Nanjing University of Aeronautics and Astronautics

www.nuaa.edu.cn
Nanjing, China

Nanjing University of Aeronautics and Astronautics is a university located in Nanjing, Jiangsu province, China. It was established in October 1952. In Chinese, the university name is sometimes shortened to Nanhang . The university is operated by Ministry of Industry and Information Technology and is one of China's leading universities on research and education. It is regarded as one of the top engineering universities in China and also has a great influence on China's aerospace industry. Wikipedia.

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A "self-heating" boron catalyst that makes particularly efficient use of sunlight to reduce carbon dioxide (CO2) serves as a light harvester, photothermal converter, hydrogen generator, and catalyst in one. In the journal Angewandte Chemie, researchers introduce a photothermocatalytic reaction that requires no additives beyond water. This could form the basis of a new, more efficient process for converting the greenhouse gas CO2 into a useful carbon source for the production of fuels and chemical products. The ideal route for making CO2 useful is considered to be reduction aided by a photocatalyst to use sunlight as the only source of energy—a process that corresponds to the first step of photosynthesis. Despite decades of research, processes for converting CO2 are still too inefficient. "This is largely due to the insufficient utilization of solar light, the high energy barrier for CO2 activation, and the sluggish kinetics of the multiple electron and proton transfer processes," explains Jinhua Ye. Working with a team for the National Institute for Materials Science (NIMS) in Tsukuba, Ibaraki, and Hokkaido University in Sapporo (Japan), as well as Tianjin University and Nanjing University of Aeronautics and Astronautics (China), Ye is now pursuing a strategy that uses both the light and thermal energy provided by sunlight. When the sun shines on a surface, it is heated. The researchers want to use this ordinary photothermic effect to increase the efficiency of catalytic systems. Their material of choice is powdered elemental boron, which very strongly absorbs sunlight and efficiently converts it photothermically, heating itself up remarkably. This allowed the team to carry out the efficient reduction of CO2 to form carbon monoxide (CO) and methane (CH4) under irradiation in the presence of water, with no additional reagents or co-catalysts. Irradiation causes the boron particles to heat up to about 378 °C. At this temperature it reacts with water, forming hydrogen and boron oxides in situ. The boron oxides act as "traps" for CO2 molecules. The hydrogen is highly reactive and, in the presence of the light-activated boron catalyst, efficiently reduces the CO2 by providing the necessary protons (H+) and electrons. "The key to our success lies in the favorable properties of the boron powder, which make it an all-in-one catalyst: light harvester, photothermic converter, hydrogen source, and catalyst," says Ye. "Our study confirms the highly promising potential of a photothermocatalytic strategy for the conversion of CO2 and potentially opens new vistas for the development of other solar-energy-driven reaction systems." Explore further: Hydrogen from sunlight—but as a dark reaction More information: Guigao Liu et al. Elemental Boron for Efficient Carbon Dioxide Reduction under Light Irradiation, Angewandte Chemie International Edition (2017). DOI: 10.1002/anie.201701370


Chen M.,Nanjing University of Aeronautics and Astronautics
IEEE Transactions on Industrial Electronics | Year: 2017

In this paper, a robust tracking control scheme is proposed for wheeled mobile robots with skidding, slipping, and input disturbance. Considering the existing skidding and slipping, a desired disturbance-observer-based virtual velocity control law is first designed. Then, the robust tracking control scheme is developed by considering the prescribed tracking performance requirement and using the disturbance observer. In the tracking control scheme design, the prescribed performance function method is employed to guarantee the desired tracking performance. To handle the skidding, slipping, and input disturbance, the disturbance observer is developed in the control scheme design. Experiment results demonstrate the effectiveness of the proposed tracking control scheme for wheeled mobile robots with skidding, slipping, and input disturbance. © 2017 IEEE.


Zhang D.,University of North Carolina at Chapel Hill | Zhang D.,Nanjing University of Aeronautics and Astronautics | Shen D.,University of North Carolina at Chapel Hill
NeuroImage | Year: 2012

Many machine learning and pattern classification methods have been applied to the diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Recently, rather than predicting categorical variables as in classification, several pattern regression methods have also been used to estimate continuous clinical variables from brain images. However, most existing regression methods focus on estimating multiple clinical variables separately and thus cannot utilize the intrinsic useful correlation information among different clinical variables. On the other hand, in those regression methods, only a single modality of data (usually only the structural MRI) is often used, without considering the complementary information that can be provided by different modalities. In this paper, we propose a general methodology, namely multi-modal multi-task (M3T) learning, to jointly predict multiple variables from multi-modal data. Here, the variables include not only the clinical variables used for regression but also the categorical variable used for classification, with different tasks corresponding to prediction of different variables. Specifically, our method contains two key components, i.e., (1) a multi-task feature selection which selects the common subset of relevant features for multiple variables from each modality, and (2) a multi-modal support vector machine which fuses the above-selected features from all modalities to predict multiple (regression and classification) variables. To validate our method, we perform two sets of experiments on ADNI baseline MRI, FDG-PET, and cerebrospinal fluid (CSF) data from 45 AD patients, 91 MCI patients, and 50 healthy controls (HC). In the first set of experiments, we estimate two clinical variables such as Mini Mental State Examination (MMSE) and Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), as well as one categorical variable (with value of 'AD', 'MCI' or 'HC'), from the baseline MRI, FDG-PET, and CSF data. In the second set of experiments, we predict the 2-year changes of MMSE and ADAS-Cog scores and also the conversion of MCI to AD from the baseline MRI, FDG-PET, and CSF data. The results on both sets of experiments demonstrate that our proposed M3T learning scheme can achieve better performance on both regression and classification tasks than the conventional learning methods. © 2011 Elsevier Inc.


Yang X.,Nanjing University of Aeronautics and Astronautics
Nonlinear Analysis, Theory, Methods and Applications | Year: 2015

Abstract We prove global-in-time and uniform-in-∈ of the strong solutions to the 3D compressible nematic liquid crystal flows in a bounded domain, where ∈ is the Mach number. Consequently, we obtain the strong solution of compressible nematic liquid crystal model that converges to that of incompressible nematic liquid crystal model. This is the first result on the low Mach number limit for compressible nematic liquid crystal flows in 3D bounded domain. Our proof relies on the dedicated estimates on the solutions and the subtle use of the boundary conditions. © 2015 Elsevier Ltd. All rights reserved.


Longbiao L.,Nanjing University of Aeronautics and Astronautics
Engineering Fracture Mechanics | Year: 2017

The effects of loading type, i.e., cyclic loading/unloading tensile, cumulative tensile fatigue loading, and tension-tension fatigue loading, temperature and oxidation on the mechanical hysteresis behavior of carbon fiber-reinforced ceramic-matrix composites (CMCs) have been investigated. Based on the damage mechanism of fiber sliding relative to the matrix in the interface debonded region, the stress-strain relationships upon unloading/reloading when the interface partially and completely debonded have been determined considering fibers fracture. The effects of material properties, damage mode and cycle number on the interface slip and hysteresis loops have been analyzed. The mechanical hysteresis behavior of different loading types of C/SiC at room temperature and 800 °C in air have been predicted. © 2016 Elsevier Ltd


Tan X.,Nanjing University of Aeronautics and Astronautics | Triggs B.,French National Center for Scientific Research
IEEE Transactions on Image Processing | Year: 2010

Making recognition more reliable under uncontrolled lighting conditions is one of the most important challenges for practical face recognition systems. We tackle this by combining the strengths of robust illumination normalization, local texture-based face representations, distance transform based matching, kernel-based feature extraction and multiple feature fusion. Specifically, we make three main contributions: 1) We present a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition; 2) We introduce local ternary patterns (LTP), a generalization of the local binary pattern (LBP) local texture descriptor that is more discriminant and less sensitive to noise in uniform regions, and we show that replacing comparisons based on local spatial histograms with a distance transform based similarity metric further improves the performance of LBP/LTP based face recognition; and 3) We further improve robustness by adding Kernel principal component analysis (PCA) feature extraction and incorporating rich local appearance cues from two complementary sourcesGabor wavelets and LBPshowing that the combination is considerably more accurate than either feature set alone. The resulting method provides state-of-the-art performance on three data sets that are widely used for testing recognition under difficult illumination conditions: Extended Yale-B, CAS-PEAL-R1, and Face Recognition Grand Challenge version 2 experiment 4 (FRGC-204). For example, on the challenging FRGC-204 data set it halves the error rate relative to previously published methods, achieving a face verification rate of 88.1% at 0.1% false accept rate. Further experiments show that our preprocessing method outperforms several existing preprocessors for a range of feature sets, data sets and lighting conditions. © 2010 IEEE.


Qiu H.,Nanjing University of Aeronautics and Astronautics | Guo W.,Nanjing University of Aeronautics and Astronautics
Physical Review Letters | Year: 2013

In sharp contrast to the prevailing view that electric fields promote water freezing, here we show by molecular dynamics simulations that monolayer ice confined between two parallel plates can melt into liquid water under a perpendicularly applied electric field. The melting temperature of the monolayer ice decreases with the increasing strength of the external field due to the field-induced disruption of the water-wall interaction induced well-ordered network of the hydrogen bond. This electromelting process should add an important new ingredient to the physics of water. © 2013 American Physical Society.


Dai Q.,Nanjing University of Aeronautics and Astronautics
Knowledge-Based Systems | Year: 2013

Ensemble pruning is crucial for the consideration of both efficiency and predictive accuracy of an ensemble system. This paper proposes a new Competitive technique for Ensemble Pruning based on Cross-Validation (CEPCV). The data to be learnt by neural computing models are mostly drifting with time and environment, therefore a dynamic ensemble pruning method is indispensable for practical applications, while the proposed CEPCV method is just the kind of dynamic ensemble pruning method, which can realize on-line ensemble pruning and take full advantage of potentially valuable information. The algorithm naturally inherits the predominance of cross-validation technique, which implies that those networks regarded as winners in selective competitions and chosen into the pruned ensemble have the "strongest" generalization capability. It is essentially based on the strategy of "divide and rule, collect the wisdom", and might alleviate the local minima problem of many conventional ensemble pruning approaches only at the cost of a little greater computational cost, which is acceptable to most applications of ensemble learning. The comparative experiments among the four ensemble pruning algorithms, including: CEPCV and the state-of-the-art Directed Hill Climbing Ensemble Pruning (DHCEP) algorithm and two baseline methods, i.e. BSM, which chooses the Best Single Model in the initial ensemble based on their performances on the pruning set, and ALL, which reserves all network members of the initial ensemble, on ten benchmark classification tasks, demonstrate the effectiveness and validity of CEPCV. © 2012 Elsevier B.V. All rights reserved.


Chen K.,Nanjing University of Aeronautics and Astronautics
International Journal of Production Economics | Year: 2012

This paper investigates the coordination mechanism for supply chain with one manufacturer and multiple competing suppliers in the electronic market. We first study two conventional price-only policies, including wholesale price policy and catalog policy, based on the reverse Vickrey auction, and show that both the buyer and the powerful suppliers (with production cost less than a special threshold value) prefer catalog policy to wholesale price policy, and the powerless suppliers prefer wholesale price policy to catalog policy. Simultaneously, neither policy can coordinate the channel composed of the manufacturer and the winning supplier. We also show that a quantity discount policy cannot coordinate the supply chain with competing suppliers unless a kind of restriction is imposed. The aim of the paper is to explore a coordination mechanism, i.e., the price-restricted quantity-discount policy. Pareto analysis indicates that the manufacturer and the winning supplier will realize the 'win-win' situation, and the channel can also be coordinated. A key managerial implication of our study is that additional restrictive condition may be necessary to eliminate system inefficiency. Some numerical examples are also given to illustrate management insights. © 2012 Elsevier B.V. All rights reserved.


Patent
Nanjing University of Aeronautics and Astronautics | Date: 2014-11-10

The present invention discloses a method for locating an impact area of a composite structure based on an energy weighted factor, which belongs to the field of structural health monitoring technologies. According to the characteristic that the closest a sensor in an impact occurring sub-area is to the impact position, the most the sensor is affected by the impact, the present invention defines a characteristic parameter of the energy weighted factor, to represent the degree that each sensor is affected by the impact within the entire impact monitoring range, then calculates the degree that each sub-area is affected by the impact within the monitoring range, and finally determines that the sub-area most affected is the impact occurring sub-area. The present invention solves the problems of localization confliction of adjacent nodes and locating blind zones of mid-areas arising during existing multi-node large-scale networking monitoring; the method unites a plurality of nodes to jointly perform impact monitoring through networking, can quickly and accurately perform impact localization on all sub-areas within the network monitoring range, and has good application prospects in the aspect of impact monitoring of large-scale composite structures.

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