Engineers at Purdue University in Lafayette, Indiana are developing a new system for keeping nuclear reactors safe with artificial intelligence (AI). In the paper published in the IEEE Transactions on Industrial Electronics journal, the researchers introduced a deep learning framework called a naïve Bayes-convolutional neural network that can effectively identify cracks in reactors by analyzing individual video frames. The method could potentially make safety inspections safer. "Regular inspection of nuclear power plant components is important to guarantee safe operations," Mohammad Jahanshahi, an assistant professor at Purdue's Lyles School of Civil Engineering, said in a press release. "However, current practice is time-consuming, tedious, and subjective and involves human technicians reviewing inspection videos to identify cracks in reactors."
Intelligence agencies have a limited number of trained human analysts looking for undeclared nuclear facilities, or secret military sites, hidden among terabytes of satellite images. But the same sort of deep learning artificial intelligence that enables Google and Facebook to automatically filter images of human faces and cats could also prove invaluable in the world of spy versus spy. An early example: US researchers have trained deep learning algorithms to identify Chinese surface-to-air missile sites--hundreds of times faster than their human counterparts. The deep learning algorithms proved capable of helping people with no prior imagery analysis experience find surface-to-air missile sites scattered across nearly 90,000 square kilometers of southeastern China. Such AI based on neural networks--layers of artificial neuron capable of filtering and learning from huge amounts of data--matched the overall 90 percent accuracy of expert human imagery analysts in locating the missile sites.
WEST LAFAYETTE, Ind. – A system under development at Purdue University uses artificial intelligence to detect cracks captured in videos of nuclear reactors and represents a future inspection technology to help reduce accidents and maintenance costs. "Regular inspection of nuclear power plant components is important to guarantee safe operations," said Mohammad R. Jahanshahi, an assistant professor in Purdue's Lyles School of Civil Engineering. "However, current practice is time-consuming, tedious, and subjective and involves human technicians reviewing inspection videos to identify cracks on reactors." Complicating the inspection process is that nuclear reactors are submerged in water to maintain cooling. Consequently, direct manual inspection of a reactor's components is not feasible due to high temperatures and radiation hazards.
You are free to share this article under the Attribution 4.0 International license. A new system that uses artificial intelligence to find cracks captured in videos of nuclear reactors could help reduce accidents as well as maintenance costs, researchers report. "Regular inspection of nuclear power plant components is important to guarantee safe operations," says Mohammad R. Jahanshahi, an assistant professor in the Lyles School of Civil Engineering at Purdue University. "However, current practice is time-consuming, tedious, and subjective and involves human technicians reviewing inspection videos to identify cracks on reactors," Jahanshahi says. The fact that nuclear reactors are submerged in water to maintain cooling complicates the inspection process.
From Fukushima in Japan to Sellafield in the UK, the world is home to a number of sites that are contaminated with radioactive waste and require clean-up. The current techniques available to do this are expensive and time consuming – but a new'super hero' robot could help to cut both costs and time. The robot, called Avexis, is designed to fit through a 100mm access port in the flooded reactors at the Fukushima site, to locate and analyse melted fuel. Many areas around Fukushima are still being decontaminated, 58,000 people are still displaced from their homes and the local food industries have been crippled. Its designers hope that the robot will be ready to deploy at the Fukushima Daiichi Nuclear power plant by February 2018.
An aquatic robot called Avexis is being tested in Japan ahead of being deployed into the damaged core of the Fukushima Daiichi Nuclear Power Plant. A link has been sent to your friend's email address. A link has been posted to your Facebook feed. An aquatic robot called Avexis is being tested in Japan ahead of being deployed into the damaged core of the Fukushima Daiichi Nuclear Power Plant.
He recently gave a talk hosted by the MIT Energy Initiative (MITEI) on using machine learning to develop a real-time warning system for impending disruptions in fusion reactors. The whole goal of fusion energy is to develop large power plants to generate electrical power on the grid, and replace today's fossil-fueled utility power plants, and even replace fission nuclear power plants. But if a fusion power plant is subject to disruptions, its electricity output would suddenly turn off. When dealing with large, complicated datasets, machine learning may be a powerful way of finding subtle patterns in the data that elude human efforts.
The Awards, given for each scenario to the best performing teams, were introduced by Alan Winfield from Bristol Robotics Laboratory and ERL Emergency Coordinator. After the earthquake and tsunami, the pipes connecting the reactor to the sea might be leaking radioactive substances, therefore the emergency team has to find the damaged ones on land or underwater. The land robots have to inspect the pipes in the building's machine room and marine robots the underwater pipes in order to close the correct valves and prevent leakage. In addition, Marta Palau Franco, Bristol Robotics Laboratory, ERL Emergency project manager introduced the referees' special awards.