Virtual Reality-Based Random Dot Kinematogram

This research implements a random dot kinematogram(RDK)using virtual reality(VR)and analyzes the results based on normal subjects.Visual motion perception is one of visual functions localized to a specific cortical area,the human motion perception area(human analogue for the middle temporal/middle superior temporal area)located in the parieto–occipito–temporal junction of the human brain.The RDK measures visual motion perception capabilities.The stimuli in conventional RDK methods are presented using a monitor screen,so these devices require a spacious dark room for installation and use.Recently,VR technology has been implemented in different medical domains.The test method proposed in this study include a VR-based RDK that can independently measure human motion perception abilities without any spatial constraints via a VR head-mounted display.Subsequently,the VR-based RDK was implemented,and the visual perception abilities of the normal subjects were measured based on varying coherences.In both screen-and VR-based RDK tests,the easier the stimulus is,the higher the correct answer rate and the shorter the reaction time.No significant differences in coherence thresholds were observed between the two test methods.The VRbased RDK proposed in this study can be used as a diagnosis tool for visual motion perception and neurodegenerative disorders affecting the posterior region of the brain.

High-emitter identification for heavy-duty vehicles by temporal optimization LSTM and an adaptive dynamic threshold

Heavy-duty diesel vehicles are important sources of urban nitrogen oxides(NOx)in actual applications for environmental compliance,emitting more than 80%of NOx and more than 90%of particulate matter(PM)in total vehicle emissions.The detection and control of heavy-duty diesel emissions are critical for protecting public health.Currently,vehicles on the road must be regularly tested,every six months or once a year,to filter out high-emission mobile sources at vehicle inspection stations.However,it is difficult to effectively screen high-emission vehicles in time with a long interval between annual inspections,and the fixed threshold cannot adapt to the dynamic changes of vehicle driving conditions.An on-board diagnostic device(OBD)is installed inside the vehicle and can record the vehicle’s emission data in real time.In this paper,we propose a temporal optimization long short-term memory(LSTM)and adaptive dynamic threshold approach to identify heavy-duty high-emitters by using OBD data,which can continuously track and record the emission status in real time.First,a temporal optimization LSTM emission prediction model is established to solve the attention bias discrepancy problem on time steps that is caused by the large number of OBD data streams in practice.Then,the concentration prediction error sequence is detected and distinguished from the anomalous emission contexts using flexible criteria,calculated by an adaptive dynamic threshold with changing driving conditions.Finally,a similarity metric strategy for the time series is introduced to correct some pseudo anomalous results.Experiments on three real OBD time-series emission datasets demonstrate that our method can achieve high accuracy anomalous emission identification.