The next time you're sitting at a railroad crossing watching the tons of steel fly past you on a narrow strip of track, remember that University of Illinois professor of electrical and computer engineering Shun-Lien Chuang is working to keep that train on that track. He is developing sensors that, among other things, detect flaws in rails and wheels. The sensors detect a train's presence and speed on a given set of tracks. "They are different from conventional track circuit systems since fiber optics are the insulators and they are immune from electromagnetic interference," says Chuang. The sensors are based on optical signal transmission through fiber-optic cables that are attached to the rail. The fiber-sensor senses changes in the strain created by cracked, broken, or buckled rails as the train passes. As a train moves, pressure creates perturbations in the fiber-optic transmission. The reflectometry system measures the distance to the perturbations for pinpointing the train's speed and location. "We calibrate the intensity of the optical transmission as a function of the applied bending pressure," says Chuang. If the optical fiber on the rail bends, some of its light leaks out. The device uses optical time-domain reflectometry, which measures signal loss in the optical fiber as a function of distance, using a time-gated pulse-detection technique. "The fiber-optic sensor can be used for measuring other environmental changes such as temperature, stress, and structural integrity of bridges, buildings, and fuel tanks," he notes. Contact Chuang at the University of Illinois, Dept. of Electrical and Computer Engineering, 1406 W. Green St., Urbana, IL 61801; or call (217) 333-3359.
According to a study by the National Institute of Standards and Technology, one of the factors in the collapse of the original World Trade Center towers on Sept. 11, 2001, was the reduction in the yield strength of the steel reinforcement as a result of the high temperatures of the fire and the loss of thermal insulation.
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Robots are getting more agile and automation systems are becoming more complex. Yet the most impressive development in robotics and automation is increased intelligence. Machines in automation are increasingly able to analyze huge amounts of data. They are often able to see, speak, even imitate patterns of human thinking. Researchers at European Automation
call this deep learning.
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