Experimental Investigation of Oblique Impact Behavior of Adhesively Bonded Composite Single-Lap Joints

Applied Composite Materials - Determining the impact behavior of adhesive joints allows the designing of high-strength joints. Therefore, the dynamic behavior of adhesive joints has recently become...     

Effect of film thickness on hydrogen content in a-Si : H

The hydrogen content, its depth distribution, and its bonding configuration have been studied in hydrogenated amorphous silicon prepared by plasma-enhanced chemical vapor deposition with hydrogen-diluted silane. Nuclear reaction analysis and infrared spectroscopy were used to determine the total amount of hydrogen and its bonded component, respectively. It has been established that the total concentration of hydrogen does not depend on the film thickness, and has a uniform depth profile. The concentration of bonded hydrogen changes with the film thickness within the measurement accuracy. The data obtained suggest the presence of molecular (non-bonded) hydrogen, uniformly distributed in concentration across the film thickness.     

Novel hybrid DNN approaches for speaker verification in emotional and stressful talking environments

Neural Computing and Applications - In this work, we conducted an empirical comparative study of the performance of text-independent speaker verification in emotional and stressful environments....     

Effect of high temperature heat treatment on intergranular corrosion of AlMgSi(Cu) model alloy

Copper containing 6000-series aluminium alloys may become susceptible to intergranular corrosion (IGC) as a result of improper thermomechanical processing. Effect of cooling rate after solution heat treatment on the corrosion behaviour of a model AlMgSi(Cu) alloy of nominal composition (wt%) 0.6 Mg, 0.6 Si, 0.2 Fe, 0.2 Mn and 0.1 Cu was investigated. Slow cooling rates were simulated by isothermal treatment for predetermined times in lower temperature baths immediately after solution heat treatment. Treatment for 10-100 s at temperatures below 400 °C introduced susceptibility to IGC. Longer heat treatment at the same temperatures introduced susceptibility to pitting. A corrosion resistant time zone was found between the zones of IGC and pitting at temperatures lower than 350 °C. Quenching in water after solution heat treatment prevented IGC. IGC was related to microgalvanic coupling between the noble Q-phase (Al₄Mg₈Si₇Cu₂) grain boundary precipitates and the adjacent depleted zone. Pitting was attributed to coarse particles in the matrix. Possible mechanisms causing the corrosion resistant intermediate zone are discussed. The results indicate possible methods for obtaining increased corrosion resistance of similar alloys by proper thermal processing.     

Fractal Characteristics of Fracture Surfaces

Quantitative fractography is often used to study material failure mechanisms. During calculation of surface or profile roughness parameters, the magnification used in obtaining fractographic data is found to influence the value of the parameters. Fractal geometry has been developed into a tool capable of defining surface and profile topography without sensitivity to magnification, and several studies have related fractal dimension ( D _F) to other physical or mechanical properties. In this study, we obtained the fractal dimension of profiled fracture surfaces of one glass and three proprietary dental porcelains. The fracture toughness ( K _1c) of these materials was also measured using the indentation-strength method. Results show the surfaces to be fractal. No quantitative relationship between fractal dimension and toughness was found. Differences in K _1c were demonstrated between some materials. It is postulated that the size range within which fractal dimension can be defined as constant is dependent on the toughening mechanism, and that the relationship between K _Ic and D _F cannot be identical for all materials.     

Distributed Learning for Planning Under Uncertainty Problems with Heterogeneous Teams

This paper considers the problem of multiagent sequential decision making under uncertainty and incomplete knowledge of the state transition model. A distributed learning framework, where each agent learns an individual model and shares the results with the team, is proposed. The challenges associated with this approach include choosing the model representation for each agent and how to effectively share these representations under limited communication. A decentralized extension of the model learning scheme based on the Incremental Feature Dependency Discovery (Dec-iFDD) is presented to address the distributed learning problem. The representation selection problem is solved by leveraging iFDD’s property of adjusting the model complexity based on the observed data. The model sharing problem is addressed by having each agent rank the features of their representation based on the model reduction error and broadcast the most relevant features to their teammates. The algorithm is tested on the multi-agent block building and the persistent search and track missions. The results show that the proposed distributed learning scheme is particularly useful in heterogeneous learning setting, where each agent learns significantly different models. We show through large-scale planning under uncertainty simulations and flight experiments with state-dependent actuator and fuel-burn- rate uncertainty that our planning approach can outperform planners that do not account for heterogeneity between agents.