Artificial intelligence is helping to monitor wind turbines in some of the world's most remote locations. Many of these turbines are going offshore where there's plenty of space and winds blow the strongest. But remote locations also make it harder to fix turbines if something goes wrong. In Denmark'sNorth Sea, service vessels aren't even able to travel to the turbines four to five months of the year because of rough seas. And it often takes a crane to work on the gigantic turbines, each with blades as big as the wingspan of an Airbus A380 airliner. Wind turbines – like a lot of machinery – have been autonomous for decades.

Summary: If you want to capitalize on all the amazing advancements in data science take a look at these two hot growth areas for IoT. It's likely that these will be where a lot of venture capital is invested over the next year or two. A lot of well deserved attention is being directed at speech, image, and text processing. The tools in this area are the CNNs and RNNs we've reviewed in recent articles. We'll continue to exploit and refine these capabilities probably for several more years but if you want to get out in front you really need to be looking for the next wave.

The US company, Orbital Insight, is using AI to analyze satellite images and identify and quantify crude oil storage tanks. The tanks have floating roofs, so the volume of oil is visible. Orbital Insight is using shadow-detection technology and calculates how full a storage tank is by the size of the crescent-shaped shadow on the tank roof.

Blind source separation is a common processing tool to analyse the constitution of pixels of hyperspectral images. Such methods usually suppose that pure pixel spectra (endmembers) are the same in all the image for each class of materials. In the framework of remote sensing, such an assumption is no more valid in the presence of intra-class variabilities due to illumination conditions, weathering, slight variations of the pure materials, etc... In this paper, we first describe the results of investigations highlighting intra-class variability measured in real images. Considering these results, a new formulation of the linear mixing model is presented leading to two new methods. Unconstrained Pixel-by-pixel NMF (UP-NMF) is a new blind source separation method based on the assumption of a linear mixing model, which can deal with intra-class variability. To overcome UP-NMF limitations an extended method is proposed, named Inertia-constrained Pixel-by-pixel NMF (IP-NMF). For each sensed spectrum, these extended versions of NMF extract a corresponding set of source spectra. A constraint is set to limit the spreading of each source's estimates in IP-NMF. The methods are tested on a semi-synthetic data set built with spectra extracted from a real hyperspectral image and then numerically mixed. We thus demonstrate the interest of our methods for realistic source variabilities. Finally, IP-NMF is tested on a real data set and it is shown to yield better performance than state of the art methods.

PDF: We made a fancy PDF of this post for printing and offline viewing. Note: The reason this post took three weeks to finish is that as I dug into research on Artificial Intelligence, I could not believe what I was reading. It hit me pretty quickly that what's happening in the world of AI is not just an important topic, but by far THE most important topic for our future. So I wanted to learn as much as I could about it, and once I did that, I wanted to make sure I wrote a post that really explained this whole situation and why it matters so much. Not shockingly, that became outrageously long, so I broke it into two parts. This is Part 1--Part 2 is here. We are on the edge of change comparable to the rise of human life on Earth. It seems like a pretty intense place to be standing--but then you have to remember something about what it's like to stand on a time graph: you can't see what's to your right. So here's how it actually feels to stand there: Imagine taking a time machine back to 1750--a time when the world was in a permanent power outage, long-distance communication meant either yelling loudly or firing a cannon in the air, and all transportation ran on hay. When you get there, you retrieve a dude, bring him to 2015, and then walk him around and watch him react to everything. It's impossible for us to understand what it would be like for him to see shiny capsules racing by on a highway, talk to people who had been on the other side of the ocean earlier in the day, watch sports that were being played 1,000 miles away, hear a musical performance that happened 50 years ago, and play with my magical wizard rectangle that he could use to capture a real-life image or record a living moment, generate a map with a paranormal moving blue dot that shows him where he is, look at someone's face and chat with them even though they're on the other side of the country, and worlds of other inconceivable sorcery.

The Trump administration has prioritized repealing the Clean Power Plan (CPP), a set of rules by the U.S. EPA aimed at limiting pollution from power plants. New analysis shows that repealing the rule would cost the U.S. economy hundreds of billions of dollars, add more than a billion tons of greenhouse gases to the atmosphere, and cause more than 100,000 premature deaths due to inhaled particulate pollution. Energy Innovation utilized the Energy Policy Simulator (EPS) to analyze the effects of repealing the CPP. The EPS is an open-source computer model developed to estimate the economic and emissions effects of various combinations of energy and environmental policies using non-partisan, published data from the U.S. Energy Information Administration (EIA), U.S. EPA, Argonne National Laboratory, U.S. Forest Service, and U.S. Bureau of Transportation Statistics, among others. The EPS has been peer reviewed by experts at MIT, Stanford University, Argonne National Laboratory, Berkeley National Laboratory, and the National Renewable Energy Laboratory.

A startup called Exyn Technologies Inc. today revealed AI software that enables drones to fly autonomously, even in dark, obstacle-filled environments or beyond the reaches of GPS. A spin out of the University of Pennsylvania's GRASP Labs, Exyn uses sensor fusion to give drones situational awareness much like a human's. In a demo video shared by the company with TechCrunch, a drone using Exyn's AI can be seen waking up and taking in its surroundings. It then navigates from a launch point in a populated office to the nearest identified exit without human intervention. The route is not pre-programmed, and pilots did not manipulate controls to influence the path that the drone takes.

Samsung will reportedly sell "refurbished" Galaxy Note 7 devices that were globally recalled last year after the phones were found to cause battery fires, Korean outlet Hankyung said. The refurbished devices will reportedly come with new cases and a smaller low-capacity battery. The new Note 7 battery will have a capacity between 3,000 and 3,200mAh, compared to its previous 3,500mAh battery. So far, the company has recovered 98 percent of the 3.6 million Note 7 phones sold worldwide. Out of the retrieved devices, 200,000 were used tested to identify the cause of the battery fires.

Across the globe, energy systems are changing, creating unprecedented challenges for the organisations tasked with ensuring the lights stay on. In the UK, large fossil fuelled power stations are being replaced by increasing levels of widely distributed wind and solar generation. This renewable power is clean and free at the point of use but it cannot always be relied upon. To date National Grid has managed this intermittency by keeping polluting power stations online to make up the difference but Artificial Intelligence offers an alternative approach. What's needed is a smart grid which can integrate renewable energy efficiently at scale without having to keep polluting power stations online to manage intermittency.

Accurate prediction of user consumption is a key part not only in understanding consumer flexibility and behavior patterns, but in the design of robust and efficient energy saving programs as well. Existing prediction methods usually have high relative errors that can be larger than 30% and have difficulties accounting for heterogeneity between individual users. In this paper, we propose a method to improve prediction accuracy of individual users by adaptively exploring sparsity in historical data and leveraging predictive relationship between different users. Sparsity is captured by popular least absolute shrinkage and selection estimator, while user selection is formulated as an optimal hypothesis testing problem and solved via a covariance test. Using real world data from PG&E, we provide extensive simulation validation of the proposed method against well-known techniques such as support vector machine, principle component analysis combined with linear regression, and random forest. The results demonstrate that our proposed methods are operationally efficient because of linear nature, and achieve optimal prediction performance. Pan Li and Baosen Zhang are with the Department of Electrical Engineering, University of Washington, Seattle, WA, 98195, (email: {pli69, zhangbao}@uw.edu). Yang Weng and Ram Rajagopal are with the Civil and Environmental Department, Stanford University, Stanford, CA, 94035, (email: {yangweng, ramr}@stanford.edu). 2 Estimated consumption at time t. Estimated variance of the noise. Electric load forecasting is an important problem in the power engineering industry and have received extensive attention from both industry and academia over the last century. Many different forecasting techniques have been developed during this time. The authors in [1] present a comprehensive literature review on different methods related to load forecasting, from regression models to expert systems. Time series methods are further discussed in [2]. A thorough research on load and price forecasting is presented in [3]. A common theme among many of the established methods is that they are used to forecast relative large loads, from substations serving megawatts to transmission networks serving more than gigawatts of power [4]. Recent advances in technology such as smart meters, bidirectional communication capabilities and distributed energy resources have made individual households active participants in the power system. Many applications and programs based on these new technologies require estimating the future load of individual homes.