Summary
In view of the problems of low routing efficiency, complex control process, and difficult network management in big data environment in the traditional integrated space‐terrestrial network, in the paper, we propose a satellite network architecture called software‐defined information centric satellite networking (SDICSN) based on software‐defined networking (SDN) and information‐centric networking (ICN), and we design a virtual node matrix routing algorithm (VNMR) under the SDICSN architecture. The SDICSN architecture realizes the flexibility of network management and business deployment through the features of the separation of forwarding and controlling by the SDN architecture and improves the response speed of requests in the network by the centric of “content” as the ICN idea. According to the periodicity and predictability of the satellite network, the VNMR algorithm obtains the routing matrix through the relative orientation of the source and destination nodes, thus reducing the spatial complexity of the input matrix of the Dijkstra algorithm and then reducing the time complexity of the routing algorithm. For forwarding information base (FIB), the mechanism of combination of event driven and polling can be quickly updated in real time. Finally, the advantages of the SDICSN architecture in routing efficiency, request delay, and request aggregation are verified by simulation.
The influence of multiple longitudinal mode pulses on laser-induced damage at the exit surface of fused silica is investigated under simultaneous exposure to multi-wavelength lasers. The typical damage morphologies are systematically observed after a series of experiments have been conducted on fused silica samples by the equipment for multi-wavelength damage test. By means of the calculation for incubation and expansion fluences determined from the diameter of the ring pattern, the role of the each wavelength laser in damage process has been identified. The results provide insight into the information on the precursor reactiveness under simultaneous exposure to dual-wavelength pulses.
Abstract:Multi-task learning can extract the correlation of multiple related machine learning problems to improve performance. This paper considers applying the multi-task learning method to learn a single task. We propose a new learning approach, which employs the mixture of expert model to divide a learning task into several related subtasks, and then uses the trace norm regularization to extract common feature representation of these sub-tasks. A nonlinear extension of this approach by using kernel is also provided. Experiments conducted on both simulated and real data sets demonstrate the advantage of the proposed approach.
In the real energy spectrum attenuation environment, many traditional nuclide identification methods for nuclear robot systems have problems such as using only part of the energy spectrum curve, being susceptible to noise, and having low recognition accuracy. Proposes an energy spectrum nuclide recognition method based on S-transform (ST) and Mahalanobis distance-based support vector machine (MSVM). Regarding the energy spectrum curve as a non-stationary signal, combined with the widely used S transformation method in signal transformation, the energy spectrum data is two-dimensional, Then use two-dimensional principal component analysis(2D-PCA) to reduce the dimension of the two-dimensional energy spectrum data for feature extraction, and design a support vector machine (SVM) classifier based on Mahalanobis distance to realize the identification of energy spectrum nuclides. Finally, experiments are carried out with simulated nuclide energy spectrum data based on Geant4. The experimental results show that this method effectively improves the accuracy of energy spectrum nuclide recognition by using full spectrum information. At the same time, experiments are carried out on the nuclide energy spectrum data of different detection distances obtained by the NaI detector in the real environment, and it is verified that the algorithm proposed in this paper also has a good recognition performance for the nuclide energy spectrum collected in the real environment.
Abstract:A model for predicting the size ranges of different potential inclusions initiating damage on the surface of fused silica has been presented. This accounts for the heating of nanometric inclusions whose absorptivity is described based on Mie Theory. The depth profile of impurities has been measured by ICP-OES. By the measured temporal pulse profile on the surface of fused silica, the temperature and thermal stress has been calculated. Furthermore, considering the limit conditions of temperature and thermal stress strength for different damage morphologies, the size range of potential inclusions for fused silica is discussed.
In this paper, we propose a new consistency measurement for classification rule sets that is based on the similarity of their classification abilities. The similarity of the classification abilities of the two rule sets is evaluated though the similarity of the corresponding partitions of the feature space using the different rule sets. The proposed consistency measure can be used to measure the equivalent symmetry of subsystems decomposed from a large, complex cyber–physical system (CPS). It can be used to verify whether the same knowledge is obtained by the sensing data in the different subsystems. In the experiments, five decision tree algorithms and eighteen datasets from the UCI machine learning repository are employed to extract the classification rules, and the consistency between the corresponding rule sets is investigated. The classification rule sets extracted from the use of the C4.5 algorithm on the electrical grid stability dataset have a consistency of 0.88, which implies that the different subsystems contain almost equivalent knowledge about the network stability.
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