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Category: robotics/AI – Page 1,658

Japanese space startup Gitai has raised a $17.1 million funding round, a Series B financing for the robotics startup. This new funding will be used for hiring, as well as funding the development and execution of an on-orbit demonstration mission for the company’s robotic technology, which will show its efficacy in performing in-space satellite servicing work. That mission is currently set to take place in 2023.

Gitai will also be staffing up in the U.S., specifically, as it seeks to expand its stateside presence in a bid to attract more business from that market.

“We are proceeding well in the Japanese market, and we’ve already contracted missions from Japanese companies, but we haven’t expanded to the U.S. market yet,” explained Gitai founder and CEO Sho Nakanose in an interview. So we would like to get missions from U.S. commercial space companies, as a subcontractor first. We’re especially interested in on-orbit servicing, and we would like to provide general-purpose robotic solutions for an orbital service provider in the U.S.”

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Innovative silicon solutions provider HPQ Silicon Resources Inc. (“HPQ” or the “Company”), announced that it has received the TREKHY® system, a portable hydrogen-based mini-power generator, jointly developed by the French companies Apollon Solar SAS (“Apollon”) and Pragma Industries SAS (“Pragma”).

While continuing to work with Apollon on the development of new generations of more efficient silicon powders for hydrogen production, HPQ signed a Memorandum of Understanding with Apollon and Pragma to study the commercial potential of the TREKHY® autonomous power generator in Canada.

The TREKHY® provides energy on demand. The system uses a compact fuel cell to provide electrical power. The integrated fuel cell combines hydrogen and oxygen to provide useful electricity + H2O. Hydrogen is produced through a chemical reaction resulting from contact between water and a powder bag. Each bag delivers 30W of power for more than one hour. (Video of the system in operation). In January 2021, a Japanese distributor purchased 300 TREKHY® systems to equip the survival shelters of the Japanese Civil Security.

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Energy researchers have been reaching for the stars for decades in their attempt to artificially recreate a stable fusion energy reactor. If successful, such a reactor would revolutionize the world’s energy supply overnight, providing low-radioactivity, zero-carbon, high-yield power – but to date, it has proved extraordinarily challenging to stabilize. Now, scientists are leveraging supercomputing power from two national labs to help fine-tune elements of fusion reactor designs for test runs.

In experimental fusion reactors, magnetic, donut-shaped devices called “tokamaks” are used to keep the plasma contained: in a sort of high-stakes game of Operation, if the plasma touches the sides of the reactor, the reaction falters and the reactor itself could be severely damaged. Meanwhile, a divertor funnels excess heat from the vacuum.

In France, scientists are building the world’s largest fusion reactor: a 500-megawatt experiment called ITER that is scheduled to begin trial operation in 2025. The researchers here were interested in estimating ITER’s heat-load width: that is, the area along the divertor that can withstand extraordinarily hot particles repeatedly bombarding it.

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Over the past few decades, researchers have developed deep neural network-based models that can complete a broad range of tasks. Some of these techniques are specifically designed to process and generate coherent texts in multiple languages, translate texts, answer questions about a text and create summaries of news articles or other online content.

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Robots that could take on basic healthcare tasks to support the work of doctors and nurses may be the way of the future. Who knows, maybe a medical robot can prescribe your medicine someday? That’s the idea behind 3D structural-sensing robots being developed and tested at Simon Fraser University by Woo Soo Kim, associate professor in the School of Mechatronic Systems Engineering.

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We’ve all met people so smart and informed that we don’t understand what they’re talking about. The investment advisor discussing derivatives, the physician elaborating about B cells and T cells, the auto mechanic talking about today’s computerized engines—we trust their decisions, even though we do not completely grasp the meaning of their words.

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A research team from Skoltech and FBK (Italy) has presented a methodology to derive 4D building models using historical maps and machine learning. The implemented method relies on geometric, neighborhood, and categorical attributes in order to predict building heights. The method is useful for understanding urban phenomena and changes that contributed to defining our cities’ actual shape. The results were published in Applied Sciences.

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In Finland, stormy weather can happen at any time of year. This is an issue because Finland is heavily forested, and falling trees can knock out power lines and disable transformers, causing power blackouts for hundreds of thousands of people a year. Researchers at Aalto University and the Finnish Meteorological Institute (FMI) are using artificial intelligence and machine learning to try and predict when these weather-inflicted blackouts happen. Their new method can now predict these storms days in advance, allowing electricity companies to prepare their repair crews before the storm has even happened.

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