![]() “In the future, we will consider a more realistic and challenging scenario where the data for training and testing may come from different distributions,” added Xu, stating that future iterations of KDnet-RUL may be able to apply a model derived from one machine to predict the lifespan of another. “These findings provide a possible model compression solution that addresses an actual industry requirement of deploying a powerful but cumbersome network into resource-limited edge devices,” Xu said. In some instances, the ‘student’ even had more accurate estimates than the ‘teacher’ and could deliver its lifespan predictions more rapidly. They found that KDnet-RUL was just as effective as its ‘teacher’ network at estimating when these engines would fail. The ‘student’ then passes through several cycles of learning-during-teaching knowledge distillation to improve its accuracy at forecasting a machine’s remaining lifespan.įor KDnet-RUL’s test run, the researchers used C-MAPSS, a public dataset that simulates how turbofan engines degrade over time. First, a generative adversarial network facilitates the actual knowledge transfer from the original, highly complex prediction algorithm to a basic convolutional neural network. To overcome this challenge, the team created a novel framework for knowledge distillation that they named KDnet-RUL.ĭesigned to be fast and take up minimal storage space, KDnet-RUL can retain its teacher’s accuracy through a specialized two-factor approach. The problem is that this distillation process is not fool-proof-compressing elaborate equations compromises the algorithm’s predictive accuracy. Upon distillation, the ‘student’ learns to copy the outputs of the ‘teacher’ using less disk space, allowing advanced calculations to be performed by regular computer hardware. The experts turned to knowledge distillation, a method by which a larger, more complicated computing system called the ‘teacher’ transfers its knowledge to a smaller, more economical ‘student’ system. However, because these algorithms are so complex, they need to be run on advanced computing systems that are often housed off-site, thus limiting their use in real-time decision making.Ī team of researchers led by Qing Xu, a Research Engineer at A*STAR’s Institute for Infocomm Research (I 2R), was interested in streamlining these computational platforms to make them more accessible for everyday, on-the-job use. Many companies use deep learning algorithms to estimate when machines are likely to start winding down, helping avoid emergencies due to unexpected failures. If only there had been a way to anticipate that the vehicle’s engine was close to its end.Īrtificial intelligence can make these predictions, at least in an industrial setting. Imagine running late for an important meeting, and your car refuses to start despite having a full gas tank and no warning signs flashing on the dash. So live, laugh and love!Įditor’s Note: This article was submitted by Barry Kolanowski.Deep learning algorithms for predicting machine failures in industrial settings can be compressed without compromising their performance, say A*STAR researchers. It was fairly easy and after entering all my information, it returned with this ominous message: “You have 0 years left to live.” The site predicted I died in 2016, which was oddly enough the year of my heart attack. I could pick up two more years by dropping 15 pounds or getting 4 inches taller. Lifespan, provided by an insurance company, resulted in an age rather than a date. It reported that Wednesday, Jwill be my last day on earth and a countdown clock was clicking away toward a ripe age of 88. Death asked my gender, if I smoked, my outlook on life and how many alcoholic drinks I had each week. I asked three of them to consider my chances: The Death Clock, Lifespan Calculator, and Fateful Day, to give me their best shot. Since it is so confusing I went to some online apps that offer to predict your day of death. ![]() The data shows that hospitals are actually a dangerous place and you are more likely to die there then at home. The final myth is that the bump in deaths is due to people being sent home from the hospital too early just for the sake of the holidays. ![]() They also eliminated the connection on a socio-economic basis as the reaper makes no distinction between affluence and poverty when it comes to the end of life. This also eliminates the theory that staying inside because of the cold weather spreads more germs and results in a higher January death rate. The spike in deaths is as true in tropical areas as it is in the snow shoveling northern parts of the United States. ![]() Known by statisticians as “excess deaths” it appears to have nothing to do with weather. For decades researchers have tried to figure out on a global scale why more people die in January than any other month of the year? ![]()
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