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.

Airline Passenger Profiling Based on Fuzzy Deep Machine Learning

Abstract: Passenger profiling plays a vital part of commercial aviation security, but classical methods become very inefficient in handling the rapidly increasing amounts of electronic records. This paper proposes a deep learning approach to passenger profiling. The center of our approach is a Pythagorean fuzzy deep Boltzmann machine (PFDBM), whose parameters are expressed by Pythagorean fuzzy numbers such that each neuron can learn how a feature affects the production of the correct output from both the positive and negative sides. We propose a hybrid algorithm combining a gradient-based method and an evolutionary algorithm for training the PFDBM. Based on the novel learning model, we develop a deep neural network (DNN) for classifying normal passengers and potential attackers, and further develop an integrated DNN for identifying group attackers whose individual features are insufficient to reveal the abnormality. Experiments on data sets from Air China show that our approach provides much higher learning ability and classification accuracy than existing profilers. It is expected that the fuzzy deep learning approach can be adapted for a variety of complex pattern analysis tasks.

Driving Forces of Changes in Air Quality during the COVID-19 Lockdown Period in the Yangtze River Delta Region, China

Abstract: During the COVID-19 lockdown period (from January 23 to February 29, 2020), ambient PM2.5 concentrations in the Yangtze River Delta (YRD) region were observed to be much lower, while the maximum da...

A Self-Adaptive Mutation Neural Architecture Search Algorithm Based on Blocks

Abstract: Recently, convolutional neural networks (CNNs) have achieved great success in the field of artificial intelligence, including speech recognition, image recognition, and natural language processing. CNN architecture plays a key role in CNNs' performance. Most previous CNN architectures are hand-crafted, which requires designers to have rich expert domain knowledge. The trial-and-error process consumes a lot of time and computing resources. To solve this problem, researchers proposed the neural architecture search, which searches CNN architecture automatically, to satisfy different requirements. However, the blindness of the search strategy causes a 'loss of experience' in the early stage of the search process, and ultimately affects the results of the later stage. In this paper, we propose a self-adaptive mutation neural architecture search algorithm based on ResNet blocks and DenseNet blocks. The self-adaptive mutation strategy makes the algorithm adaptively adjust the mutation strategies during the evolution process to achieve better exploration. In addition, the whole search process is fully automatic, and users do not need expert knowledge about CNNs architecture design. In this paper, the proposed algorithm is compared with 17 state-of-the-art algorithms, including manually designed CNN and automatic search algorithms on CIFAR10 and CIFAR100. The results indicate that the proposed algorithm outperforms the competitors in terms of classification performance and consumes fewer computing resources.

Size-controlled electrochemical synthesis and properties of SnO2 nanotubes

Abstract: SnO2 nanotubes with controlled diameter and length were synthesized using an electrochemical method at room temperature. The length and wall thickness of the nanotubes increased monotonically with the deposition time and the diameter of the nanotubes was altered by varying the pore size of the scaffolds. Post-annealing at 400??C in dry air significantly improved the crystallinity while maintaining the nanotube structure. The temperature-dependent photoluminescence spectra indicated an activation energy of 58?meV for emission centered at 410?nm. The temperature-dependent electrical resistance revealed that the dominant electrical conduction mechanism alters from the ionization of the main donor centers to impurity scattering as the temperature decreases. The electrical conductance of 200?nm diameter nanotubes increased to 33 times the original value upon UV illumination at 254?nm.