基于SNMP的影院观影人数清点系统

随着智能影院建设,各种新的自动化智能设备部署进影院.对TMS/NOC系统来说,兼容功能协议各不相同的智能系统是一个很大挑战.本文采用业界通用的网络管理协议SNMP,能够通过IP网络管理众多厂家生产的软硬件平台.本文通过在AAM的TMS/NOC系统上部署基于SNMP的影院观影人数清点系统,展现了SNMP协议对新设备的前所未有的兼容性与灵活性,希望能为智能影院的建设添砖加瓦.     

Temporal evolution of flame stretch due to turbulence and the hydrodynamic instability

The temporal evolution of the strain rate on a turbulent premixed flame was measured experimentally using cinema-stereoscopic particle image velocimetry. Turbulence strains a flame due to velocity gradients associated both directly with the turbulence and those caused by the hydrodynamic instability, which are initiated by the turbulence. The development of flame wrinkles caused by both of these mechanisms was observed. Wrinkles generated by the turbulence formed around vortical structures, which passed through the flame and were attenuated. After the turbulent structures had passed, the hydrodynamic instability flow pattern developed and caused additional strain. The hydrodynamic instability also caused the growth of small flame front perturbations into large wrinkles. In the moderately turbulent flame investigated, it was found that the evolution of the strain rate caused by turbulence–flame interactions followed a common pattern involving three temporal regimes. In the first, the turbulence exerted extensive (positive) strain on the flame, creating a wrinkle that had negative curvature (concave towards the reactants). This was followed by a transition period, leading into the third regime in which the flow pattern and strain rate were dominated by the hydrodynamic instability mechanism. It was also found that the magnitudes of the strain rate in the first and third regimes were similar. Hence, the hydrodynamic instability mechanism caused significant strain on a flame and should be included in turbulent combustion models.     

A classification model based on goal programming with non-standard preference functions with application to the prediction of cinema-going behaviour

This paper presents a goal programming model that allows for the flexible handling of the two group classification problem. The goal programming model is based around the concepts of non-standard preference functions and penalty function modelling. An extension to a generalised distance metric case is given. The inclusion of multiple levels of classification based upon different levels of certainty is incorporated into the model. The model is tested on a real-life data set pertaining to cinema-going attendance and conclusions are drawn both in the context of the methodology and of the application.