Application Advantages of Staring Area Imaging Technology(SAIT) for Microsatellites

The Staring Area Imaging Technology(SAIT) satellite continuously "images" the target over a certain time range, and can realize continuous imaging and multi-angle imaging of the area of interest. It has the characteristics of flexible imaging parameter setting and fast image preprocessing speed, enabling dynamic target detection and tracking, super-resolution, surface 3 D model construction, night-time imaging and many other application tasks. Based on the technical characteristics of the SAIT satellite, this paper analyzes the challenges in satellite development and data processing, focuses on the quasi-realtime application of SAIT satellite data, and looks at the development trend of the SAIT satellite.

Thrust estimate method of an on-orbit Hall thruster using Hall drift current

In order to realize the thrust estimation of the Hall thruster during its flight mission,this study establishes an estimation method based on measurement of the Hall drift current.In this method,the Hall drift current is calculated from an inverse magnetostatic problem,which is formulated according to its induced magnetic flux density detected by sensors,and then the thrust is estimated by multiplying the Hall drift current with the characteristic magnetic flux density of the thruster itself.In addition,a three-wire torsion pendulum micro-thrust measurement system is utilized to verify the estimate values obtained from the proposed method.The errors were found to be less than 8%when the discharge voltage ranged from 250 V to 350 V and the anode flow rate ranged from 30 sccm to 50 sccm,indicating the possibility that the proposed thrust estimate method could be practically applied.Moreover,the measurement accuracy of the magnetic flux density is suggested to be lower than 0.015 mT and improvement on the inverse problem solution is required in the future.

A Vehicle Detection Method for Aerial Image Based on YOLO

With the application of UAVs in intelligent transportation systems, vehicle detection for aerial images has become a key engineering technology and has academic research significance. In this paper, a vehicle detection method for aerial image based on YOLO deep learning algorithm is presented. The method integrates an aerial image dataset suitable for YOLO training by pro-cessing three public aerial image datasets. Experiments show that the training model has a good performance on unknown aerial images, especially for small objects, rotating objects, as well as compact and dense objects, while meeting the real-time requirements.

A Road Extraction Method Based on Region Growing and Mathematical Morphology from Remote Sensing Images

Road traffic is the important driving factor for economic and social development. With the rapid increase of vehicle population, road traffic problems such as traffic jam and traffic accident have become the bottleneck which restricts economic development. In recent years, natural disasters frequently occur in China. Therefore, it is essential to extract road information to compute the degree of road damage for traffic emergency management. A road extraction method based on region growing and mathematical morphology from remote sensing images is proposed in this paper. According to the road features, the remote sensing image is preprocessed to separate road regions from non-road regions preliminarily. After image thresholding, region growing algorithm is used to extract connected regions. Then we sort connected regions by area to exclude the small regions which are probably non-road objects. Finally, the mathematical morphology algorithm is used to fill the holes inside the road regions. The experimental results show that the method proposed can effectively extract roads from remote sensing images. This research also has broad prospects in dealing with traffic emergency management by the government.

Image motion and experimental study of a 0.1″space pointing measuring instrument for micro-vibration conditions

There exists an increasing need for Milli-Arc-Seconds(MAS)accuracy pointing measurement for current and future space systems.To meet the 0.1″space pointing measurement accuracy requirements of spacecraft in future,the influence of spacecraft micro-vibration on a 0.1″Space Pointing Measuring Instrument(SPMI)is studied.A Quasi-Zero Stiffness Device(QZSD)with adaptive adjustment and variable stroke was proposed.Then,a series of micro-vibration experiments of the SPMI were carried out.The influence of the micro-vibration generated by Guidance Navigation Control(GNC)attitude control components under different attitudes on the SPMI was analyzed.Point spread function of image motion in micro-vibration was also derived.Further,the changes of image motion under the micro-vibration environment were evaluated by extracting the gray centroid of the images,and the experiment processes and results are deeply discussed.The results show that the firstorder frequency of the QZSD system is 0.114 Hz,and it is induced by a double pendulum system;the image motion of single flywheel spinning reached 0.015 pixels;whilst the image motion reached 0.03 pixels when three flywheels are combined spinning.These latest findings provide a beneficial theoretical and technical support for the development of spacecraft with 0·1″pointing accuracy.

Continual learning fault diagnosis:A dual-branch adaptive aggregation residual network for fault diagnosis with machine increments

As a data-driven approach, Deep Learning(DL)-based fault diagnosis methods need to collect the relatively comprehensive data on machine fault types to achieve satisfactory performance. A mechanical system may include multiple submachines in the real-world. During condition monitoring of a mechanical system, fault data are distributed in a continuous flow of constantly generated information and new faults will inevitably occur in unconsidered submachines, which are also called machine increments. Therefore, adequately collecting fault data in advance is difficult. Limited by the characteristics of DL, training existing models directly with new fault data of new submachines leads to catastrophic forgetting of old tasks, while the cost of collecting all known data to retrain the models is excessively high. DL-based fault diagnosis methods cannot learn continually and adaptively in dynamic environments. A new Continual Learning Fault Diagnosis method(CLFD) is proposed in this paper to solve a series of fault diagnosis tasks with machine increments. The stability–plasticity dilemma is an intrinsic issue in continual learning. The core of CLFD is the proposed Dual-branch Adaptive Aggregation Residual Network(DAARN).Two types of residual blocks are created in each block layer of DAARN: steady and dynamic blocks. The stability–plasticity dilemma is solved by assigning them with adaptive aggregation weights to balance stability and plasticity, and a bi-level optimization program is used to optimize adaptive aggregation weights and model parameters. In addition, a feature-level knowledge distillation loss function is proposed to further overcome catastrophic forgetting. CLFD is then applied to the fault diagnosis case with machine increments. Results demonstrate that CLFD outperforms other continual learning methods and has satisfactory robustness.

Design and Optimization of the CFRP Mirror Components

As carbon fiber reinforced polymer （CFRP） material has been developed and demonstrated as an effective material in lightweight telescope reflector manufacturing recently, the authors of this article have extended to apply this material on the lightweight space camera mirror design and fabrication. By CFRP composite laminate design and optimization using finite element method （FEM） analysis, a spherical mirror with φ316 mm diameter whose core cell reinforcement is an isogrid configuration is fabricated. Compared with traditional ways of applying ultra-low-expansion glass （ULE） on the CFRP mirror surface, the method of nickel electroplating on the surface effectively reduces the processing cost and difficulty of the CFRP mirror. Through the FEM analysis, the first order resonance frequency of the CFRP mirror components reaches up to 652.3Hz. Under gravity affection coupling with ＋5℃ temperature rising, the mirror surface shape root-mean-square values （RMS） at the optical axis horizontal state is 5.74 nm, which meets mechanical and optical requirements of the mirror components on space camera.

Off-axis two-mirror laser communication antenna designed using differential equations

In satellite laser communication technology, which is built between planets and between a planet and the Earth, the optical antenna is the key point. Thus, research on optical system design is important. The off-axis reflective system has no obscuration and hence possesses a high efficiency for energy transfer. This study proposes a novel method for designing a free-form off-axis reflective imaging system. This study also introduces differential equations that depend on Fermat＇s principle and sine condition. Finally, a free-form off-axis two-mirror optical system was designed by using the differential equation method. This system includes one intermediate image plane, in which the field of view （FOV） is -5° to -4° in the y-axis and -1° to 0° in the x-axis. The modulation transfer function was greater than 0.7 at 50 lp/mm, and the efficiency of energy transmission was high. The free-form off-axis two-mirror optical system involves a wide range of application prospects in the optical antenna used in the satellite laser communication systems. Moreover, the design method that used differ- ential equations was proven simple and effective.

Improved simulation method for urban free-space optical links based on the finite Markov state model

An improved method is proposed to simulate the scintillation introduced by the turbulence, based on the finite Markov state model. As a contrast to the literatures, uniformly distributed variables take place during a certain state, which contributes to equivalent simulation of the intensity fluctuations with fewer states than the traditional Markov model. It's also discovered that the proposed Markov model with 20 states provides a satisfactory approximation to the experimental results in the auto-covariance analysis. Moreover, the outage probability and mean fading time are more accurate than those of the traditional Markov model with equivalent states.