Join the audience for a live webinar at 6 p.m. BST/1 p.m. EST on 12 August 2020 on the discovery of a novel battery electrolyte that was guided by machine-learning software without human intervention Want to take part in this webinar? Innovations in batteries take years to formulate and commercialize, requiring extensive experimentation during the design and optimization phases. We approached the design and selection of a battery electrolyte through a black-box optimization algorithm directly integrated into a robotic test stand. We report here the discovery of a novel battery electrolyte by this experiment completely guided by the machine-learning software without human intervention. Motivated by the recent trend toward super-concentrated aqueous electrolytes for high-performance batteries, we utilize Dragonfly – a Bayesian machine-learning software package – to search mixtures of commonly used lithium and sodium salts for super-concentrated aqueous electrolytes with wide electrochemical stability windows.

Industrial Control Systems (ICS) are used to control physical processes in critical infrastructure. These systems are used in a wide variety of operations such as water treatment, power generation and distribution, and manufacturing. While the safety and security of these systems are of serious concern, recent reports have shown an increase in targeted attacks aimed at manipulating physical processes to cause catastrophic consequences. This trend emphasizes the need for algorithms and tools that provide resilient and smart attack detection mechanisms to protect ICS. In this paper, we propose an anomaly detection framework for ICS based on a deep neural network. The proposed methodology uses dilated convolution and long short-term memory (LSTM) layers to learn temporal as well as long term dependencies within sensor and actuator data in an ICS. The sensor/actuator data are passed through a unique feature engineering pipeline where wavelet transformation is applied to the sensor signals to extract features that are fed into the model. Additionally, this paper explores four variations of supervised deep learning models, as well as an unsupervised support vector machine (SVM) model for this problem. The proposed framework is validated on Secure Water Treatment testbed results. This framework detects more attacks in a shorter period of time than previously published methods.

Matt Carlson is the Vice President of Business Development at WiBotic Inc, a company that provides reliable wireless power solutions to charge aerial, mobile and aquatic robot systems. Why are wireless charging solutions so important to the future of robotics? Robots need the ability to autonomously charge for most applications. It simply isn't cost effective to hire a staff of workers to manage battery charging or battery swapping. However, most autonomous charging today is done using docking stations that require physical mating of electrical contacts.

I'm not sure if Moflin is supposed to be a robotic hamster, guinea pig, baby bunny, or some alternate take on a Tribble, but goddamn this robo-pet is cute. Launched as part of a Kickstarter campaign from Vanguard Industries that went live earlier this week, Moflin looks to follow in the steps of Sony's Aibo or other robo-pets like Qoobo. However, instead of simply a disembodied tail attached to a fluffy base like Qoobo, Moflin apparently uses AI to have "emotional capabilities" meant to more accurately mimic real pets, so that it can express feelings and potentially even serve as a therapeutic aid. In order to make that happen, Vanguard Industries said it created its own Emotion AI tech that allows Moflin's feelings to react and evolve over time based on contact with humans. Individual Moflins can even have unique personalities based on their experiences, and learn to react differently depending on the actions of their owners.

To meet the growing quest for enhanced network capacity, mobile network operators (MNOs) are deploying dense infrastructures of small cells. This, in turn, increases the power consumption of mobile networks, thus impacting the environment. As a result, we have seen a recent trend of powering mobile networks with harvested ambient energy to achieve both environmental and cost benefits. In this paper, we consider a network of virtualized small cells (vSCs) powered by energy harvesters and equipped with rechargeable batteries, which can opportunistically offload baseband (BB) functions to a grid-connected edge server depending on their energy availability. We formulate the corresponding grid energy and traffic drop rate minimization problem, and propose a distributed deep reinforcement learning (DDRL) solution. Coordination among vSCs is enabled via the exchange of battery state information. The evaluation of the network performance in terms of grid energy consumption and traffic drop rate confirms that enabling coordination among the vSCs via knowledge exchange achieves a performance close to the optimal. Numerical results also confirm that the proposed DDRL solution provides higher network performance, better adaptation to the changing environment, and higher cost savings with respect to a tabular multi-agent reinforcement learning (MRL) solution used as a benchmark.

This summer I've been testing several lawn mowers, the most unique of which is this robot from WORX. This is the WORX Landroid M robotic mower, a fully automated, cordless, rechargeable battery powered piece of equipment that'll do all your work for you. The biggest obstacle you'll face is setup, and that's pretty straightforward if you follow the directions step-by-step. The Parts Included in our review is the basic WORX Landroid M robotic mower and a few add-ons. If you're looking at the WORX website (or WORX in a store) there are at least two versions of this Landroid M, one with GPS, one without.

AI techniques are being applied by researchers aiming to extend the life and monitor the health of batteries, with the aim of powering the next generation of electric vehicles and consumer electronics. Researchers at Cambridge and Newcastle Universities have designed a machine learning method that can predict battery health with ten times the accuracy of the current industry standard, according to an account in ScienceDaily. The promise is to develop safer and more reliable batteries. In a new way to monitor batteries, the researchers sent electrical pulses into them and monitored the response. The measurements were then processed by a machine learning algorithm to enable a prediction of the battery's health and useful life.

A battery pioneer has invented a new kind of battery that is 90 per cent cheaper to produce than standard lithium-ion batteries, and potentially much safer. Hideaki Horie – who has worked on battery technology since 1990 and led Nissan's development of the Leaf electric car – discovered a way to replace the batteries basic components in order to speed up and simplify the manufacturing process. "The problem with making lithium batteries now is that it's device manufacturing, like semiconductors," Mr Horie told The Japan Times. "Our goal is to make it more like steel production." Manufacturing the new batteries is significantly simplified by replacing the metal-lined electrodes and liquid electrolytes typically found within lithium-ion units with a resin construction.

Comau has created an innovative, in-line testing and quality control paradigm that optimizes the construction and assembly of batteries. MI.RA/Thermography is one of the newest solutions within Comau's cutting-edge vision systems family of Machine Inspection Recognition Archetypes, named MI.RA. Designed for industrial-scale battery manufacturing, MI.RA/Thermography uses thermal imaging and artificial intelligence to perform non-invasive automated assessment and control of welded joints, to ensure battery integrity and prevent waste. Its non-destructive testing methodology protects cycle times without changing the existing manufacturing layout. Battery packs are composed of a large number of individual battery cells that are structurally held and electrically connected by numerous welded joints.

GL Robotics, an advanced technology business, is offering robotic lawnmowers for sale. Obviously, the lawnmower operates on its own and can operate from three to seven hours a day in the yard, so your grass will stay looking sharp at all times. And if you're wondering, the lawnmower is very quiet you can have the lawnmower programmed to run at night while you're at work or sleeping. Owner Laurie Summerlin says not only is it a timesaver but it has other perks as well. "It's very good for the environment it doesn't need gas or oil its a rechargeable battery, so you don't have the carbon footprint and the emission. A lawnmower actually uses as much carbon footprint as 11 cars for the same amount of time, so its good for the environment in that way. Its also very good for the grass it cuts a small amount for each day and it doesn't stress the grass," Summerlin said.