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

The proper management of energy resources is essential for any wireless sensing system. With applications that span industrial, civil, and aerospace infrastructure, it is necessary for sensors and sensor nodes to be physically robust and power efficient. In many applications, a sensor network must operate in locations that are difficult to access, and often these systems have a desired operational lifespan which exceeds that of conventional battery technologies. In the present study, the use of microwave energy is examined as an alternate method for powering compact, deployable wireless sensor nodes. A prototype microstrip patch antenna has been designed to operate in the 2.4 GHz ISM band and is used to collect directed radio frequency (RF) energy to power a wireless impedance device that provides active sensing capabilities for structural health monitoring applications. The system has been demonstrated in the laboratory, and was deployed in field experiments on the Alamosa Canyon Bridge in New Mexico in August 2007. The transmitted power was limited to 1 W in field tests, and was able to charge the sensor node to 3.6 V in 27 s. This power level was sufficient to measure two piezoelectric sensors and transmit data back to a base station on the bridge.

RF Energy Transmission for a Low-Power Wireless Impedance Sensor Node

An Outlier Analysis Framework for Impedance-based Structural Health Monitoring

Over the course of the last few years, the Robot Operating System (ROS) has become a highly popular software
framework for robotics research. ROS has a very active developer community and is widely used for robotics research in
both academia and government labs. The prevalence and modularity of ROS cause many people to ask the question:
“What prevents ROS from being used in commercial or government applications?” One of the main problems that is
preventing this increased use of ROS in these applications is the question of characterizing its security (or lack thereof).
In the summer of 2012, a crowd sourced cyber-physical security contest was launched at the cyber security conference
DEF CON 20 to begin the process of characterizing the security of ROS. A small-scale, car-like robot was configured as
a cyber-physical security “honeypot” running ROS. DEFFCON-20 attendees were invited to find exploits and
vulnerabilities in the robot while network traffic was collected. The results of this experiment provided some interesting
insights and opened up many security questions pertaining to deployed robotic systems. The Federal Aviation
Administration is tasked with opening up the civil airspace to commercial drones by September 2015 and driverless cars
are already legal for research purposes in a number of states. Given the integration of these robotic devices into our daily
lives, the authors pose the following question: “What security exploits can a motivated person with little-to-no
experience in cyber security execute, given the wide availability of free cyber security penetration testing tools such as
Metasploit?” This research focuses on applying common, low-cost, low-overhead, cyber-attacks on a robot featuring
ROS. This work documents the effectiveness of those attacks.

A preliminary cyber-physical security assessment of the Robot Operating System (ROS)

The primary objective of novelty detection is to examine a system's dynamic response to determine if the system significantly deviates from an initial baseline condition. In reality, the system is often subject to changing environmental and operation conditions that affect its dynamic characteristics. Such variations include changes in loading, boundary conditions, temperature, and moisture. Most damage diagnosis techniques, however, generally neglect the effects of these changing ambient conditions. Here, a novelty detection technique is developed explicitly taking into account these natural variations of the system in order to minimize false positive indications of true system changes. Auto-associative neural networks are employed to discriminate system changes of interest such as structural deterioration and damage from the natural variations of the system.

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Novelty detection under changing environmental conditions

This special issue provides readers with a picture of the current state of the art in the structural health monitoring field while highlighting the new research avenues that are being aggressively explored.

Charles R. Farrar

Papers

RF Energy Transmission for a Low-Power Wireless Impedance Sensor Node

An Outlier Analysis Framework for Impedance-based Structural Health Monitoring

A preliminary cyber-physical security assessment of the Robot Operating System (ROS)

Novelty detection under changing environmental conditions

Structural health monitoring: technological advances to practical implementations [scanning the issue]