Abstract:Accurate capacity evaluation in runway system at an airport is prerequisite for air traffic flow management and an essential content of airport planning. To deal with the problem that analytic logic is lack in MACAD (MANTEA Airfield Capacity and Delays) model in evaluating the capacity of two closespaced parallel runways, a new ultimate capacity model for two closespaced parallel runways is established by using a mathematical analysis method. The effect of departures in previous interarrival gaps in calculating the probability of inserting departures in current interarrival gap is specially analyzed and FAA airport capacity model's Qlogic is introduced so as to improve the accuracy of departure capacity evaluation result. The runway capacity envelope of an American commercial airport based on its reprehensive data is calculated respectively by using MACAD and this mode. The results show that the computation results of the model are basically consistent with those of MACAD model, but this model is more suitable for the definition of the runway capacity envelope.

Abstract:This paper evaluates the properties of composite modified asphalt by selecting four kinds of nanomaterials combined with composite modified the base bitumen with QingChuan rock asphalt. Asphalt experiments contain the three major indexes, rotational viscosity test, dynamic shear rheological test and bending beam rhememer test and rotating film drying oven test, and asphalt mixture tests include rutting test, oil immersion stability test, water immersion stability test and freezethaw splitting test. Compared with base bitumen and rock asphalt, the penetration and ductility of composite modified asphalt reduce while softening point increases. Nanomaterials and rock asphalt can significantly improve the consistency of asphalt, high temperature performance as well as anti ageing property. But low temperature properties have adverse effects. The mixture test shows that the addition of rock asphalt and nano materials can improve the antirutting ability, anti water damage and anti erosion performance of aviation kerosene. The study shows that the composite modified asphalt meets the requirements of the technical indicators of airport pavement.

Abstract:A new tracking algorithm of near space hypersonic target is proposed aimed at the problem that the multiple model algorithms are prone to choose only one single type of models in the process of adaptive conversion based on the degree of model matching. In four nonactivated filter period of different models, detections of model matching between realtime model set and real motion model are used to decide whether to activate different classes of models. By so doing, the algorithm effectively solves not only the defects in the model transformation but also the decreasing robustness of the algorithm caused by the judgment in single filter period. The simulation results show that the improved algorithm can effectively improve the tracking performance compared with the traditional algorithm.

Abstract:Aimed at the problem that the SBN network model fails to meet the requirements of the dynamic performance and regularly and accidentally mistake target recognition, a new target recognition model is designed based on variable structure dynamic Bayesian network to improve the capability of target recognition under high dynamic and complex electromagnetic conditions of environment. This modified model is developed by Static Bayesian Network model, has a good dynamic expression and filtering function, makes up for the lack of SBN, and has a good fault tolerance capability. The simulation results show that the effect of target recognition based on dynamic Bayesian networks is better than that of target fused recognition based on parameter learning Bayesian. The accuracy of target identification and the stability of the algorithm are significantly improved. By so doing, the model effectively solves the problem of missing data and information in the process of target identification.

Abstract:In consideration of the principles that attack benefits of network combined targets with targets are maximal and its own consumption is minimal in total, a multiobjective optimization model is established under conditions of network confrontation environment in fire distribution. Under conditions of random network topology introduced, the effect of fire distribution corresponding to the random network is analyzed. This paper adopts quantuminspired immune clonic multiobjective optimization algorithm to solve the model of fire distribution. Though experimental simulation, the change circumstances of the total attack benefits are analyzed by using different cost ammunition. The attack efficiency of the fire distribution scheme increases by 23% by using the improved algorithm over the fire distribution scheme by using standard algorithm. The convergence of the algorithm and superiority of Pareto solution distribution are studied. The experiments demonstrate that the Pareto efficiency solution distribution increases 42% by using the improved algorithm over using the standard algorithm. The superiority of the model and the efficiency of the algorithm are verified.

Abstract:Aimed at the problems that the electromagnetic environments are becoming more complex day by day, and the radar operation mode recognition is of the limitations, a featurelevel fusion recognition algorithm of radar operation mode based on DS evidence theory is presented to overcome the limitations of recognition method on singleplatform under condition of the background of multiplatforms cooperative combat. Firstly, the BPAF (Basic Probability Assignment Function) is gotten with the member function. Then, according to the Dempster fusion rule, the fusion among platforms and the fusion among parameters are carried out successively. Finally, the radar operation mode is recognized by fusion results and decision rule of radar mode. Through the simulation experiments, the recognition rate of multiplatform is averagely increased by 27%. The result shows that the method can improve the performance of recognition effectively.

Abstract:In airborne nonside looking MIMO radar system, conventional statisticalbased spacetime adaptive processing (STAP) algorithms always cannot obtain enough independent identically distributed training data, and fail to estimate the clutter covariance accurately, thus degrading the performance of clutter suppression. In order to reduce the requirement of training data, the concept of clutter range azimuth spectrum (RA) is first presented. Then, the clutter RA is obtained by utilizing a sparse recovery algorithm and a preconstructed range filter. At last, the covariance matrix of clutter is calculated by the estimated clutter RA. Due to the application of the priori information and the relation between the spatial frequency and temporal frequency of clutter, the proposed STAP method based on RA, i.e. RASTAP, can estimate the covariance matrix of clutter with a few training cells accurately. Therefore, the method can suppress the clutter and detect lowmoving target effectively. The theoretical analysis and the experimental simulations demonstrate that the method is effective.

Abstract:Space backbone network is an integrated network composed by the space-air-ground largescale backbone nodes, and the core of supporting space information network. On the basis of analyzing the status quo of research on space information network of the Europe and the United States of America, space backbone network architecture and protocol framework are proposed according to the relevant characteristics and overall requirements. The network is composed of spacebased network, airbased network, and groundbased network. By so doing, this realizes mutual communication and cooperation through intersatellite links, satelliteair links, satelliteground links, airtoair links, airground links and terrestrial communications lines. The spacebased backbone network based on multilayer satellite network and airbased backbone network based on wireless mesh are mainly studied. Finally, the paper explores the key technologies such as network structure, routing protocols, data transmission technique, heterogeneous compatibility, and network management. This research has some contributions for reference at a certain degree to the construction of future space backbone network.

Abstract:Aimed at the problem that the VFTO originating from operations of isolating switch in GIS may produce a fearful interference with the switch and its peripherals, a collaborative simulation method by Fieldcircuitwave of VFTO radiation interference is proposed in this paper. Combined with project living example, the GIS equivalent circuit is extracted by means of quasistatic electromagnetic field analysis firstly. Furthermore, the equivalent timevarying voltage source of VFTO can be obtained via associating the timevarying resistance model of switching arc and the broadband circuit method. Finally, using transient full wave technique in time domain, the spatial electromagnetic interference by VFTO is simulated. This method has significance for reference to the high voltage switch design.

Abstract:In order to improve the accuracy of fault diagnosis in analog circuits, aimed at the instability structure and error transferring of the existing directed acyclic graph support vector machine (DAGSVM), a novel approach based on fault distinguish degree to construct DAGSVM is proposed. According to the fault information acquired from all of the testable points, this paper defines the concept of fault distinguish degree, and takes this as a basis to optimize the topology of the DAGSVM to eliminate the inherent instability of DAGSVM structure. For this reason, there is a stable and quite good accuracy of diagnosis. The experimental results show that this method improves obviously diagnosis accuracy compared with "1 vs 1",SVM, and traditional DAGSVM, and simultaneously the method can be used for reference in analog circuit fault diagnosis.

Abstract:In order to realize the blind separation of FH signals based on the needs of its networkstation sorting, most of existing approaches based on joint diagonalization for FH signals request the orthogonality exactly, whereas this requirement is always limited in many practical applications. In order to soften the terms, a new blind separation algorithm based on nonorthogonal joint diagonalization is proposed. The algorithm effectively obtains the autosource (timefrequency) TF points with the eigenmatrix structure through gradient norm based on noise reduction algorithm in timeslots area, computes a sequence of matrices of timefrequency distributions (TFDs), and then estimates the separate matrix through nonorthogonal joint diagonalization to realize blind source separation of mixed frequencyhopping signals. The results of the simulations illustrate that the proposed algorithm is effective in the blind separation of frequencyhopping signals, and the robustness is better than that of the other blind separation algorithms of frequencyhopping signals.

Abstract:Effective detection problem for BPSK (binary phase shift keying) signals under condition of Gaussian white noise environments is studied. In cognitive radio, when second user senses spectrum from first user, this makes the best detection threshold with the minimum of sum of false alarm and missed detection rate in existence. By theoretical derivation for distribution character of energy detection static, the best detection threshold problem is converted to solution of the minimum of the sum, computation expression is determined by sampling length, noise variance and signal to noise ratio (SNR) are given. The simulation of sum value curve variety in different SNR shows that the best detection threshold is in existence. The performance of the best threshold is even better than before by analysis for detection ratio performance between the threshold and the one in constant false alarm ratio (CFAR), false alarm ratio performance between the threshold and the one in constant detection ratio (CDR), other two thresholds' convergence to the threshold performance. Besides, Monte Carlo experiment shows that the mathematic analysis is correct.

Abstract:A miniaturized dualband bandstop frequency selective surface (BSFSS) is proposed and analyzed. The resonance frequencies of the proposed BSFSS can be adjusted by changing the structure parameters, and a miniaturized BSFSS centered at 3.84 GHz and 6.67 GHz is designed and simulated. The simulated results indicate that the designed BSFSS obtains bandwidths of 57.8% at 3.84 GHz and 12.9% at 6.67% GHz, and performs very well in the angle stability and the polarization stability within the operating bands. Besides, a whole size of the proposed BSFSS unit cell is only 7×7 mm2, compared with the conventional FSS unit, the integral size is reduced by 56.9% at least, and the advantages of miniaturization are obvious.

Abstract:Situation forecast is a key link in network situation awareness, because this can provide necessary decision support for network managers. In order to realize the big data model of network management, a forecast method based on association rules is proposed aimed at the problem that the current forecast methods fail to take advantages of big data. The method takes the characteristics of big data into full account combined with requirement of situation forecast, and the basic idea and processes of the method are given. The experiment results show that the proposed method reduces time complexity greatly through finding relevance instead of nonlinear registration, and improves accuracy of forecast compared with the traditional methods.

Abstract:This paper proposes a multitask learning method of joint optimization based on sparse linear array position of elements and weights of the array. The method can get more information from the target pattern features without adequate prior knowledge, and convert the sparse array antennas into a linear regression problem sparse matrix. By identifying sparse vector support area, the paper transforms the underdetermined steering vector matrix equation to the over determined conversion feature matrix equation. The results show that the proposed algorithm can optimize the position and weights of elements, and at the same time, this can realize the shaped beam pattern effectively.

Abstract:The diversity existed in most recently hashing methods leads to the binary codes cannot efficiently preserve the data similarity. This paper, taking the ensemble learning theory and the parallel algorithm as a support, proposes a novel hashing method, i.e. Unsupervised Ensemble Hashing Learning (UEH). Firstly, the ensemble method is utilized to balance the diversity so as to reduce the quantization error. Specially, the higher accuracy and the larger diversity the base learner has, the more effective the ensemble method is. Then the bootstrap aggregating (bagging) method is used to increase the diversity. Finally, the paper uses iterative quantization to guarantee equivalent information of each hashing bits to effectively enhance the generalization ability. The paper validates the method on two large scale datasets CIFAR-10 and MINIST for image retrieval, and the experimental results show that the performance gains of the proposed method is improved by 6%~15% compared with the stateoftheart methods. In addition, an important benefit of bagging scheme for hashing is inherently favorable to parallel computing

Abstract:Aimed at the problems that Symbiotic Organisms Search (SOS) algorithm is poor in convergence, low in searching precision and ease of premature convergence, a multistrategy adaptive algorithm is proposed. Firstly, according to the fitness, populations can be divided into three groups, and each group with different strategies can achieve different functions. Secondly, a hybrid search strategy based on adaptive scaling factor can make its search strategy realization of the adaptive adjustment. Finally, in order to maintain the population diversity, a mutation is utilized when the individual beyond the boundary. Experiments are conducted on the 14 benchmark functions, and the results show that the MSASOS algorithm improves obviously the performance in convergence speed, precision and global optimization.

Abstract:Selecting the red sandstone from a project of the Yunnan Diqing region to do the split Hopkinson pressure bar (SHPB) experiment and the scanning electronic microscopy (SEM) experiment under different moisture state and different pulse intensity, the paper studies the dynamic strength characteristics and the failure mechanism of waterbearing rock from macroscopic and microscopic point of view. The results show that: The strength of rock decreases with the increase of the moisture content in the static or dynamic uniaxial compression; According to the stressstrain whole process curves of the SHPB experiment, the waterbearing specimen has obvious volume compression deformation, higher peak strain and strain softening characteristics after peak, while dry specimen has higher peak intensity and its post peak stress drop rapidly. The compressive strength decreases with the increase of water content when pulse strength is the same, but increases with the increase of pulse intensity under the same water condition based on the dynamic compressive strength curve for each specimen. The SEM images show that the particle structure of watersaturated rock is loose and swelling, and the impact failure mechanism is dominated by the fracture of initial microcracks and mineral particle or phase boundary, performance as dynamic compressive strength reduction, while the particle structure of dry rock is compact and the impact failure mechanism also include the fracture of mineral particles cut directly, and this shows that the strength increases.