A Novel Method for Calculating the Radiated Disturbance from Pantograph Arcing in High-speed Railway

Pantograph arcing is a key electromagnetic disturbance source to affect train control system in high-speed railway. Since the characteristics of pantograph arcing is related to train speed, it is necessary to investigate effective numerical modeling and measurement method. However, due to the uncontrollable train speed during on-site measurement, it is difficult to study the radiated disturbance from arcing in the corresponding speed and repeat the same measurement. Therefore, a method combined numerical modeling and reverberation chamber measurements for calculating the radiated disturbance from pantograph arcing in a high-speed railway is proposed. Numerical models of train and sensitive equipment are built to calculate the coupling coefficient in CONCEPT II. And a new measurement procedure in reverberation chamber using pulse signal as the reference source is proposed based on a speed-controllable laboratory replica to measure the total radiated power of pantograph arcing. Then the radiated disturbance from pantograph arcing to the sensitive equipment is achieved with the coupling coefficient and the total radiated power of arcing. The method is verified laboratory experiments. This method can solve the uncontrollable train speed problem during on-site measurement and improve the repeatability of measurement

Saliency detection using suitable variant of local and global consistency

In existing local and global consistency (LGC) framework, the cost functions related to classifying functions adopt the sum of each row of weight matrix as an important factor. Some of these classifying functions are successfully applied to saliency detection. From the point of saliency detection, this factor is inversely proportional to the colour contrast between image regions and their surroundings. However, an image region that holds a big colour contrast against it surroundings does not denote it must be a salient region. Therefore a suitable variant of LGC is introduced by removing this factor in cost function, and a suitable classifying function (SCF) is decided. Then a saliency detection method that utilises the SCF, content‐based initial label assignment scheme, and appearance‐based label assignment scheme is presented. Via updating the content‐based initial labels and appearance‐based labels by the SCF, a coarse saliency map and several intermediate saliency maps are obtained. Furthermore, to enhance the detection accuracy, a novel optimisation function is presented to fuse the intermediate saliency maps that have a high detection performance for final saliency generation. Numerous experimental results demonstrate that the proposed method achieves competitive performance against some recent state‐of‐the‐art algorithms for saliency detection

Computational exploration of dynamic mechanisms of steady state visual evoked potentials at the whole brain level

Periodic visual stimulation can induce stable steady-state visual evoked potentials (SSVEPs) distributed in multiple brain regions and has potential applications in both neural engineering and cognitive neuroscience. However, the underlying dynamic mechanisms of SSVEPs at the whole-brain level are still not completely understood. Here, we addressed this issue by simulating the rich dynamics of SSVEPs with a large-scale brain model designed with constraints of neuroimaging data acquired from the human brain. By eliciting activity of the occipital areas using an external periodic stimulus, our model was capable of replicating both the spatial distributions and response features of SSVEPs that were observed in experiments. In particular, we confirmed that alpha-band (8–12 Hz) stimulation could evoke stronger SSVEP responses; this frequency sensitivity was due to nonlinear entrainment and resonance, and could be modulated by endogenous factors in the brain. Interestingly, the stimulus-evoked brain networks also exhibited significant superiority in topological properties near this frequency-sensitivity range, and stronger SSVEP responses were demonstrated to be supported by more efficient functional connectivity at the neural activity level. These findings not only provide insights into the mechanistic understanding of SSVEPs at the whole-brain level but also indicate a bright future for large-scale brain modeling in characterizing the complicated dynamics and functions of the brain