Physiological Measurements in Social Acceptance of Self Driving Technologies

The goal of the present study is to examine the cognitive/physiological correlates of passenger travel experience in autonomously driven transportation systems. We investigated the social acceptance and cognitive aspects of self-driving technology by measuring physiological responses in real-world experimental settings using eye-tracking and EEG measures simultaneously on 17 volunteers. A typical test run included human-driven and autonomous conditions in the same vehicle, in a safe environment. In the spectrum analysis of the eye-tracking data we found significant differences in the complex patterns of eye movements: the structure of movements of different magnitudes were less variable in the autonomous drive condition. EEG data revealed less positive affectivity and slightly higher arousal in the autonomous condition compared to the human-driven condition. Correlates with personality traits are also discussed. These preliminary findings reinforced our initial hypothesis that passenger experience in human and machine navigated conditions entail different physiological and psychological correlates, and those differences are accessible using state of the art in-world measurements. These useful dimensions of passenger experience may serve as a source of information both for the improvement and design of self-navigating technology and for market-related concerns.

Narrowband multivariate source separation for semi-blind discovery of experiment contrasts

BackgroundElectrophysiological recordings contain mixtures of signals from distinct neural sources, impeding a straightforward interpretation of the sensor-level data. This mixing is particularly detrimental when distinct sources resonate in overlapping frequencies. Fortunately, the mixing is linear and instantaneous. Multivariate source separation methods may therefore successfully separate statistical sources, even with overlapping spatial distributions.New MethodWe demonstrate a feature-guided multivariate source separation method that is tuned to narrowband frequency content as well as binary condition differences. This method — comparison scanning generalized eigendecomposition, csGED — harnesses the covariance structure of multichannel data to find directions (i.e., eigenvectors) that maximally separate two subsets of data. To drive condition specificity and frequency specificity, our data subsets were taken from different task conditions and narrowband-filtered prior to applying GED.ResultsTo validate the method, we simulated MEG data in two conditions with shared noise characteristics and unique signal. csGED outperformed the best sensor at reconstructing the ground truth signals, even in the presence of large amounts of noise. We next applied csGED to a published empirical MEG dataset on visual perception vs. imagery. csGED identified sources in alpha, beta, and gamma bands, and successfully separated distinct networks in the same frequency band.Comparison with Existing Method(s)GED is a flexible feature-guided decomposition method that has previously successfully been applied. Our combined frequency- and condition-tuning is a novel adaptation that extends the power of GED in cognitive electrophysiology.ConclusionsWe demonstrate successful condition-specific source separation by applying csGED to simulated and empirical data.

Emergency Stop and Drive Analysis of Bolted Flange Joints

The strength and tightness of flange joints will be weaker due to the temperature fluctuations. There exists no mature calculation procedure that can accounts for the temperature fluctuations’ effect on the performance of flanged joint. Based on the finite element simulation of a flanged joint under emergency stop and drive condition using ANSYS, the strength integrity and sealing performance of it are evaluated according to code JB4732-2005. The results show that stress of every component increases after experiencing an emergency stop and drive, flange deflection is more serious, resulting in integrity and tightness failure and can’t meet the sealing requirements. Repeatedly stop and drive will lead to discontinuity and alternating loads for the flanged joints, which will increase the leakage trend.