We present a novel unsupervised deep learning approach that utilizes the encoder-decoder architecture for detecting anomalies in sequential sensor data collected during industrial manufacturing. Our approach is designed not only to detect whether there exists an anomaly at a given time step, but also to predict what will happen next in the (sequential) process. We demonstrate our approach on a dataset collected from a real-world testbed. The dataset contains images collected under both normal conditions and synthetic anomalies. We show that the encoder-decoder model is able to identify the injected anomalies in a modern manufacturing process in an unsupervised fashion. In addition, it also gives hints about the temperature non-uniformity of the testbed during manufacturing, which is what we are not aware of before doing the experiment.

This paper presents approaches to determine a network based pricing for 3D printing services in the context of a two-sided manufacturing-as-a-service marketplace. The intent is to provide cost analytics to enable service bureaus to better compete in the market by moving away from setting ad-hoc and subjective prices. A data mining approach with machine learning methods is used to estimate a price range based on the profile characteristics of 3D printing service suppliers. The model considers factors such as supplier experience, supplier capabilities, customer reviews and ratings from past orders, and scale of operations among others to estimate a price range for suppliers' services. Data was gathered from existing marketplace websites, which was then used to train and test the model. The model demonstrates an accuracy of 65% for US based suppliers and 59% for Europe based suppliers to classify a supplier's 3D Printer listing in one of the seven price categories. The improvement over baseline accuracy of 25% demonstrates that machine learning based methods are promising for network based pricing in manufacturing marketplaces. Conventional methodologies for pricing services through activity based costing are inefficient in strategically pricing 3D printing service offering in a connected marketplace. As opposed to arbitrarily determining prices, this work proposes an approach to determine prices through data mining methods to estimate competitive prices. Such tools can be built into online marketplaces to help independent service bureaus to determine service price rates.

A futuristic 3D-printed house that lets guests'experience Mars on Earth' will soon offer you the chance to experience what an interplanetary vacation of the future may be like, its creators say. Nestled in the woods of upstate New York along the Hudson River, Tera will be hired out to holiday-makers hoping to experience what sustainable life could be like on Mars. 'Tera' is the brainchild of AI SpaceFactory, a New York City design agency that was awarded $500,000 (£386,000) earlier this year for winning NASA'S 3D-Printed Habitat Challenge with its previous'Marsha' habitat. Each stay will be used to fund the mission of the firm behind its design, which hopes to research and develop the renewable and sustainable technologies of the future. This technology will be used both here on Earth and, they say, will be one day form the basis of a sustainable colony on the red planet.

Into 3D printing world, "spaghetti" is the common term for the tangled mess of stringy plastic that's often the result of a failed print. Fear of their print bed turning into a hot plate of PLA spaghetti is enough to keep many users from leaving their machines operating overnight or while they're out of the house. The Spaghetti Detective, an open source project that lets machine learning take over when you can't sit watching the printer all day, might help those users to overcome that fear. This software monitors your prints for you, and notify you if it detects a possible print failure. The Spaghetti Detective is a plugin for OctoPrint, which runs on a Raspberry Pi, and gives you the ability to remotely control your 3D printer and view a live video feed as it runs.

Volvo Group and NVIDIA are delivering autonomy to the world's transportation industries, using AI to revolutionize how people and products move all over the world. At its headquarters in Gothenburg, Sweden, Volvo Group announced Tuesday that it's using the NVIDIA DRIVE end-to-end autonomous driving platform to train, test and deploy self-driving AI vehicles, targeting public transport, freight transport, refuse and recycling collection, construction, mining, forestry and more. By injecting AI into these industries, Volvo Group and NVIDIA can create amazing new vehicles and deliver more productive services. The two companies are co-locating engineering teams in Gothenburg and Silicon Valley. Together, they will build on the DRIVE AGX Pegasus platform for in-vehicle AI computing and utilize the full DRIVE AV software stack for 360-degree sensor processing, perception, map localization and path planning.

This paper introduces a novel framework to construct the region of attraction (ROA) of a power system centered around a stable equilibrium by using stable state trajectories of system dynamics. Most existing works on estimating ROA rely on analytical Lyapunov functions, which are subject to two limitations: the analytic Lyapunov functions may not be always readily available, and the resulting ROA may be overly conservative. This work overcomes these two limitations by leveraging the converse Lyapunov theorem in control theory to eliminate the need of an analytic Lyapunov function and learning the unknown Lyapunov function with the Gaussian Process (GP) approach. In addition, a Gaussian Process Upper Confidence Bound (GP-UCB) based sampling algorithm is designed to reconcile the trade-off between the exploitation for enlarging the ROA and the exploration for reducing the uncertainty of sampling region. Within the constructed ROA, it is guaranteed in probability that the system state will converge to the stable equilibrium with a confidence level. Numerical simulations are also conducted to validate the assessment approach for the ROA of the single machine infinite bus system and the New England $39$-bus system. Numerical results demonstrate that our approach can significantly enlarge the estimated ROA compared to that of the analytic Lyapunov counterpart.

Objects made with 3-D printing can be lighter, stronger, and more complex than those produced through traditional manufacturing methods. But several technical challenges must be overcome before 3-D printing transforms the production of most devices. Commercially available printers generally offer only high speed, high precision, or high-quality materials. Rarely do they offer all three, limiting their usefulness as a manufacturing tool. Today, 3-D printing is used mainly for prototyping and low-volume production of specialized parts.

Power system emergency control is generally regarded as the last safety net for grid security and resiliency. Existing emergency control schemes are usually designed off-line based on either the conceived "worst" case scenario or a few typical operation scenarios. These schemes are facing significant adaptiveness and robustness issues as increasing uncertainties and variations occur in modern electrical grids. To address these challenges, for the first time, this paper developed novel adaptive emergency control schemes using deep reinforcement learning (DRL), by leveraging the high-dimensional feature extraction and non-linear generalization capabilities of DRL for complex power systems. Furthermore, an open-source platform named RLGC has been designed for the first time to assist the development and benchmarking of DRL algorithms for power system control. Details of the platform and DRL-based emergency control schemes for generator dynamic braking and under-voltage load shedding are presented. Extensive case studies performed in both two-area four-machine system and IEEE 39-Bus system have demonstrated the excellent performance and robustness of the proposed schemes.

Coffee cups are being grown from fruit by a design company in a bid to cut down on plastic waste. They are made from gourds, a large fruit with a hard skin in the pumpkin family, using custom-designed 3D-printed moulds. The company claims that these biodegradable cups can be manufactured on a mass scale – offering a more environmentally friendly alternative to paper coffee cups. Gourds are fast-growing plants that bear strong fruit each season. Once dried, the gourds' strong outer skin and fibrous inner flesh becomes watertight.

Machine learning embodies the principles of data mining, but can also make automatic correlations and learn from them to apply to new algorithms. It's the technology behind self-driving cars that can quickly adjust to new conditions while driving. Machine learning also provides instant recommendations when a buyer purchases a product from Amazon. These algorithms and analytics are constantly meant to be improving, so the result will only get more accurate over time. Machine learning isn't artificial intelligence, but the ability to learn and improve is still an impressive feat.