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This article expands on research that has been done to develop a recurrent neural network (RNN) capable of predicting aircraft engine vibrations using long short-term memory (LSTM) neurons. LSTM RNNs can provide a more generalizable and robust method for prediction over analytical calculations of engine vibration, as analytical calculations must be solved iteratively based on specific empirical engine parameters, making this approach ungeneralizable across multiple engines. In initial work, multiple LSTM RNN architectures were proposed, evaluated and compared. This research improves the performance of the most effective LSTM network design proposed in the previous work by using a promising neuroevolution method based on ant colony optimization (ACO) to develop and enhance the LSTM cell structure of the network. A parallelized version of the ACO neuroevolution algorithm has been developed and the evolved LSTM RNNs were compared to the previously used fixed topology. The evolved networks were trained on a large database of flight data records obtained from an airline containing flights that suffered from excessive vibration. Results were obtained using MPI (Message Passing Interface) on a high performance computing (HPC) cluster, evolving 1000 different LSTM cell structures using 168 cores over 4 days. The new evolved LSTM cells showed an improvement of 1.35%, reducing prediction error from 5.51% to 4.17% when predicting excessive engine vibrations 10 seconds in the future, while at the same time dramatically reducing the number of weights from 21,170 to 11,810.

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Virtual reality (VR) is an emerging technology with a broad range of applications in training, entertainment, and business. To maximize the potentials of virtual reality as a medium, the unwelcome feeling of cybersickness needs to be minimized. Cybersickness is a type of simulation sickness that is experienced in virtual reality. It is a significant challenge for the usability of virtual reality systems. Even with advancements in virtual reality, the usability concerns are barriers for a wide-spread acceptance. Several factors (hardware, software, human) play a part towards a pleasant virtual reality experience. In this paper, we review the potential factors which cause sickness and minimize the usability of virtual reality systems. The reviewed scientific articles are mostly part of documents indexed in digital libraries. We review the best practices from a developer’s perspective and some of the safety measures a user must follow while using the virtual reality systems from existing research. Even after following some of the guidelines and best practices virtual reality environments do not guarantee a pleasant experience for users. Limited research in virtual reality environments towards requirements specification, design, and development for maximum usability and adaptability was the main motive for this work.

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