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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.

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.

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.

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.

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.

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.

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.

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.

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.

In order to guarantee the QoS requirements of multiple services in airborne all-domain heterogeneous and flexible networks, we propose a novel multi-priority and multi-path based QoS routing (MP2R) protocol in this paper. In the protocol, in terms of the route effectiveness and reliability, the route cost penalty function is constructed based on the multi-priority M/M/1 queueingqueuing system with the preemptive-resume policy. The minimum value of the function is derived through the optimization theory, and the optimum routing solution is acquired. The simulation results show that the MP2R protocol not only has the capabilities of differentiation services for different kinds of traffic and QoS provision, but also can utilize the network resource rationally, avoid congestion, achieve the load balancing, and meet the requirements of airborne all-domain heterogeneous and flexible networks effectively.

Rapid evaluation of aerodynamic forces is a key technology in conceptual and preliminary performance assessment and design optimization for supersonic/hypersonic vehicles, which is expected to achieve a balance between accuracy and time. In this paper, the preliminary evaluation platform of aerodynamic forces is built based on the physical theories and engineering methods. This platform computes the local velocity vector by using the streamline tracing technology, and extracts the pressure and friction coefficients by calling methods in method database. Furthermore, an example based on HL-20 is brought out. A good agreement is obtained by comparing the calculated results via CFD with the experimental. Also, a good angle of attack compatibility of streamline tracing technology is concluded. This platform has a computation time less than that via CFD and an flight range greater than that via wind tunnel. Finally, the simple flight dynamics simulation is taken out and the synergetic process is verified.

Aim. MRO2 system is a data management platform. It has the ability to manage and store all kinds of data in the product's lifecycle, that is both the mass storage capacity and the scalability are required. For the existing big-data stores for MRO2 systemeither only focus on the storage problem, or only do the scalability issue. In this paper, a two-layer data management model is proposed, in which the top layer uses the memory storage for scalability and the bottom layer uses the distributed key-value storage for mass storage. By adding a middle layer of the key group between the application and the KV storage system, the keys for real-time processing are combined to cache in a node. It satisfies the characteristics of the real-time application and improves the dynamic scalability. The present protocol of the dynamic key groups for real-time distributed computation for MRO2 system is explained in detail. And then the protocol for creating and deleting key groups is introduced. The third topic is an implement of a big-data store for supporting MRO2 system. In this topic, the delay times for creating and deleting the dynamic transaction groups to estimation are used. Finally, the experiments to appraise the present method are done. The response time of the present method is quite efficient in comparison with the other methods to be used inbig data storage systems.

In underwater acoustic target recognition, the target signal is usually complex and the samples which are difficult to obtain also have some uncertain information. In order to effectively solve these problems, the evidence clustering recognition algorithm (TECRA) is presented. In this new method, the k-nearest neighbor are first determined by using the feature distance between the object and its neighbors in each class of the training set, and a reasonable initial basic belief assignments (bba's) for each target data are constructed by the improved k-nearest neighbor classification algorithm. Then the final global bba's of the target is obtained by optimizing the objective function of the algorithm. Finally the object can be recognized by the fusion result and the classification rule presented in the paper. Several experiments based on real underwater acoustic data sets are made to test the effectiveness of TECRA in comparison with some other methods. The results indicate that TECRA can effectively improve the recognition accuracy.