This paper describes a noise-aware dominance operator for evolutionary algorithms to solve the multiobjective optimization problems (MOPs) that contain noise in their objective functions. This operator takes objective value samples of given two individuals (or solution candidates), estimates the impacts of noise on the samples and determines whether it is confident enough to judge which one is superior/inferior between the two individuals. Since the proposed operator assumes no noise distributions a priori, it is well applicable to various MOPs whose objective functions follow unknown noise distributions. Experimental results show that it operates reliably in noisy MOPs and outperforms existing noise-aware dominance operators.

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Mechanical tests, for example, tensile and hardness tests, are usually used to evaluate the properties of rubber materials. In this work, mechanical properties of selected rubber materials, that is, natural rubber (NR), styrene butadiene rubber (SBR), nitrile butadiene rubber (NBR), and ethylene propylene diene monomer (EPDM), were evaluated using a near infrared (NIR) spectroscopy technique. Here, NR/NBR and NR/EPDM blends were first prepared. All of the samples were then scanned using a FT-NIR spectrometer and fitted with an integration sphere working in a diffused reflectance mode. The spectra were correlated with hardness and tensile properties. Partial least square (PLS) calibration models were built from the spectra datasets with preprocessing techniques, that is, smoothing and second derivative. This indicated that reasonably accurate models, that is, with a coefficient of determination [R2] of the validation greater than 0.9, could be achieved for the hardness and tensile properties of rubber materials. This study demonstrated that FT-NIR analysis can be applied to determine hardness and tensile values in rubbers and rubber blends effectively.

Abstract. Developing applications for wireless sensor networks (WSN) is a complicated process because of the wide variety of WSN applications and lowlevel implementation details. Model-Driven Engineering offers an effective solution to WSN application developers by hiding the details of lower layers and raising the level of abstraction. However, balancing between abstraction level and unambiguity is challenging issue. This paper presents Baobab, a metamodeling framework for designing WSN applications and generating the corresponding code, to overcome the conflict between abstraction and reusability versus unambiguity. Baobab allows users to define functional and non-functional aspects of a system separately as software models, validate them and generate code automatically.

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