At a time when artificial intelligence (AI) is moving from the realm of fantasy to practical application, NuEnergy.ai Healthcare Lead Orchid Jahanshahi says the issues of trust and transparency are becoming critical. In a recent interview with ITWC President Fawn Annan conducted as part of the CMO Talks podcast series, Jahanshahi noted many banks are now using AI to decide the credit worthiness of customers and ultimately decide to whom they'll offer loans. That may be a huge advance on some front, but she warned that unless the AI algorithms are designed on certain parameters, the system could be biased and might unfairly exclude some customers. "Machine learning can eventually surpass our human ability to catch up. But some oversight is needed to make sure no consequences in terms of bias or lack of transparency occur. Jahanshahi extended the discussion around trust and transparency into the pharmaceuticals industry. "In pharma, it's not enough to discuss features and benefits of a drug.

As I write this, Futurama's Bender is on my TV expressing his opinions about the flaws of us humans. Although he may take it a little farther than I would, it's true that we don't have the best natural detection capabilities. And when you're talking about detecting structural flaws in something like a nuclear reactor, human error isn't something with which I'd want to take a chance. Luckily, technology is able to help us with this, and it's sure to be much more helpful than Bender the Robot. A system in development at Purdue University is poised to help operators detect cracks and their severity in nuclear reactors, according to a recent article by Chris Adam.

A tiny crack in a nuclear reactor, skyscraper, bridge or dam can cause catastrophic consequences. The Minneapolis bridge collapse, which killed 13 people in 2007, is just one example of what can happen when structural integrity is compromised. Unidentified or under-identified structural damage in nuclear reactors can be cataclysmic. Inspection of critical systems such as nuclear reactors is complicated and time-consuming. Videos captured by an automatic crack detection system can easily misidentify small scratches or welds as cracks, so technicians must review videos frame by frame.

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.

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."

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.