Nanjing University of Information Science and Technology

About: Nanjing University of Information Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Precipitation & Aerosol. The organization has 14129 authors who have published 17985 publications receiving 267578 citations. The organization is also known as: Nan Xin Da.

Finger vein secure biometric template generation based on deep learning

Abstract: Leakage of unprotected biometric authentication data has become a high-risk threat for many applications. Lots of researchers are investigating and designing novel authentication schemes to prevent such attacks. However, the biggest challenge is how to protect biometric data while keeping the practical performance of identity verification systems. For the sake of tackling this problem, this paper presents a novel finger vein recognition algorithm by using secure biometric template scheme based on deep learning and random projections, named FVR-DLRP. FVR-DLRP preserves the core biometric information even with the user’s password cracked, whereas the original biometric information is still safe. The results of experiment show that the algorithm FVR-DLRP can maintain the accuracy of biometric identification while enhancing the uncertainty of the transformation, which provides better protection for biometric authentication.

A Numerical Study of Typhoon Megi (2010). Part I: Rapid Intensification

Abstract: Typhoon Megi (15W) was the most powerful and longest-lived tropical cyclone (TC) over the western North Pacific during 2010. While it shared many common features of TCs that crossed Luzon Island in the northern Philippines, Megi experienced unique intensity and structural changes, which were reproduced reasonably well in a simulation using the Advanced Research Weather Research and Forecasting Model (ARW-WRF) with both dynamical initialization and large-scale spectral nudging. In this paper processes responsible for the rapid intensification (RI) of the modeled Megi before it made landfall over Luzon Island were analyzed. The results show that Megi experienced RI over the warm ocean with high ocean heat content and decreasing environmental vertical shear. The onset of RI was triggered by convective bursts (CBs), which penetrate into the upper troposphere, leading to the upper-tropospheric warming and the formation of the upper-level warm core. In turn, CBs with their roots inside of the eyewall in...

Scenario dependence of future changes in climate extremes under 1.5 °C and 2 °C global warming.

Abstract: The 2015 Paris Agreement aims to limit global warming below 2 °C and pursue efforts to even limit it to 1.5 °C relative to pre-industrial levels. Decision makers need reliable information on the impacts caused by these warming levels for climate mitigation and adaptation measures. We explore the changes in climate extremes, which are closely tied to economic losses and casualties, under 1.5 °C and 2 °C global warming and their scenario dependence using three sets of ensemble global climate model simulations. A warming of 0.5 °C (from 1.5 °C to 2 °C) leads to significant increases in temperature and precipitation extremes in most regions. However, the projected changes in climate extremes under both warming levels highly depend on the pathways of emissions scenarios, with different greenhouse gas (GHG)/aerosol forcing ratio and GHG levels. Moreover, there are multifold differences in several heavily polluted regions, among the scenarios, in the changes in precipitation extremes due to an additional 0.5 °C warming from 1.5 °C to 2 °C. Our results demonstrate that the chemical compositions of emissions scenarios, not just the total radiative forcing and resultant warming level, must be considered when assessing the impacts of global 1.5/2 °C warming.

Future extreme climate changes linked to global warming intensity

Abstract: Based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) daily dataset, we investigate changes of the terrestrial extreme climates given that the global mean temperature increases persistently under the Representative Concentration Pathways 8.5 (RCP8.5) scenario. Compared to preindustrial conditions, more statistically significant extreme temperatures, precipitations, and dry spells are expected in the 21st century. Cold extremes decrease and warm extremes increase in a warmer world, and cold extremes tend to be more sensitive to global warming than the warm ones. When the global mean temperature increases, cold nights, cold days, and warm nights all display nonlinear relationships with it, such as the weakening of the link projected after 3 °C global warming, while the other indices generally exhibit differently, with linear relationships. Additionally, the relative changes in the indices related to extreme precipitation show significantly consistent linear changes with the global warming magnitude. Compared with the precipitation extremes, changes in temperature extremes are more strongly related to the global mean temperature changes. For the projection of the extreme precipitation changes, models show higher uncertainty than that in extreme temperature changes, and the uncertainty for the precipitation extremes becomes more remarkable when the global warming exceeds 5 °C.