The Space-based TT&C network connects many kinds of orbital satellites downward and deep space detectors upward. During the operation of the whole network, dynamic links with a series of space nodes such as launch vehicles, satellites, space stations and missiles need to be considered. According to the dynamic characteristics of space-based TT&C network topology, this paper uses time-space graph to model the dynamic topology. Based on the consideration of cost-effectiveness ratio, reliability and connectivity, the topology control of space-based TT&C network is described. Further, we propose two topology control algorithms. The simulation results show that our proposed topology control algorithm can ensure the reliability threshold, significantly reduce the total cost of the network, and maintain a low cost-effectiveness ratio while building a sparse topology.
The change Point Detection (CPD) method identifies the time series method related to the trend and attributes the change of time series data to describe the fundamental behavior of the system. For example, detecting changes and anomalies related to web service usage, application usage or human behavior, and system state can provide valuable insights for downstream modeling tasks. We propose comparative learning based on a self-monitoring time series change point detection method. Compared with traditional methods, we can apply them to a broader range. We prove that our method is superior to the state-of-the-art CPD method through experiments on three different and widely used time-series data sets.
With the development of technology, network technology is growing rapidly and there are more and more problems in the network, how to deal with anomaly traffic quickly and accurately in the network is a major problem that must be solved to achieve network security. This paper gives an overview of anomaly traffic and introduces three types of anomalies: point anomaly, contextual anomaly and collective anomaly, argues the relationship between anomalies, summarizes the latest publicly available datasets, focuses on the current classification approaches for handling network traffic anomalies, divides them into supervised multi-class classification approaches and unsupervised one-class classification approaches according to their learning types, and introduces some major development processes according to their respective. Finally, the advantages and shortcomings of multi-class classification and one-class classification approaches are summarized.
With a large number of distributed power sources connected to the distribution network, it has brought a huge impact to the fault waveform of the distribution network. Considering the governance problems after the failure of the distributed distribution network, how to realize the failure after the failure of the active distribution network positioning research is a key link for active distribution network failure recovery. This paper considers the characteristics of the fault current flow in the active distribution network, and uses the artificial bee colony algorithm to search for the faulty section. Simulation results show the effectiveness of the proposed algorithm.
The effects of different kinds of communication channels on the key indexes such as bit error rate and communication rate of quantum communication system, represented by quantum secure communication system, are systematically studied. For the actual channel, the transformation of quantum states caused by physical effects such as loss, dispersion and polarization mode dispersion in optical fiber channel is analyzed. With building in the spatial channel noise experimental data under different weather conditions, the mathematical model is established, and the software is designed and implemented.
