Tuesday July 23, 2019
Home Lead Story Indian Railwa...

Indian Railways to use artificial intelligence

Earlier, railways used a manual maintenance system

0
//
Indian Railways is one of the most important and controversial transport in India. Wikimedia Commons
Indian Railways is one of the most important and controversial transport in India. Wikimedia Commons

New Delhi. November 21, 2017:

Aiming to reduce the possibilities of signals failing, Indian Railways has undertaken remote condition monitoring of the system, a new approach for the national transporter, to predict failures through the effective use of Artificial Intelligence.

The Signalling system is vital for safe train operations and the railways completely depend on the health of its signalling assets along with real-time information.

Currently, the railways follow a manual maintenance system and adopt find-and-fix methods rather than predict-and-prevent approach.

“Now, we are introducing remote condition monitoring using non-intrusive sensors for continuous online monitoring of signals, track circuits, axle counters and their sub-systems of interlocking, power supply systems including the voltage and current levels, relays, timers,” said a senior Railway Ministry official involved with the project.

The system entails the collection of inputs on a pre-determined interval and sending this to a central location.

As a result, any flaws or problems in the signalling system would be detected on a real-time basis and rectified to avoid possible delays and mishaps.

The failure of signals is one of the major reasons for train accidents and delays.

Currently, remote monitoring of signalling is operational in Britain.

The system envisages data transfer through a wireless medium (3G, 4G and high-speed mobile) and data based on these inputs will be utilised, with help of Artificial Intelligence (AI), for predictive and prescriptive Big Data analytics.

This will enable prediction of signalling asset failures, automated self-correction and informed decisions on intervention strategies, said the official.

The railways have decided that trial is taken up in two sections of Western Railway and South Western Railway at Ahmedabad-Vadodara and Bengaluru-Mysuru.

Depending on the feedback, the system would gradually be extended to other sections. (IANS)

Next Story

Researchers Develop Artificial Intelligence Tool in Chest X-Rays to Predict Long Term Mortality

Each image was paired with a key piece of data: Did the person die over a 12-year period?

0
artificial intelligence
The goal was for CXR-risk to learn the features or combinations of features on a chest X-ray image that best predict health and mortality. Pixabay

Researchers have developed an Artificial Intelligence (AI)-powered tool that can harvest information in chest X-rays to predict long-term mortality.

The findings of this study, published in the journal JAMA Network Open, could help to identify patients most likely to benefit from screening and preventive medicine for heart disease, lung cancer and other conditions.

“This is a new way to extract prognostic information from everyday diagnostic tests,” said one of the researchers, Michael Lu, from Massachusetts General Hospital (MGH) of Harvard Medical School. “It’s information that’s already there that we’re not using, that could improve people’s health,” Lu said. Lu and his colleagues developed a convolutional neural network – an AI tool for analysing visual information – called CXR-risk.

artificial Intelligence
Next, Lu and colleagues tested CXR-risk using chest X-rays for 16,000 patients from two earlier clinical trials. Pixabay

It was trained by having the network analyse more than 85,000 chest X-rays from 42,000 participants who took part in an earlier clinical trial. Each image was paired with a key piece of data: Did the person die over a 12-year period? The goal was for CXR-risk to learn the features or combinations of features on a chest X-ray image that best predict health and mortality.

ALSO READ: Why Virtual Reality Headsets Failed to Create Craze Among Masses?

Next, Lu and colleagues tested CXR-risk using chest X-rays for 16,000 patients from two earlier clinical trials. They found that 53 per cent of people the neural network identified as “very high risk” died over 12 years, compared to fewer than four per cent of those that CXR-risk labeled as “very low risk.”

The study found that CXR-risk provided information that predicts long-term mortality, independent of radiologists’ readings of the x-rays and other factors, such as age and smoking status. Lu believes this new tool will be even more accurate when combined with other risk factors, such as genetics and smoking status. (IANS)