Anomaly Detection in Noisy Images

Finding rare events in multidimensional data is an important detection problem that has applications in many fields, such as risk estimation in insurance industry, finance, flood prediction, medical diagnosis, quality assurance, security, or safety in transportation. The occurrence of such anomalies is so infrequent that there is usually not enough training data to learn an accurate statistical model of the anomaly class. In some cases, such events may have never been observed, so the only information that is available is a set of normal samples and an assumed pairwise similarity function. Such metric may only be known up to a certain number of unspecified parameters, which would either need to be learned from training data, or fixed by a domain expert. Sometimes, the anomalous condition may be formulated algebraically, such as a measure exceeding a predefined threshold, but nuisance variables may complicate the estimation of such a measure. Change detection methods used in time series analysis are not easily extendable to the multidimensional case, where discontinuities are not localized to a single point. On the other hand, in higher dimensions, data exhibits more complex interdependencies, and there is redundancy that could be exploited to adaptively model the normal data. In the first part of this dissertation, we review the theoretical framework for anomaly detection in images and previous anomaly detection work done in the context of crack detection and detection of anomalous components in railway tracks. In the second part, we propose new anomaly detection algorithms. The fact that curvilinear discontinuities in images are sparse with respect to the frame of shearlets, allows us to pose this anomaly detection problem as basis pursuit optimization. Therefore, we pose the problem of detecting curvilinear anomalies in noisy textured images as a blind source separation problem under sparsity constraints, and propose an iterative shrinkage algorithm to solve it. Taking advantage of the parallel nature of this algorithm, we describe how this method can be accelerated using graphical processing units (GPU). Then, we propose a new method for finding defective components on railway tracks using cameras mounted on a train. We describe how to extract features and use a combination of classifiers to solve this problem. Then, we scale anomaly detection to bigger datasets with complex interdependencies. We show that the anomaly detection problem naturally fits in the multitask learning framework. The first task consists of learning a compact representation of the good samples, while the second task consists of learning the anomaly detector. Using deep convolutional neural networks, we show that it is possible to train a deep model with a limited number of anomalous examples. In sequential detection problems, the presence of time-variant nuisance parameters affect the detection performance. In the last part of this dissertation, we present a method for adaptively estimating the threshold of sequential detectors using Extreme Value Theory on a Bayesian framework. Finally, conclusions on the results obtained are provided, followed by a discussion of possible future work

Some NASA contributions to human factors engineering: A survey

This survey presents the NASA contributions to the state of the art of human factors engineering, and indicates that these contributions have a variety of applications to nonaerospace activities. Emphasis is placed on contributions relative to man's sensory, motor, decisionmaking, and cognitive behavior and on applications that advance human factors technology

Aeronautical engineering: A continuing bibliography with indexes (supplement 303)

This bibliography lists 211 reports, articles, and other documents introduced into the NASA scientific and technical information database. 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

A Bio-inspired Framework for Highly Efficient Structural Health Monitoring and Vibration Analysis

Civil engineering structures are continuously exposed to the risk of damage whether due to ageing effects, excessive live loads or extreme events, such as earthquakes, blasts and cyclones. If not readily identified, damage will inevitably compromise the structural integrity, leading the system to stop operating and undergo in-depth interventions. The economic and social impacts associated with such an adverse condition can be significant, therefore effective methods able to early identify structural vulnerabilities are needed for these systems to keep meeting the required life-safety standards and avoid the impairment of their normal function. In this context, vibration-based analysis approaches play a leading role as they allow to detect structural faults which lie beneath the surface of the structure by identifying and quantifying anomalous changes in the system’s inherent vibration characteristics. However, although the considerable degree of maturity attained within the fields of experimental vibration analysis (EVA) and structural health monitoring (SHM), several technical issues still need to be addressed in order to ensure the successful implementation of these powerful tools for damage identification purposes. The scope of this paper is to present a bio-inspired framework for optimal structural health monitoring and vibration analysis. After a critical overview on current methods and tools, three main sources of bio-inspiration are described along with the relative algorithms derived for SHM applications. It is shown how uncovering the general principles behind the functioning of selected biological systems can foster the development of efficient solutions to the technical conflicts of actual SHM architectures and lead to new sensing paradigms for optimal network topology and sensors location. A compatibility-matrix is proposed to help compare biological and SHM systems and discriminate desired from unwanted features. Such a framework will ultimately assist in seeking for the most suitable nature-inspired solutions for more accurate condition screening and robust vibration analysis.FEDER funds through the Competitiveness Factors Operational Programme - COMPETE and by national funds through FCT – Foundation for Science and Technology within the scope of the project POCI-01-0145-FEDER-007633info:eu-repo/semantics/publishedVersio

Structural Adhesive Bonding Conference

Conference on design, materials, processes, and technology evaluation of structural adhesive bondin

Index to 1985 NASA Tech Briefs, volume 10, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1985 Tech Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

Altered Trabecular Bone Structure and Delayed Cartilage Degeneration in the Knees of Collagen VI Null Mice

Mutation or loss of collagen VI has been linked to a variety of musculoskeletal abnormalities, particularly muscular dystrophies, tissue ossification and/or fibrosis, and hip osteoarthritis. However, the role of collagen VI in bone and cartilage structure and function in the knee is unknown. In this study, we examined the role of collagen VI in the morphology and physical properties of bone and cartilage in the knee joint of Col6a1−/− mice by micro-computed tomography (microCT), histology, atomic force microscopy (AFM), and scanning microphotolysis (SCAMP). Col6a1−/− mice showed significant differences in trabecular bone structure, with lower bone volume, connectivity density, trabecular number, and trabecular thickness but higher structure model index and trabecular separation compared to Col6a1+/+ mice. Subchondral bone thickness and mineral content increased significantly with age in Col6a1+/+ mice, but not in Col6a1−/− mice. Col6a1−/− mice had lower cartilage degradation scores, but developed early, severe osteophytes compared to Col6a1+/+mice. In both groups, cartilage roughness increased with age, but neither the frictional coefficient nor compressive modulus of the cartilage changed with age or genotype, as measured by AFM. Cartilage diffusivity, measured via SCAMP, varied minimally with age or genotype. The absence of type VI collagen has profound effects on knee joint structure and morphometry, yet minimal influences on the physical properties of the cartilage. Together with previous studies showing accelerated hip osteoarthritis in Col6a1−/− mice, these findings suggest different roles for collagen VI at different sites in the body, consistent with clinical data