网络综合布线之系统配置设计

本文从"工作区、配线子系统、干线子系统、建筑群子系统、设备间、进线间、管理"七个方面阐述了网络综合布线中系统设计的方法,以期实现网络综合布线的系统化和规范化。

Fracture energy evaluation on 7075-T651 aluminum alloy welds determined by instrumented impact pendulum

By using an instrumented impact pendulum, the force versus time curves of 7075-T651 aluminum welds were obtained from standard Charpy-V samples. Considering the force-time curves and constant impact velocity, the fracture energies for different zones were quantified. A fracture energy improvement for the HAZ(33.6 J) was observed in comparison with the weld metal(7.88 J), and base metal(5.37 J and 7.37 J for longitudinal and transverse directions, respectively). This toughness increment was attributed to the microstructural transformation caused by the thermodynamic instability of η′ precipitates during the welding. Fracture energy for weld metal was higher than that for base metal, probably due to pores created during solidification. Regarding the dynamic yielding force obtained from the force-time curves, an approximation to the dynamic yield strength for weld, HAZ and base metal was determined. Fracture surfaces revealed an intergranular failure for base metal in longitudinal direction, whereas a predominately brittle failure(cleavage) with some insights of ductile characteristics was observed for the transverse direction. In contrast, a ductile failure was observed for weld metal and HAZ.

Study and Application of Fault Prediction Methods with Improved Reservoir Neural Networks

Time-series prediction is one of the major methodologies used for fault prediction. The methods based on recurrent neural networks have been widely used in time-series prediction for their remarkable non-liner mapping ability. As a new recurrent neural network, reservoir neural network can effectively process the time-series prediction. However, the ill-posedness problem of reservoir neural networks has seriously restricted the generalization performance. In this paper, a fault prediction algorithm based on time-series is proposed using improved reservoir neural networks. The basic idea is taking structure risk into consideration, that is, the cost function involves not only the experience risk factor but also the structure risk factor. Thus a regulation coefficient is introduced to calculate the output weight of the reservoir neural network. As a result, the amplitude of output weight is effectively controlled and the ill-posedness problem is solved. Because the training speed of ordinary reservoir networks is naturally fast, the improved reservoir networks for time-series prediction are good in speed and generalization ability. Experiments on Mackey–Glass and sunspot time series prediction prove the effectiveness of the algorithm. The proposed algorithm is applied to TE process fault prediction. We first forecast some timeseries obtained from TE and then predict the fault type adopting the static reservoirs with the predicted data.The final prediction correct rate reaches 81%.

Asymptotically Isometric Copies of l^∞ in Some Banach Lattices

In this paper, we show that any a-complete Banach lattice, with a σ-order semicontinuous but not σ-order continuous norm, contains an asymptotically isometric copy of l^∞. We also get that the Fenchel-Orlicz space with the Orlicz norm may not contain an asymptotically isometric copy of l^∞.

Time Optimal Feedrate Generation with Confined Tracking Error Based on Linear Programming

In this paper,the problem of time optimal feedrate generation under confined feedrate,axis accelerations,and axis tracking errors is considered.The main contribution is to reduce the tracking error constraint to constraints about the axis velocities and accelerations,when the tracking error satisfies a second order linear ordinary differential equation.Based on this simplification on the tracking error,the original feedrate generation problem is reduced to a new form which can be efficiently solved with linear programming algorithms.Simulation results are used to validate the methods.