On-road measurements of pollutant concentration profiles inside Yangkou tunnel, Qingdao, China.

To obtain physical properties of pollutant concentrations encountered by vehicle commuters during travelling Yangkou tunnel (7.76 km) of Qingdao City, particle concentration measurements are accompanied by the measurements of gaseous species (CO and CO2). The field campaigns are on-road conducted from April 26 to September 23, 2014. Results demonstrate that the mean particle number concentrations observed within the tunnel at the normal traffic volume are 1.15 × 105 and 1.24 × 105 particles cm-3 for the southbound and northbound trip, respectively. Furthermore, the significance level of traffic volume to particle number concentration is analyzed by multivariate regression model. And a high correlation between pollutant concentrations and traffic intensity has been demonstrated. Consequently, the fuel-based emission factors of pollutants inside the tunnel are calculated and the personal exposures are derived. In addition, the profile of particle number concentration exhibits distinct dilution features between the exit of northbound bore and the exit of southbound bore. The explanation is attributed to the different long uphill trip within the tunnel. Results in this study offer meaningful understanding to explore the nature of pollutants within long tunnels.

A batch copyright scheme for digital image based on deep neural network.

Digital signature and watermarking are effective image copyright protection techniques. However, these methods come with some inherent drawbacks, including the incapacity of carrying information and inevitable fidelity loss, respectively. To improve this situation, this paper proposes a neural network-based image batch copyright protection scheme, with which a copyright message bitstream can be extracted from each registered image while no modifications are introduced. Taking advantage of the pattern extraction capability and the error tolerance of the neural network, the proposed scheme achieves perfect imperceptibility and superior robustness. Moreover, the network's preference for diverse data content makes it especially appropriate for multiple images copyright verification. These claims will be further supported by the experimental results in this paper.