Showing papers by "Civil Aviation University of China published in 2017"

Achieving reactive species specificity within plasma‐activated water through selective generation using air spark and glow discharges

Abstract: Plasma-activated liquids (PAL) attract increasing interest with demonstrated biological effects. Plasma exposure in air produces stable aqueous reactive species which can serve as chemical diagnostics of PAL systems. Here, we tailor aqueous reactive species inside plasma-activated water (PAW) through treating water with AC air spark and glow discharges in contact with water. Chemical probing demonstrated species specificity between two types of PAW. Spark discharge PAW contains H2O2 and NO3−, while NO2−and NO3− are generated in glow discharge PAW. Species formation in different PAWs have been discussed in terms of discharge mechanisms and liquid phase chemistry process. Species specificity can provide richer parametric spaces for producing PALs with controlled impact and dosage achievable by combining discharge modes or mixing different PALs.

A Nonhomogeneous Fractional p-Kirchhoff Type Problem Involving Critical Exponent in ℝ N

Abstract: Abstract This paper concerns itself with the nonexistence and multiplicity of solutions for the following fractional Kirchhoff-type problem involving the critical Sobolev exponent: [ a + b ⁢ ( ∬ ℝ 2 ⁢ N | u ⁢ ( x ) - u ⁢ ( y ) | p | x - y | N + p ⁢ s ⁢ 𝑑 x ⁢ 𝑑 y ) θ - 1 ] ⁢ ( - Δ ) p s ⁢ u = | u | p s * - 2 ⁢ u + λ ⁢ f ⁢ ( x ) in ⁢ ℝ N , \\Biggl{[}a+b\\biggl{(}\\iint_{\\mathbb{R}^{2N}}\\frac{\\lvert u(x)-u(y)\\rvert^{p}}{% \\lvert x-y\\rvert^{N+ps}}\\,dx\\,dy\\biggr{)}^{\\theta-1}\\Biggr{]}(-\\Delta)_{p}^{s}% u=\\lvert u\\rvert^{p_{s}^{*}-2}u+\\lambda f(x)\\quad\\text{in }\\mathbb{R}^{N}, where a ≥ 0 {a\\kern-1.0pt\\geq\\kern-1.0pt0} , b > 0 , θ > 1 {b\\kern-1.0pt>\\kern-1.0pt0,\\theta\\kern-1.0pt>\\kern-1.0pt1} , ( - Δ ) p s {(-\\Delta)_{p}^{s}} is the fractional p-Laplacian with 0 < s < 1 {0\\kern-1.0pt<\\kern-1.0pts\\kern-1.0pt<\\kern-1.0pt1} and 1 < p < N / s {1\\kern-1.0pt<\\kern-1.0ptp\\kern-1.0pt<\\kern-1.0ptN/s} , p s * = N ⁢ p / ( N - p ⁢ s ) {p_{s}^{*}\\kern-1.0pt=\\kern-1.0ptNp/(N-ps)} is the critical Sobolev exponent, λ ≥ 0 {\\lambda\\geq 0} is a parameter, and f ∈ L p s * / ( p s * - 1 ) ⁢ ( ℝ N ) ∖ { 0 } {f\\in L^{p_{s}^{*}/(p_{s}^{*}-1)}(\\mathbb{R}^{N})\\setminus\\{0\\}} is a nonnegative function. When λ = 0 {\\lambda=0} , we show that the multiplicity and nonexistence of solutions for the above problem are related with N, θ, s, p, a, and b. When λ > 0 {\\lambda>0} , by using Ekeland’s variational principle and the mountain pass theorem, we show that there exists λ * * > 0 {\\lambda^{**}>0} such that the above problem admits at least two nonnegative solutions for all λ ∈ ( 0 , λ * * ) {\\lambda\\in(0,\\lambda^{**})} . In the latter case, in order to overcome the loss of compactness, we derive a fractional version of the principle of concentration compactness in the setting of the fractional p-Laplacian.

Combining FMEA with DEMATEL models to solve production process problems.

Abstract: Failure mode and effects analysis (FMEA) is an analysis tool for identifying and preventing flaws or defects in products during the design and process planning stage, preventing the repeated occurrence of problems, reducing the effects of these problems, enhancing product quality and reliability, saving costs, and improving competitiveness. However, FMEA can only analyze one influence factor according to its priority, rendering this method ineffective for systems containing multiple FMs whose effects are simultaneous or interact with one another. Accordingly, when FMEA fails to identify the influence factors and the factors being influenced, the most crucial problems may be placed in lower priority or remain unresolved. Decision-Making Trial and Evaluation Laboratory (DEMATEL) facilitates the determination of cause and effect factors; by identifying the causal factors that should be prioritized, prompt and effective solutions to core problems can be derived, thereby enhancing performance. Using the photovoltaic cell manufacturing industry in China as the research target, the present study combined FMEA with DEMATEL to amend the flaws of FMEA and enhance its effectiveness. First, FMEA was used to identify items requiring improvement. Then, DEMATEL was employed to examine the interactive effects and causal relationships of these items. Finally, the solutions to the problems were prioritized. The proposed method effectively combined the advantages of FMEA and DEMATEL to facilitate the identification of core problems and prioritization of solutions in the Chinese photovoltaic cell industry.

A strong convergence result involving an inertial forward–backward algorithm for monotone inclusions

Abstract: Our interest in this paper is to prove a strong convergence result for finding a zero of the sum of two monotone operators, with one of the two operators being co-coercive using an iterative method which is a combination of Nesterov’s acceleration scheme and Haugazeau’s algorithm in real Hilbert spaces. Our numerical results show that the proposed algorithm converges faster than the un-accelerated Haugazeau’s algorithm.

Indirect Boundary Integral Equation Method for the Cauchy Problem of the Laplace Equation

Abstract: In this paper, we examine the Cauchy problem of the Laplace equation. Motivated by the incompleteness of the single-layer potential function method, we investigate the double-layer potential function method. Through the use of a layer approach to the solution, we devise a numerical method for approximating the solution of the Cauchy problem, which are well known to be highly ill-posed in nature. The ill-posedness is dealt with Tikhonov regularization, whilst the optimal regularization parameter is chosen by Morozov discrepancy principle. Convergence and stability estimates of the proposed method are then given. Finally, some examples are given for the efficiency of the proposed method. Especially, when the single-layer potential function method does not give accurate results for some problems, it is shown that the proposed method is effective and stable.

Calculation of Siphons and Minimal Siphons in Petri Nets Based on Semi-Tensor Product of Matrices

Abstract: In this paper, we address the problems of enumerating siphons and minimal siphons in ordinary Petri nets (PNs) by resorting to the semi-tensor product (STP) of matrices. First, a matrix equation, called the siphon equation (SE), is established by using STP. Second, an algorithm is proposed to calculate all siphons in ordinary PNs. An example is presented to illustrate the theoretical results and show that the proposed method is more effective than other existing methods in calculating all siphons of PNs. Third, an efficient recursion algorithm is also proposed, which can be applied to computing all minimal siphons for any ordinary PNs. Last, some results on the computational complexity of the proposed algorithms, in this paper, are provided, as well as experimental results.

Elevated temperature compressive properties and energy absorption response of in-situ grown CNT-reinforced Al composite foams

Abstract: Carbon nanotube (CNT) reinforced Al composite foams were successfully fabricated by the combination of an in-situ chemical vapor deposition (CVD), short-time ball-milling and space-holder method. The CNTs are homogeneously dispersed and embedded in the Al foam matrix after 90 min ball-milling while maintaining the structural integrity. Both compressive properties and energy absorption capacity of the composite foams increase with the increment of CNT content but decrease with the temperature rising between 25 and 250 °C. The compressive yield strength and the plateau stress of 3.0 wt%-CNT/Al composite foams maintain 16.8 and 20.2 MPa at 150 °C, respectively, which are much higher than the corresponding yield stress (5.7 MPa) and plateau stress (8.6 MPa) of the pure Al foam. Especially, the energy absorption capacity of the 3.0 wt%-CNT/Al composite foams reaches 19.8 MJ/m3 at 150 °C, which is ~2.5 times higher than that of pure Al foam. Fracture analysis shows that the failure mode of the Al foam changes from ductile type to brittle type combined with ductile type, as a result of the CNT addition in the matrix.

Evaluation and mechanisms on heat damage of WC particles in Ni60/WC composite coatings by laser induction hybrid cladding

Abstract: The evaluation and mechanisms on the heat damage of WC particles in Ni60/WC composite coatings produced by laser induction hybrid cladding (LIHC) were investigated. A semi-quantitative formula, R = 49 46 × b M × η 1 − η × 1 − η 2 + 0.45 , was deduced to evaluate the heat damage degree of WC particles in Ni60/WC composite coatings according to the proposed trapping model. The results show that the heat damage degree of WC particles decreases as the laser scanning speed increases, whereas firstly increases as the content of WC particles increases then decreases. WC particles in Ni60/WC composite coatings produced by LIHC present the mechanisms of the dissolution-diffusion controlled heat damage, the collapsibility-dissolution-diffusion controlled heat damage and the dissolution-precipitation controlled heat damage, which of mechanism depends on the size of WC particles and the processing parameters. The first mechanism results in the formation of the alloyed reaction layer with a thickness of several micrometers around WC particles. The second one results in the formation of alloying carbides. The third one leads to the precipitation of the different carbides with the different morphologies.

An empirical research on relationships between subjective judgement, technology acceptance tendency and knowledge transfer

Abstract: The purpose of this study was to explore the relationships among employees' usage intention pertaining to mobile information devices, focusing on subjective judgement, technology acceptance tendency, information sharing behavior and information transfer. A research model was established to verify several hypotheses. The research model based on integrated concepts of knowledge management and technology acceptance modeling. Participants were employees of enterprises in Taiwan, selected by combining snowball and convenience sampling. Data obtained from 779 e-surveys. Multiple-regression analysis was employed for hypothesis verification. The results indicate that perceived ease-of-use of mobile devices was affected by computer self-efficacy and computer playfulness directly; meanwhile, perceived ease-of-use directly affects perceived usefulness. In addition, perceived ease-of-use and perceived usefulness can predict information-sharing behavior in a positive manner, and impact knowledge transfer as well. Based on the research findings, it suggested that enterprises should utilize mobile information devices to create more contact with customers and enrich their service network. In addition, it is recommended that managers use mobile devices to transmit key information to their staff and that they use these devices for problem-solving and decision-making. Further, the staff's skills pertaining to the operation of mobile information devices and to fully implement their features are reinforced in order to inspire the users' knowledge transfer. Enhancing the playfulness of the interface is also important. In general, it is useful to promote knowledge transfer behavior within an organization by motivating members to share information and ideas via mobile information devices. In addition, a well-designed interface can facilitate employees' use of these devices.

Quasi-Polar-Based FFBP Algorithm for Miniature UAV SAR Imaging Without Navigational Data

Abstract: Because of flexible geometric configuration and trajectory designation, time-domain algorithms become popular for unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) applications. In this paper, a new quasi-polar-coordinate-based fast factorized back-projection (FFBP) algorithm combined with data-driven motion compensation is proposed for miniature UAV-SAR imaging. By utilizing wavenumber decomposition, the analytical spectrum of a quasi-polar grid image is obtained, where the phase errors arising from the trajectory deviations can be conveniently investigated and the phase autofocusing can be compatibly incorporated. Different from the conventional FFBP based on a polar coordinate system, the proposed algorithm operates in a quasi-polar coordinate system, where the phase errors become spacial invariant and can be accurately estimated and easily compensated. Moreover, the relationship between phase errors and nonsystematic range cell migration (NsRCM) is revealed according to the analytical image spectrum, based on which the NsRCM correction is developed to further improve the image focusing quality for high-resolution SAR applications. Promising experimental results from the raw data experiments of miniature UAV-SAR test bed are presented and analyzed to validate the advantages of the proposed algorithm.

MIKE: Keyphrase Extraction by Integrating Multidimensional Information

Abstract: Traditional supervised keyphrase extraction models depend on the features of labelled keyphrases while prevailing unsupervised models mainly rely on structure of the word graph, with candidate words as nodes and edges capturing the co-occurrence information between words. However, systematically integrating all these multidimensional heterogeneous information into a unified model is relatively unexplored. In this paper, we focus on how to effectively exploit multidimensional information to improve the keyphrase extraction performance (MIKE). Specifically, we propose a random-walk parametric model, MIKE, that learns the latent representation for a candidate keyphrase that captures the mutual influences among all information, and simultaneously optimizes the parameters and ranking scores of candidates in the word graph. We use the gradient-descent algorithm to optimize our model and show the comprehensive experiments with two publicly-available WWW and KDD datasets in Computer Science. Experimental results demonstrate that our approach significantly outperforms the state-of-the-art graph-based keyphrase extraction approaches.

Jet electrochemical machining of TB6 titanium alloy

Abstract: TB6 titanium alloy (Ti-10V-2Fe-3Al) is a prospective material to replace the traditional titanium alloys in aviation industry due to its excellent comprehensive properties. However, machining of TB6 titanium alloy with traditional processes is characterized by the low machining efficiency, high tool wear, reduced machining accuracy, and inferior surface finish. Electrochemical machining (ECM) is a potential processing technology for titanium alloy, but almost no investigation has been performed on TB6. This paper aims to study the feasibility and select the optimum process parameters for Jet-ECM machining of TB6 titanium alloy. The dissolution behavior of TB6 titanium alloy in sodium nitrate and sodium chloride electrolyte with different concentration and temperature is analyzed. Then, the effect of main parameters, including the composition and concentration of electrolyte, machining voltage, electrolyte flow rate, and inter-electrode gap (IEG), on machining performance is investigated in detail. From experiments, 24 V voltage, 0.6 mm IEG, 2.1 L/min flow rate, and 15 % sodium chloride electrolyte are selected as optimum parameters. The experimental results by using the chosen parameters reveal that the comprehensive machining performance, including material removal rate (10.062 g/min), surface toughness (0.231 μm), taper (2.5), and average overcut (1.01 mm), is acceptable from the viewpoint of industry.

Existence of solutions for a critical fractional Kirchhoff type problem in ℝ N

Abstract: This paper concerns with the existence of solutions for the following fractional Kirchhoff problem with critical nonlinearity: $${\left( {\int {\int {_{{\mathbb{R}^{2N}}}\frac{{{{\left| {u\left( x \right) - u\left( y \right)} \right|}^2}}}{{{{\left| {x - y} \right|}^{N + 2s}}}}dxdy} } } \right)^{\theta - 1}}{\left( { - \Delta } \right)^s}u = \lambda h\left( x \right){u^{p - 1}} + {u^{2_s^* - 1}} in {\mathbb{R}^N},$$ where (−Δ) s is the fractional Laplacian operator with 0 < s < 1, 2 s * = 2N/(N − 2s), N > 2s, p ∈ (1, 2 s *), θ ∈ [1, 2 s */2), h is a nonnegative function and λ a real positive parameter. Using the Ekeland variational principle and the mountain pass theorem, we obtain the existence and multiplicity of solutions for the above problem for suitable parameter λ > 0. Furthermore, under some appropriate assumptions, our result can be extended to the setting of a class of nonlocal integro-differential equations. The remarkable feature of this paper is the fact that the coefficient of fractional Laplace operator could be zero at zero, which implies that the above Kirchhoff problem is degenerate. Hence our results are new even in the Laplacian case.

Weak convergence theorem for a class of split variational inequality problems and applications in a Hilbert space.

Abstract: In this paper, we consider the algorithm proposed in recent years by Censor, Gibali and Reich, which solves split variational inequality problem, and Korpelevich’s extragradient method, which solves variational inequality problems. As our main result, we propose an iterative method for finding an element to solve a class of split variational inequality problems under weaker conditions and get a weak convergence theorem. As applications, we obtain some new weak convergence theorems by using our weak convergence result to solve related problems in nonlinear analysis and optimization.

Microstructure evolution of phase separated Fe-Cu-Cr-C composite coatings by laser induction hybrid cladding

Abstract: To investigate the cooling rate on the microstructure evolution of phase separated Fe-Cu composite coatings by laser induction hybrid cladding (LIHC), the different laser scanning speed is adopted to estimate the temperature profiles and cooling rate of the molten pool. When the low laser scanning speed is adopted during LIHC, the Cu-rich spherical particles are embedded dispersedly in the Fe-rich interdendrites at the bottom of coating, while the nanostructured Fe-rich dendrites and the Fe-rich particles are distributed simultaneously in the Cu-rich matrix at the top of coatings. However, the increasing of laser scanning speed can decrease the temperature of the molten pool and increase the cooling rate of the molten pool. This in turn leads to an increase in dynamic supercooling and solute trapping of Cu in the enhanced Fe-rich dendritic refinement, but a decrease in size of Fe- and Cu-rich particles as well as a reduction in size of nanostructured Cu-rich grains inside the Fe-rich particles due to the secondary liquid phase separation.

Interval Neutrosophic Muirhead mean Operators and Their Application in Multiple Attribute Group Decision Making

Abstract: In recent years, neutrosophic sets (NSs) have attracted widespread attentions and been widely applied to multiple attribute decision-making (MADM). The interval neutrosophic set (INS) is an extension of NS, in which the truth-membership, indeterminacy-membership and falsity-membership degree are expressed by interval values, respectively. Obviously, INS can conveniently describe complex information.

Multipath interference mitigation in GNSS via WRELAX

Abstract: In order to suppress the multipath interference in global navigation satellite system, two algorithms based on NLS (nonlinear least square) parameter estimation are proposed. Instead of the classic delay lock loop, the first proposed algorithm estimates the parameters of the line of sight signal and the multipath interference in the correlation domain. The NLS cost function is solved by WRELAX (weighted Fourier transform and RELAXation), which decouples the multidimensional optimization problem into a sequence of one-dimensional optimization problems in a conceptually and computationally simple way. In order to further reduce the complexity, the second NLS algorithm utilizing the characteristic of the C/A code is proposed, which estimate the parameters in the data domain. Finally, the two proposed algorithms are compared with the existing multipath interference methods and show excellent performance and less computational burden.

Co-Evolutionary path optimization by Ripple-Spreading algorithm

Abstract: Static path optimization (SPO) is a foundation of computational intelligence, but in reality, the routing environment is usually time-varying (e.g., moving obstacles, spreading disasters and uncertainties). Thanks to scientific and technical advances in many relevant domains nowadays, changes in the routing environment are often more or less predictable. This study mainly focuses on path optimization in a given dynamic routing environment (POGDRE). A common practice to deal with dynamic routing environment is to conduct online re-optimization (OLRO), i.e., at each time t , environmental parameters are measured/predicted first, and then the best path is re-calculated by resolving SPO based on the newly measured/predicted environmental parameters. In theory, POGDRE is equivalent to time-dependent path optimization (TDPO), which is usually resolved as SPO on a time-expanded hypergraph (TEHG) with a significantly enlarged size. In other words, during a single online run of OLRO-based methods or a single run of TEHG-based methods, the route network is actually fixed and static. Inspired by the multi-agent co-evolving nature reflected in many methods of evolutionary computation, this paper proposes a methodology of co-evolutionary path optimization (CEPO) to resolve the POGDRE. Distinguishing from OLRO and TEHG methods, in CEPO, future routing environmental parameters keep changing during a single run of optimization on a network of original size. In other words, the routing environment co-evolves with the path optimization process within a single run. This paper then reports a ripple-spreading algorithm (RSA) as a realization of CEPO to resolve the POGDRE with both optimality and efficiency. In just a single run of RSA, the optimal actual travelling trajectory can be achieved in a given dynamic routing environment. Simulation results clearly demonstrate the effectiveness and efficiency of the proposed CEPO and RSA for addressing the POGDRE.

High strain rate dynamic compressive properties and deformation behavior of Al matrix composite foams reinforced by in-situ grown carbon nanotubes

Abstract: In this paper, high strain rate dynamic compressive properties of the CNT/Al composite foams reinforced by in-situ grown carbon nanotubes (CNTs) have been investigated. The results show that the compressive strength and energy absorption capacity of the CNT/Al composite foams not only increase with the increment of CNT content but also increase with the strain rate increasing, revealing a typical strain rate sensitivity. For the 3.0 wt%-CNT/Al composite foams, the peak stress at 2130 s −1 is 52.5 MPa, which is ~86% higher than that at 0.001 s −1 (28.2 MPa). Besides, the deformation behavior of the pure Al foam matrix can be significantly affected by the CNT addition and the strain rate. As the strain rate increases from 0.001 to 800 s −1 , the macro deformation way of the pure Al foam transits from the local failure to layers of collapsing, while the micro failure modes in both cases belong to a “Tension mode”. Whereas, whether under quasi-static or dynamic conditions, the macro failure characteristics of the CNT/Al composite foams mainly manifest as the shear deformation and correspond to a “Shear mode” in the micro scale. Moreover, the pore fracture, collapsing and friction, as well as the crack propagation during the compressive processes are all beneficial to the energy absorption enhancement of the CNT/Al composite foams.

Short-term 4D Trajectory Prediction Using Machine Learning Methods

Abstract: 4D trajectory prediction is the core element of future air transportation system, which is intended to improve the operational ability and the predictability of air traffic. In this paper, we introduce a novel model to address the short-term trajectory prediction problem in Terminal Manoeuvring Area (TMA) by application of machine learning methods. It consists of two parts: clustering-based preprocessing part and Multi-cells Neural Network (MCNN)-based machine learning part. First, in the preprocessing part, Principle Component Analysis (PCA) is applied to the real 4D trajectory dataset for reducing the vector variable dimensions. Then, the trajectories are clustered into partitions and noises by Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method. After that, the Neural Network (NN) model is chosen as machine learning method to find out the good predicting model for each individual cluster cell. Finally, with the real traffic data in Beijing TMA, the predicted Estimated Time of Arrival (ETA) for each flight is generated. Experiment results demonstrate that our proposed method is effective and robust in the short-term 4D trajectory prediction. In addition, it can make an accurate trajectory prediction in terms of MAE and RMSE with regards to comparative models.