Research of Fiber Optic Connector Ferrule End-Face Degradation Influence on Optical Pulse Distortion during Propagation over Indoor Multi-Gigabit Optical Network Link with Crypto-Fibers

This article presents results of approbation of developed model of piece-wise regular fiber optic link, operating in a few-mode regime, with series-connected couple of special multimode optical crypto-fibers “encryptor-decoder”. Unlike the previously developed solution, the model was modified with an ability to take into account influence of fiber optic connector end-face contamination on laser-excited optical signal launching conditions. We present comparison results of computed optical pulse response envelops, distorted during propagation over 10GBase-LX network fiber optic links, containing optical crypto-fibers, depending on various conditions of transceiver laser source connector ferrule end-face contamination.

Tdnn-Based Engine In-Cylinder Pressure Estimation from Shaft Velocity Spectral Representation

Pressure is one of the essential variables to give information about engine condition and monitoring. Direct recording of this signal is complex and invasive, while angular velocity can be measured. Nonetheless, the challenge is to predict the cylinder pressure using the shaft kinematics accurately. In this paper, a time-delay neural network (TDNN), interpreted as a finite pulse response (FIR) filter, is proposed to estimate the in-cylinder pressure of a single-cylinder internal combustion engine (ICE) from fluctuations in shaft angular velocity. The experiments are conducted over data obtained from an ICE operating in 12 different states by changing the angular velocity and load. The TDNN’s delay is adjusted to get the highest possible correlation-based score. Our methodology can predict pressure with an R2 >0.9, avoiding complicated pre-processing steps.