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...     

Microarchitectures for Managing Chip Revenues under Process Variations

As transistor feature sizes continue to shrink intothe sub-90nm range and beyond, the effects of process variationson critical path delay and chip yields have amplified. A commonconcept to remedy the effects of variation is speed-binning, bywhich chips from a single batch are rated by a discrete range offrequencies and sold at different prices. In this paper, we discussstrategies to modify the number of chips in different bins andhence enhance the profits obtained from them. Particularly, wepropose a scheme that introduces a small Substitute Cacheassociated with each cache way to replicate the data elementsthat will be stored in the high latency lines. Assuming a fixedpricing model, this method increases the revenue by as much as13.8% without any impact on the performance of the chips.     

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.     

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.     

An adaptive support vector regressor controller for nonlinear systems

In this study, a novel online support vector regressor (SVR) controller based on system model estimated by a separate online SVR is proposed. The main idea is to obtain an SVR controller based on an estimated model of the system by optimizing the margin between reference input and system output. For this purpose, “closed-loop margin” which depends on tracking error is defined, then the parameters of the SVR controller are optimized so as to optimize the closed-loop margin and minimize the tracking error. In order to construct the closed-loop margin, the model of the system estimated by an online SVR is utilized. The parameters of the SVR controller are adjusted via the SVR model of system. The stability of the closed-loop system has also been analyzed. The performance of the proposed method has been evaluated by simulations carried out on a continuously stirred tank reactor (CSTR) and a bioreactor, and the results show that SVR model and SVR controller attain good modeling and control performances.