Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

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Anomaly detection: A survey

物件導向軟體之架構(Object-Oriented Software Construction)探討

A review on machinery diagnostics and prognostics implementing condition-based maintenance

This report contains a review of the technical literature concerning the detection, location, and characterization of structural damage via techniques that examine changes in measured structural vibration response. The report first categorizes the methods according to required measured data and analysis technique. The analysis categories include changes in modal frequencies, changes in measured mode shapes (and their derivatives), and changes in measured flexibility coefficients. Methods that use property (stiffness, mass, damping) matrix updating, detection of nonlinear response, and damage detection via neural networks are also summarized. The applications of the various methods to different types of engineering problems are categorized by type of structure and are summarized. The types of structures include beams, trusses, plates, shells, bridges, offshore platforms, other large civil structures, aerospace structures, and composite structures. The report describes the development of the damage-identification methods and applications and summarizes the current state-of-the-art of the technology. The critical issues for future research in the area of damage identification are also discussed.

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Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review

Mechanoreciprocity refers to a cell's ability to maintain tensional homeostasis in response to various types of forces. Physical forces are continually being exerted upon cells of various tissue types, even those considered static, such as the brain. Through mechanoreceptors, cells sense and subsequently respond to these stimuli. These forces and their respective cellular responses are prevalent in regulating everything from embryogenic tissue-specific differentiation, programmed cell death, and disease progression, the last of which being the subject of extensive attention. Abnormal mechanical remodeling of cells can provide clues as to the pathological status of tissues. This becomes particularly important in cancer cells, where cellular stiffness has been recently accepted as a novel biomarker for cancer metastasis. Several studies have also elucidated the importance of cell stiffness in cancer metastasis, with data highlighting that a reversal of tumor stiffness has the capacity to revert the metastatic properties of cancer. In this review, we summarize our current understanding of extracellular matrix (ECM) homeostasis, which plays a prominent role in tissue mechanics. We also describe pathological disruption of the ECM, and the subsequent implications toward cancer and cancer metastasis. In addition, we highlight the most novel approaches toward understanding the mechanisms which generate pathogenic cell stiffness and provide potential new strategies which have the capacity to advance our understanding of one of human-kinds' most clinically significant medical pathologies. These new strategies include video-based techniques for structural dynamics, which have shown great potential for identifying full-field, high-resolution modal properties, in this case, as a novel application.

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YAP/TAZ Related BioMechano Signal Transduction and Cancer Metastasis.

This paper presents a variety of structural health monitoring (SHM) techniques, based on the use of piezoelectric active-sensors, used to determine the structural integrity of wind turbine blades. Specifically, Lamb wave propagations, frequency response functions, and time series based methods are utilized to estimate the condition of wind turbine blades. For experiments, a 1m section of a 9m CX100 blade is used. Overall, these three methods yielded a sufficient damage detection capability to warrant further investigation into field deployment. A full-scale fatigue test of a CX-100 wind turbine blade is also conducted. This paper summarizes considerations needed to design such SHM systems, experimental procedures and results, and practical implementation issues that can be used as guidelines for future investigations.

SHM of wind turbine blades using piezoelectric active-sensors

In order to facilitate damage detection and structural health monitoring (SHM) research for composite unmanned aerial
vehicles (UAV) a specialized test-bed has been developed. This test-bed consists of four 2.61 m all-composite test-pieces
emulating composite UAV wings, a series of detailed finite element models of the test-pieces and their
components, and a dynamic testing setup including a mount for simulating the cantilevered operation configuration of
real wings. Two of the wings will have bondline damage built in; one undamaged and one damaged wing will also be
fitted with a range of embedded and attached sensors-piezoelectric patches, fiber-optics, and accelerometers. These
sensors will allow collection of realistic data; combined with further modal testing they will allow comparison of the
physical impact of the sensors on the structure compared to the damage-induced variation, evaluation of the sensors for
implementation in an operational structure, and damage detection algorithm validation. At the present time the pieces
for four wings have been fabricated and modally tested and one wing has been fully assembled and re-tested in a
cantilever configuration. The component part and assembled wing finite element models, created for MSC.Nastran,
have been correlated to their respective structures using the modal information. This paper details the design and
manufacturing of the test-pieces, the finite element model construction, and the dynamic testing setup. Measured natural
frequencies and mode shapes for the assembled cantilevered wing are reported, along with finite element model
undamaged modal response, and response with a small disbond at the root of the top main spar-skin bondline.

Development of a composite UAV wing test-bed for structural health monitoring research

WITHDRAWN: Singularity detection for structural health monitoring using holder exponents

A damage detection problem is cast in the context of a statistical pattern recognition paradigm In particular, this paper focuses on applying statistical process control methods referred to as control charts to vibration-based damage detection First, an auto-regressive (AR) model is fitted to the measured time histories from an undamaged structure Residual errors, which quantify the difference between the prediction from the AR model and the actual measured time history at each time interval, are used as the damage-sensitive features Next, the average and variability of the selected features are monitored by the X-bar and S control charts A statistically significant number of error terms outside the control limits indicate a system transit from a healthy state to a damage state For demonstration, this statistical process control is applied to vibration test data acquired from a concrete bridge column as the column is progressively damaged

Charles R. Farrar

Papers

YAP/TAZ Related BioMechano Signal Transduction and Cancer Metastasis.

SHM of wind turbine blades using piezoelectric active-sensors

Development of a composite UAV wing test-bed for structural health monitoring research

WITHDRAWN: Singularity detection for structural health monitoring using holder exponents

Continuous structural monitoring using statistical process control