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The optical network unit management and control interface (OMCI) is a crucial protocol for interconnectivity between the optical line terminal (OLT) and optical network unit (ONU) in Gigabit- capable passive optical networks (GPON) systems. When addressing OMCI interoperability issues, developers often need to conduct exception analysis on OMCI service models. However, due to the complexity of OMCI domain knowledge, directly analyzing OMCI service models can be extremely challenging, time-consuming, and laborious for inexperienced developers. To address these challenges, the OMCI model exception analysis method based on graph neural networks (GNNs) is proposed. This method leverages graph similarity computation algorithms to search for similar OMCI models from a database as references, compares the differences, and identifies anomalies. Firstly, real OMCI data is structured into graph data. Subsequently, the similarity graph neural network (SimGNN) is improved by integrating graph isomorphism networks and self-attention pooling for fast graph similarity computation. Finally, the similarity scores between each graph in the OMCI graph database and the anomalous graph data are calculated, and the most similar OMCI service model graphs are recommended based on the score ranking. Experimental results show that the improved graph similarity computation model outperforms the baseline model on the OMCI dataset used in this study. Moreover, it proves effective in practical applications, offering valuable assistance in analyzing OMCI interoperability issues. [ABSTRACT FROM AUTHOR]

The Gleeble-3500 thermal simulation tester was used to perform hot deformation behavior on the extruded AZ40 Mg alloy to analyze the trend of the true stress-strain curve after compression and to obtain the influence of the flow stress on the deformation temperature and strain rate. Subsequently, the constitutive equation was constructed for extruded AZ40 alloy based on the hyperbolic-sine relationship, and the thermal processing map of extruded AZ40 alloy was established based on the dynamic material model (DMM), thereby estimating the processing range of extruded AZ40 alloy. The results show that the rheological curve of extruded AZ40 alloy is characterized by obvious dynamic recrystallization. Furthermore, during the compression process, the peak stress of extruded AZ40 alloy decreases with the increase of deformation temperature, while increases with the increase of strain rate. Moreover, the proportion of dynamic recrystallized grains (DRGs) decreases with the increase of the strain rate under the same deformation temperature condition; while the DRGs size increases with the increase of the deformation temperature under the same strain rate condition. The coarse uncrystallized grains show obvious crystallographic orientations of 〈1010〉‖ND and 〈2110〉‖ND, while crystallographic orientation of DRGs is random distributed. Finally, through thermal processing map and tissue analysis, the optimal processing window was identified as T=573 K, $\dot \varepsilon $=0.1 s-1.

The microstructure of as-cast Mg-6Zn-xCe alloy was investigated by optical microscope (OM), X-ray diffraction (XRD), scanning election microscopy (SEM), energy dispersive spectrometer (EDS), transmission electron microscopy (TEM)and the changing rule of thermal conductivity with the content of Ce was investigated using the flash method. The results show that Mg-6Zn alloy is mainly composed of α-Mg and Mg7Zn3 phases. The ternary phase Ce5 (Mg,Zn)41 which is mainly distributed between grain boundaries and dendrites appears in the alloy after the addition of the rare earth element Ce, and the appearance of ternary phase has an inhibitory effect on Mg7Zn3 phase. The content of eutectic structure is increased by the addition of Ce, and the distribution continuity is improved with the increase of Ce content. The thermal conductivity of the alloy is decreased with the increase of Ce content, which may be caused by the increase of volume fraction, more continuous distribution of eutectic structure in the alloy, the enhanced electron scattering, the extended electron conduction paths and the increased thermal resistance, ultimately the thermal conductivity of the alloy is reduced.

This paper presents a multi-point parameterized model reduction method based on Krylov-subspace techniques for large parametric systems where the parametric dependence in system matrices can be nonaffine and the parameters are in a wide range. First, a large parametric system is approximated as a weighted combination of different linear parametric systems, which are obtained by Taylor expansion on the different selected parameter expansion points. Then, a numerically stable algorithm based on self-moments and cross-moments is proposed to obtain a reduced parametric system with high fidelity in the full ranges of the parameters. The accuracy of the obtained reduced parametric systems can be improved by applying the multi-point expansions and weighted functions proposed in this paper. Two benchmarks in practical applications are assessed by employing different parameterized model reduction methods. The numerical simulation results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a structure‐preserving model reduction method for second‐order systems based on H2 optimal interpolation. In the iterative process of the proposed method, an algorithm is presented for selecting interpolation points in order to control the dimension of the reduced system. Result about error analysis of the interpolation points selection algorithm is obtained and the property of the new model reduction method is also given. Finally, three numerical examples are performed to illustrate the effectiveness of the new method. [ABSTRACT FROM AUTHOR]