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For the issue of limited filtering accuracy of interactive multiple model particle filter algorithm caused by the resampling particles don't contain the latest observation information, we made improvements on interactive multiple model particle filter algorithm in this paper based on mixed kalman particle filter algorithm. Interactive multiple model particle filter algorithm is proposed. In addition, the composed methods influence to tracking accuracy are discussed. In the new algorithm the system state estimation is generated with unscented kalman filter (UKF) first and then use the extended kalman filter (EKF) to get the proposal distribution of the particles, taking advantage of the measure information to update the particles' state. We compare and analyze the target tracking performance of the proposed algorithm of IMM-MKPF in this paper, IMM-UPF and IMM-EPF through the simulation experiment. The results show that the tracking accuracy of the proposed algorithm is superior to other two algorithms. Thus, the new method in this paper is effective. The method is of important to improve tracking accuracy further for maneuvering target tracking under the non-linear and non-Gaussian circumstances.

With the widespread using of GPU hardware facilities, more and more distributed machine learning applications have begun to use CPU-GPU hybrid cluster resources to improve the efficiency of algorithms. However, the existing distributed machine learning scheduling framework either only considers task scheduling on CPU resources or only considers task scheduling on GPU resources. Even considering the difference between CPU and GPU resources, it is difficult to improve the resource usage of the entire system. In other words, the key challenge in using CPU-GPU clusters for distributed machine learning jobs is how to efficiently schedule tasks in the job. In the full paper, we propose a CPU-GPU hybrid cluster schedule framework in detail. First, according to the different characteristics of the computing power of the CPU and the computing power of the GPU, the data is divided into data fragments of different sizes to adapt to CPU and GPU computing resources. Second, the paper introduces the task scheduling method under the CPU-GPU hybrid. Finally, the proposed method is verified at the end of the paper. After our verification for K-Means, using the CPU-GPU hybrid computing framework can increase the performance of K-Means by about 1.5 times. As the number of GPUs increases, the performance of K-Means can be significantly improved.

Aiming at the distance measurement of moving sound sources in shallow seas, this paper proposes a method of histogram filtering to realize underwater distance estimation of moving sound sources in shallow seas. The algorithm used the transmission loss, target motion parameter in the sound propagation and receival signal as prior knowledge to updated the state vector of the sound source, so as to realize the distance estimation of the shallow sea sound source, and this paper used SwellEx-96 database for experimental verification. The experimental results shown that: the depth estimating error of moving sound source is small, and when the detected horizontal distance is in the range of 10 km, the maximum range error of the horizontal distance is ±10 m, meanwhile the accuracy of ranging can be improved by improving the prior knowledge of the target motion parameters, which verifies that the histogram filtering algorithm can achieve better ranging for underwater moving targets.

Compared with traditional gear transmission, the multi-branch split-torsion gear transmission system has the advantages of large transmission power, small size and high reliability, so it is more and more used in high-speed heavy load occasions such as ships and aircraft. Since the transmission system of multi-branch split torsional gears belongs to over-constrained configuration, it is necessary to meet strict tooth matching condition in the design process in order to realize the correct synchronous meshing of each branch, which is of great significance to ensure its uniform installation and motion synchronization.Aiming at the coaxial six-branch twisted herringbone gear transmission system, this paper establishes a calculation method for the proper meshing conditions of each branch on the basis of considering the movement synchronization of each branch and preventing geometric interference.In addition, the calculation of gear allocation was carried out for a ship's power transmission system, and a parameter scheme that satisfies the requirements of transmission ratio, concentricity and synchronous meshing was obtained.The correctness of the calculation method of tooth matching in this paper is verified by three-dimensional modeling. This method has universal application value to the tooth matching design of other coaxial multi-branch gear transmissions.

Most of the solutions to existing test selection problems are based on single-objective optimization algorithms and multi-signal models, which maybe lead to some problems such as rough index calculation and large solution set limitations. To solve these problems, a test optimization selection method based on NSGA-3 algorithm and Bayesian network model is proposed. Firstly, the paper describes the improved Bayesian network model, expounds the method of model establishment, and introduces the model's learning ability and processing ability on uncertain information. According to the constraints and objective functions established by the design requirements, NSGA-3 is used to calculate the test optimization selection scheme based on the improved Bayesian network model. Taking a certain component of the missile airborne radar as an example, the fault detection rate and isolation rate are selected as constraints, and the false alarm rate, misdiagnosis rate, test cost, and test quantity are the optimization goals. The method of this paper is used for test optimization selection. It has been verified that this method can effectively solve the problem of multi-objective test selection, and has guiding significance for testability design.

The fault detection function of GLT method is approximately proportional to the square of fault value, the value of the fault detection function is lower than the detection threshold when the soft fault is small, which leads to the low sensitivity of soft fault detection and the high rate of missing detection. The IM-SPRT method rapidly increases the value of fault detection function above the threshold of fault detection by accumulating small soft fault values, so it has high sensitivity for soft fault detection, however, the end time of the fault cannot be judged timely, resulting in a high false alarm for fault detection. In view of the above problems, this paper proposes a high-sensitivity fault detection method for redundant systems based on GLT/IM-SPRT. This method uses the IM-SPRT method to detect weak and soft faults and sets the IM-SPRT method fault detection function value to zero by the GLT fault detection result, and isolates the fault with GLT. On this basis, a framework of high-sensitivity fault detection and isolation methods for redundant ADS and INS systems is designed. The simulation results of soft and hard fault detection and isolation of three-redundancy ADS subsystem level and INS sensor level show that the method in this paper can improve the sensitivity of fault detection and reduce the rate of missed detection.

Path planning is the key technology for UAV to achieve autonomous flight. Considering the shortcomings of path planning based on the conventional potential field method, this paper proposes an improved optimization algorithm based on the artificial potential field method and extends it to three-dimensional space to better achieve flight constrained 3D online path planning for UAVs. The algorithm is improved and optimized aiming at the three problems of goal nonreachable with obstacle nearby (GNWON), easy to fall into local minimum, and path oscillation in traditional artificial potential field method. First, an improved potential field function with relative distance is used to solve the GNWON, and an optimized repulsive potential field calculation method based on different obstacles or threat models is proposed to optimize the planned path. Secondly, in order to make the drone jump out of the local minimum trap, a method of setting heuristic sub-target points is proposed. For local path oscillation, a method using memory sum force was proposed to improve the oscillation. The simulation results show that the improved optimization algorithm in this paper effectively makes up for the shortcomings of the traditional artificial potential field method, and the designed 3D online path planning algorithm for the UAV is practical and feasible.

Aiming at the requirement of attitude information module with high precision, small size and low power consumption for the control of miniature UAV, a practical attitude estimation algorithm based on the micro-electro-mechanical sensor is proposed in this paper, which realizes the accurate estimation of the attitude of the UAV under the condition of low acceleration. A low-cost MEMS gyroscope, accelerometer, and magnetometer are used in the system. The Euler angle is obtained by the state observer method based on Direction Cosine Matrix (DCM) which can be got by fusing the sensor data. Firstly, based on the basic idea of TRIAD algorithm, a method to determine the attitude rotation matrix by accelerometer and magnetometric measurement is proposed. Compared with the traditional method, this method does not have to calculate the inverse of the matrix. Secondly, a state observer is intended to estimate the attitude of the system. The state observer doesn't have to observe the bias of the gyroscope, but still ensures the convergence of the Euler angle. Finally, the simulation based on the actual sampling data of the MEMS sensor shows that the output of the state observer designed in this paper still has high accuracy and good dynamic characteristics under the condition of gyroscope noise and bias.

It is crucial to develop an effective controller for the multi-UAV system to contribute to the frontier fields, such as the electronic warfare. To address the dilemma of the cooperative formation with the high dimensional data, a deep neural network(NN) controller is developed in this paper. Firstly, a deep NN model is used to tune parameters of PID controller online. Secondly, this paper introduces an improved deep NN model integrating the momentum to improve the performance of the classical NN model and satisfy the condition for the real time cooperative formation. Lastly, the cooperative formation task is achieved by extending the proposed cooperative controller with an improved NN to the complex multi-UAV system. The simulation result of multi-UAV formation demonstrates the effectiveness of the proposed method, which achieves a faster formation than competitors.

In this paper, based on the linear inverted pendulum (LIP) model, the multi-walking of biped robot is analogous to the multi-swing of a three-dimensional inverted pendulum. In terms of the concept of 'divergent component of motion (DCM)', the dynamic equations expressed by using Center of Mass (COM) and DCM are studied. Two DCM closed-loop controllers are designed:one-step DCM terminal invariant disturbance rejection controller and real-time DCM trajectory tracking closed-loop controller. Both controllers can effectively suppress the disturbance, so that the DCM of the actual robot does not diverge, and which is used to plan the COM trajectory of the biped walking process. Based on the COM trajectory and biped end trajectory, the numerical method for solving inverse kinematics of biped robot is studied. The whole set of solving problems from input footprint to output joint angle in biped walking process is completed, and systematize the method of biped gait planning. Finally, combining with a ubiquitous robot model, all the algorithms in this paper are simulated via MATLAB platform. The simulation results verify the effectiveness of the method.

To intercept a higher-speed target in the terminal guidance phase, this paper proposes a generalized relative biased proportional navigation (BPN) law. In order to enlarge the capture domain of the classical proportional navigation (PN) law and make full use of the maneuverability of a missile, the paper designs time-varying navigation coefficients; thus the modified PN guidance law integrates the advantages of the PN guidance law with those of the retro-PN guidance law. In order to intercept high-speed targets with impact angle constraints, the relative BPN law is introduced, and the impact angle is achieved by controlling the relative flight-path angle. In order to improve the performance of the guidance law for intercepting higher-speed maneuvering targets, some compensation measures are designed for guidance commands. Extensive simulations are conducted to verify the design features of the proportional navigation law.

Aiming at the high computational complexity and large storage of IGRF geomagnetic field model in Attitude Determination and Control System (ADCS), the uniform sampling model and K-Curvature weighted model have been established. First of all, storage format and track deduction method of uniform look-out-table (LUT) model is proposed. Then, this paper uses U-Discrete curvature calculation method to solve the discrete curvature of magnetic field data. Finally, the uniform sampling method and K-Curvature Weighted model in fitting the original curve has been compared. In general, this paper presents K curvature weighted non-uniform LUT model with only 80 points needed, and it only needs 0.96 kB. It proves great advantages in terms of computing speed and storage space compared with the existing technology.

Using microfluidic technology to achieve integrated chip cooling is becoming a promising method to extend Moore law effective period. The thermal management is always critical for 3D integrated circuit design. Hot spots due to spatially non-uniform heat flux in integrated circuits can cause physical stress that further reduces reliability. The critical point for chip cooling is to use microfluidic cooling accurately on the hot spots. First, based on electro-wetting on dielectric, the paper presents an adaptive chip cooling technique using the digital microfluidics. Then, a two-plans 3D chip cooling model has been given with its working principle and characteristics. And single plan chip cooling model is presented, including its capacitance performance and models. Moreover, the dentate electrode is designed to achieve droplet continuing movement. Next, the ant colony optimization is adopted to get optimal route during electrode moving. Last, the experiments demonstrate the adaptive chip cooling technique proposed in this paper is effective and efficiency.

With the development of the more/All electrical aircraft technology, the dual active bridge converter has been applied to the energy storage device to realize the bidirectional power flow in aircraft electrical system. The power loss of power device in DAB converter affects the operational life of the device and the reliability of the converter. So it is an important performance index to keep the temperature balance for power device in DAB converter. In this paper, the operational mode of dual active bridge bidirectional DC-DC converter in extended-phase-shift (EPS) control is analyzed and verified by experiment, find the reasons for difficult to achieve soft-switching under light load conditions, then proposes a novel thermal balance phase-shift control strategy on the basis of EPS. Experimental methods in this paper are alternation control mode and temperature feedback control mode. The thermal image and temperature was analyzed. The efficiency curve was plotted. The switching waveform was observed. the thermal balance was achieved to verify the effectiveness. and finally achieves the goal for improving the converter efficiency, reduces the devices heat stress, improves the reliability of the DAB converters and increases device lifetime.

A research method for multi-echelon inventory allocation of civil aircraft spare parts with lateral transshipments and maintenance ratio is proposed in this paper, and the maintenance rate is considered into the inventory model, aiming at the problem of multi-echelon inventory allocation with lateral transshipments for civil aircraft spare parts. Firstly, the principle of the multi-echelon inventory model with lateral transshipments and maintenance ratio is proposed. Then, by considering the fleet availability and the satisfaction ratio as the constraints, a mathematical model is built with the inventory system total cost as the optimization objective. Secondly, the procedure of solving multi-echelon inventory allocation model considering the influence of lateral transshipments and maintenance ratio is discussed based on the marginal analysis method. Finally, some components of Boeing 737 are selected as the research objects, the multi-echelon inventory allocation is investigated by considering lateral transshipments and maintenance ratio. The analysis results in this paper show the best number of multi echelon inventory allocation, the minimum inventory system total cost and the fleet availability are 1.205 million dollars and 0.9909 respectively. Comparing the traditional inventory model without considering lateral transshipments and maintenance ratio, and the model which only considers the maintenance ratio, the results indicate that the model offered in this paper could reduce the total cost of inventory system significantly, and provides theoretical support and method guidance for the accurate allocation of multi echelon inventory optimization for civil aircraft spare parts.

In the case of nonlinear systems with random bias, the Optimal Two-Stage Unscented Kalman Filter (OTSUKF) can obtain the optimal estimation of system state and bias. But it requires random bias to be accurately modeled, while it is always very difficult in actual situation because the aircraft is a typical nonlinear system. In this paper, the faults of the Inertial Measurement Unit (IMU) are treated as a random bias, and the random walk model is used to describe the fault. The accuracy of the random walk model depends on the degree of matching between the covariance of the random walk model and the actual situation. For the IMU fault diagnosis method based on OTSUKF, the covariance of the random walk model is assigned with a constant matrix, and the value of the matrix is initialized empirically. It is very difficult to select a matching matrix in practical applications. For this problem, in this paper, the covariance matrix of the random walk model is adaptively adjusted online based on the innovation covariance matching technique, and an adaptive Two-Stage Unscented Kalman Filter (ATSUKF) is proposed to solve the fault diagnosis problem of the IMU. The simulation experiment compares the IMU fault diagnosis performance of OTSUKF and ATSUKF, and verifies the effectiveness of the proposed adaptive method.

Cardinality estimation is an important component of query optimization. Its accuracy and efficiency directly decide effect of query optimization. Traditional cardinality estimation strategy is based on original table or sample to collect statistics, then inferring cardinality by collected statistics. It will be low-efficiency when handling big data; Statistics exist update latency and are gotten by inferring, which can not guarantee correctness; Some strategies can get the actual cardinality by executing some subqueries, but they do not keep the result, leading to low efficiency of fetching statistics. Against these problems, this paper proposes a novel cardinality estimation strategy, called cardinality estimation based on query result(CEQR). For keeping correctness of cardinality, CEQR directly gets statistics from query results, which is not related with data size; we build a cardinality table to store the statistics of basic tables and middle results under specific predicates. Cardinality table can provide cardinality services for subsequent queries, and we build a suit of rules to maintain cardinality table; To improve the efficiency of fetching statistics, we introduce the source aware strategy, which hashes cardinality item to appropriate cache. This paper gives the adaptability and deviation analytic of CEQR, and proves that CEQR is more efficient than traditional cardinality estimation strategy by experiments.

When the hypersonic vehicle is cruising, they face the aerothermoelastic, as well as the matching, unmatched uncertainty and the disturbance. This paper presents a control method of command filter Backstepping sliding in order to cope with the problems above. First, this method does the problem of calculation expands to Backstepping using the command filter. Then, introducing sliding control settles the matching problem and disturbance; Consideration to decline the shake of sliding control and deal with the unmatched uncertainty, using the idea of higher order sliding mode control, this paper augments an addition virtual control state equation in each control loop and makes the output of the control system act on an integral, which not only declines the shake of sliding control, but also deals with the unmatched uncertainty using adaptive Backstepping. However, introducing an additional virtual control equation of state increases the system order, which has a growing in complexity when using the adaptive Backstepping to design the control system. Nevertheless the problem could be resolved by the method of dynamic surface. At last, the global stability of the proposed method is proved in theory and the effectiveness and robust of this method are verified by the simulation.

Aiming at the problem that the edge preserving effect of the existing improved bilateral filtering algorithm in the process of dealing with the haze weather traffic images is not ideal, and the algorithm is time consuming and computationally inefficient, which is unacceptable in the traffic video defogging. Thus, this paper proposed a new algorithm based on median guide filter for this target. In the full paper, we explain in some detail about median guide filter; in this abstract, we just add some pertinent remarks to listing the title of each section of this paper. The six sections of this paper are:introduction(section 0), traffic image degradation model in fog and haze weather(section 1), guide filter(section 2), the design of median guide filter(section 3), Optimization of transmittance estimation(section 4), experimental result(section 5), conclusion(section 6). In section 2, we introduce the basic principle of guide filter. In section 3, we propose median guide filter and the formulas of algorithm are deduced and point out the deficiency of the traditional algorithm. In section 4, we introduce a simple method to optimize transmittance estimation. In section 5, In order to verify the effect of defog, we perform a simulation on MATLAB, the analyze of simulation results, indicates that our median guide filter algorithm can not only guarantee the effectiveness of defog, but also ensure the efficiency of calculation.

Natural and convenient physiological health monitoring and the technology of health information sharing has important practical value in preventing the occurrence of disease and enhancing people’s health level, this paper combines physiological health monitoring technology and mobile application technology, studying the mobile application of collaborative physiological health monitoring and sharing technology. Heart rate monitoring as a breakthrough point of health monitoring, this paper studies non-contact heart rate monitoring technology based on visible light and constructs the physiological signs network for group users. Finally, combined with mobile terminals and social networking platforms, the paper achieves natural and convenient physiological health monitoring and community health information sharing for network relationship support.

The electric motor drive load simulator can simulate the hinge torque of the steering gear during the flight of the aircraft, which is one of the key equipment of the servo test. This paper bases on the composition and working principle of the electric load simulator, and based on the Narendra model reference adaptive control theory, the reference model of the controlled object is designed. Considering the large step disturbance of steer gear, the load simulator system the paper presented is simulated. The results show that the model can increase the bandwidth of the load system from 20 Hz to 30 Hz, realize fast, high-precision control of the electric load simulator, and improve the system's anti-perturbation performance effectively.

Complex subcarrier modulation signal, is a kind of BOC signal which is used complex exponential function as subcarrier, has been widely applied in GNSS(Global Navigation satellite system). The existence of multiple correlation peaks of the autocorrelation function of the complex subcarrier signal, will cause the acquisition error and tracking to the side peak when the signal is synchronized. To solve this problem, an unambiguous combined tracking loop for complex subcarrier signals is proposed. In this loop architecture, the main loop completes the coarse synchronization with BPSK-like algorithm, and the assisted loop realizes the accurate tracking together main loop. In this paper, the dual estimate tracking algorithm and the side-peak cancellation scheme are employed respectively in the assisted loop. The performance of tracking accuracy and anti-multipath capability of the proposed architecture is analyzed, and the practicability is verified by using the actual collected data. Simulation and experimental results show that the tracking structure proposed in this paper is practical and has good performance of anti-noise and anti-multipath.

Mobile navigation service is an important and popular location-based services, which help to recommend routes for mobile users to their destinations. Modern mobile navigation service provides various navigation strategies and considers many complex situations, which make validation and verification of mobile navigation service becomes very difficult. In this paper, we present an approach to prioritize test cases for regression testing of mobile navigation Service. The approach is based on the assumption that there may be a failure if the user's actual route deviates from the navigation recommended route. In this paper, we analyze the mass mobile navigation logs, compare the recommended routes with the user travel routes, identify the deviation of the intersection point, and then priority regression test data by deviation. To evaluate our approach, we conduct a case study on a popular navigation software. By compare proposed prioritizing test approach with random test approach, the approach helps to improve test efficiency.

The traditional underwater sonar system usually achieve high angle resolution by increasing array aperture and the number of array elements, but this method will inevitably lead to complex system and high cost. Given that big brown bats have obtained surprisingly high resolution using a simple system, this paper proposes a bionic target localization method. First, a range-azimuth joint dictionary was constructed based on the bionic system of multi-harmonic emission and double random array reception. Then, the coherence characteristic of the dictionary was analyzed and the range and azimuth of the target were estimated, and at last the experimental verification was completed. The results show that the bionic range-azimuth joint estimation based on sparse signal representation can achieve high-precision target localization under the condition of echo high aliasing.

To solve the combat task assignment of reconnaissance unmanned aerial vehicle (RUAV)/unmanned combat aerial vehicle(UCAV), this paper proposed an efficient task assignment method that takes into account the expected destruction probability of target. This method improves the utility function and constraint of the model that based on the goal of destroying the total sum of the target value. The adjustment factor is added to the model to achieve a balanced distribution of RUAVs/UCAVs resources; the expected destruction probability of target is introduced as a constraint to prevent the excessive distribution of RUAVs/UCAVs resources. Subsequently, a greedy algorithm based on maximizing marginal-return is designed to solve the proposed model. The simulation results show that the improved algorithm not only meets the combat effectiveness but also improves the economic performance on the basis of real-time task allocation.

The parameter identification of a non-cooperative target is a key problem of on-orbit servicing. Due to poor priori information about the motion and structure of non-cooperative targets, the traditional cooperative estimation architecture is inadequate. In addition, the existing method based on single observation spacecraft is difficult to ensure a good observation state for a long time. This paper proposes a method for observing and estimating attitude kinematic state of a non-cooperative space target based on multi-spacecrafts cooperative observation. The space target's 3D point cloud data is reconstructed based on the data with different viewpoints. The target's attitude change is calculated based on the 3D point cloud matching, and the kinematics information is estimated via EKF filtering. The simulation results demonstrate the efficiency of the proposed approach. As the proposed method does not require priori model, structural information or accurate motion information of the tumbling non-cooperative target, it can avoid the influence of occlusion or date missing in space observation, it is suitable for space on-orbit servicing with non-cooperative target in future.

Considering the precise landing demand for carrier-based aircraft flight control, this paper proposes an adaptive fuzzy landing control method for the strong time-varying, parameter uncertainty and various complex coupling in the actual state aircraft model. This method is applied to the flap channel to achieve direct lift control, and the fuzzy system is utilized to approximate the six-degree-of-freedom nonlinear system model of a carrier aircraft that is difficult to accurately describe, to achieve accurate tracking of the landing glideslope, and improve the landing accuracy. Lyapunov method was used to judge the stability of the adaptive fuzzy control algorithm. During the simulation, the airwake and deck motion disturbance were introduced to simulate the landing environment of the aircraft. The effectiveness of the landing control system was verified by the Matlab software. Monte-Carlo random test was utilized to carry out the landing point accuracy for the conventional control scheme and the adaptive fuzzy direct lift control scheme respectively. Through the response curve and landing point statistical results, it is confirmed that the direct lift control scheme has better control effect, and the landing precision has improvement compared with the conventional control scheme.

Walker constellation is the most effective constellation for global coverage and is often used for earth observation, navigation and internet communication. A scenario that several faulted satellites in a Walker constellation lead to its performance degradation can be quickly repaired to a certain extent by reconstructing the on-orbit satellite configuration. Different from the classical strategies such as adjusting the phase of the adjacent satellites, uniform the on-satellites' phase and adjusting the plane of the adjacent satellites, this paper proposes a bionic reconstruction method which uses the elastic mechanics method of thin film plate to generate the satellite maneuver strategy in the constellation reconstruction process, minimizes the performance degradation between the repaired configuration and the previous configuration. Thus, the maneuver strategy of each satellite can be calculated in reversely. The simulation example shows that the maneuver strategy by the bionic reconstruction method is more harmonious and natural than the classical strategies.

In future naval warfare, the accurate attack of underwater intelligent vehicles on targets will become more and more important. Therefore, it is very important to study the guiding method which has the key influence on tracking targets. In the guidance process of intelligent navigation system, the estimation of guidance information by the agent is influenced by confrontation and interference, which often results in the decrease of the guidance accuracy of UUV under the single guidance law. In this paper, the estimated information is classified according to the different types of guidance target information combining to the advantages of three guidance methods, namely fixed advance angle, extended proportional guidance law and adaptive sliding mode guidance law. Then the fuzzy weight coefficient calculation method is used to get the weight coefficients of all kinds of information to form the adaptive weighted guidance law. The target tracking effects of each guidance method is simulated and compared in the simulation environment of the target maneuver and the target non-maneuver respectively.

Data processing can be roughly divided into two categories, online transaction processing OLTP(on-line transaction processing) and online analytical processing OLAP(on-line analytical processing). OLTP is the main application of traditional relational databases, and it is some basic daily transaction processing, such as bank pipeline transactions and so on. OLAP is the main application of the data warehouse system, it supports some more complex data analysis operations, focuses on decision support, and provides popular and intuitive analysis results. As the amount of data processed by enterprises continues to increase, distributed databases have gradually replaced stand-alone databases and become the mainstream of applications. However, the current business supported by distributed databases is mainly based on OLTP applications, lacking OLAP implementation. This paper proposes an implementation method of HTAP for distributed database CBase, which provides an implementation method of OLAP analysis for CBase, and can easily deal with data analysis of large amounts of data.

Spaceborne SAR(synthetic aperture radar) imaging requires real-time processing of enormous amount of input data with limited power consumption. Designing advanced heterogeneous array processors is an effective way to meet the requirements of power constraints and real-time processing of application systems. To design an efficient SAR imaging processor, the on-chip data organization structure and access strategy are of critical importance. Taking the typical SAR imaging algorithm-chirp scaling algorithm-as the targeted algorithm, this paper analyzes the characteristics of each calculation stage engaged in the SAR imaging process, and extracts the data flow model of SAR imaging, and proposes a storage strategy of cross-region cross-placement and data sorting synchronization execution to ensure FFT/IFFT calculation pipelining parallel operation. The memory wall problem can be alleviated through on-chip multi-level data buffer structure, ensuring the sufficient data providing of the imaging calculation pipeline. Based on this memory organization and access strategy, the SAR imaging pipeline process that effectively supports FFT/IFFT and phase compensation operations is therefore optimized. The processor based on this storage strategy can realize the throughput of up to 115.2 GOPS, and the energy efficiency of up to 254 GOPS/W can be achieved by implementing 65 nm technology. Compared with conventional CPU+GPU acceleration solutions, the performance to power consumption ratio is increased by 63.4 times. The proposed architecture can not only improve the real-time performance, but also reduces the design complexity of the SAR imaging system, which facilitates excellent performance in tailoring and scalability, satisfying the practical needs of different SAR imaging platforms.

Distributed electric propulsion technology brings new ideas to the design of unmanned aerial vehicle(UAV), such as improving aerodynamic efficiency and propulsive efficiency, and new concept of vertical/short takeoff and landing configurations. However, compared with conventional UAV, the propulsion system of distributed electric propulsion UAV is more complex, which brings difficulties and challenges to the design of distributed electric propulsion UAV. Based on its special aerodynamic/propulsive coupling characteristics, this paper studies the design method and process of primary parameters of distributed electric propulsion UAV. A short takeoff and landing UAV with distributed electric propulsion system is taken as an example for the conceptual design and primary parameter design, and the influence of design parameters on the takeoff mass and endurance is analyzed. Finally, the validity of the established design method is verified by the flight test of the prototype. Results indicate that the distributed electric propulsion system accounts for more than 20% of the takeoff mass; the electric ducted fan efficiency, mass specific power of the motor, mass specific power of the electronic speed controller and the resistivity of power wires are the most significant design parameters that affect the performance of the UAV; with the improvement of technologies, the takeoff mass is expected to be reduced by more than 20%, and the endurance is expected to be increased by more than three times.

DC-DC converter is the core component of power conversion module of integrated modular avionics. Condition monitoring and fault diagnosis of DC-DC converter can effectively improve the reliability of avionics equipment, reduce the maintenance cost and greatly improve the use efficiency of aircraft. In this paper, firstly, a typical DC-DC converter model based on SEPIC topology is designed in PSPICE environment, and the failure modes of DC-DC converter are analyzed. Secondly, the typical fault types of DC-DC converter are simulated, and the corresponding original data are obtained through simulation. Finally, the processing framework including data preprocessing, feature extraction and selection, and multi model fusion is used to do fault classification of the DC-DC converter. The fault diagnosis of the converter is simulated. Simulation results show the effectiveness of the proposed method.

During the blisk side milling, the blade is thin and the tool suspension length is large, which is easy to occur milling system chatter. The wavy surface left by chatter has great effect on the surface roughness and service performance of the aero-engine blisk. To accurately predict the stability of the blisk side milling, the influence of random variables on the machining stability was considered. In this paper, the chatter reliability model of the blisk side milling system was established using the first-order second-moment method. The model takes structural parameters and spindle speed as random variables and chatter frequency as intermediate variables. The chatter reliability model was used to draw the reliability lobe diagram, which can be used to divide the machining reliability area. The effectiveness of this method was verified by experiments and compared with Monte Carlo method. The validation results showed that the reliability lobe diagram can be used to determine the stability probability prediction of blisk side milling and can replace the traditional stability lobe diagram.

In this paper, a novel multi-stage trajectory transfer and fixed-point landing time optimal guidance method for the lunar surface emergency rescue mission is proposed. Firstly, the whole process motion and dynamics model for the lunar surface emergency rescue with four stages are established. Then, in the initial orbit transfer phase, the Lambert algorithm based on "prediction + correction" is designed for the non spherical gravitational perturbation of the moon. In the powered descent phase, the Hamiltonian function is used to design a time suboptimal explicit guidance law that can be applied in orbit in real time. Finally, aiming at the multi-stage global time optimal guidance, the whole time process guidance law is obtained by establishing the allowable control set for each stage in the whole process. The simulation results show that compared with the piecewise optimal control method, the present method has better optimization effect and shorter whole process time. It is of great significance to the possible emergency rescue mission of manned lunar exploration in the future.

Aiming at the problem that the current WSoS mission modeling and analysis methods are difficult to describe the property of multi-state of each equipment system, combining with Petri net and Markov process, an O-PPN based mission thread modeling and analysis method is proposed in this paper. Based on the definition of WSoS, the mission of WSoS is formally described. To improve the traditional Petri net, an operation performance Petri net (O-PPN) is constructed, in order to meet the modeling needs of the general mission thread of the WSoS; and aiming at the problems of the performance degradation of the equipment system and the possible "ahead of time" state, the performance degradation model of the equipment system based on Markov is constructed. The corresponding state probability set is obtained by inputting the reliability parameters of each equipment system. And according to the requirements of each operation in the mission thread for the performance level of each equipment system and combined with the mission success and failure criteria of the combat task and operation, the mission success probability of the whole WSoS is calculated. Finally, the feasibility of the proposed method is verified by the simulation of the case.

Aiming at the issue of incomplete trajectories in the 2D epipolar image of circular light field, this paper proposes a 3D reconstruction method by using 3D Hough transformation. This method computes 3D point clouds by computing the parameters of feature trajectories in 3D image volume. By analyzing the 3D distribution of circular light field trajectories, binary curves in image volume are extracted, and their local orientation are further estimated by the 3D structure tensor. The 3D Hough space generation and the parameter selection method are proposed to the 3D curves detection. The parameters of these curves are converted to 3D point clouds on each view and then merged to final 3D reconstruction. The ambiguity of Hough transformation solution on 2D epipolar image is overcome by the 3D analyzing method. The experiments are carried out on both synthetic and real datasets. The experiment results show that this method can improve the reconstruction performance compared with the state-of-the-art in circular light field.

Aircraft lug joint is the key part of load transfer. In order to improve the safety of lug joint, on the premise of meeting the design requirements of static strength and fatigue, the composite connection lug structure design technology of different metal materials is proposed in this paper. Firstly, the damage safety design and life reliability analysis of the lug structure are studied theoretically. Secondly, based on the concept of damage safety design and the design principle of deformation coordination, the design method of composite connection lug with deformation coordination is proposed, and the thickness ratio of single ear is 0.8:1:0.8. Finally, the reliability of the composite lug is analyzed. The results show that the structural design scheme of aluminum-titanium composite ear piece can meet the requirements of static strength and damage tolerance, and compared with the conventional ear structure, the failure probability of structure mission life is greatly reduced when the weight of the composite connection lug is only increased by 4.9%. The proposed method can effectively guide the structural design of composite ear piece.

The feature image code represented by the two-dimensional code is the key reference for global positioning in the visual navigation of mobile robots. Although reducing the acquired low-resolution image helps to reduce the real-time performance of the algorithm, the acquired feature image is more susceptible to motion blur-based interference and affects the accuracy of recognition, which causes the positioning failure of the whole multi-intelligence, in which the body control system is invalid. In this paper, an optimized low-resolution feature image code recognition method is proposed. In the preprocessing part, the characteristic image is converted into the characteristic signal matrix of Hu invariant moments, and then the characteristic image is added to the characteristic signal matrix as a characteristic component, and then the Hu-DBN neural network signal classifier is used to construct the signal matrix so as to achieve accurate recognition of low-resolution custom image signature images under high motion tolerance conditions. It not only avoids the problem of classical pattern recognition relying on model experience and poor adaptability of the scene, but also avoids the problem of high computational complexity and recognition efficiency of directly deep learning methods such as YOLO. The deployment of the mobile robot instance deployment test shows that the average recognition rate is of 96.3% at a resolution of 640×480@Pixs and motion speed of 0.5 m/s, which proves the effectiveness of the present method.

In order to solve the fault detection problem of flush air data sensing (FADS), an advanced airborne sensor, a new method is proposed in this paper. First, the high-precision FADS model is established on the basis of the database obtained from the CFD software and aerodynamics knowledge. Then, the distribution characteristics of each group of signals under fault condition are derived through strict formulas. Meanwhile, the threshold of alarm times is designed with statistical knowledge. For verifying the effectiveness of the newly proposed method, a comparison with other two widely adopted methods, including the methods based on parity equation and Chi-square χ2 distribution, is conducted under different measurement noise. Simulation results show that the proposed fault detection method for FADS possess higher accuracy and stronger anti-interference.

The method of using unmanned aerial vehicle (UAV) to obtain aerial image information of target scene has the characteristics of wide coverage, strong mobility and high efficiency, which is widely used in urban traffic monitoring, vehicle detection, oil pipeline inspection, regional survey and other aspects. Aiming at the difficulties of the object to be detected in the process of aerial image object detection, such as multiple orientations, small image pixel size and UAV body vibration interference, a novel aerial image object detection model based on the rotation-invariant deep denoising auto encoder is proposed in this paper. Firstly, the interest region of the aerial image is extracted by the selective search method, and the radial gradient of interest region is calculated. Then, the rotation invariant feature descriptor is obtained from the radial gradient feature, and the noise in the original data is filtered out by the deep denoising automatic encoder and the deep feature of the feature descriptors is extracted. Finally, the experimental results show that this method can achieve high accuracy for aerial image target detection and has good rotation invariance.

Because the evaluation of the location performance of AUVs with the lower bound of the Cramer-Rao inequality is not intuitive, the geometric interpretation method is proposed based on geometric ellipse. The Fisher information matrix is used to replace the Cramer-Rao inequality. The priori information matrix and the measurement information matrix are synthesized into the posterior information matrix through singular value decomposition. The geometric ellipse is used to geometrically represent the posterior information matrix. The posterior information ellipse area is used to establish the location performance evaluation function. By analyzing the performance evaluation function, the optimal formation configurations of single-master, dual-master and three-master AUVs are designed. The implementation of the special formation configuration for the three-master AUV formation proposed in the paper is easier, while the location performance of the optimal formation configuration is not much inferior. The simulation results verify that the optimal formation configuration has a higher location accuracy and that the special formation configuration is effective.

The initial satellite telemetry data acquired by ground stations usually contain noise and outlier interference. In order to ensure the accurate analysis of satellite status, the telemetry data need to be filtered. In this paper, a sliding window optimal tracking differentiator filtering (SWOTDF) method for satellite telemetry data is proposed. Aiming at the problem of parameter selection during the filtering of the optimal tracking differentiator, the amplitude-frequency characteristics of the maximum tracking differentiator are analyzed by sine sweep frequency method, and the mapping relationship between tracking factors and filtering effects is established. On this basis, the telemetry data are divided by sliding windows, and the relationship between local stability of data in each window and tracking factors is further analyzed. The calculation method of local data tracking factor is given to realize dynamic optimal tracking differentiator filtering of telemetry data in each window. Experimental results show that the SWOTDF method can effectively avoid the limitations of traditional digital filters in processing nonlinear telemetry data, and can effectively filter out noise and outliers in satellite telemetry data.

In this paper, the full strap-down seeker of rotating bomb is taken as the research object, and the method of extracting the LOS (line-of-sight) angle and angular rate of the full strap-down seeker of the rotating bomb is studied. The structure of the full strap-down seeker is quite different from that of the conventional rate gyro seeker. The measurement system of full strap-down seeker is fixed to the missile, the seeker can only obtain the measurement information in the projectile coordinate system, and the measurement information is coupled with the body posture information, so it cannot be directly used for the control guidance of the rotating projectile. First, based on the conversion relationship between coordinate systems, the mathematical model of the inertial LOS angle of the rotating bomb is established, and the mathematical model of the extraction of the inertial LOS angle and angular rate of the rotating bomb is further established. Then, the Kalman filter is designed by using the unscented Kalman filter method (UKF), and the extracted LOS angle containing noise information is filtered. Finally, the mathematical simulation is carried out to verify the validity of the mathematical model of LOS angle and angular rate extraction. Compared with the Extended Kalman filter method (EKF), the UKF has a higher accuracy for estimating the navigation information of the full strap-down rotating projectile.

Currently developed underwater gliders can be roughly divided into the two types:traditional configuration and unconventional configuration. As a type of underwater gliders with unconventional configuration, a blended-wing-body (BWB) underwater glider has better fluid dynamic performances because of its unique shape. However, it is difficult to design the shape of the BWB underwater glider that has excellent hydrodynamic performances. Therefore, it is of great significance to optimize its shape, which this paper carries out by using the free-form deformation (FFD). The complete and automatic shape optimization framework is established by jointly using FFD parameterization method, CFD solver, optimization algorithm and mesh deformation method. The framework is used to optimize the shape of a BWB underwater glider. The average drag coefficient of the BWB underwater glider during its sinking and floating in one working period is used as the objective function to optimize its shape, with the volume constraints considered. The optimization results show that the gliding performance of the BWB underwater glider is remarkably enhanced.

Under the modern high-tech condition, the demand of air combat has been stricter on the combat performance and the integration of fighter, which impels the application of integrated vehicle management technology and makes it become the core technology of the advanced fighter about the mission management and vehicle management. From an overview of tactical flight management system's technical background and the state of development, this paper systematically introduces the issues, including the development process & history, architecture framework of the integrated vehicle management system (IVMS) which divided into perception layers, decision layer and execution layer, and function system architecture and software system architecture. Through a comprehensive analysis for the functions of each level, comprehensively expounds the basic function and key technology of IVMS which included situation awareness and assessment, mission path planning, integrated navigation, automatic attack guidance, health management and prognostic and intelligent pilot-vehicle interface, and the future development directions are also discussed.

Feature selection is an essential process in the identification task because the irrelevant and redundant features contained in the unselected feature set can reduce both the performance and efficiency of recognition. However, when identifying the underwater targets based on their radiated noise, the diversity of targets, and the complexity of underwater acoustic channels introduce various complex relationships among the extracted acoustic features. For this problem, this paper employs the normalized maximum information coefficient (NMIC) to measure the correlations between features and categories and the redundancy among different features and further proposes an NMIC based feature selection method (NMIC-FS). Then, on the real-world dataset, the average classification accuracy estimated by models such as random forest and support vector machine is used to evaluate the performance of the NMIC-FS. The analysis results show that the feature subset obtained by NMIC-FS can achieve higher classification accuracy in a shorter time than that without selection. Compared with correlation-based feature selection, laplacian score, and lasso methods, the NMIC-FS improves the classification accuracy faster in the process of feature selection and requires the least acoustic features to obtain classification accuracy comparable to that of the full feature set.

Discretization of multidimensional attributes can improve the training speed and accuracy of machine learning algorithm. At present, the discretization algorithms perform at a lower level, and most of them are single attribute discretization algorithm, ignoring the potential association between attributes. Based on this, we proposed a discretization algorithm based on forest optimization and rough set (FORDA) in this paper. To solve the problem of discretization of multi-dimensional attributes, the algorithm designs the appropriate value function according to the variable precision rough set theory, and then constructs the forest optimization network and iteratively searches for the optimal subset of breakpoints. The experimental results on the UCI datasets show that:compared with the current mainstream discretization algorithms, the algorithm can avoid local optimization, significantly improve the classification accuracy of the SVM classifier, and its discretization performance is better, which verifies the effectiveness of the algorithm.

When the target is tracked based on the current traditional reference point guidance method by standoff mode, if the angle between the relative speed of the UAV and the target and the tangential direction of the corresponding point on the circular expected flight path of UAV is close to 90 degrees, and UAV is close to the desired path, the efficiency of the UAV converging to the circular desired path is not high enough. In this paper, the small angle hypothesis for reference point guidance method tracking desired circular path is studied. According to this, we designed a maneuvering entry guiding strategy for UAV standoff tracking fixed/moving target based on reference point guidance method. Simulation results show effectiveness of the guidance strategy. UAV might quickly converge to the circular desired path.

The autonomous security situation awareness on industrial networks communication has been a critical subject for industrial networks security analysis. In this paper, a CNN-based feature mining method for networks communication dataflow was proposed to intrusion detect industrial networks to extract security situation awareness. Specifically, a normalization technique uniforming different sorts of networks dataflow features was designed for dataflow features fusion in the proposed feature mining method. The proposed methods were used to detect the security situation of traditional IT networks and industrial control networks. Experiment results showed that the proposed feature analysis method had good transferability in the two network data, and the accuracy rate of network anomaly detection was ideal and had higher stability.