Nanjing University of Science and Technology

About: Nanjing University of Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.

Unsupervised Feature Selection via Nonnegative Spectral Analysis and Redundancy Control

Abstract: In many image processing and pattern recognition problems, visual contents of images are currently described by high-dimensional features, which are often redundant and noisy Toward this end, we propose a novel unsupervised feature selection scheme, namely, nonnegative spectral analysis with constrained redundancy, by jointly leveraging nonnegative spectral clustering and redundancy analysis The proposed method can directly identify a discriminative subset of the most useful and redundancy-constrained features Nonnegative spectral analysis is developed to learn more accurate cluster labels of the input images, during which the feature selection is performed simultaneously The joint learning of the cluster labels and feature selection matrix enables to select the most discriminative features Row-wise sparse models with a general $\ell _{2,p}$ -norm ( $0 ) are leveraged to make the proposed model suitable for feature selection and robust to noise Besides, the redundancy between features is explicitly exploited to control the redundancy of the selected subset The proposed problem is formulated as an optimization problem with a well-defined objective function solved by the developed simple yet efficient iterative algorithm Finally, we conduct extensive experiments on nine diverse image benchmarks, including face data, handwritten digit data, and object image data The proposed method achieves encouraging the experimental results in comparison with several representative algorithms, which demonstrates the effectiveness of the proposed algorithm for unsupervised feature selection

Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning

Abstract: Accurate short-term traffic flow forecasting facilitates active traffic control and trip planning. Most existing traffic flow models fail to make full use of the temporal and spatial features of tr...

Self-formed adaptor PCR: a simple and efficient method for chromosome walking.

Abstract: We developed a self-formed adaptor PCR (termed SEFA PCR) which can be used for chromosome walking. Most of the amplified flanking sequences were longer than 2.0 kb, and some were as long as 6.0 kb. SEFA PCR is simple and efficient and should have broad applications in the isolation of unknown sequences in complex genomes.

Effects of Nanometer Ni, Cu, Al and NiCu Powders on the Thermal Decomposition of Ammonium Perchlorate

Abstract: A study of the decomposition behaviour for Ammonium Perchlorate(AP) was carried out by differential thermal analysis and the two decomposition peaks were observed. The high temperature peak was found to shift to lower temperatures, but the corresponding shift in the low temperature peak was smaller due to the effect of nanometer metal powders. Results shows that Cu and NiCu nanopowders decreased both the high and low decomposition temperature, while Ni and Al nanopowders just decreased the high decomposition temperature and increased the low decomposition temperature. Metal micron-sized powders show catalytic effects on the thermal decomposition of AP, but their effects are less than that of nanometer metal powders. With the increase in content, nanometer metal powders enhanced their catalytic effect on the high temperature decomposition of AP, however their effect was weakened on the low temperature decomposition.