Naoyuki Tamaru

Automated large-scale fiber cross-connecting switch using a fiber handling method

Summary form only given. Fiber termination modules (FTMs), which terminate both subscriber and office-equipment cables and cross connect between these cables, are one of the most important equipment in optical access networks. For future optical access networks that employ fiber-to-the-home (FTTH) architecture, it is important to lower the total cost, including the operational cost for cross-connecting at FTMs. Compactness and high reliability are also required for FTMs. To satisfy these requirements, automated cross-connecting switches are crucial. In this paper a new configuration for a large-scale automated fiber cross-connecting switch, which is capable of cross connecting between more than 4000 subscriber cables and 4090 office-equipment cables, is proposed.

Current estimation for state variables using a Kalman filter with dual models - influence of delay time error

With the aim of obtaining current state variables of a mechanical system with time delay, we estimate current state variables from the delayed measurement signal by using a novel Kalman filter. In order to increase the stability of the estimation system, we propose a Kalman filter with dual mathematical models and show that it is more robust and precise than a conventional Kalman filter with a single model. We examine the influence on the estimation error caused by delay time error of the estimation system. From the results, we infer that it is safer to set the delay time higher than the actual delay time if uncertain, because the increment of the estimation error is smaller. Moreover, the variance of the estimation error increases more than that at lower input noise when the delay time increases.

Novel estimation method of current state variables for time delay system using a Kalman filter with dual models-influence of modeling error

The article concerns remote measurement. Aiming to obtain current state variables of a mechanical system with time delay, we estimated current state variables from the delayed measurement signal by using a Kalman filter. In order to increase the stability of the estimation system, we have proposed a novel Kalman filter with dual mathematical models, and also have shown that it was very effective because it was more robust and precise than a conventional Kalman filter with a single model. We have examined an influence for the estimation error that was caused by modeling errors of the plant, and have examined one for the sampling time error. From these results it is safer to set the variance of the measurement noise and input noise higher if uncertain, because the increment of the estimation error is smaller. Moreover the increment of the estimation error caused by the sampling time error keeps almost constant from the experiment and the simulation, when the sampling time is set longer.