视频监控是目前安防产业中最重要的一部分,网络视频监控由于其易管理、易扩展、可远程监控等优势,已经成为发展的大趋势。但由于传统以太网100米传输距离的限制,网络视频监控系统的监控距离受到了很大的限制。本文主要介绍如何通过 LRE(...视频监控是目前安防产业中最重要的一部分,网络视频监控由于其易管理、易扩展、可远程监控等优势,已经成为发展的大趋势。但由于传统以太网100米传输距离的限制,网络视频监控系统的监控距离受到了很大的限制。本文主要介绍如何通过 LRE(Long Range Ethernet)物理层芯片如何使10M以太网在普通网线上的传输距离达到一千米以上,并比较了这种应用相对现有解决方案的优势。展开更多
Ensuring the safe operation of liquid rocket engine(LRE)systems requires reliable fault diagnosis,yet the scarcity of real fault data limits deep learning applications despite their modeling strengths.We address this ...Ensuring the safe operation of liquid rocket engine(LRE)systems requires reliable fault diagnosis,yet the scarcity of real fault data limits deep learning applications despite their modeling strengths.We address this by developing an offline detection method based on piecewise stationary vector autoregressive modeling,employing a two-phase approach that first identifies candidate change points through block fused LASSO regularization and subsequently refines them using smoothly clipped absolute deviation regularization to leverage its asymptotic unbiasedness.Validated on a high-fidelity LRE simulation dataset(26 sensors,2000 time points)with injected faults including turbopump efficiency degradation,hydrogen turbine leakage,and valve failures across 48 scenarios,our method achieves 100% precision(±50-sample tolerance)in fault timing detection without requiring training data,demonstrating superior performance to conventional autoregressive moving average models while overcoming the data dependency of neural networks.展开更多
文摘视频监控是目前安防产业中最重要的一部分,网络视频监控由于其易管理、易扩展、可远程监控等优势,已经成为发展的大趋势。但由于传统以太网100米传输距离的限制,网络视频监控系统的监控距离受到了很大的限制。本文主要介绍如何通过 LRE(Long Range Ethernet)物理层芯片如何使10M以太网在普通网线上的传输距离达到一千米以上,并比较了这种应用相对现有解决方案的优势。
文摘Ensuring the safe operation of liquid rocket engine(LRE)systems requires reliable fault diagnosis,yet the scarcity of real fault data limits deep learning applications despite their modeling strengths.We address this by developing an offline detection method based on piecewise stationary vector autoregressive modeling,employing a two-phase approach that first identifies candidate change points through block fused LASSO regularization and subsequently refines them using smoothly clipped absolute deviation regularization to leverage its asymptotic unbiasedness.Validated on a high-fidelity LRE simulation dataset(26 sensors,2000 time points)with injected faults including turbopump efficiency degradation,hydrogen turbine leakage,and valve failures across 48 scenarios,our method achieves 100% precision(±50-sample tolerance)in fault timing detection without requiring training data,demonstrating superior performance to conventional autoregressive moving average models while overcoming the data dependency of neural networks.
