Sparse feature learning for image analysis in segmentation, classification, and disease diagnosis.

The success of machine learning algorithms generally depends on intermediate data representation, called features that disentangle the hidden factors of variation in data. Moreover, machine learning models are required to be generalized, in order to reduce the specificity or bias toward the training dataset. Unsupervised feature learning is useful in taking advantage of large amount of unlabeled data, which is available to capture these variations. However, learned features are required to capture variational patterns in data space. In this dissertation, unsupervised feature learning with sparsity is investigated for sparse and local feature extraction with application to lung segmentation, interpretable deep models, and Alzheimer\u27s disease classification. Nonnegative Matrix Factorization, Autoencoder and 3D Convolutional Autoencoder are used as architectures or models for unsupervised feature learning. They are investigated along with nonnegativity, sparsity and part-based representation constraints for generalized and transferable feature extraction

Reliability-Based Safety Evaluation of the BISTOON Historic Masonry Arch Bridge

AbstractThis research examines the probabilistic safety assessment of the historic BISTOON arch bridge. Probabilistic analysis based on the Load-Resistance model was performed. The evaluation of implicit functions of load and resistance was performed by the finite element method, and the Monte-Carlo approach was used for experiment simulation. The sampling method used was Latin Hypercube. Four random variables were considered including modulus of elasticity of brick and infilled materials and the specific mass of brick and infilled materials. The normal distribution was used to express the statistical properties of the random variables. The coefficient of variation was defined as 10%. Linear behavior was assumed for the bridge materials. Three output parameters of maximum bridge displacement, maximum tensile stress, and minimum compressive stress were assigned as structural limit states. A sensitivity analysis for probabilistic analysis was performed using the Spearman ranking method. The results showed that the sensitivity of output parameters to infilled density changes is high. The results also indicated that the system probability of failure is equal to p fsystem =1.55 × 10−3. The bridge safety index value obtained is βt = 2.96, which is lower than the recommended target safety index. The required safety parameters for the bridge have not been met and the bridge is at the risk of failure