The Rattlesnake Creek Wind Project will be built between the towns of Allen, Emerson and Wakefield, the Sioux City Journal reported . Demand for power from Facebook helped resurrect the project that had been at a standstill since 2013 after its former owners, Trade Winds, couldn't find buyers for the energy the farm.

Artificial intelligence (AI) poses the greatest threat to the oil and gas industry over the next five to 10 years, according to Microsoft's oil and gas director for the Middle East and Africa.

Artificial intelligence has the potential to transform a multitude of industries, including retail, small business accounting and even product design. The energy and utilities markets could be next. It's still early days in terms of adoption, but there are numerous use cases for AI in the energy and utilities industries. AI can be used to make smart electric grids more efficient in delivering energy, can predict when batteries and other equipment will fail and can also help make energy exploration easier and more economical. AI and one of its subsets, machine learning, are digital trends poised to disrupt the energy industry, according to a recent report from Wood Mackenzie, an energy, chemicals, renewables, metals and mining research and consultancy group, Greentech Media (GTM) reports.

Artificial intelligence is slowly but steadily embedding itself into the core processes of multiple industries and changing the industrial landscape in so many ways -- be it deep learning-powered autonomous cars or bot-powered medical diagnostic processes. The industrial and energy sectors are not immune to the disruption that comes with embracing AI. As upstream and downstream companies gear up for AI, there is one important lesson I want to share that might seem counterintuitive. The Internet of Things (IoT) world may be exciting, but there are serious technical challenges that need to be addressed, especially by developers. In this handbook, learn how to meet the security, analytics, and testing requirements for IoT applications.

Lithium battery safety is an important issue as there are more and more reports of fires and explosions. Fires have been reported in everything from cell phones to airplanes to robots. If you don't know why we need to discuss this, or even if you do know, watch this clip or click here. I am not a fire expert. This post is based on things I have heard and some basic research.

There are thousands of oil spills each year in US waters alone. One major source is illegal dumping of oil in harbours when ships empty their bilges, typically at night to avoid detection. However, a new kind of polarising camera can now spot offenders immediately. Its ability to detect otherwise invisible oil sheens could even lead investigators to lost planes.

Mitsui O.S.K. Lines, Ltd. (MOL) today announced that it teamed up with Fujitsu Laboratories Ltd., and Tokyo University of Marine Science and Technology to verify the accuracy of technology to estimate vessel performance at sea by applying Fujitsu's artificial intelligence (AI) technology, "FUJITSU Human Centric AI Zinrai." This project is a part of MOL's initiative to assess the effectiveness of AI technology, and aims to reduce fuel consumption and vessels' environmental impact by verifying the accuracy of the technology, using Fujitsu's AI Technology to estimate vessel performance at sea. MOL provided actual voyage data collected from MOL fleet in operation to Fujitsu Laboratories, which, along with Tokyo University of Marine Science and Technology, verified the data by using their jointly developed machine learning method. Learned the correlation of each item of operation data using Fujitsu's unique AI technology and high-dimensional statistics analysis technology, and established the technology that estimates vessel performance. Estimated the ship speed from the data other than the speed and verified the comparison between that estimated value and actual operation data, in case to assess allowance of speed.

This paper considers two important problems - on the supply-side and demand-side respectively and studies both in a unified framework. On the supply side, we study the problem of energy sharing among microgrids with the goal of maximizing profit obtained from selling power while meeting customer demand. On the other hand, under shortage of power, this problem becomes one of deciding the amount of power to be bought with dynamically varying prices. On the demand side, we consider the problem of optimally scheduling the time-adjustable demand - i.e., of loads with flexible time windows in which they can be scheduled. While previous works have treated these two problems in isolation, we combine these problems together and provide for the first time in the literature, a unified Markov decision process (MDP) framework for these problems. We then apply the Q-learning algorithm, a popular model-free reinforcement learning technique, to obtain the optimal policy. Through simulations, we show that our model outperforms the traditional power sharing models.

Solving inverse problems is central to geosciences and remote sensing. Radiative transfer models (RTMs) represent mathematically the physical laws which govern the phenomena in remote sensing applications (forward models). The numerical inversion of the RTM equations is a challenging and computationally demanding problem, and for this reason, often the application of a nonlinear statistical regression is preferred. In general, regression models predict the biophysical parameter of interest from the corresponding received radiance. However, this approach does not employ the physical information encoded in the RTMs. An alternative strategy, which attempts to include the physical knowledge, consists in learning a regression model trained using data simulated by an RTM code. In this work, we introduce a nonlinear nonparametric regression model which combines the benefits of the two aforementioned approaches. The inversion is performed taking into account jointly both real observations and RTM-simulated data. The proposed Joint Gaussian Process (JGP) provides a solid framework for exploiting the regularities between the two types of data. The JGP automatically detects the relative quality of the simulated and real data, and combines them accordingly. This occurs by learning an additional hyper-parameter w.r.t. a standard GP model, and fitting parameters through maximizing the pseudo-likelihood of the real observations. The resulting scheme is both simple and robust, i.e., capable of adapting to different scenarios. The advantages of the JGP method compared to benchmark strategies are shown considering RTM-simulated and real observations in different experiments. Specifically, we consider leaf area index (LAI) retrieval from Landsat data combined with simulated data generated by the PROSAIL model.

You are free to share this article under the Attribution 4.0 International license. A new system that uses artificial intelligence to find cracks captured in videos of nuclear reactors could help reduce accidents as well as maintenance costs, researchers report. "Regular inspection of nuclear power plant components is important to guarantee safe operations," says Mohammad R. Jahanshahi, an assistant professor in the Lyles School of Civil Engineering at Purdue University. "However, current practice is time-consuming, tedious, and subjective and involves human technicians reviewing inspection videos to identify cracks on reactors," Jahanshahi says. The fact that nuclear reactors are submerged in water to maintain cooling complicates the inspection process.