Chinese airports usually use cement concrete road slabs with a slab width of no more than 5 m, and existing airports in other countries use cement concrete slabs with a width more than 6 m. Small size cement concrete pavement slabs are more likely to suffer from disease than large size. In order to study the effect of pavement slab size on pavement performance, a finite element model of pavement structure was established based on the elastic layered theoretical system. By changing the size of the pavement slab, the stress changes of the pavement slab under the combined action of aircraft load and temperature gradient are studied. The internal stress change of the slab is used to analyze the influence of the plane size of the pavement slab on the service life of the pavement slab under the combined action of temperature gradient and aircraft load through an example of real airport. The results show that the internal stress of the pavement slab is greatly affected by the temperature gradient. Under the action of common aircraft load, more than 25% of the internal tensile stress of the pavement slab in summer is caused by the temperature gradient load; under the same load, the damage of different sizes of pavement slabs is different, and choosing the appropriate pavement size can significantly increase the expected service life of the cement concrete pavement.

The existing flight operation quality assurance (FOQA) standard only uses the integral distance of the ground speed to define the long touchdown exceedance (LTE), which cannot detect and explain the exceedance using multiple quick access recorder (QAR) parameters. The QAR samples with multiple parameters were segmented by the sliding window, and the segmented sample sets were generated according to the segmentation position. The representation of the QAR sample segmentation and the vector within each QAR segment was obtained by the long short-term memory (LSTM) networks autoencoder, and the representation vectors were clustered by K-means to realize the symbolization of the QAR samples and the QAR segments. The hidden Markov model (HMM) model was built by using the symbolic sequence of the QAR sample set of the normal flights, which was used to detect the flights with the LTE. The second HMM model was constructed from the symbolic sequences of the segment of the normal QAR samples and the QAR samples including the LTE. Then the Viterbi algorithm was used to determine the specific positon of the LTE in the QAR sample segment. Experimental results on real QAR data sets show that, compared with other multi-dimensional time series anomaly detection methods, the proposed method can not only effectively detect the LTE, but also obtain the outliers of multiple QAR parameters, which can assist domain experts to analyze the cause of the exceedance.

In view of the important influence of void defects on the mechanical properties of materials, the off-axis tensile mechanical properties of 3D five-directional braided composites with voids are studied by introducing randomly distributed void defects into the meso-scale model of material. For two specimens with typical braided angles, the effect of void contents on the on-axis mechanical properties was discussed and the appropriate void contents were determined by comparing with the available experimental data. Based on the periodic boundary conditions, the off-axis load was applied to obtain the stress-strain curves of the material under different off-axis angles and the strength properties were thus predicted. The meso-scale damage initiation and evolution processes of the composites under typical off-axis angles were simulated and the failure mechanism was analyzed in detail, which provides a proper reference for the numerical analysis of void defects and off-axis loading problems of other composite structures.

To standardize the plan generation of craniosynostosis surgery, we propose a skull cutting trajectory generation method which combines deep learning, stereo vision and point cloud processing technology to establish cutting plan template library and generate cutting trajectory for new cases. The proposed method, for the first time, uses the deep learning to segment the external surface of skull. First, it takes the advantage of Mask R-CNN to detect and segment the surgical cutting area. Then, a simplified contour extraction algorithm is explored to extract the surgical cutting trajectory. After that, the point cloud processing technology is used to map the surgical cutting trajectory to three-dimensional coordinates and realize the automatic extraction of cutting trajectory. Finally, a template library with typical cases is established and the cutting plans of new cases are automatically generated by template matching method. The experiment shows that the proposed trajectory extraction method can detect the skull surgical cutting trajectory accurately and efficiently and conduct three-dimensional mapping of trajectory coordinate.The depth measurement error of point cloud is less than 3 mm, which meets the clinical available standard. Using the template matching method, the new case's cutting trajectory can be generated effectively, which conforms to surgical plan of senior doctors.

With the rapid development of artificial intelligence technology, the moving target tracking technology for the power system has gradually attracted researchers' attention. Although the existing methods have achieved great success, most of them are based on the fixed camera surveillance video recording, which cannot track the moving target flexibly. When the moving object leaves the camera's field of view, there is a problem of losing the moving object. In light of this, we propose a moving target tracking scheme based on UAV vision (MTTS_UAV) in electric scenario. In particular, to ensure the real-time feature, we combine the improved target tracking algorithm and target detection algorithm to track the hidden dangers. To ensure the accuracy, we introduce two UAV flight control modules: heuristic mode and data-driven mode, so that the UAV's flight speed and direction can be adjusted adaptively according to the target movement. Extensive experiments have been conducted on a real-world dataset of hardhat personnel in real substations, which demonstrate that the average pixel error (APE) and average overlap rate (AOR) are 2.37 and 0.67 respectively and verify the effectiveness of the proposed method.

Automated fiber placement is an advanced automatic manufacturing technology for composite materials, which is widely used in manufacturing composite components with complex shapes. However, the compaction quality of the fiber in the area of large curvature is poor. In addition, it is difficult to satisfy the directional deviation and the turning radius of the placement path at the same time, resulting in various placement defects. In this paper, the influence of surface curvature on path performance is studied and a new path planning method for complex surfaces with area partition is established. The distance between roller and the mold surface is used to estimate the fiber compaction quality and the fiber number placed simultaneously can be calculated by the surface curvature. The local direction deviation and steering radius are evaluated during the path generation process, and a new sector is partitioned when the evaluation results are out of constraints. The method fully considers the influence of compaction quality, direction deviation and turning radius on paving path, so that the placement quality and efficiency of the entire curved surface can be ensured. Simulations and experiments are completed on the winglet surface to verify the path planning method, and the results show that, by the proposed method, satisfactory component placement quality can be obtained on complex surface.

The stability of fuel assembly transfer system and the reliability of system structure are very important to ensure the safety of fuel assembly in nuclear power plant. In order to obtain the vibration of system during the running process, the dynamic model of coupling system of the beam-truck+link-mass+spring+damping was established. Considering the effect of water, the nonlinear differential equation of the system is derived. The response of the system under different traveling speeds of truck was calculated by numerical algorithm. After comparison and analysis, it is considered that the smaller the truck speed is, the weaker the system vibration is and the more stable it is; due to the static deformation, even when the speed is very low, the basket will still bear a certain lateral force, so it is necessary to pay attention to the strength of the weak part of the basket; the position near the guide rail support is the dangerous position for the basket, and monitoring points can be set here if necessary to ensure the safety of the fuel assembly.

The U-K theory provides a new concept for obtaining the explicit dynamic equation of constraint multibody system. However, one consequence of the numerical approximation and truncation error is the constraint violation of the dynamic equation at the position and velocity level. Baumgarte's constraint violation stability methods (BSM) provide a stable dynamic equation by constraint modification. Nevertheless, the selection of Baumgarte parameters usually involve a trial-and-error process, which may result in the failure of simulation results. Consequently, the Baumgarte parameters selection problem is studied by using the classical fourth-order Runge-Kutta method, and the explicit dynamic equation of robot system based on the modified U-K theory by BSM is established. Furthermore, the lower limb rehabilitation robot is taken as the research object for simulation analysis. The results show that the constraint violation can be effectively suppressed. The joint angle errors are controlled within the range of -5×10^-3(°)-5×10^-3(°), the joint angular velocity errors are controlled within the range of -2×10^-4 rad/s-2×10^-4 rad/s, and the operation trajectory of the robot end-effector can be well close to the predetermined target of the system.

Biomass for production of alternative jet fuel has a very important impact on global carbon reduction and the control of greenhouse gas emissions. Wide sources and large annual output of cellulosic biomass have become its significant advantages as a biomass raw material to produce aviation alternative fuels. Based on the latest research results of cellulosic biomass, the process parameters and yield of the key process units for the production of jet fuel precursor (furfural (FF), 5-hydroxymethylfurfural (5-HMF) and levulinic acid (LA)) from cellulosic biomass were studied in depth in this paper. Through Aspen Plus process simulation, the material flow and energy flow of FF and LA, FF and 5-HMF were studied and compared. The influence of different process parameters on the yield was obtained, and energy consumption analysis of the advantageous process flow was carried out, which provides a theoretical basis for increasing the yield of platform compounds and reducing energy consumption of production of alternative jet fuel from biomass.

based on strapdown inertial navigation system (SINS) and radio frequency identification (RFID), an integrated positioning method is proposed for the position acquisition of high-speed trains in satellite-denied environment such as tunnels. The positioning accuracy of RFID tags is calculated by the response time model. The tags could also calibrate the attitude by adding actual railway information. Setting RFID tags on tunnel wall and at the same time combining with inertial navigation system provide continuous dynamic positioning data. Simulation results show that utilizing RFID tags in position calibration significantly decreases the error accumulation of inertial navigation system and increases positioning accuracy in 30 km tunnel. After the addition of attitude information, the positioning accuracy of the whole tunnel railway maintains at the level of meters in a variety of combinations of gyroscope performance and RFID tag interval, the best of which is 0.5 m.

The data accuracy and density of the stratospheric horizontal wind field have direct influence on the trajectory prediction accuracy and flight control accuracy of the high-altitude balloon experiment. Focusing on stratospheric wind field, this paper puts forward an interpolation method based on geostrophic wind model. The method improves the wind field accuracy in low-latitude area and calculation speed of the geostrophic wind model by improving the formula of Coriolis frequency and using two-dimensional convolution. Meanwhile, the method reduces the influence of observation error on interpolation result by using iterative binary linear regression and an improved adaptive bias weight matrix for the observed field. The experimental results show that the proposed interpolation method can effectively improve the calculation speed of the interpolation of stratospheric wind field and the interpolation accuracy of stratospheric horizontal wind field.

Through the investigation of cross-modal retrieval, the use of attribute information can enhance the semantic representation of extracted features. The attributes of the pedestrian image and text are not used adequately in the existing cross-modal pedestrian Re-ID algorithms based on natural language. To tackle the above issues, a novel cross-modal pedestrian Re-ID algorithm based on dual attribute information is proposed. Specifically, the attribute information of the pedestrian image and the attribute information of pedestrian text descriptions are fully and simultaneously explored, and the dual attribute space is also built to improve the distinguishability and semantic expression of extracted image and text features. Extensive experimental results on a public cross-modal pedestrian Re-ID dataset CUHK-PEDES demonstrate that the proposed algorithm is comparable with state-of-the-art algorithm CMAAM (Top-1 56.68%), the retrieval accuracy Top-1 of the proposed algorithm reaches 56.42%, and Top-5 and Top-10 are improved by 0.45% and 0.29% respectively. Besides, the retrieval accuracy of cross-modal pedestrian images can be significantly improved if the class information is provided in the gallery image pool and is used to extract attribute features, and Top-1 can reach 64.88%. The ablation study also proves the importance of the text attribute and image attribute used by the proposed algorithm and the effectiveness of the dual attribute space.

Satellite-borne paraboloid antenna may vibrate during orbit operation due to attitude adjustment or environmental factors, thus reducing its performance. In order to reduce the influence of vibration, it is necessary to add tension rope to enhance the antenna stiffness. Therefore, a new multi-layer design method of tension rope is proposed. In this method, the paraboloid antenna is divided into multiple layers and the tension of each layer is adjusted to improve the structural stiffness of the antenna and reduce the shape error caused by the tension force as far as possible. In order to verify the effectiveness of the above method, the finite element model of the antenna is established. On this basis, the finite element analysis is carried out, and the structural stiffness and shape error under different tension parameters are studied. In order to improve the stiffness of the structure and reduce the shape error caused by the tension force, the tension parameters are optimized. In order to improve the efficiency of optimization calculation, the response surface method is used to establish a surrogate model to participate in the optimization iterative calculation. Non-inferior sorting genetic algorithm (NSGA-Ⅱ) is used to complete the iterative calculation. The optimized tension parameters further improve the performance of the antenna. This research can provide theoretical guidance for the design of truss deployable paraboloid antenna.

Micro air vehicles (MAV) have wide appications in both miltary and civilian fields, and flexible membrane wings are an effective method to improve the aerodynamic of MAV. In order to better control the flexible wing, the vibration and deformation characteristics of the flexible membrane wing and their impact on aerodynamic force are measured synchronously. Compared with the rigid wing, for the flexible membrane wing, the stall angle of attack is delayed by 6°, the maximum lift coefficient is increased by 47.4%, and the lift-drag ratio is increased by 17.8%. In addition to the characteristics of large amplitude and small static deformation at α=0°~2°, the amplitude of the periodic vibration of the flexible membrane wing undergoes a transition from no obvious crest, three crests to one crest as the angle of attack increases. When the lift coefficient is maximum, the corresponding membrane deformation and vibration amplitude both reach their maxima. Besides, the chordwise position of the maximum deformation changes with the angle of attack, which determines the pitching moment characteristics. Based on these results, an active control method of improving aerodynamic performance by applying deformation and vibration excitation with specific frequency is proposed.

In the operation status evaluation of smart electricity meters, it is often difficult to collect labeled data and the data distribution in different regions is inconsistent. To solve this problem, we introduce the joint distribution adaption (JDA) method in transfer learning in the field of operation status evaluation of smart electricity meters. This method tries to find an optimized transformation matrix to minimize the distance between edge distribution and conditional distribution in different regions in the transformed space. In order to solve the problem that there is no data label in the target domain when the conditional distribution adaptation is made, the JDA method uses pseudo-tag iteration method to make the target domain pseudo-label constantly approach the real tag. The classification model trained from the data in the post-transformation space can be applied to the new region to realize the transfer. The experimental results demonstrate the effectiveness of JDA method in the operation status evaluation of smart electricity meters.

In order to investigate the drag reduction mechanism of the base-cavity projectile, the flow field characteristics of M910 projectile are numerically simulated through the 3-dimensional steady CFD method. The zero-lift drag coefficient variation with Mach number are presented. The computational results have a good agreement with the experimental data. On this basis, a base cavity is introduced for M910 projectile that is named M910BC in this paper and then numerically simulated. The base flow field characteristics of the projectile with different base structures are compared and the drag reduction mechanism of the base cavity is analyzed. The results show that at subsonic speed, the drag reduction of the base-cavity projectile is found to be mainly due to the introduction of the high-pressure "dead zone" in base cavity and the displacement of the solid base with the compliant fluid boundary of the cavity base. Because of that, the forming location, shape and strength of the wake vortex are slightly changed. At transonic speed, the drag reduction effect of base cavity is vanished since the wake vortex is further from the base of projectile and the effects of the solid base and fluid boundary are the same. At supersonic speed, the drag reduction mechanism of the base cavity is that the mass of the recirculation region is increased by the flow of the base cavity, which is similar to the drag reduction mechanism of the base bleed projectile.

In order to explore the altimetry performance of GNSS-R code phase delay measurement with different modulation methods in GNSS LI band signals, a coastal dual-antenna GNSS-R ocean altimetry experiment was carried out, and the original intermediate frequency data with a sampling rate of 40 MHz was collected. The experimental data was processed using the self-developed GNSS-R altimetry software receiver and inversion software, and the GNSS-R ocean altimetry results of QZSS L1C/A code and L1C code were obtained simultaneously. Then we compare them with the measured values of the radar altimeter that were simultaneously observed coastally, and used to evaluate the accuracy of the GNSS-R inversion result. The experimental results show that the ocean altimetry accuracy based on L1C/A code and L1C code is about 0.63 m and 0.4 m, respectively. It can be seen that the GNSS-R measurement accuracy of the L1C code delay is significantly higher than that of the L1C/A code. In addition, the accuracy of GNSS-R will be improved as the satellite elevation increases gradually.

Aiming at the potential risk of hailstone impact on the safety of composite structures, a continuum damage mechanics based nonlinear finite element model was developed to study the mechanical behavior of carbon fiber composite laminates under high-velocity hailstone impact. The Lagrangian method and smoothed particle hydrodynamics (SPH) method were used together to model the impact of hailstone, and the equation of state of water was introduced to describe the flow characteristics of the hailstone after breaking. A rate-dependent constitutive model of unidirectional composite, as well as 3D Hashin failure criteria and material stiffness reduction rule, was applied to predict the in-plane damage in composite layers. Interface elements governed by bilinear cohesive model were employed to simulate the inter-laminar delamination phenomena induced by impact. A user material subroutine VUMAT was coded and implemented to obtain the numerical solution based on ABAQUS/Explicit solver. The transient process of composite laminates under hailstone impact was reproduced and the damage characteristics and failure mechanism were analyzed in detail. The effects of impact velocity and impact angle of hailstone on the impact properties of composite laminates are discussed, which provides proper reference for numerical investigation of hailstone impact problems in composite structures.

Blockchain data is growing linearly, eventually reaching a point where single node cannot provide sufficient storage capacity, and resulting in storage scalability problems. Although the light node model greatly reduces the demand for storage capacity, it also leads to a reduction in full nodes and decentralization, which threaten the security of blockchain networks. At present, there is no mature and scalable storage solution proposed for account-based blockchains. Considering that the access frequency of state data is much higher than that of block data, this paper proposes the scalable storage model for account-based blockchain (SSMAB). SSMAB saves state data in a completely redundant manner to guarantee its transaction verification function, stores block data via sharding storage to reduce redundancy, and adopts an economic incentive mechanism to ensure data availability while reducing storage consumption. Experiments show that SSMAB can ensure data reliability and availability, while reducing storage data volume to 13% of the full node model.

Cubature Kalman filter (CKF) with good filtering performance is one of the deterministic sampling filtering algorithms, but it is not able to overcome the impact caused by the target model uncertainty or the mutation of the target state. Constructing strong tracking CKF can effectively improve the adaptability of the algorithm, but the computation is greatly increased when solving the fading factor. A low-complexity adaptive CKF algorithm is proposed to solve the above problems. By establishing adaptive judgment criteria and amending method based on innovation sequence, the predicted state value is directly amended, so that the filtering algorithm can keep up with the real state of the target in time, and thus improve the filtering accuracy. The complexity of CKF, strong tracking CKF and the proposed algorithm are calculated and analyzed by using floating point operations. At the same time, the above three algorithms are applied to target tracking with inaccurate modeling, and are verified through simulation. The simulation results show that both the proposed algorithm and the strong tracking CKF algorithm can maintain better filtering accuracy and numerical stability in the case of mismatched target modeling, and the proposed algorithm has obvious improvement in algorithm complexity.