In this episode of Robots in Depth, Per Sjöborg speaks with Andrew Graham about snake arm robots that can get into impossible locations and do things no other system can. Andrew tells the story about starting OC Robotics as a way to ground his robotics development efforts in a customer need. He felt that making something useful gave a great direction to his projects. We also hear about some of the unique properties of snake arm robots: – They can fit in any space that the tip of the robot can get through – They can operate in very tight locations as they are flexible all along and therefore do not sweep large areas to move – They are easy to seal up so that they don't interact with the environment they operate in – They are set up in two parts where the part exposed to the environment and to risk is the cheaper part Andrew then shares some interesting insights from the many projects he has worked on, from fish processing and suit making to bomb disposal and servicing of nuclear power plants. This interview was recorded in 2015.
Drones have become the ultimate option for monitoring, surveying and inspection. Enel, the multinational energy company has implemented this strategy and selected Percepto's Sparrow system to monitor the Torrevaldaliga Nord power plant in Italy. The Sparrow's AI and computer vision technology will allow it to operate as independently as possible, and the collected aerial footage, photography is transmitted to Enel in real-time. "While drones are touted as the technology of the future, the ability to act autonomously unlocks their true potential, enabling them to act as a responsible, independent and smart team member that provides not only a bird's eye view of facilities, but real, actionable insights," said Percepto CEO, Dor Abuhasira. The goal is to introduce cost-effective and practical drone support to a business model attempting to continuously refine itself.
A convolutional sequence to sequence non-intrusive load monitoring model is proposed in this paper. Gated linear unit convolutional layers are used to extract information from the sequences of aggregate electricity consumption. Residual blocks are also introduced to refine the output of the neural network. The partially overlapped output sequences of the network are averaged to produce the final output of the model. We apply the proposed model to the REDD dataset and compare it with the convolutional sequence to point model in the literature. Results show that the proposed model is able to give satisfactory disaggregation performance for appliances with varied characteristics.
What do you think when you hear the words'artificial intelligence?' Robots? Do images of a world that looks less human flash across your mind? Before you panic, know this. Artificial Intelligence (AI) is much more than the idea of pending doom. Defined as"intelligence displayed by machines, in contrast with the natural intelligence displayed by humans and other animals," AI is simply another means to improve the way that we, as a society, address some of the biggest challenges we face.
Identifying the location of a disturbance and its magnitude is an important component for stable operation of power systems. We study the problem of localizing and estimating a disturbance in the interconnected power system. We take a model-free approach to this problem by using frequency data from generators. Specifically, we develop a logistic regression based method for localization and a linear regression based method for estimation of the magnitude of disturbance. Our model-free approach does not require the knowledge of system parameters such as inertia constants and topology, and is shown to achieve highly accurate localization and estimation performance even in the presence of measurement noise and missing data.
Electricity is produced by a variety of generating units, each with different lead times and costs to be readied for service, and production costs once brought online. Because electricity is a commodity that cannot be easily stored, generation should match consumption at any given time; therefore, the cost of generating electricity has a direct relationship to electricity demand, typically referred to as electricity load. An accurate load forecast enables generators to optimize the mix of generating units that can serve the expected load while minimizing the production costs. This holds true for generators in both regulated and deregulated markets. In several deregulated markets, the electricity market operator is in charge of dispatching the available generation units according to the market's expected load and individual units' offered generation costs.
The energy industry has undergone many changes over the past couple of years. With new advances in industrial processes, multiple sources of energy are now available which can drive forward a more efficient use of energy resources. With the newest breakthroughs artificial intelligence (AI) has offered in the fields of robotics, self-driven cars, finance, and healthcare, energy companies are now exploring the possibilities of incorporating AI to increase the prospects of more efficient consumption of energy. Several artificial intelligence courses are already being developed to facilitate learning in the field of AI. The ability to compress and analyze large sets of data can help brands monitor and interpret the data produced by energy industries to optimize energy consumption.
For instance, any of the utility company wants to have a close look of the remote power lines, the Airbus aerial satellites would help pull off data and for the closer look; it might contract with a local company to run a plane or drone flight over the area. Lately, Airbus announced its partnership with DroneBase for better results and inspection. Airbus has also started mapping the runways at Atlanta's Hartsfield-Jackson, the world's busiest airports. Here, the company used Sensefly's fixed-wing drone which flies autonomously, capturing images of the ground, and then the results are checked and complied, including 3D maps to show bumps and cracks, and GPS data to locate busted lights. "In the coming time, drones would be taking up certain projects that would make a great impact," says FAA.
Year by year control of normal and emergency conditions of up-to-date power systems becomes an increasingly complicated problem. With the increasing complexity the existing control system of power system conditions which includes operative actions of the dispatcher and work of special automatic devices proves to be insufficiently effective more and more frequently, which raises risks of dangerous and emergency conditions in power systems. The paper is aimed at compensating for the shortcomings of man (a cognitive barrier, exposure to stresses and so on) and automatic devices by combining their strong points, i.e. the dispatcher's intelligence and the speed of automatic devices by virtue of development of the intelligent system "Artificial dispatcher" on the basis of deep machine learning technology. For realization of the system "Artificial dispatcher" in addition to deep learning it is planned to attract the game theory approaches to formalize work of the up-to-date power system as a game problem. The "gain" for "Artificial dispatcher" will consist in bringing in a power system in the normal steady-state or post-emergency conditions by means of the required control actions.
Structure plays a key role in learning performance. In centralized computational systems, hyperparameter optimization and regularization techniques such as dropout are computational means to enhance learning performance by adjusting the deep hierarchical structure. However, in decentralized deep learning by the Internet of Things, the structure is an actual network of autonomous interconnected devices such as smart phones that interact via complex network protocols. Adjustments in the learning structure are a challenge. Uncertainties such as network latency, node and link failures or even bottlenecks by limited processing capacity and energy availability can significantly downgrade learning performance. Network self-organization and self-management is complex, while it requires additional computational and network resources that hinder the feasibility of decentralized deep learning. In contrast, this paper introduces reusable holarchic learning structures for exploring, mitigating and boosting learning performance in distributed environments with uncertainties. A large-scale performance analysis with 864000 experiments fed with synthetic and real-world data from smart grid and smart city pilot projects confirm the cost-effectiveness of holarchic structures for decentralized deep learning.