Abstract

Neuroscience-based or neuroscience-informed design is a new application area of Brain-Computer Interaction (BCI). It takes its roots in study of human well-being in architecture, human factors study in engineering and manufacturing including neuroergonomics. In traditional human factors studies and/or well-being study, mental workload, stress, and emotion are obtained through questionnaires that are administered upon completion of some task and/or the whole experiment. Recent advances in BCI research allow for using Electroencephalogram (EEG) based brain state recognition algorithms to assess the interaction between brain and human performance. We propose and develop an EEG-based system CogniMeter to monitor and analyze human factors measurements of newly designed software/hardware systems and/or working places. Machine learning techniques are applied to the EEG data to recognize levels of mental workload, stress and emotions during each task. The EEG is used as a tool to monitor and record the brain states of subjects during human factors study experiments. We describe two applications of CogniMeter system: human performance assessment in maritime simulator and EEG-based human factors evaluation in Air Traffic Control (ATC) workplace. By utilizing the proposed EEG-based system, true understanding of subjects working patterns can be obtained. Based on the analyses of the objective real time EEG-based data together with the subjective feedback from the subjects, we are able to reliably evaluate current systems/hardware and/or working place design and refine new concepts and design of future systems.

Abstract

LIXIL Co. handles a wide range of water-related products such as showers, toilets, baths, and kitchens. The fluid behavior control is a key technology for developing of these products. Therefore, the numerical simulation with CFD plays an important role in product and technical development. The flow of water in showers has been simulated with the finite volume method (FVM). However, the simulation with fine droplets such as in a shower needs a lot of computational cost. To deal with this problem, MPS particle method [1] has been introduced to take an advantage of enabling for simulating such as splashing, droplets, and mixing. In this paper, the MPS particle method is studied to simulate the shower head design because it is the first use case for simulating the spout, coupling with mechanism analysis. Because computational efficiency is also an important factor for the product development, the computational performance on GPU against multi-CPU for the MPS particle method is also discussed.