Contemporary smart power grids are being challenged by rapid voltage fluctuations, due to large-scale deployment of renewable generation, electric vehicles, and demand response programs. In this context, monitoring the grid's operating conditions in real time becomes increasingly critical. With the emergent large scale and nonconvexity however, past optimization based power system state estimation (PSSE) schemes are computationally expensive or yield suboptimal performance. To bypass these hurdles, this paper advocates deep neural networks (DNNs) for real-time power system monitoring. By unrolling a state-of-the-art prox-linear SE solver, a novel modelspecific DNN is developed for real-time PSSE, which entails a minimal tuning effort, and is easy to train. To further enable system awareness even ahead of the time horizon, as well as to endow the DNN-based estimator with resilience, deep recurrent neural networks (RNNs) are pursued for power system state forecasting. Deep RNNs exploit the long-term nonlinear dependencies present in the historical voltage time series to enable forecasting, and they are easy to implement. Numerical tests showcase improved performance of the proposed DNN-based estimation and forecasting approaches compared with existing alternatives. Empirically, the novel model-specific DNN-based PSSE offers nearly an order of magnitude improvement in performance over competing alternatives, including the widely adopted Gauss-Newton PSSE solver, in our tests using real load data on the IEEE 118-bus benchmark system.

This method of concurrent 3D-printing, known as swarm printing, paves the way for a team of mobile robots to print even bigger structures in future. Developed by Assistant Professor Pham Quang Cuong and his team at NTU's Singapore Centre for 3D Printing, this new multi-robot technology was published in Automation in Construction, a top tier journal for civil engineering. The NTU scientist was also behind the Ikea Bot earlier this year where two robots assembled an Ikea chair in 8 min 55s. Using a specially formulated cement mix suitable for 3-D printing, this new development will allow for unique concrete designs currently not possible with conventional casting. Structures can also be produced on demand and in a much shorter period.

Results from Fanuc Corp. and Komatsu were a mixed bag Monday. Factory-automation giant Fanuc reported an 8.4 percent drop in fiscal first-half operating income and saw its shares rise, while construction-equipment-maker Komatsu posted an 80 percent profit surge that was rewarded with a stock decline. Put that perplexing share reaction down to the topsy-turvy world of machinery-makers, where investors tend to view dismal earnings as a sign that a company is nearing the bottom, and good results as a warning that it's close to the top. The overall picture, though, is that concerns sparked by U.S. bellwether Caterpillar Inc. last week of late-cycle cost pressures and a deteriorating China outlook have been overdone, at least as far as the Japanese firms are concerned. China's faltering economy has been a key focus. Fanuc's sales in the country, already shrinking, fell a further 42 percent in the quarter through Sept. 30, compared with the previous three months.

When you think of artificial intelligence (AI), chances are that a vision of supremely intelligent computers and robots taking over the world and enslaving the human race spring to mind. We've been conditioned by sci-fi books and movies to fear the worst. The reality is far different – mundane even. AI is basically the operation of algorithms that automatically optimize themselves as they go – a process known as'machine learning'. It may sound simple, but it yields powerful results that are revolutionizing the world we live in.

Accurately estimating the remaining useful life (RUL) of industrial machinery is beneficial in many real-world applications. Estimation techniques have mainly utilized linear models or neural network based approaches with a focus on short term time dependencies. This paper introduces a system model that incorporates temporal convolutions with both long term and short term time dependencies. The proposed network learns salient features and complex temporal variations in sensor values, and predicts the RUL. A data augmentation method is used for increased accuracy. The proposed method is compared with several state-of-the-art algorithms on publicly available datasets. It demonstrates promising results, with superior results for datasets obtained from complex environments.

One thing about airplanes--especially ones that fly from aircraft carriers, where they're battered by saltwater and tough deck landings--is that they need lots of spare parts that are not always on hand. Instead of flying in new parts, though, future Navy ships may be able to make new ones to order. Picutre an intelligent, laser-wielding robot that can analyze the damage and 3D-print the needed titanium alloy parts from an onboard supply of metallic dust. This is one glimpse of the future proposed by the Office of Naval Research (ONR), which today announced a two-year, $5.8 million contract to create a new generation of super-smart 3D printers. The printers would not only make parts on order wherever they are needed, but can "observe, learn and make decisions by themselves," according to Lockheed.

For years, Lawrence Livermore National Laboratory engineers and scientists have used an array of sensors and imaging techniques to analyze the physics and processes behind metal 3-D printing in an ongoing effort to build higher quality metal parts the first time, every time. Now, researchers are exploring machine learning to process the data obtained during 3-D builds in real time, detecting within milliseconds whether a build will be of satisfactory quality. In a paper published online Sept. 5 by Advanced Materials Technologies, a team of Lab researchers report developing convolutional neural networks (CNNs), a popular type of algorithm primarily used to process images and videos, to predict whether a part will be good by looking at as little as 10 milliseconds of video. "This is a revolutionary way to look at the data that you can label video by video, or better yet, frame by frame," said principal investigator and LLNL researcher Brian Giera. "The advantage is that you can collect video while you're printing something and ultimately make conclusions as you're printing it. A lot of people can collect this data, but they don't know what to do with it on the fly, and this work is a step in that direction."

What's possible with 3D printing is largely driven by two things: How patient you are, since printing large or complex structures can take a while, and what kind of build volume you have to work with. Most 3D printers are boxes, and inside those boxes are smaller boxes, and inside those boxes are the area in which a thing can be printed. If your thing is larger than that box, you've either got to print it in pieces that can be assembled later, buy yourself a new printer, or give up entirely. You can certainly 3D print very large things, but there are still usually build volume constraints. We've seen examples of robot arms that can print anywhere they can reach, as well as gantry systems that can print structures like houses, as long as the structures are slightly smaller than they are.

There are a lot of changes that occur in companies under the influence of the information technology innovations. Those changes help significantly increase the quality of products and services, which increases the level of customer loyalty and satisfaction. Manufacturers also do not stand aside. New approaches and business models born in Industry 4.0 allow them increasing profit and investing more in the product enhancement. The term "industry 4.0" is now used as a synonym for the fourth industrial revolution.

Today, you will find 3D printers in the most surprising places--and all over the world. Not only that, but they are often busy doing the most surprising things for the human race. If you have been following 3D printing for even the shortest amount of time, then you may have learned to continually expect the unexpected. Machine learning and data calculations are perfect examples of this as they are now being applied in 3D via a new artificial intelligence system that performs its work through bending light. AI is built on looping calculations of numbers and data that ultimately result in recognition.