Quality-based Multimodal Classification Using Tree-Structured Sparsity

Recent studies have demonstrated advantages of information fusion based on sparsity models for multimodal classification. Among several sparsity models, tree-structured sparsity provides a flexible framework for extraction of cross-correlated information from different sources and for enforcing group sparsity at multiple granularities. However, the existing algorithm only solves an approximated version of the cost functional and the resulting solution is not necessarily sparse at group levels. This paper reformulates the tree-structured sparse model for multimodal classification task. An accelerated proximal algorithm is proposed to solve the optimization problem, which is an efficient tool for feature-level fusion among either homogeneous or heterogeneous sources of information. In addition, a (fuzzy-set-theoretic) possibilistic scheme is proposed to weight the available modalities, based on their respective reliability, in a joint optimization problem for finding the sparsity codes. This approach provides a general framework for quality-based fusion that offers added robustness to several sparsity-based multimodal classification algorithms. To demonstrate their efficacy, the proposed methods are evaluated on three different applications - multiview face recognition, multimodal face recognition, and target classification.Comment: To Appear in 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014

Transform-domain sparse representation based classification for machinery vibration signals

The working state of machinery can be reflected by vibration signals. Accurate classification of these vibration signals is helpful for the machinery fault diagnosis. A novel classification method for vibration signals, named Transform Domain Sparse Representation-based Classification (TDSRC), is proposed. The method achieves high classification accuracy by three steps. Firstly, time-domain vibration signals, including training samples and test samples, are transformed to another domain, e.g. frequency-domain, wavelet-domain etc. Then, the transform coefficients of the training samples are combined as a dictionary and the transform coefficients of the test samples are sparsely coded on the dictionary. Finally, the class label of the test samples is identified by their minimal reconstruction errors. Although the proposed method is very similar to the Sparse Representation-based Classification (SRC), experimental results illustrates its performance is far superior to SRC in the classification of vibration signals. These experiments include: frequency-domain classification of bearing vibration data from the Case Western Reserve University (CWRU) Bearing Data Center and wavelet-domain classification of six fault-types gearbox vibration data from our rotating machinery experimental platform

Joint sparse representation based classification of rub-impact in rotating machinery with multiple acoustic emission sensors

Acoustic emission (AE) technique has been widely used for the classification of rub-impact in rotating machinery due to its high sensitivity, wide frequency response range and dynamic detection property. However, it is still unsatisfied to effectively classify the rub-impact in rotating machinery under complicated environment using traditional classification method tailored to a single AE sensor. Recently, motivated by the theory of compressed sensing, a sparse representation based classification (SRC) method has been successfully used in many classification applications. Moreover, when dealing with multiple measurements the joint sparse representation based classification (JSRC) method could improve the classification accuracy with the aid of employing structural complementary information from each measurement. This paper investigates the use of multiple AE sensors for the classification of rub-impact in rotating machinery based on the JSRC method. First, the cepstral coefficients of each AE sensor are extracted as the features for the rub-impact classification. Then, the extracted cepstral features of all AE sensors are concatenated as the input matrix for the JSRC based classifier. Last, the backtracking simultaneous orthogonal matching pursuit (BSOMP) algorithm is proposed to solve the JSRC problem aiming to get the rub-impact classification results. The BSOMP has the advantages of not requiring the sparsity to be known as well as deleting unreliable atoms. Experiments are carried out on real-world data sets collected from in our laboratory. The results indicate that the JSRC method with multiple AE sensors has higher rub-impact classification accuracies when compared to the SRC method with a single AE sensor and the proposed BSOMP algorithm is more flexible and it performs better than the traditional SOMP algorithm for solving the JSRC method

Exclusive lasso-based k-nearest-neighbor classification

Conventionally, the k nearest-neighbor (kNN) classification is implemented with the use of the Euclidean distance-based measures, which are mainly the one-to-one similarity relationships such as to lose the connections between different samples. As a strategy to alleviate this issue, the coefficients coded by sparse representation have played a role of similarity gauger for nearest-neighbor classification as well. Although SR coefficients enjoy remarkable discrimination nature as a one-to-many relationship, it carries out variable selection at the individual level so that possible inherent group structure is ignored. In order to make the most of information implied in the group structure, this paper employs the exclusive lasso strategy to perform the similarity evaluation in two novel nearest-neighbor classification methods. Experimental results on both benchmark data sets and the face recognition problem demonstrate that the EL-based kNN method outperforms certain state-of-the-art classification techniques and existing representation-based nearest-neighbor approaches, in terms of both the size of feature reduction and the classification accuracy

Availability modeling and evaluation on high performance cluster computing systems

Cluster computing has been attracting more and more attention from both the industrial and the academic world for its enormous computing power, cost effective, and scalability. Beowulf type cluster, for example, is a typical High Performance Computing (HPC) cluster system. Availability, as a key attribute of the system, needs to be considered at the system design stage and monitored at mission time. Moreover, system monitoring is a must to help identify the defects and ensure the system\u27s availability requirement. In this study, novel solutions which provide availability modeling, model evaluation, and data analysis as a single framework have been investigated. Three key components in the investigation are availability modeling, model evaluation, and data analysis. The general availability concepts and modeling techniques are briefly reviewed. The system\u27s availability model is divided into submodels based upon their functionalities. Furthermore, an object oriented Markov model specification to facilitate availability modeling and runtime configuration has been developed. Numerical solutions for Markov models are examined, especially on the uniformization method. Alternative implementations of the method are discussed; particularly on analyzing the cost of an alternative solution for small state space model, and different ways for solving large sparse Markov models. The dissertation also presents a monitoring and data analysis framework, which is responsible for failure analysis and availability reconfiguration. In addition, the event logs provided from the Lawrence Livermore National Laboratory have been studied and applied to validate the proposed techniques

A hierarchical structure built on physical and data-based information for intelligent aero-engine gas path diagnostics

Future trends in engine health management (EHM) systems are information fusion, advanced analytical methods, and the concept of the Intelligent Engines. Machine Learning (ML)-based aero-engine gas path diagnostic methods are promising under the motivation of these trends. However, previous ML-based diagnostic structures are rarely applied in actual engineering practice because they are purely mathematical and lack physical insight or are limited by the error accumulation problem. Developing an accurate, flexible and interpretable intelligent diagnostic method has always posed a challenge, especially when physical knowledge is also available for more diagnostic information. Instead of modifying and applying existing ML methods for classification or regression, this study proposes a novel hierarchical diagnostic method to get insight into the physical systems, build hierarchies automatically, and recommend the classification structures. The proposed hierarchical diagnostic method is evaluated against a NASA model high-bypass two-spool turbofan engine. NASA's blind test case results show that Kappa Coefficient of the proposed hierarchical diagnostic method is 0.693 and is at least 0.008 higher than the other diagnostic methods in the open literature. It has been proved that the proposed method can quantify the dependence relationships between the fault classes for enhanced diagnostic information, recommend the best diagnostic structure for reduced complexity, and solve the error accumulation problem for improved diagnostic accuracy. The proposed method could support intelligent condition monitoring systems by effectively exploiting physical and data-based information for improved model interpretability, model flexibility, diagnostic visibility, diagnostic accuracy, and diagnostic reliability

Novel Deep Learning Techniques For Computer Vision and Structure Health Monitoring

This thesis proposes novel techniques in building a generic framework for both the regression and classification tasks in vastly different applications domains such as computer vision and civil engineering. Many frameworks have been proposed and combined into a complex deep network design to provide a complete solution to a wide variety of problems. The experiment results demonstrate significant improvements of all the proposed techniques towards accuracy and efficiency