PLA Air Force Xi'an Flying College

An attitude estimation approach based on the hybrid particle swarm optimization (HPSO) and radar cross section (RCS) precise prediction for the three-axis stabilized satellites in space is presented in this paper. The modified strategies with quantum behavior operator and cloud mutation based on the multi-phase particle swarm optimization are used to increase the diversity of particles, reduce the objective function evaluation times and improve the global searching ability. The simulated RCS sequences of three-axis stabilized satellites at the specified epoch are determined by the method combined the precise orbit and the RCS of satellites calculated by fast multipole method. The mean square error between measured RCS sequences and simulated RCS sequences is considered as the objective function. The attitude estimation problem is transferred to an optimization problem by minimizing the objective function with attitude angles, which is solved by HPSO. The operation procedure is shown clearly, and the set of measurement is analyzed. The numerical results of optimizing the test functions showing better behavior of the proposed algorithm method than standard particle swarm optimization in terms of accuracy are provided. Representative examples validate the accuracy and the anti-noise jamming ability of the proposed algorithm for attitude estimation of three-axis stabilized satellites.

Precise fault diagnosis is an important part of prognostics and health management. It can avoid accidents, extend the service life of the machine, and also reduce maintenance costs. For gas turbine engine fault diagnosis, we cannot install too many sensors in the engine because the operating environment of the engine is harsh and the sensors will not work in high temperature, at high rotation speed, or under high pressure. Thus, there is not enough sensory data from the working engine to diagnose potential failures using existing approaches. In this paper, we consider the problem of engine fault diagnosis using finite sensory data under complicated circumstances, and propose deep belief networks based on information entropy, IE-DBNs, for engine fault diagnosis. We first introduce several information entropies and propose joint complexity entropy based on single signal entropy. Second, the deep belief networks (DBNs) is analyzed and a logistic regression layer is added to the output of the DBNs. Then, information entropy is used in fault diagnosis and as the input for the DBNs. Comparison between the proposed IE-DBNs method and state-of-the-art machine learning approaches shows that the IE-DBNs method achieves higher accuracy.

Remaining useful life estimation of the prognostics and health management technique is a complicated and difficult research question for maintenance. In this article, we consider the problem of prognostics modeling and estimation of the turbofan engine under complicated circumstances and propose a kernel principal component analysis–based degradation model and remaining useful life estimation method for such aircraft engine. We first analyze the output data created by the turbofan engine thermodynamic simulation that is based on the kernel principal component analysis method and then distinguish the qualitative and quantitative relationships between the key factors. Next, we build a degradation model for the engine fault based on the following assumptions: the engine has only had constant failure (i.e. no sudden failure is included), and the engine has a Wiener process, which is a covariate stand for the engine system drift. To predict the remaining useful life of the turbofan engine, we built a health index based on the degradation model and used the method of maximum likelihood and the data from the thermodynamic simulation model to estimate the parameters of this degradation model. Through the data analysis, we obtained a trend model of the regression curve line that fits with the actual statistical data. Based on the predicted health index model and the data trend model, we estimate the remaining useful life of the aircraft engine as the index reaches zero. At last, a case study involving engine simulation data demonstrates the precision and performance advantages of this prediction method that we propose. At last, a case study involving engine simulation data demonstrates the precision and performance advantages of this proposed method, the precision of the method can reach to 98.9% and the average precision is 95.8%.

In the Dempster-Shafer theory, how to quantitatively evaluate the quality of information is an essential issue and also an open issue. Many of measures of uncertainty have been proposed in previous work, whereas some measures among them had been proved to have a few shortcomings. The validity and rationality of the measures proposed in recent years have been explored and analyzed preliminarily, and then an empirical measure of uncertainty with exponential function form which is directly based on the framework of the evidence theory is proposed to overcome the shortcomings. Several numerical examples have been presented to illustrate the validity and rationality of the empirical measure.

A new proportional navigation (PN) guidance law, called combined proportional navigation (CPN), is proposed. The guidance law is designed to intercept high-speed targets, which is a common case for ballistic targets. The range of target-to-interceptor speed ratio during target interception is derived when guidance laws are applied in high-speed targets interception, and the effectiveness of negative navigation ratio in the PN-based guidance law is proven analytically in some lemmas. Based on the lemmas, the lateral acceleration command of CPN is defined, and the solution to the appearance of singularity in time-varying navigation ratio is given. The simulation results show that CPN can determine head-on engagement (as PN) or tail-chase engagement (as RPN) through initial path angle compared with PN and retro proportional navigation (RPN), and can adjust the value of navigation ratio for head-on engagement or tail-chase engagement. Therefore, the capture region of CPN is larger than that of other guidance laws using PN-based methods.

Breast mass segmentation in digital mammography is one of the most significant methods of breast cancer prevention. An integrated approach for mammographic mass segmentation is proposed in this paper. Given a mammographic image, it is first eliminated interference and enhanced in the preprocessing states. Then, the preprocessed images are detected and segmented by level set method. A preliminary evaluation of the proposed method performs on a known public database, namely Mini Mammographic Image Analysis Society (MIAS) database. In order to present that our proposed algorithm is more excellent, we make comparison experiments to demonstrate that our proposed approach can potentially obtain better masses detection results.

With respect to the features of dynamicality and uncertainty of emergency events, a prospect-theory-based method for dynamic adjustment of emergency alternatives is proposed. The problems of emergency event are firstly described, and the problems needed to be solved are presented. And, the method and processes for dynamic adjustment of emergency alternatives are elaborated, according to the idea of value function and weighting function of prospect theory. Finally, a numerical example is provided to illustrate the feasibility and effectiveness of the proposed method.

For the problem of the traditional discrete optimal tracking differentiator (TD) with complex expressions and large phase delay, a more general and terse discrete optimal tracking differentiator without chattering is proposed. By introducing a phase adjusting gene, a new optimal control synthesis function is deduced. The phase characteristic of the tracking differentiator can be adjusted and the mathematic expression of the differentiator is terser. The traditional discrete optimal tracking differentiator is only an especial case of the new tracking differentiator (when the gene is equal to zero). The numerical simulation validated the effectiveness of the proposed approach.

A full polarization radar generally has four polarization channels, whose signals are non-coherently accumulated in general. In this paper, a coherent fusion rule is presented for a full polarization diversity radar. It works in the target tracking mode and uses received signals to estimate and then remove the initial phases of target returns with the phase-locked loop, such that non-coherent target returns can be coherently combined. Simulation results indicate that this method can outperform the non-coherent one significantly in the target tracking mode.

Aiming at the problem of cooperative search for aircraft swarm in an unknown environment without prior information, a cooperative search algorithm with the coverage rate as the optimization objective is proposed, based on the Model Predictive Control theory and the Differential Evolution algorithm. Firstly, the Area Coverage Map (ACM) is established to describe the mission area, and a rapid method of updating the ACM based on Hadamard product is given. Then, the search effect is measured by coverage rate calculated based on the ACM. We regard the aircraft swarm as a control system, and establish a systematic prediction model. The maximum coverage rate in the predicted period is defined as the optimization goal, and the DE is used to solve this problem. Finally, the simulation results verify the effectiveness of the proposed method.

A general evaluation method for coverage performance of LEO constellations with different patterns and altitudes is proposed. The minimum elevation angle characteristics at different latitudes is transformed into the number of standard satellites with the same coverage performance,and the ratio with respect to the actual number of satellites in a constellation is define as the index of coverage performance. According to the evaluation method,the coverage indexes of four kinds of Walker-δ constellation including Iridium,Globalstar,Celestri and NeLS are computed,and the improved design method is brought forward.

To improve the prediction ability of stock price, an integration prediction method based on Rough Set (RS) and Wavelet Neural Network (WNN) is proposed. First RS is used to reduce the dimensions of feature of stock price, then the WNN prediction model is established for stock price movement on the basis of feature dimension reduction; finally, the built model is applied to predict the stock price movement. The simulations on daily closing price index of SSE Composite Index indicate that, the proposed method has advantages of simple structure, strong implementation and good prediction accuracy with average correct rate 64%, and gets better stock price prediction in contrast with single neural network, genetic neural network and WNN.

Hyperspectral image (HSI) is a three-dimensional data cube containing two spatial information dimensions and one spectral information dimension. The spectral vectors of different classes may have similar tendency and value that may bring about negative influences on classification. It is, therefore, important to introduce signal preprocessing techniques in the spatial domain to improve classification accuracy of HSIs. Assuming that local pixels in HSI have some correlations with each other, this paper proposes a spatial filtering model based on adaptive manifold (AM) for HSI. The AM for spatial filtering emphasizes the similar neighboring pixels and is robust to resist the noisy points with fast speed. The rich information in the filtered data is effective for improving the performance of the subsequent classification. The filtered data are classified by an extreme learning machine (ELM). The experimental results indicate that the framework built based on AM and ELM provides competitive performance. Specifically, by classifying the filtered data, the average accuracy of ELM can be improved as high as 30.54%, while performing tens to hundreds times faster than those state-of-the-art classifiers.

Owing to the limitations of the imaging sensor and theoretical aspect, hyperspectral images (HSIs) are contaminated with some unwanted components such as noise and a lack of spatial information. This article proposes a spatial–spectral feature enhancement model to eliminate interference, modify spectral distortion, and increase the useful features. The framework firstly proposes an effective spatial feature-based strategy for selecting a band with the most edge information to serve as alpha channel. Given the alpha channel, the continuous foreground and background are estimated by the closed form solution. Finally, feature-enhanced HSI is obtained by linearly combining the selected band, hyper foreground and background. Experimental results of the ground-based data and remotely sensed data indicate that the proposed feature enhancement algorithm provides effective performance in enhancing spatial–spectral features and reducing noise. Especially, the feature-enhanced data have positive influence on both unmixing and classification.

In this paper, a multi-passband microwave photonic filter (MPF) based on multiple dispersive devices has been proposed and experimentally demonstrated. The Mach-Zehnder interferometer (MZI) divides the broadband light source (BBS) into multiple optical taps, and with the combination of different dispersion mediums such as chirped fiber Bragg grating (CFBG) and single mode fiber (SMF) to delay the optical tap, a MPF with multiple passbands can be simply achieved. The number of the passbands can be easily controlled by changing the number of the dispersion medium. In the experiment, the frequency response result of the four passbands is obtained by accessing two CFBGs and two SMFs. In addition, by adjusting the wavelength interval of the interference spectrum with a variable optical delay line (VODL), all passbands of the filter can be simultaneously tuned. The filter has broad application prospects in the fields of modern wireless and satellite communication, optoelectronic oscillator and optical sensing.

Abstract A new type of two-dimensional composite right/left handed transmission line (2D CRLH TL) is proposed in this letter. Utilizing this structure, an antenna operating on GLONASS and WLAN based on dual zeroth-order resonance (ZOR) mode and first-order resonance (FOR) mode is designed and fabricated. By taking advantage of coaxially center feed and symmetric structure, nearly all omnidirectional radiations in XOY plane at three operating frequencies is obtained. Furthermore, the antenna gain has a high value of −1.27 dB in f 1 =1.60 GHz, −3.89 dB in f 2 =2.39 GHz and 0.67 dB in f 3 =5.12 GHz respectively.

Quadrotor aircraft has become the forefront and hotspot in the research of the aviation industry. At present, the study of the quadrotor aircraft mainly concentrated in the category of electric quadrotor aircraft. But the electric quadrotor aircraft has a relatively short range and small payload, mainly restricted by battery capacity. In order to improve its shortage, select fuel engine as a driving force of the quadrotor aircraft and variable pitch propeller as its propeller. Using computer CAD technology to establish the modeling of different diameter and pitch propeller, then classify the structural grid, and finally with the help of fluid mechanics computing platforms to simulate the propeller's hover state of different diameter and pitch. Matching characteristics analysis of different calculation results with engine performance parameters was carried and conclusion was drawn about the matching law of fuel-powered quadrotor aircraft propeller and engine. This work can provide reference for the overall design of fuel-powered quadrotor aircraft.

Military network technologist lack of comprehensive ability evaluation system,which lead that we couldn't effectively manage the process of their select,train and use. Considering that all indexes' weights are different in system's design,development and use process,an analysis method based on dynamic weight is proposed. And also fuzzy theory is introduced into gray evaluation method which builds a fuzzy-gray evaluation system. This system solves the problems that technologist indexes are hard to quantization and poor in stability,and provides an useful way to evaluate comprehensive ability of military network technologists.

This paper considers the factor of a mechanism based on customer, with the purpose of getting to know the different characteristics that must be taken into account during the development of software as a procedure. This also involves the attending of all kinds of relevant person of the Information system model together with the users’ message exchanging experience. This paper offers a mechanism of the related role in the design of a software component as a procedure, with the target of confirming their attending outside and within the organization. The purpose is to create a component centered on the user, involving clients and experts to work collaboratively; and get usable software as information system.

0-1 invertible matrice which has the largest branch number is widely used in the design of diffusion structures in block ciphers.In view of how to construct such 16×16 matrix,this paper divides 16×16 matrix into 4×4 block matrix by 4×4 0-1 matrix as a unit.Using the weight distribution peculiarity of the sum of 4-dimensional 0-1vectors with weight 2 in field of characteristic 2,it constructs 4×4 0-1 matrix unit group with some special structures in the permutation of isomorphism.On the basis of the structure characteristic of Hadamard matrice,it presents the methods of constructing 16×16 invertible 0-1 matrice with maximum branch number 8 using the matrix block construction method.Further,it presents the methods of constructing 16×16 involutory 0-1 matrice with maximum branch number 8 and their number in the permutation of isomorphism.