Abstract:The future battlefield environment puts forward higher requirements for the construction of protective engineering. Building materials are the important factors that affecting the comprehensive protective ability of protective engineering. As the main material of protective engineering construction, traditional silicate concrete has the characteristics of brittleness, low tensile strength, poor impact resistance, easy cracking and function. It is difficult to meet the requirements of the actual battlefield environment for the construction of protection projects. Fiber concretes have been widely used in civil engineering and protection engineering because of their good performance. The development history, classification and performance of fiber reinforced concrete are introduced. The research status of mechanical properties, durability, absorbing property and high temperature resistance of fiber reinforced concrete are expounded with the application background of protection engineering. The development trend of fiber reinforced concrete is analyzed in order to provide reference for the research and application of new materials for protection engineering.

Abstract:A nonlinear model of faulttolerant switched reluctance starter/generator system is proposed in this paper. Based on the mathematical model of switched reluctance motor, including circuit equation and mechanical equation, an open model is built with modularization idea. The feasibility of the model is analyzed:① when the rated voltage is 380V under condition of electromotive state, the output torque can meet the needs of constant torque starting; ② under condition of electric power state, driven by the prime mover with 2 000 r/min, the noload rapid voltage building can be realized, and the voltage can be quickly stabilized at near 270 V and regulated. In view of the high opencircuit fault rate of power devices, a faulttolerant power converter is designed based on the idea of hardware redundancy. In order to verify the faulttolerant performance in the system, the simulation results show that:① the faulttolerant system at the electric state not only eliminates the deadtime of the torque, but also improves the average torque and reduces the torque ripple; ② the output voltage restores to about 270 V within 0.2 s, and the phase current also drops to the normal operating range, the faulttolerant performance is good.

Abstract:Aimed at the problems that the risk of collision between military and civil aviation in the airspace is increasing sharply, the current flight conflict detection methods are difficult to grasp the overall conflict situation in the airspace, and the controllers fail to make accurate judgment of different conflict situations, a method for identifying flight conflict key nodes based on complex network theory is proposed. Firstly, a flight conflict situation network model is built based on ACAS protected area model. Then the importance of all conflict nodes is evaluated based on the centrality of node degree and closeness, PageRank in complex network theory and AHP method to evaluate nodes’ conflict levels. And the key aircraft and locations with high threat levels are found. The simulation results show that the level of security situation in the airspace can be divided reasonably by establishing the flight conflict situation network. Simultaneously, according to the evaluation index of node importance of complex network, aircraft with serious conflict security threats can be effectively identified to assist air traffic controllers to fully grasp the flight safety situation in the airspace.

Abstract:Aeroengine fault prognostic can provide basis for maintenance decisionmaking which can help to avoid catastrophic failures and minimize economic losses. According to the characteristics of aeroengine sensor data, a fault prognostic method based on bidirectional long shortterm memory (LSTM) neural network is proposed. A fault prognostic model is established, including data preprocessing, network design, training and testing. The model structure has strong generalization ability under various working conditions and faults is obtained. The model was validated using the CMAPSS data set. Compared with the RNN, GRU and LSTM time series models, the results show that the proposed Bidirectional LSTM fault prognostic model has an average error of 33.58%, which has better adaptability. The asymmetric score decreased by 71.22%, resulting in higher prediction accuracy.

Abstract:Aimed at the problems that at the present time the model of air materials ordering is relying solely on human experience, the stagnancy responsed to variation of consume rules exists in and the credibility is low, and the amount of work is too heavy, a heuristic ordering model is established to calculate the classification, the properties’ discretation and the interval weight by using PAM cluster, Rough Set global discretation and PSO under the framework of ordinary ording model. Then, the MSE between the two models is compared. The result indicates that the heuristic model could help people get rid of the tedious work of calculate model by experience, and improve the veracity and response timeliness of model.

Abstract:The accurate estimation of the state of charge (SOC) of a battery is increasingly important in the context of the wide application of batteries. However，it is difficult to build accurate physical models，and the use of pure datadriven methods is prone to overfitting problems due to individual differences of batteries. To solve these problems，propose a method based on belief rule base (BRB) to estimate SOC of lithium battery. This method allows experts to overcome the overfitting problem of datadriven methods through empirical knowledge and the inaccuracy of expert knowledge through parameter training. A lithium iron phosphate (LiFePO-4) battery is taken as an example to verify the proposed method，and the results are compared with those of the neural network. The results show that this method has high accuracy in SOC estimation，the estimated error is not more than 10%，and can overcome the overfitting problem of traditional neural network methods.

Abstract:A Prediction method for storage life of conducting film resistor based on accelerated degradation data is proposed in this paper. First of all, the accelerated performance degradation test of the conducting film resistor is carried out by using the temperature stress. In the experiment, the total resistance of the conductive film is used as a criterion of the performance, and the accelerated performance degradation data tested by online and offline testing are obtained under conditions of different accelerated stress; Secondly, the temperature drift effect of the online data is removed by introducing the temperature factor, and the online data is fused with the offline data to identify the degenerate trajectory parameters, and the pseudo life of the conducting film resistor at each acceleration stress level is obtained; Then, the storage life of the conducting film resistor under normal stress is obtained by combining the modified three parameter temperature acceleration model; Finally, taking a conducting film resistor as an example, the applicability and effectiveness of the proposed method are verified.

Abstract:Aiming at the NP-hard characteristics of directional antenna network topology design under cluster UAV background, an elite strategy for deep reinforcement learning communication network topology generation algorithm is introduced with the requirements of high survivability, low power consumption and high stability of the network, which has the rewarding of invulnerability (3-connectivity), link quantity, link power consumption and stability. Compared with traditional DQN, elite experience pool verifies the acceleration training effect by effectively accelerating the convergence of the model and reducing the training time by more than three times. Rather than genetic algorithm, this algorithm separates the processes of use and training . When the network training is completed, the communication network topology can be calculated in real time with the needs of scene. In experimental stage, a 3-connected communication network topology with randomly given spatial location is designed which includes 6 nodes, 10 nodes, 24 nodes and 36 nodes . The experimental results has shown that this proposed algorithm has strong realtime and applicability, it can help network topology which has less than 36 nodes update in 183 ms so that meeting the realtime requirements of practical application.

Abstract:A distributed UAV network coverage optimization algorithm is proposed for the hotspot coverage coverage optimization scenario in the nonuniform target area. Firstly, the number of minimum UAV nodes that satisfy the network connectivity and the coverage of the hotspot area are estimated. Secondly, the hotspot information is added to improve the location update equation of the cuckoo algorithm and the optimization objective function is reconstructed. Then the adaptive probability parameters are adaptively adjusted. Finally, the key optimization of hotspot area coverage is achieved. In the simulation experiment analysis, compared with the standard cuckoo algorithm and other classical algorithms in the same simulation environment, the results show that the coverage of the hotspot area of the proposed algorithm is improved by about 4% compared with other algorithms, and the number of iterations is reduced by about 30 times. It is proved that the algorithm has fast convergence speed and less time, which can improve the coverage of hotspots more effectively.

Abstract:In the controller in-band connection mode, a control message intensive based flow update strategy is proposed to reduce the flow table update time and simplify the process of pushing flows. The flow table update is discussed from two aspects, i.e. path creation and path switching. The source routing and tracking packet are used to intensify the form of flow table update message and plan the way to send it. This strategy is aimed at guaranteeing the consistency of the flow table update and simultaneously reducing the amount of information of controllers receiving and sending and updating time. The simulation results show that rather than classification based flow update strategy and path and feedback based policy update strategy, by this strategy the flow table update time is reduced severely and no great fluctuation is caused in data transmission under different link delay and transmission rate conditions. In addition, the amount of information that controllers receive and send by this strategy is similar to PF-FUS, and both the amounts of information are lower than that of C-FUS.

Abstract:Aimed at the problems that he traditional linear Support Vector Machine (SVM) is equal to each dimension of input features in training data sets, and the classification in the original space directly leads to the low prediction accuracy, a method for combining lowdegree polynomial mappings and feature weighted is proposed to improve the classification performance of linear SVM. First of all, each sample is mapped into the twodegree explicit feature space by using the polynomial trick to increase the implicit information of samples. Then, the feature weights of each dimension are calculated by using the fuzzy entropy feature weighted algorithm. By feature weighting, the magnitude of contribution to the result of classification can be measured. To verify the robust of the proposed method, the totally seven data sets from different database are tested. Making a comparison between Kernel SVM and other improved Linear SVM algorithms in training time and prediction accuracy, the results show that the training time reduces an order of magnitude with the expansion of data set, and the prediction accuracy can keep up with few training samples even close to Kernel SVM. The running results show that the proposed method can effectively improve the overall performance of the linear vector machine.

Abstract:Aimed at the problems that the blind equalization algorithm does not require a sequence and can effectively reduce intersymbol interference, but under the impulse noise environment, the existing single filter equalization algorithm fails to effectively balance the convergence rate and steady state error, and fails to effectively reduce ISI, a convex combination blind equalization algorithm is proposed based on cosine cost function. The algorithm utilizes two blind equalizers in parallel ( one as a fast filter ) for guaranteeing the convergence rate and ( the other as a slow filter) for reducing the equalization error. In order to further reduce the impact of impulse noise, the fractional loworder statistic is introduced into the blind equalization algorithm based on the cosine cost function and the blind equalization algorithm based on the decisiondetection algorithm. These two algorithms are used as weight vector update algorithms for fast and slow filters, respectively. The simulation results show that when the noise is set to Gaussian white noise of 28 dB, the ISI of the new algorithm will be lower than CMA and CCF and the constellation diagram is also clear. When the noise environment is 28 dB α stable distribution noise, the new algorithm uses the fractional loworder statistic to suppress the impulse noise to obtain lower ISI and clear constellation, and the convex combination structure takes into account the steadystate error and convergence rate, further reducing the steadystate error while ensuring a faster convergence rate.

Abstract:Aimed at the problems that the VDSR model convolution kernel is single and the DRRN model fails to take advantages of global features, a combined convolution image superresolution model is proposed based on parallel residual convolution neural networks. Firstly, the combined convolution neural layer is structured by the original convolution layer and dilated convolution layer, and the skip connection approach is employed to connect the different layers to take advantage of different level features, completing superresolution network. There are two advantages of this model:①Combination of dilated convolution neural layers and original convolution layers can capture multiscale features without computationconsuming. Based on this approach, the network can get more presentation capacity. ②Skip connection approach fuse lowlevel information and highlevel information. From this approach, different level features can be learned. This means that stronger learning ability can be obtained. Based on the experiment results on multiple data sets, more than 0.1 IFC improvement is achieved, compared with the stateoftheart models VDSR, DRRN, SRCNN in most tasks.

Abstract:Aimed at the problems that the optimization based on ambiguity function is an important means of Frequency Diverse Array (FDA) radar waveform design, the ambiguity function of subarraybased Frequency Diverse Array is lack of research in the existing literature, a data model for FDA transmitting simple pulses under narrowband conditions is established and the beampattern characteristics of FDA arrays is compared to the phased arrays. On this basis, based on the time domain convolution of the signal through the matched filter output, the ambiguity function of the FDA array under three received signal processing architectures is derived. Then, the transmit beampattern characteristics and the ambiguity function characteristics of the cross subarraybased FDAs with different frequency offset are analyzed separately. The result shows that the FDA with sinusoidal frequency offset has a good interference suppression performance. The correctness of the analysis is verified and an important foundation is laid by this method for a series of studies in designing radar waveforms based on the ambiguity function of the subarraybased FDA to achieve interference suppression.

Abstract:Aimed at the problems that the traditional ballistic targets micromotion classification is lack of intelligence and the classification performance is poor under noise conditions , by using the highdimensional feature generalization learning ability of deep learning, a method of using deep convolution neural network for ballistic target micromotion classification is proposed. Firstly, based on the establishment of the ballistic target micromotion model, the microDoppler representations of the three micromotion forms are analyzed, and the timefrequency map of the radar echo signals is generated as the data set for training, verification and testing; The transfer learning in deep convolution neural network is used to retrain AlexNet and GoogLeNet. Finally, the target network classification in three micromotion forms is realized by using the trained network, and the influence of signaltonoise ratio on classification performance is studied. The simulation results show that compared with the traditional micromotion target classification method, the method is not only high in intelligence, but also is good in classification accuracy under low SNR conditions, and is guidable in the classification of ballistic targets.

Abstract:Sparse recovery algorithm can realize sparse MIMO planar array twodimensional imaging. When the traditional 1D-CS algorithm is adopted to treat with the dimensionsorting in processing, there will be a loss of the coupling information, the migration of cell will be caused by, the image is poor in quality, and time is too long in operation. For the reason mentioned above, the structure characteristics of MIMO planar array are studied in this paper. The paper analyzes the joint sparse feature of the twodimensional data accepted by MIMO and realizes the joint reconstruction of the twodimensional data by adopt 2D-SOONE algorithm. The algorithm uses sequential order one negative exponential function instead of Gaussian function of the traditional SL0 algorithm, extends to the two dimensions and solved by gradient projection, and has the performance of the two dimensional joint reconstruction, and the precision of reconstruction is improved. Through experiments, the imaging effect of the algorithm for MIMO sparse array is simulated under different array sparsity and SNR. The simulation results show that the 2D-SOONE algorithm suppresses the cell migration problem of the traditional 1DCS algorithm, and reduces the operation time. The imaging quality is better than that of the 2D-SL0 algorithm.