Machine learning techniques have been used in the past using Monte Carlo samples to construct predictors of the dynamic stability of power systems. In this paper we move beyond the task of prediction and propose a comprehensive approach to use predictors, such as Decision Trees (DT), within a standard optimization framework for pre- and post-fault control purposes. In particular, we present a generalizable method for embedding rules derived from DTs in an operation decision-making model. We begin by pointing out the specific challenges entailed when moving from a prediction to a control framework. We proceed with introducing the solution strategy based on generalized disjunctive programming (GDP) as well as a two-step search method for identifying optimal hyper-parameters for balancing cost and control accuracy. We showcase how the proposed approach constructs security proxies that cover multiple contingencies while facing high-dimensional uncertainty with respect to operating conditions with the use of a case study on the IEEE 39-bus system. The method is shown to achieve efficient system control at a marginal increase in system price compared to an oracle model.

Another such invention, 3-D printing, is now scaling up. All over the world, an impressive diversity of people and organizations, ranging from startups and hobbyists to construction and engineering firms, are successfully prototyping 3-D-printed buildings. The government of Dubai has set a goal of 3-D printing 25% of every new building by 2030. Prototype single-family dwellings have been 3-D-printed in China, Italy, Russia--and Texas. Global infrastructure firm AECOM ACM 2.59% uses 3-D printing to prefabricate jail cells and hospital rooms.

If you've been following 3D printing in recent years, you may have come across a "next new thing" that sounds like it was dreamed up for the twilight zone: 4D printing, so dubbed and promoted by Skylar Tibbits, director of MIT's Self-Assembly Lab. You unlock this technology, as Rod Serling would say, with the keys of chemistry, physics, engineering and materials science, and move into a realm of both molecular properties and computer-aided design (CAD). Then you cross over into programmable materials -- and that's the term that leads to most of the news made in this field since 2014. Once they're produced on 3D printers, objects made of programmable materials continue to take shape, folding, unfolding or assembling themselves in response to outside stimuli such as light, movement, heat, pressure or water. Tibbits' TED Talk videos demonstrate multimaterial, printed 2D strands that curl themselves into the letters "MIT," and printed flat sheets of programmable materials that, once robotically cut, transform themselves into shoes. Tibbits said 4D printing started as a way to "print" robots, but taking out all the electromechanical systems.

Senvol, a 3D printing data specialist based in New York City, is developing data-driven machine learning additive manufacturing (AM) software for the U.S. Navy's Office of Naval Research (ONR). The software will help the Navy cut out the process of trial and error during material development. New York's Senvol creates additive manufacturing software that analyzes the relationships between AM process parameters and material performance. With this software to hand, ONR will be able to develop what Senvol describes as "statistically substantiated material properties" in order to reduce the conventional material characterization and testing that is needed to develop design allowables (the statistically determined material property values ascertained from test data). "Our software's capabilities will allow ONR to select the appropriate process parameters on a particular additive manufacturing machine given a target mechanical performance," commented Senvol President Annie Wang.

First invented in the 1980s by Chuck Hull, an engineer and physicist, 3D printing technology – also called additive manufacturing – is the process of making an object by depositing material, one layer at a time. Similarly to how an inkjet printer adds individual dots of ink to form an image, a 3D printer adds material where it is needed, based on a digital file. Many conventional manufacturing processes involved cutting away excess materials to make a part, and this can lead to wastage of up to 30 pounds (13.6 kilograms) for every one pound of useful material, according to the Energy Department's Oak Ridge National Laboratory in Tennessee. By contrast, with some 3D printing processes about 98 per cent of the raw material is used in the finished part, and the method can be used to make small components using plastics and metal powders, with some experimenting with chocolate and other food, as well as biomaterials similar to human cells.

Robotic assistants are one of the most enduring features when we envision our technological future. Whether they're Roombas sweeping the floor or chauffeurs in the form of self-driving cars, our collective image of the future is almost always accented by machinic helpers. Now, a team of scientists is building a robotic assistant, not necessarily to help around the home, but to fabricate 3D digital designs in real time. Dubbed the Robotic Modeling Assistant (RoMA), the system combines augmented reality (AR) with robotic modeling, allowing users to design primitive 3D models on one side of a platform while the robotic arm fabricates the object on the other. "One of the most exciting things about RoMA is the combination of AR with fast 3D printing, which makes it possible for the designer to not only receive the visual cue of their design, but also the fast, in-situ tangible feedback that otherwise couldn't be possible," Huaishu Peng, an information science doctoral student at Cornell University who helped create RoMa, told Digital Trends.

The construction of New York's Empire State Building is often seen as the figurative and literal pinnacle of construction efficiency, rising 1,250 feet and 102 stories from the ground to its rooftop spire in just over 13 months' time, at a human cost of just five lives. Indeed, most of today's construction projects would be lucky to come close to that level of speed, regardless of the building's size. While the construction industry traditionally has been slow to change the way it operates, several new technologies are poised to usher in a new era of faster and more automated construction practices. Three-dimensional (3D) printing is among the key technologies that are expected to change the way structures are built in the future, as construction engineers and contractors seek methods for completing buildings more quickly, more efficiently, and, in many cases, with a greater attention paid to sustainability. Large printers that can print construction materials such as foam or concrete into specific shapes can drastically speed up the creation of walls, decorative or ornamental pieces, and even certain structural elements.

Stunning concept images have revealed a glimpse of what homes of the future could look like, as preparations are made for the world's first freeform 3D-printed property. Experts will build a house called'Curve Appeal' whose structure will be made from 28 panels, which will be 3D-printed off-site before being slotted together on-site to create two exterior walls, a roof and interior core. These four main sections will then be hoisted into place and joined together, and will feature a complex blend of curved angles and glazed windows. Although 3D printed buildings have been made before, this futuristic home - to be constructed in Chattanooga, Tennessee, later this year - will be the first of its kind, as it will contain no regular shapes or angles. Designers are planning to build the world's first freeform 3D-printed house later this year, taking a competition winning idea from concept to reality.

India has a severe shortage of engineers in these technologies, especially when countries like the US have a talent pool of 850,000 AI engineers. Finally, India begins to take Artificial Intelligence (AI), robotics, 3D Printing, and IOT technologies seriously. The finance minister announced during his Budget 2018 speech emphasised the need to set up centres of excellence to enhance their impact on business and the economy. The budget has allocated Rs 3,073 crore for this purpose, which has doubled from last year. This comes at a time when China has put in $2 billion for its centre for excellence.

Niti Aayog, the main policy think tank of the government, will direct the Centre's efforts to research on new technologies such as Internet of Things, artificial intelligence and 3D printing in various areas, finance minister Arun Jaitley said on Thursday. "Global economy is transforming into a digital economy thanks to development of cutting-edge technologies in digital space – machine learning, artificial intelligence, internet of things, 3D printing and the like," he said in his budget speech. Jaitley said the NITI Aayog will initiate a national programme to direct the government's efforts in the area of artificial intelligence and that the Department of Science and Technology will launch a Cyber Physical Systems Mission to establish centres of excellence to train people in robotics, AI, digital manufacturing, big data analytics, quantum communication and Internet of Things. He also said that the government was doubling the allocation for the Digital India programme to Rs 3,073 crore for 2018-19. The budget also proposed to set up five lakh WiFi hotspots to provide broadband access to five crore rural citizens. Besides, the budget has allocated Rs 10,000 crore to create and augment telecom infrastructure for the next year.