A versatile software tool for microwave planar radar absorbing materials design using global optimization algorithms

A computer-aided design (CAD) tool for the design of planar multi-layer coatings with high absorption for a desired frequency and angle range is presented. The tool uses deterministic and evolutionary optimization design methods. Both single and multi-objective design algorithms can be used and a single absorber design or the Pareto front can be found accordingly. A novel design technique utilizing PSO is also presented. A user-defined or a pre-defined design case can be selected interchangeably. The choice of selecting materials from pre-defined database is also available. The tool can be useful for both educational and research purposes. The efficiency of the tool is demonstrated through several design cases that are in agreement with existing literature data.     

Representing and reasoning about preferences in requirements engineering

The priorities that stakeholders associate with requirements may vary from stakeholder to stakeholder and from one situation to the next. Differing priorities, in turn, imply different design decisions for the system to be. While elicitation of requirement priorities is a well-studied activity, modeling and reasoning with prioritization has not enjoyed equal attention. In this paper, we address this problem by extending a state-of-the-art goal modeling notation to support the representation of preference (??nice-to-have??) requirements. In our extension, preference goals are distinguished from mandatory ones. Then, quantitative prioritizations of the former are constructed and used as criteria for evaluating alternative ways to achieve the latter. To generate solutions, an existing preference-based planner is utilized to efficiently search for alternatives that best satisfy a given set of mandatory and preferred requirements. With such a planning tool, analysts can acquire a better understanding of the impact of high-level stakeholder preferences on low-level design decisions.     

Optimizing the nanofibrous morphology of electrospun poly[(butylene succinate)‐ co‐(butylene adipate)]/clay nanocomposites and revealing the effect of the fibre nano‐dimension on the attained material properties

Electrospun mats of biodegradable polymer/clay nanocomposites were prepared in order to investigate the qualitative and quantitative correlations between electrospinning‐related parameters and the fibrous morphology of the mats. A full factorial design of experiments was used for that purpose and scanning electron microscopy was employed for dimensional characterization and determination of the chosen responses. Statistical analysis revealed that solution concentration and clay loading of nanocomposite were the most important parameters affecting the morphology of the fibrous webs. This, subsequently, allowed prediction of the domain of these two parameter settings where purely fibrous morphology can be achieved and further allowed optimization of the process in the framework of response surface methodology (RSM). The structure and thermal behaviour of the nanocomposites were also characterized before and after electrospinning using X‐ray diffraction, thermogravimetric analysis and differential scanning calorimetry. A multi‐analyte platform is provided by the combination of RSM results for improving the nanofibrous quality and post‐spinning characterization for predicting the overall performance of the electrospun web. Copyright © 2011 Society of Chemical Industry     

A reservoir-driven non-stationary hidden Markov model

In this work, we propose a novel approach towards sequential data modeling that leverages the strengths of hidden Markov models and echo-state networks (ESNs) in the context of non-parametric Bayesian inference approaches. We introduce a non-stationary hidden Markov model, the time-dependent state transition probabilities of which are driven by a high-dimensional signal that encodes the whole history of the modeled observations, namely the state vector of a postulated observations-driven ESN reservoir. We derive an efficient inference algorithm for our model under the variational Bayesian paradigm, and we examine the efficacy of our approach considering a number of sequential data modeling applications.     

A nonparametric Bayesian approach toward robot learning by demonstration

In the past years, many authors have considered application of machine learning methodologies to effect robot learning by demonstration. Gaussian mixture regression (GMR) is one of the most successful methodologies used for this purpose. A major limitation of GMR models concerns automatic selection of the proper number of model states, i.e., the number of model component densities. Existing methods, including likelihood- or entropy-based criteria, usually tend to yield noisy model size estimates while imposing heavy computational requirements. Recently, Dirichlet process (infinite) mixture models have emerged in the cornerstone of nonparametric Bayesian statistics as promising candidates for clustering applications where the number of clusters is unknown a priori. Under this motivation, to resolve the aforementioned issues of GMR-based methods for robot learning by demonstration, in this paper we introduce a nonparametric Bayesian formulation for the GMR model, the Dirichlet process GMR model. We derive an efficient variational Bayesian inference algorithm for the proposed model, and we experimentally investigate its efficacy as a robot learning by demonstration methodology, considering a number of demanding robot learning by demonstration scenarios.