Evolutionary Algorithms have been applied to single and multiple objectives optimization problems, with a strong emphasis on problems, solved through numerical simulations. However in several engineering problems, there is limited availability of suitable models and there is need for optimization of realistic or experimental configurations. The multiobjective optimization of an experimental setup is addressed in this work. Experimental setups present a number of challenges to any optimization technique including: availability only of pointwise information, experimental noise in the objective function, uncontrolled changing of environmental conditions and measurement failure. This work introduces a multiobjective evolutionary algorithm capable of handling noisy problems with a particular emphasis on robustness against unexpected measurements (outliers). The algorithm is based on the Strength Pareto Evolutionary Algorithm (SPEA) of Zitzler and Thiele and includes the new concepts of domination dependent lifetime, reevaluation of solutions and modifications in the update of the archive population. Several tests on prototypical functions underline the improvements in convergence speed and robustness of the extended algorithm. The proposed algorithm is implemented to the Pareto optimization of the combustion process of a stationary gas turbine in an industrial setup. The Pareto front is constructed for the objectives of minimization of NO emissions and reduction of the pressure fluctuations (pulsation) of the flame. Both objectives are conflicting affecting the environment and the lifetime of the turbine, respectively. The optimization leads a Pareto front corresponding to reduced emissions and pulsation of the burner. The physical implications of the solutions are discussed and the algorithm is evaluated.

Micro-electromechanical systems (MEMS) have reduced drastically in size, cost, and power consumption, while improving accuracy. The combination of different sensor technologies is considered a promising step in the monitoring of athletes. Those “wearables” enable the capturing of relevant physiological and tactical information in individual and team sports and thus replacing subjective, time-consuming and qualitative methods with objective, quantitative ones. Prior studies mainly comprised sports categories such as: targeting sports, batting and fielding games as well as net and wall games, focusing on the detection of individual, non-locomotive movements. The increasing capabilities of wearables allow for more complex and integrative analysis expanding research into the last category: invasion sports. Such holistic approaches allow the derivation of metrics, estimation of physical conditions and the analysis of team strategic behavior, accompanied by integrative knowledge gains in technical, tactical, physical, and mental aspects of a sport. However, prior and current researchers find the precise measurement of the actual movement within highly dynamic and non-linear movement difficult. Thus, the present article showcases an overview of the environments in which the wearables are employed. It elaborates their use in individual as well as team-related performance analyses with a special focus on reliability and validity, challenges, and future directions.

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Videos have emerged as a dominant medium for educational purposes in many Massive Open Online Course (MOOC) platforms. Online learning videos are visual components of the online learning platforms. They are particularly interesting for students who are visual learners and who learn best by watching the short format engaging videos rather than by just reading or listening to course materials. The paper focuses on the categorization of factors that are responsible for the success/popularity of online learning videos. The research is based on the literature review and video observations of the MOOC platforms, in particular of the Khan Academy and Coursera. In this paper, we propose a tabular and a spider graph as a condensed overview of categories that should commonly be utilized by the online learning video production to make them successful.