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

An Introduction to MultiAgent Systems.

Applications of Machine Learning (ML) algorithms in Structural Health Monitoring (SHM) have become of great interest in recent years owing to their superior ability to detect damage and deficiencies in civil engineering structures. With the advent of the Internet of Things, big data and the colossal and complex backlog of aging civil infrastructure assets, such applications will increase very rapidly. ML can efficiently perform several analyses of clustering, regression and classification of damage in diverse structures, including bridges, buildings, dams, tunnels, wind turbines, etc. In this systematic review, the diverse ML algorithms used in this domain have been classified into two major subfields: vibration-based SHM and image-based SHM. The efficacy of deploying ML algorithms in SHM has been discussed and detailed critical analysis of ML applications in SHM has been provided. Accordingly, practical recommendations have been made and current knowledge gaps and future research needs have been outlined.

Machine Learning Algorithms in Civil Structural Health Monitoring: A Systematic Review

Comparative studies of metamodeling and AI-Based techniques in damage detection of structures

Inspection strategies with guided wave-based approaches give to structural health monitoring (SHM) applications several advantages, among them, the possibility of the use of real data from the structure which enables continuous monitoring and online damage identification. These kinds of inspection strategies are based on the fact that these waves can propagate over relatively long distances and are able to interact sensitively with and uniquely with different types of defects. The principal goal for SHM is oriented to the development of efficient methodologies to process these data and provide results associated with the different levels of the damage identification process. As a contribution, this work presents a damage detection and classification methodology which includes the use of data collected from a structure under different structural states by means of a piezoelectric sensor network taking advantage of the use of guided waves, hierarchical nonlinear principal component analysis (h-NLPCA), and machine learning. The methodology is evaluated and tested in two structures: (i) a carbon fibre reinforced polymer (CFRP) sandwich structure with some damages on the multilayered composite sandwich structure and (ii) a CFRP composite plate. Damages in the structures were intentionally produced to simulate different damage mechanisms, that is, delamination and cracking of the skin.

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A Damage Classification Approach for Structural Health Monitoring Using Machine Learning

 Abstract—We report on work that is part of the development of an agent-based structural health monitoring system. The data used are acoustic emission signals, and we classify these signals according to source mechanisms, those associated with crack growth being particularly significant. The agents are proxies for communication- and computation-intensive techniques and respond to the situation at hand by determining an appropriate constellation of techniques. It is critical that the system have a repertoire of classifiers with different characteristics so that a combination appropriate for the situation at hand can generally be found. We use unsupervised learning for identifying the existence and location of damage but supervised learning for identifying the type and severity of damage. The supervised learning techniques investigated are support vector machines (SVM), naive Bayes classifiers, and feed-forward neural networks (FNN). The unsupervised learning techniques investigated are k-means (with k equal to 3, 4, 5, and 6) and self-organizing maps (SOM, with 3, 4, 5, and 6 output neurons). For each technique except SOM, we tested versions with and without principal component analysis (PCA) to reduce the dimensionality of the data. We found significant differences in the characteristics of these machine learning techniques, with trade-offs between accuracy and fast classification runtime that can be exploited by the agents in finding appropriate combinations of classification techniques. The approach followed here can be generalized for exploring the characteristics of machine-learning techniques for monitoring various kinds of structures. requirements. Agents negotiate to determine a team of techniques for solving the task at hand, and they communicate a workflow to a workflow engine, which

A Study of Machine Learning Techniques for Detecting and Classifying Structural Damage

Writing secure code is critical because a large fraction of security incidents result from flaws in the code. In order to effectively teaching knowledge of secure software engineering we have developed a course module titled “Introduction to Writing Secure Code”. This paper presents the content of this module and reports our teaching experiences. This module has been successfully taught in GEEN 163 Introduction to Java Programming class and GEEN 165 Computer Programming Design class in Spring 2011 in the Department of Computer Science at xxx University. Our experience exhibits that teaching this module in freshman and sophomore levels help students not only understand the impacts of insecure code, but also gain significant knowledge of safe programming practice. Students' survey and feedback reflected that this module is very valuable in their educational experience. This module could be taught in first year freshman and second year sophomore classes of software engineering, computer science and information technology.

Teaching secure software engineering: Writing secure code

Increasing the pool of qualified workers in the Science, Technology, Engineering and Mathematics (STEM) areas has become one of the nation's key priorities, as those professions are the backbone of innovation and critical to our country's economic future. The 2015 Females in Technology (FiT) Summer Boot Camp project was a summer residential technology boot camp for low income rising high school junior and senior girls. The objective of the 2015 Summer FiT boot camp was to increase the number of low income female students who pursue degrees in technology disciplines at the collegiate level and who are prepared for a technologically rich workforce. As a recruitment tool, we anticipate increasing the enrollment of female students in the School of Technology. The methodology for the FiT Boot Camp was a short-term intensive hands-on experience which was the first of its kind at NCA&T. The preliminary results have been more than promising. The pre and post-test surveys indicate a positive trend in terms of knowledge gained, interest, and attitude towards technology. Not only did this program stimulate interest in the technology profession, it also introduced female students to new and innovative technology concepts while reinforcing and improving technology and leadership skills.

Using technology summer camp to stimulate the interest of female high school students in technology careers

Feature selection techniques are investigated to increase the accuracy of classification while reducing the dimensionality of the feature space Dimensionality reduction techniques investigated include principal component analysis (PCA), recursive feature elimination (RFE), and Genetic and Evolutionary Feature Weighting & Selection (GEFeWS) A support vector machine (SVM) with linear kernel functions was used with all three techniques for consistency In our experiment, RFE and GEFeWS performed comparably and both resulted in more accurate classifiers than PCA

Comparing dimensionality reduction techniques

This paper reports on work on a structural health monitoring system done for NASA at North Carolina A&T State University. The system combines a multiagent system as the "brains" and a workflow system as the "brawn." Agents here typically serve as proxies for techniques with intensive communication and computation requirements. Agents negotiate to determine a constellation of techniques for solving the task at hand and communicate it to a workflow engine, which actually carries out the tasks. A hierarchically structured collection of monitor agents determines efficient ways to use the computation resources of the workflow engine. A monitor agent at the lowest level in the hierarchy is associated with a wireless mote that transmits data from an acoustic sensor (or perhaps an optic fiber or strain gauges). It negotiates with specialized agents to find techniques for extracting feature vectors from signal samples and classifying the associated events. The classifiers (in the workflow engine) are trained by several machine-learning techniques. The regions of the structure for which the various monitor agents are responsible form an inclusion hierarchy, reflected in the hierarchy among the agents. This hierarchy is set up among the agents using the contract net protocol. As alternatives, we have considered the iterated contract net protocol and coalition game theory. The current prototype architecture is restricted to a single member, but the general principles generalize to structures as complex as aircraft (the ultimate target of this project).

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Gina Bullock

Papers

A Study of Machine Learning Techniques for Detecting and Classifying Structural Damage

Teaching secure software engineering: Writing secure code

Using technology summer camp to stimulate the interest of female high school students in technology careers

Comparing dimensionality reduction techniques

A Multiagent and Workflow System for Structural Health Monitoring Using the Contract Net Protocol and Alternatives.