Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Generative models of brain connectivity for population studies

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 131-139).Connectivity analysis focuses on the interaction between brain regions. Such relationships inform us about patterns of neural communication and may enhance our understanding of neurological disorders. This thesis proposes a generative framework that uses anatomical and functional connectivity information to find impairments within a clinical population. Anatomical connectivity is measured via Diffusion Weighted Imaging (DWI), and functional connectivity is assessed using resting-state functional Magnetic Resonance Imaging (fMRI). We first develop a probabilistic model to merge information from DWI tractography and resting-state fMRI correlations. Our formulation captures the interaction between hidden templates of anatomical and functional connectivity within the brain. We also present an intuitive extension to population studies and demonstrate that our model learns predictive differences between a control and a schizophrenia population. Furthermore, combining the two modalities yields better results than considering each one in isolation. Although our joint model identifies widespread connectivity patterns influenced by a neurological disorder, the results are difficult to interpret and integrate with our regioncentric knowledge of the brain. To alleviate this problem, we present a novel approach to identify regions associated with the disorder based on connectivity information. Specifically, we assume that impairments of the disorder localize to a small subset of brain regions, which we call disease foci, and affect neural communication to/from these regions. This allows us to aggregate pairwise connectivity changes into a region-based representation of the disease. Once again, we use a probabilistic formulation: latent variables specify a template organization of the brain, which we indirectly observe through resting-state fMRI correlations and DWI tractography. Our inference algorithm simultaneously identifies both the afflicted regions and the network of aberrant functional connectivity. Finally, we extend the region-based model to include multiple collections of foci, which we call disease clusters. Preliminary results suggest that as the number of clusters increases, the refined model explains progressively more of the functional differences between the populations.by Archana Venkataraman.Ph.D

Learning motion patterns using hierarchical Bayesian models

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 163-179).In far-field visual surveillance, one of the key tasks is to monitor activities in the scene. Through learning motion patterns of objects, computers can help people understand typical activities, detect abnormal activities, and learn the models of semantically meaningful scene structures, such as paths commonly taken by objects. In medical imaging, some issues similar to learning motion patterns arise. Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) is one of the first methods to visualize and quantify the organization of white matter in the brain in vivo. Using methods of tractography segmentation, one can connect local diffusion measurements to create global fiber trajectories, which can then be clustered into anatomically meaningful bundles. This is similar to clustering trajectories of objects in visual surveillance. In this thesis, we develop several unsupervised frameworks to learn motion patterns from complicated and large scale data sets using hierarchical Bayesian models. We explore their applications to activity analysis in far-field visual surveillance and tractography segmentation in medical imaging. Many existing activity analysis approaches in visual surveillance are ad hoc, relying on predefined rules or simple probabilistic models, which prohibits them from modeling complicated activities. Our hierarchical Bayesian models can structure dependency among a large number of variables to model complicated activities. Various constraints and knowledge can be nicely added into a Bayesian framework as priors. When the number of clusters is not well defined in advance, our nonparametric Bayesian models can learn it driven by data with Dirichlet Processes priors.(cont.) In this work, several hierarchical Bayesian models are proposed considering different types of scenes and different settings of cameras. If the scenes are crowded, it is difficult to track objects because of frequent occlusions and difficult to separate different types of co-occurring activities. We jointly model simple activities and complicated global behaviors at different hierarchical levels directly from moving pixels without tracking objects. If the scene is sparse and there is only a single camera view, we first track objects and then cluster trajectories into different activity categories. In the meanwhile, we learn the models of paths commonly taken by objects. Under the Bayesian framework, using the models of activities learned from historical data as priors, the models of activities can be dynamically updated over time. When multiple camera views are used to monitor a large area, by adding a smoothness constraint as a prior, our hierarchical Bayesian model clusters trajectories in multiple camera views without tracking objects across camera views. The topology of multiple camera views is assumed to be unknown and arbitrary. In tractography segmentation, our approach can cluster much larger scale data sets than existing approaches and automatically learn the number of bundles from data. We demonstrate the effectiveness of our approaches on multiple visual surveillance and medical imaging data sets.by Xiaogang Wang.Ph.D

Software Newsroom – an approach to automation of news search and editing

We have developed tools and applied methods for automated identification of potential news from textual data for an automated news search system called Software Newsroom. The purpose of the tools is to analyze data collected from the internet and to identify information that has a high probability of containing new information. The identified information is summarized in order to help understanding the semantic contents of the data, and to assist the news editing process. It has been demonstrated that words with a certain set of syntactic and semantic properties are effective when building topic models for English. We demonstrate that words with the same properties in Finnish are useful as well. Extracting such words requires knowledge about the special characteristics of the Finnish language, which are taken into account in our analysis. Two different methodological approaches have been applied for the news search. One of the methods is based on topic analysis and it applies Multinomial Principal Component Analysis (MPCA) for topic model creation and data profiling. The second method is based on word association analysis and applies the log-likelihood ratio (LLR). For the topic mining, we have created English and Finnish language corpora from Wikipedia and Finnish corpora from several Finnish news archives and we have used bag-of-words presentations of these corpora as training data for the topic model. We have performed topic analysis experiments with both the training data itself and with arbitrary text parsed from internet sources. The results suggest that the effectiveness of news search strongly depends on the quality of the training data and its linguistic analysis. In the association analysis, we use a combined methodology for detecting novel word associations in the text. For detecting novel associations we use the background corpus from which we extract common word associations. In parallel, we collect the statistics of word co-occurrences from the documents of interest and search for associations with larger likelihood in these documents than in the background. We have demonstrated the applicability of these methods for Software Newsroom. The results indicate that the background-foreground model has significant potential in news search. The experiments also indicate great promise in employing background-foreground word associations for other applications. A combined application of the two methods is planned as well as the application of the methods on social media using a pre-translator of social media language.Peer reviewe

Neurobiology of incremental speech comprehension

Understanding spoken language requires the rapid transition from perceptual processing of the auditory input through a variety of cognitive processes involved in constructing the mental representation of the message that the speaker is intending to convey. Listeners carry out these complex processes very rapidly and accurately as they hear each word incrementally unfolding in a sentence. However, little is known about the specific spatiotemporal patterning of this wide range of incremental processing operations that underpin the dynamic transitions from the speech input to the development of a meaning interpretation of an utterance. This thesis aims to address this set of issues by investigating the spatiotemporal dynamics of brain activity as spoken sentences unfold over time in order to illuminate the neurocomputational properties of the human language processing system and determine how the representation of a spoken sentence develops incrementally as each upcoming word is heard. Using a novel application of multidimensional probabilistic modelling combined with models from computational linguistics, I developed models of a variety of computational processes associated with accessing and processing the syntactic and semantic properties of sentences and tested these models at various points as sentences unfolded over time. Since a wide range of incremental processes occur very rapidly during speech comprehension, it is crucial to keep track of the temporal dynamics of the neural computations involved. To do this, I used combined electroencephalography and magnetoencephalography (EMEG) to record neural activity with millisecond resolution and analyzed the recordings in source space using univariate and/or multivariate approaches. The results confirm the value of this combination of methods in examining the properties of incremental speech processing. My findings corroborate the predictive nature of human speech comprehension and demonstrate that the effects of early semantic constraint are not dependent on explicit syntactic knowledge

Neurobiological markers for remission and persistence of childhood attention-deficit/hyperactivity disorder

Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurodevelopmental disorders in children. Symptoms of childhood ADHD persist into adulthood in around 65% of patients, which elevates the risk for a number of adverse outcomes, resulting in substantial individual and societal burden. A neurodevelopmental double dissociation model is proposed based on existing studies in which the early onset of childhood ADHD is suggested to associate with dysfunctional subcortical structures that remain static throughout the lifetime; while diminution of symptoms over development could link to optimal development of prefrontal cortex. Current existing studies only assess basic measures including regional brain activation and connectivity, which have limited capacity to characterize the functional brain as a high performance parallel information processing system, the field lacks systems-level investigations of the structural and functional patterns that significantly contribute to the symptom remission and persistence in adults with childhood ADHD. Furthermore, traditional statistical methods estimate group differences only within a voxel or region of interest (ROI) at a time without having the capacity to explore how ROIs interact in linear and/or non-linear ways, as they quickly become overburdened when attempting to combine predictors and their interactions from high-dimensional imaging data set. This dissertation is the first study to apply ensemble learning techniques (ELT) in multimodal neuroimaging features from a sample of adults with childhood ADHD and controls, who have been clinically followed up since childhood. A total of 36 adult probands who were diagnosed with ADHD combined-type during childhood and 36 matched normal controls (NCs) are involved in this dissertation research. Thirty-six adult probands are further split into 18 remitters (ADHD-R) and 18 persisters (ADHD-P) based on the symptoms in their adulthood from DSM-IV ADHD criteria. Cued attention task-based fMRI, structural MRI, and diffusion tensor imaging data from each individual are analyzed. The high-dimensional neuroimaging features, including pair-wise regional connectivity and global/nodal topological properties of the functional brain network for cue-evoked attention process, regional cortical thickness and surface area, subcortical volume, volume and fractional anisotropy of major white matter fiber tract for each subject are calculated. In addition, all the currently available optimization strategies for ensemble learning techniques (i.e., voting, bagging, boosting and stacking techniques) are tested in a pool of semi-final classification results generated by seven basic classifiers, including K-Nearest Neighbors, support vector machine (SVM), logistic regression, Naïve Bayes, linear discriminant analysis, random forest, and multilayer perceptron. As hypothesized, results indicate that the features of nodal efficiency in right inferior frontal gyrus, right middle frontal (MFG)-inferior parietal (IPL) functional connectivity, and right amygdala volume significantly contributed to accurate discrimination between ADHD probands and controls; higher nodal efficiency of right MFG greatly contributed to inattentive and hyperactive/impulsive symptom remission, while higher right MFG-IPL functional connectivity strongly linked to symptom persistence in adults with childhood ADHD. The utilization of ELTs indicates that the bagging-based ELT with the base model of SVM achieves the best results, with the most significant improvement of the area under the receiver of operating characteristic curve (0.89 for ADHD probands vs. NCs, and 0.9 for ADHD-P vs. ADHD-R). The outcomes of this dissertation research have considerable value for the development of novel interventions that target mechanisms associated with recovery

Gene Regulatory Network Reconstruction Using Dynamic Bayesian Networks

High-content technologies such as DNA microarrays can provide a system-scale overview of how genes interact with each other in a network context. Various mathematical methods and computational approaches have been proposed to reconstruct GRNs, including Boolean networks, information theory, differential equations and Bayesian networks. GRN reconstruction faces huge intrinsic challenges on both experimental and theoretical fronts, because the inputs and outputs of the molecular processes are unclear and the underlying principles are unknown or too complex. In this work, we focused on improving the accuracy and speed of GRN reconstruction with Dynamic Bayesian based method. A commonly used structure-learning algorithm is based on REVEAL (Reverse Engineering Algorithm). However, this method has some limitations when it is used for reconstructing GRNs. For instance, the two-stage temporal Bayes network (2TBN) cannot be well recovered by application of REVEAL; it has low accuracy and speed for high dimensionality networks that has above a hundred nodes; and it even cannot accomplish the task of reconstructing a network with 400 nodes. We implemented an algorithm for DBN structure learning with Friedman\u27s score function to replace REVEAL, and tested it on reconstruction of both synthetic networks and real yeast networks and compared it with REVEAL in the absence or presence of preprocessed network generated by Zou and Conzen\u27s algorithm. The new score metric improved the precision and recall of GRN reconstruction. Networks of gene interactions were reconstructed using a Dynamic Bayesian Network (DBN) approach and were analyzed to identify the mechanism of chemical-induced reversible neurotoxicity through reconstruction of gene regulatory networks in earthworms with tools curating relevant genes from non-model organism\u27s pathway to model organism pathway