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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.