Robust Adaptive Relative Position and Attitude Control for Noncooperative Spacecraft Hovering under Coupled Uncertain Dynamics

Liu, Jianghui; Li, Haiyang; Zhang, YaKun; Zhou, Jiangping; Lü, Lin; Li, Fuqi

doi:10.1155/2019/8678473

Cited by 40 publications

(1 citation statement)

References 38 publications

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“…At present, researchers are gradually transitioning from ground-based detection to space-based detection [3,23,24], and the close detection distance in space-based detection enables hyperspectral images to show relatively rich spatial features. Existing material identification methods using spatial features are usually designed under ideal imaging scenarios, in which the detector and space target are relatively stationary during the measurement of hyperspectral images (e.g., the detector is executing a hovering operation [25] on the space target with three-axis stabilization). Our work is also conducted on this premise.…”

Section: Complex Illumination Conditions Of the Space Targetmentioning

confidence: 99%

Space Target Material Identification Based on Graph Convolutional Neural Network

Gong

Zhao

et al. 2023

Remote Sensing

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Under complex illumination conditions, the spectral data distributions of a given material appear inconsistent in the hyperspectral images of the space target, making it difficult to achieve accurate material identification using only spectral features and local spatial features. Aiming at this problem, a material identification method based on an improved graph convolutional neural network is proposed. Superpixel segmentation is conducted on the hyperspectral images to build the multiscale joint topological graph of the space target global structure. Based on this, topological graphs containing the global spatial features and spectral features of each pixel are generated, and the pixel neighborhoods containing the local spatial features and spectral features are collected to form material identification datasets that include both of these. Then, the graph convolutional neural network (GCN) and the three-dimensional convolutional neural network (3-D CNN) are combined into one model using strategies of addition, element-wise multiplication, or concatenation, and the model is trained by the datasets to fuse and learn the three features. For the simulated data and the measured data, the overall accuracy of the proposed method can be kept at 85–90%, and their kappa coefficients remain around 0.8. This proves that the proposed method can improve the material identification performance under complex illumination conditions with high accuracy and strong robustness.

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Section: Complex Illumination Conditions Of the Space Targetmentioning

confidence: 99%

Space Target Material Identification Based on Graph Convolutional Neural Network

Gong

Zhao

et al. 2023

Remote Sensing

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Dynamic adaptive moving mesh finite‐volume method for the blood flow and coagulation modeling

Terekhov

Butakov

Danilov

et al. 2023

Numer Methods Biomed Eng

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In this work, we develop numerical methods for the solution of blood flow and coagulation on dynamic adaptive moving meshes. We consider the blood flow as a flow of incompressible Newtonian fluid governed by the Navier–Stokes equations. The blood coagulation is introduced through the additional Darcy term, with a permeability coefficient dependent on reactions. To this end, we introduce moving mesh collocated finite‐volume methods for the Navier–Stokes equations, advection–diffusion equations, and a method for the stiff cascade of reactions. A monolithic nonlinear system is solved to advance the solution in time. The finite volume method for the Navier–Stokes equations features collocated arrangement of pressure and velocity unknowns and a coupled momentum and mass flux. The method is conservative and inf‐sup stable despite the saddle point nature of the system. It is verified on a series of analytical problems and applied to the blood flow problem in the deforming domain of the right ventricle, reconstructed from a time series of computed tomography scans. At last, we demonstrate the ability to model the coagulation process in deforming microfluidic capillaries.

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An active defense model based on situational awareness and firewalls

Xiao

et al. 2023

Concurrency and Computation

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With the rapid development of the internet, cyberspace security issues have become increasingly prominent. The importance of constructing a cyberspace security system is self-evident, but compared with attackers, defenders in cyberspace are in a castle-like passive defense state in most cases. Therefore, building a reliable, accurate, timely, and active defense system is challenging. The key is to accurately focus on defense priorities, the anticipation of attackers who will likely succeed, and blocking attacks in a timely manner. In this article, we propose an active defense model based on the interaction of situational awareness and firewalls. First, by biasing the integrity, confidentiality, and availability of assets to get the score of assets, and using the Common Vulnerability Scoring System to assess the threat level of assets, we combine the two to determine the maximum system damage that the asset will suffer if it is lost, and then focus on defense. Meanwhile, log analysis of the network situational awareness platform can predict successful attackers, and then the linked firewall strategy can block these attacks in time before the attackers obtain attack gains. After that, we force the attackers to give up their attacks on the target by increasing the attack cost.We compared our model with iptables auto-blocking and nginx auto-blocking, and our model excelled them across the board in terms of comprehensiveness and false positive rate. The experimental results verify thar our active defense model proposed in this article can better reduce the defense cost and increase the attack cost, thus achieving the relatively defense goal. K E Y W O R D Sactive defense, cyber attack and defense, defense cost, game theory INTRODUCTIONWith the development of information technology, cyberspace has become the fifth space after "land, sea, air, and sky", and the security of cyberspace is directly related to the sovereign security of the country. Cyber attack and defense have become hot spots in current cybersecurity research. The objectives of cyber attackers include collecting information about a target, paralyzing the target service, entering the target and stealing data, while the objectives of cyber defenders are to keep the service running normally and stop attackers. Scanning of the target system is something that a cyber attacker must do before launching cyber attacks, which helps the attacker gain access to exploitable vulnerabilities in the target system to compromise the network. The purpose of such a scan is to understand the configurations of target systems, including but not limited to the target system's operating system, IP address, running services, software versions, existing common vulnerabilities & exposures(CVEs), weak passwords, and so on. How to prevent attackers from obtaining the scan results or prevent the attacker from using the scan results for other benefits is the goal

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Robust Adaptive Relative Position and Attitude Control for Noncooperative Spacecraft Hovering under Coupled Uncertain Dynamics

Cited by 40 publications

References 38 publications

Space Target Material Identification Based on Graph Convolutional Neural Network

Space Target Material Identification Based on Graph Convolutional Neural Network

Dynamic adaptive moving mesh finite‐volume method for the blood flow and coagulation modeling

An active defense model based on situational awareness and firewalls

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