The Single Training Sample Extraction of Visual Evoked Potentials Based on Wavelet Transform

Based on the good localization characteristic of the wavelet transform both in time and frequency domain, a de-noising method based on wavelet transform is presented, which can make the extraction of visual evoked potentials in single training sample from the EEG background noise in favor of studying the changes between the single sample response happen. The information is probably related with the different function, appearance and pathologies of the brain. At the same time this method can also be used to remove those signal’s artifacts that do not appear with EP within the same scope of time or frequency. The traditional Fourier filter can hardly attain the similar result. This method is different from other wavelet de-noising methods in which different criteria are employed in choosing wavelet coefficient. It has a biggest virtue of noting the differences among the single training sample and making use of the characteristics of high time frequency resolution to reduce the effect of interference factors to a maximum extent within the time scope that EP appear. The experiment result proves that this method is not restricted by the signal-to-noise ratio of evoked potential and electroencephalograph (EEG) and even can recognize instantaneous event under the condition of lower signal-to-noise ratio, as well as recognize the samples which evoked evident response more easily. Therefore, more evident average evoked response could be achieved by de-nosing the signals obtained through averaging out the samples that can evoke evident responses than de-nosing the average of original signals. In addition, averaging methodology can dramatically reduce the number of record samples needed, thus avoiding the effect of behavior change during the recording process. This methodology pays attention to the differences among single training sample and also accomplishes the extraction of visual evoked potentials from single trainings sample. As a result, system speed and accuracy could be improved to a great extent if this methodology is applied to brain-computer interface system based on evoked responses.

Feasibility of hydrogen fuel cell technology for railway intercity services:a case study for the Piedmont in North Carolina

Diesel fuel combustion results in exhaust containing air pollutants and greenhouse gas emissions.Many railway vehicles use diesel fuel as their energy source.Exhaust emissions,as well as concerns about economical,alternative power supply,have driven efforts to move to hydrogen motive power.Hydrogen fuel cell technology applied to railways offers the opportunity to eliminate harmful exhaust emissions and the potential for a low-or zero-emission energy supply chain.Currently,only multiple-unit trains with hydrail technology operate commercially.Development of an Integrated Hybrid Train Simulator for intercity railway is presented.The proposed tool incorporates the effect of powertrain components during the wheel-to-tank process.Compared to its predecessors,the proposed reconfigurable tool provides high fidelity with medium requirements and minimum computation time.Single train simulation and the federal government’s Greenhouse gases,Regulated Emissions,and Energy use in Transportation(GREET)model are used in combination to evaluate the feasibility of various train and powertrain configurations.The Piedmont intercity service operating in North Carolina is used as a case study.The study includes six train configurations and powertrain options as well as nine hydrogen supply options in addition to the diesel supply.The results show that a hydrail option is not only feasible,but a low-or zero-carbon hydrogen supply chain could be possible.