Moment-to-Moment Continuous Attention Fluctuation Monitoring through Consumer-Grade EEG Device

While numerous studies have explored using various sensing techniques to measure attention states, moment-to-moment attention fluctuation measurement is unavailable. To bridge this gap, we applied a novel paradigm in psychology, the gradual-onset continuous performance task (gradCPT), to collect the ground truth of attention states. GradCPT allows for the precise labeling of attention fluctuation on an 800 ms time scale. We then developed a new technique for measuring continuous attention fluctuation, based on a machine learning approach that uses the spectral properties of EEG signals as the main features. We demonstrated that, even using a consumer grade EEG device, the detection accuracy of moment-to-moment attention fluctuations was 73.49%. Next, we empirically validated our technique in a video learning scenario and found that our technique match with the classification obtained through thought probes, with an average F1 score of 0.77. Our results suggest the effectiveness of using gradCPT as a ground truth labeling method and the feasibility of using consumer-grade EEG devices for continuous attention fluctuation detection.

The Numerical Prediction and Analysis of Propeller Cavitation Benchmark Tests of YUPENG Ship Model

The numerical simulation of propeller cavitation benchmark tests of YUPENG ship model is studied based on OpenFOAM, an open-source CFD (Computational Fluid Dynamics) platform, and the benchmark tests are introduced as well. The propeller cavitation shape and the hull pressure fluctuation are measured and predicted, respectively. The uncertainty in hull pressure fluctuation measurement is also analyzed, and the analysis showed the uncertainty is below 10%. The cavitation shape and the hull pressure fluctuation predicted show quite good agreement with the observations and measured data, and the influences of the grid resolutions on the unsteady propeller cavitation and the hull pressure fluctuation are investigated as well. The monotonic convergence achieved and the quite small grid uncertainty illustrate the reliability of the numerical simulation methods.

Low Complexity Fluctuation Measurement in Image Processing Considering Order

The standard deviation can measure the spread out of a set of numbers and entropy can measure the randomness. However, they do not consider the order of the numbers. This can lead to misleading results where the order of the numbers is vital. An image is a set of numbers (i.e. pixel values) that is sensitive to order. In this paper, a low complexity and efficient method for measuring the fluctuation is proposed considering the order of the numbers. The proposed method sums up the changes of consecutive numbers and can be used in image processing applications. Simulation shows that the proposed method is 8 to 33 times faster than other related works.