred tank heater“ Benchmark und einer simulierten Trockenpartie einer Papiermaschine durchgeführt. Die Effektivität der vorgeschlagenen Methoden konnte an Hand der aufgeführten Benchmark Prozesse erfolgreich gezeigt werden

Structural Health Monitoring Damage Detection Systems for Aerospace

This open access book presents established methods of structural health monitoring (SHM) and discusses their technological merit in the current aerospace environment. While the aerospace industry aims for weight reduction to improve fuel efficiency, reduce environmental impact, and to decrease maintenance time and operating costs, aircraft structures are often designed and built heavier than required in order to accommodate unpredictable failure. A way to overcome this approach is the use of SHM systems to detect the presence of defects. This book covers all major contemporary aerospace-relevant SHM methods, from the basics of each method to the various defect types that SHM is required to detect to discussion of signal processing developments alongside considerations of aerospace safety requirements. It will be of interest to professionals in industry and academic researchers alike, as well as engineering students. This article/publication is based upon work from COST Action CA18203 (ODIN - http://odin-cost.com/), supported by COST (European Cooperation in Science and Technology). COST (European Cooperation in Science and Technology) is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion

Advanced robust non-invasive foetal heart detection techniques during active labour using one pair of transabdominal electrodes

The thesis proposes and evaluates three state-of-the-art signal processing techniques to detect fetal heartbeats within each maternal cardiac cycle, during labour contractions, using only a pair of transabdominal electrodes. The first and second techniques are, namely, the structured third- order cumulant-slice-template matching and the bispectral-contours-template matching for fetal QRS identification, respectively. The third technique is based on the modified and appropriately weighted spectral multiple signal classification (MUSIC) with incorporated covariance matrix for uterine contraction noise-like interfering signals also contaminated with noise. Essentially, two modifications to the standard MUSIC have been developed in order to enhance the performance of the spectral estimator in our applied work. The first modification involves the introduction of an optimised weighting function to the segmented ECG covariance matrix, and is chiefly aimed at enhancing the fetal QRS major spectral peak which occurs at around 30 Hz against the mother QRS major spectral peak usually occurring around 17 Hz and all other noise contributions. Additional optional pseudo-bispectral enhancement to sharpen the maternal and fetal spectral peaks, in particular when the mother and fetal R-waves are temporally coincident, have been achieved. The second modification to the spectral MUSIC is the removal of the unjustified assumption that only white Gaussian noise is present and the incorporation of the actual measured labour uterine contraction covariance matrix in reconfigured subspace analysis. This inevitably leads to the generalised eigenvectors - eigenvalues decomposition modern signal processing. This is now coined the modified, interference incorporated pseudo-spectral MUSIC. The above mentioned first and second techniques are higher-order statistics-based (HOS) and hybrid involving both signal processing and NN classifiers. The third technique is second-order statistics-based (SOS). In all techniques, the removal of signal non-linearity with the aid of non-linear Volterra synthesisers plays a crucial part in the fetal detection integrity. Accurately assessed fetal heart classification rates as high as 95% have been achieved during labour, thus helping to provide non-invasive transparency to fetal intrapartum welfare. Performance analysis and evaluation processes involved more than 30 critical cases classified as “fetal under stress in labour” recorded in a London hospital database and used both transbadominal ECG electrodes and fetal scalp electrodes. The latter facilitates detection of the instantaneous fetal heart rate which is then used as the Reference Fetal Heart Rate in the assessment of the classification rate of each of the above mentioned techniques. It will be shown that the fetal heartbeats are completely masked by uterine activity and noise artefacts in all the recorded transabdominal maternal ECG signals. The fetal scalp electrode was, therefore, deemed necessary to provide the highest accurate measure of fetal heart functionality (from the hospital viewpoint), and in the assessment of the three non-invasive techniques presented in this thesis. The techniques may also be used during gestation and as early as 10 weeks