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Solar storm forecasts for Earth improved with help from the public

Solar storm analysis carried out by an army of citizen scientists has helped researchers devise a new and more accurate way of forecasting when Earth will be hit by harmful space weather. Scientists at the University of Reading added analysis carried out by members of the public to computer models designed to predict when coronal mass ejections (CMEs) — huge solar eruptions that are harmful to satellites and astronauts — will arrive at Earth.

The team found forecasts were 20% more accurate, and uncertainty was reduced by 15%, when incorporating information about the size and shape of the CMEs in the volunteer analysis. The data was captured by thousands of members of the public during the latest activity in the Solar Stormwatch citizen science project, which was devised by Reading researchers and has been running since 2010.

The findings support the inclusion of wide-field CME imaging cameras on board space weather monitoring missions currently being planned by agencies like NASA and ESA.

Dr Luke Barnard, space weather researcher at the University of Reading’s Department of Meteorology, who led the study, said: “CMEs are sausage-shaped blobs made up of billions of tonnes of magnetised plasma that erupt from the Sun’s atmosphere at a million miles an hour. They are capable of damaging satellites, overloading power grids and exposing astronauts to harmful radiation.

“Predicting when they are on a collision course with Earth is therefore extremely important, but is made difficult by the fact the speed and direction of CMEs vary wildly and are affected by solar wind, and they constantly change shape as they travel through space.

“Solar storm forecasts are currently based on observations of CMEs as soon as they leave the Sun’s surface, meaning they come with a large degree of uncertainty. The volunteer data offered a second stage of observations at a point when the CME was more established, which gave a better idea of its shape and trajectory.

“The value of additional CME observations demonstrates how useful it would be to include cameras on board spacecraft in future space weather monitoring missions. More accurate predictions could help prevent catastrophic damage to our infrastructure and could even save lives.”

In the study, published in AGU Advances, the scientists used a brand new solar wind model, developed by Reading co-author Professor Mathew Owens, for the first time to create CME forecasts.

The simplified model is able to run up to 200 simulations — compared to around 20 currently used by more complex models — to provide improved estimates of the solar wind speed and its impact on the movement of CMEs, the most harmful of which can reach Earth in 15-18 hours.

Adding the public CME observations to the model’s predictions helped provide a clearer picture of the likely path the CME would take through space, reducing the uncertainty in the forecast. The new method could also be applied to other solar wind models.

The Solar Stormwatch project was led by Reading co-author Professor Chris Scott. It asked volunteers to trace the outline of thousands of past CMEs captured by Heliospheric Imagers — specialist, wide-angle cameras — on board two NASA STEREO spacecraft, which orbit the Sun and monitor the space between it and Earth.

The scientists retrospectively applied their new forecasting method to the same CMEs the volunteers had analysed to test how much more accurate their forecasts were with the additional observations.

Using the new method for future solar storm forecasts would require swift real-time analysis of the images captured by the spacecraft camera, which would provide warning of a CME being on course for Earth several hours or even days in advance of its arrival.

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Future autonomous machines may build trust through emotion

Army research has extended the state-of-the-art in autonomy by providing a more complete picture of how actions and nonverbal signals contribute to promoting cooperation. Researchers suggested guidelines for designing autonomous machines such as robots, self-driving cars, drones and personal assistants that will effectively collaborate with Soldiers.

Dr. Celso de Melo, computer scientist with the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory at CCDC ARL West in Playa Vista, California, in collaboration with Dr. Kazunori Teradafrom Gifu University in Japan, recently published a paper in Scientific Reports where they show that emotion expressions can shape cooperation.

Autonomous machines that act on people’s behalf are poised to become pervasive in society, de Melo said; however, for these machines to succeed and be adopted, it is essential that people are able to trust and cooperate with them.

“Human cooperation is paradoxical,” de Melo said. “An individual is better off being a free rider, while everyone else cooperates; however, if everyone thought like that, cooperation would never happen. Yet, humans often cooperate. This research aims to understand the mechanisms that promote cooperation with a particular focus on the influence of strategy and signaling.”

Strategy defines how individuals act in one-shot or repeated interaction. For instance, tit-for-tat is a simple strategy that specifies that the individual should act as his/her counterpart acted in the previous interaction.

Signaling refers to communication that may occur between individuals, which could be verbal (e.g., natural language conversation) and nonverbal (e.g., emotion expressions).

This research effort, which supports the Next Generation Combat Vehicle Army Modernization Priority and the Army Priority Research Area for Autonomy, aims to apply this insight in the development of intelligent autonomous systems that promote cooperation with Soldiers and successfully operate in hybrid teams to accomplish a mission.

“We show that emotion expressions can shape cooperation,” de Melo said. “For instance, smiling after mutual cooperation encourages more cooperation; however, smiling after exploiting others — which is the most profitable outcome for the self — hinders cooperation.”

The effect of emotion expressions is moderated by strategy, he said. People will only process and be influenced by emotion expressions if the counterpart’s actions are insufficient to reveal the counterpart’s intentions.

For example, when the counterpart acts very competitively, people simply ignore-and even mistrust-the counterpart’s emotion displays.

“Our research provides novel insight into the combined effects of strategy and emotion expressions on cooperation,” de Melo said. “It has important practical application for the design of autonomous systems, suggesting that a proper combination of action and emotion displays can maximize cooperation from Soldiers. Emotion expression in these systems could be implemented in a variety of ways, including via text, voice, and nonverbally through (virtual or robotic) bodies.”

According to de Melo, the team is very optimistic that future Soldiers will benefit from research such as this as it sheds light on the mechanisms of cooperation.

“This insight will be critical for the development of socially intelligent autonomous machines, capable of acting and communicating nonverbally with the Soldier,” he said. “As an Army researcher, I am excited to contribute to this research as I believe it has the potential to greatly enhance human-agent teaming in the Army of the future.”

The next steps for this research include pursuing further understanding of the role of nonverbal signaling and strategy in promoting cooperation and identifying creative ways to apply this insight on a variety of autonomous systems that have different affordances for acting and communicating with the Soldier.

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Scientists take new spin on quantum research

Army researchers discovered a way to further enhance quantum systems to provide Soldiers with more reliable and secure capabilities on the battlefield.

Specifically, this research informs how future quantum networks will be designed to deal with the effects of noise and decoherence, or the loss of information from a quantum system in the environment.

As one of the U.S. Army’s priority research areas in its Modernization Strategy, quantum research will help transform the service into a multi-domain force by 2035 and deliver on its enduring responsibility as part of the joint force providing for the defense of the United States.

“Quantum networking, and quantum information science as a whole, will potentially lead to unsurpassed capabilities in computation, communication and sensing,” said Dr. Brian Kirby, researcher at the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory. “Example applications of Army interest include secure secret sharing, distributed network sensing and efficient decision making.”

This research effort considers how dispersion, a very common effect found in optical systems, impacts quantum states of three or more particles of light.

Dispersion is an effect where a pulse of light spreads out in time as it is transmitted through a medium, such as a fiber optic. This effect can destroy time correlations in communication systems, which can result in reduced data rates or the introduction of errors.

To understand this, Kirby said, consider the situation where two light pulses are created simultaneously and the goal is to send them to two different detectors so that they arrive at the same time. If each light pulse goes through a different dispersive media, such as two different fiber optic paths, then each pulse will be spread in time, ultimately making the arrival time of the pulses less correlated.

“Amazingly, it was shown that the situation is different in quantum mechanics,” Kirby said. “In quantum mechanics, it is possible to describe the behavior of individual particles of light, called photons. Here, it was shown by research team member Professor James Franson from the University of Maryland, Baltimore County, that quantum mechanics allows for certain situations where the dispersion on each photon can actually cancel out so that the arrival times remain correlated.”

The key to this is something called entanglement, a strong correlation between quantum systems, which is not possible in classical physics, Kirby said.

In this new work, Nonlocal Dispersion Cancellation for Three or More Photons, published in the peer-reviewed Physical Review A, the researchers extend the analysis to systems of three or more entangled photons and identify in what scenarios quantum systems outperform classical ones. This is unique from similar research as it considers the effects of noise on entangled systems beyond two-qubits, which is where the primary focus has been.

“This informs how future quantum networks will be designed to deal with the effects of noise and decoherence, in this case, dispersion specifically,” Kirby said.

Additionally, based on the success of Franson’s initial work on systems of two-photons, it was reasonable to assume that dispersion on one part of a quantum system could always be cancelled out with the proper application of dispersion on another part of the system.

“Our work clarifies that perfect compensation is not, in general, possible when you move to entangled systems of three or more photons,” Kirby said. “Therefore, dispersion mitigation in future quantum networks may need to take place in each communication channel independently.”

Further, Kirby said, this work is valuable for quantum communications because it allows for increased data rates.

“Precise timing is required to correlate detection events at different nodes of a network,” Kirby said. “Conventionally the reduction in time correlations between quantum systems due to dispersion would necessitate the use of larger timing windows between transmissions to avoid confusing sequential signals.”

Since Kirby and his colleagues’ new work describes how to limit the uncertainty in joint detection times of networks, it will allow subsequent transmissions in quicker succession.

The next step for this research is to determine if these results can be readily verified in an experimental setting.

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Safer, more comfortable soldier uniforms are in the works

Uniforms of U.S. Army soldiers must meet a long list of challenging requirements. They need to feel comfortable in all climates, be durable through multiple washings, resist fires and ward off insects, among other things. Existing fabrics don’t check all of these boxes, so scientists have come up with a novel way of creating a flame-retardant, insect-repellent fabric that uses nontoxic substances.

The researchers will present their results today at the American Chemical Society (ACS) Fall 2020 Virtual Meeting & Expo. 

“The Army presented to us this interesting and challenging requirement for multifunctionality,” says study leader Ramaswamy Nagarajan, Ph.D. “There are flame-resistant Army combat uniforms made of various materials that meet flame retardant requirements. But they are expensive, and there are problems with dyeing the fabrics. Also, some of the raw materials are not produced in the U.S. So, our goal was to find an existing material that we could modify to make it flame retardant and insect repellent, yet still have a fabric that a soldier would want to wear.”

Because Nagarajan’s research group focuses on sustainable green chemistry, the team sought nontoxic chemicals and processes for this study. They chose to modify a commercially available 50-50 nylon-cotton blend, a relatively inexpensive, durable and comfortable fabric produced in the U.S. The material is used in a wide range of civilian and military applications because the nylon is strong and resistant to abrasion, whereas the cotton is comfortable to wear. But this type of textile doesn’t inherently repel bugs and is associated with a high fire risk.

“We started with making the fabric fire retardant, focusing on the cotton part of the blend,” explains Sourabh Kulkarni, a Ph.D. student who works with Nagarajan at the University of Massachusetts Lowell Center for Advanced Materials. “Cotton has a lot of hydroxyl groups (oxygen and hydrogen bonded together) on its surface, which can be activated by readily available chemicals to link with phosphorus-containing compounds that impart flame retardancy.” For their phosphorus-containing compound, they chose phytic acid, an abundant, nontoxic substance found in seeds, nuts and grains.

Next, the researchers tackled the issue of making the material repel insects so that soldiers wouldn’t have to spray themselves repeatedly or carry an additional item in their packs. The team took permethrin, an everyday nontoxic insect repellent, and attached it to the fabric using plasma-assisted deposition in collaboration with a local company, LaunchBay. Through trial and error, the researchers eventually got both the phytic acid and permethrin to link to the fabric’s surface molecules.

Using methods to measure heat release capacity and total heat release, as well as a vertical flame test, they found that the modified material performed at least 20% better than the untreated material. They also used a standard insect repellency test with live mosquitoes and found that the efficacy was greater than 98%. Finally, the fabric remained “breathable” after treatment as determined by air permeability studies.

“We are very excited,” Nagarajan says, “because we’ve shown we can modify this fabric to be flame retardant and insect repellent — and still be fairly durable and comfortable. We’d like to use a substance other than phytic acid that would contain more phosphorus and therefore impart a greater level of flame retardancy, better durability and still be nontoxic to a soldier’s skin. Having shown that we can modify the fabric, we would also like to see if we can attach antimicrobials to prevent infections from bacteria, as well as dyes that remain durable.”

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Learning capabilities of drone swarms

Army researchers developed a reinforcement learning approach that will allow swarms of unmanned aerial and ground vehicles to optimally accomplish various missions while minimizing performance uncertainty.

Swarming is a method of operations where multiple autonomous systems act as a cohesive unit by actively coordinating their actions.

Army researchers said future multi-domain battles will require swarms of dynamically coupled, coordinated heterogeneous mobile platforms to overmatch enemy capabilities and threats targeting U.S. forces.

The Army is looking to swarming technology to be able to execute time-consuming or dangerous tasks, said Dr. Jemin George of the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory.

“Finding optimal guidance policies for these swarming vehicles in real-time is a key requirement for enhancing warfighters’ tactical situational awareness, allowing the U.S. Army to dominate in a contested environment,” George said.

Reinforcement learning provides a way to optimally control uncertain agents to achieve multi-objective goals when the precise model for the agent is unavailable; however, the existing reinforcement learning schemes can only be applied in a centralized manner, which requires pooling the state information of the entire swarm at a central learner. This drastically increases the computational complexity and communication requirements, resulting in unreasonable learning time, George said.

In order to solve this issue, in collaboration with Prof. Aranya Chakrabortty from North Carolina State University and Prof. He Bai from Oklahoma State University, George created a research effort to tackle the large-scale, multi-agent reinforcement learning problem. The Army funded this effort through the Director’s Research Award for External Collaborative Initiative, a laboratory program to stimulate and support new and innovative research in collaboration with external partners.

The main goal of this effort is to develop a theoretical foundation for data-driven optimal control for large-scale swarm networks, where control actions will be taken based on low-dimensional measurement data instead of dynamic models.

The current approach is called Hierarchical Reinforcement Learning, or HRL, and it decomposes the global control objective into multiple hierarchies — namely, multiple small group-level microscopic control, and a broad swarm-level macroscopic control.

“Each hierarchy has its own learning loop with respective local and global reward functions,” George said. “We were able to significantly reduce the learning time by running these learning loops in parallel.”

According to George, online reinforcement learning control of swarm boils down to solving a large-scale algebraic matrix Riccati equation using system, or swarm, input-output data.

The researchers’ initial approach for solving this large-scale matrix Riccati equation was to divide the swarm into multiple smaller groups and implement group-level local reinforcement learning in parallel while executing a global reinforcement learning on a smaller dimensional compressed state from each group.

Their current HRL scheme uses a decupling mechanism that allows the team to hierarchically approximate a solution to the large-scale matrix equation by first solving the local reinforcement learning problem and then synthesizing the global control from local controllers (by solving a least squares problem) instead of running a global reinforcement learning on the aggregated state. This further reduces the learning time.

Experiments have shown that compared to a centralized approach, HRL was able to reduce the learning time by 80% while limiting the optimality loss to 5%.

“Our current HRL efforts will allow us to develop control policies for swarms of unmanned aerial and ground vehicles so that they can optimally accomplish different mission sets even though the individual dynamics for the swarming agents are unknown,” George said.

George stated that he is confident that this research will be impactful on the future battlefield, and has been made possible by the innovative collaboration that has taken place.

“The core purpose of the ARL science and technology community is to create and exploit scientific knowledge for transformational overmatch,” George said. “By engaging external research through ECI and other cooperative mechanisms, we hope to conduct disruptive foundational research that will lead to Army modernization while serving as Army’s primary collaborative link to the world-wide scientific community.”

The team is currently working to further improve their HRL control scheme by considering optimal grouping of agents in the swarm to minimize computation and communication complexity while limiting the optimality gap.

They are also investigating the use of deep recurrent neural networks to learn and predict the best grouping patterns and the application of developed techniques for optimal coordination of autonomous air and ground vehicles in Multi-Domain Operations in dense urban terrain.

George, along with the ECI partners, recently organized and chaired an invited virtual session on Multi-Agent Reinforcement Learning at the 2020 American Control Conference, where they presented their research findings.

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Australian Army successfully conducts field test of SPEE3D metal 3D printer

The Australian Army has carried out a field test of metal 3D printer manufacturer SPEE3D’s WarpSPEE3D additive manufacturing (AM) systems.  Taking place in various locations across the Australian Northern Territories, the three day trial demonstrated the efficacy of metal 3D printed parts during field training. Throughout testing, SPEE3D’s 3D printers proved capable of being deployed […]

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New research leads to drones changing shape mid-flight

Soon, the U.S. Army will be able to deploy autonomous air vehicles that can change shape during flight, according to new research presented at the AIAA Aviation Forum and Exposition’s virtual event June 16.

Researchers with the U.S. Army’s Combat Capabilities Development Command’s Army Research Laboratory and Texas A&M University published findings of a two-year study in fluid-structure interaction. Their research led to a tool, which will be able to rapidly optimize the structural configuration for Future Vertical Lift vehicles while properly accounting for the interaction between air and the structure.

Within the next year, this tool will be used to develop and rapidly optimize Future Vertical Lift vehicles capable of changing shape during flight, thereby optimizing performance of the vehicle through different phases of flight.

“Consider an [Intelligence, Surveillance and Reconnaissance] mission where the vehicle needs to get quickly to station, or dash, and then attempt to stay on station for as long as possible, or loiter,” said Dr. Francis Phillips, an aerospace engineer at the laboratory. “During dash segments, short wings are desirable in order to go fast and be more maneuverable, but for loiter segments, long wings are desirable in order to enable low power, high endurance flight.”

This tool will enable the structural optimization of a vehicle capable of such morphing while accounting for the deformation of the wings due to the fluid-structure interaction, he said.

One concern with morphing vehicles is striking a balance between sufficient bending stiffness and softness to enable to morphing,” Phillips said. “If the wing bends too much, then the theoretical benefits of the morphing could be negated and also could lead to control issues and instabilities.”

Fluid-structure interaction analyses typically require coupling between a fluid and a structural solver.

This, in turn, means that the computational cost for these analyses can be very high — in the range of about 10,000s core hours — for a single fluid and structural configuration.

To overcome these challenges, researchers developed a process that decouples the fluid and structural solvers, which can reduce the computational cost for a single run by as much as 80 percent, Phillips said.

The analysis of additional structural configurations can also be performed without re-analyzing the fluid due to this decoupled approach, which in turn generates additional computational cost savings, leading to multiple orders of magnitude reductions in computational cost when considering this method within an optimization framework.

Ultimately, this means the Army could design multi-functional Future Vertical Lift vehicles much more quickly than through the use of current techniques, he said.

For the past 20 years, there have been advances in research in morphing aerial vehicles but what makes the Army’s studies different is its look at the fluid-structure interaction during vehicle design and structural optimization instead of designing a vehicle first and then seeing what the fluid-structure interaction behavior will be.

“This research will have a direct impact on the ability to generate vehicles for the future warfighter,” Phillips said. “By reducing the computational cost for fluid-structure interaction analysis, structural optimization of future vertical lift vehicles can be accomplished in a much shorter time-frame.”

According to Phillips, when implemented within an optimization framework and coupled with additive manufacturing, the future warfighter will be able to use this tool to manufacture optimized custom air vehicles for mission specific uses.

Phillips presented this work in a paper, Uncoupled Method for Massively Parallelizable 3-D Fluid-Structure Interaction Analysis and Design, co-authored by the laboratory’s Drs. Todd Henry and John Hrynuk, as well as Texas A&M University’s Trent White, William Scholten and Dr. Darren Hartl.

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U.S. Army Researchers create 3D printed customized ear plugs for soldiers

Researchers from the US Army Aeromedical Research Laboratory have used 3D printing to produce and test customizable earplugs for members of the US Armed Forces.  The army scientists’ new technique for producing ear protection, could be deployed to prevent hearing loss among members of the armed forces. Damage to soldiers’ hearing can cause them to […]

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Author: Paul Hanaphy

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U.S. Army investigates predictive maintenance for 3D printed steel parts

The U.S. Army CCDC Army Research Laboratory (ARL) has discovered a method of predicting the performance of 3D printed parts and understanding any imperfections that can affect their performance. Detailed in a new study, the ARL will detect and monitor the wear and tear of 3D printed maraging steel through sensor measurement. Such measurements can […]

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Author: Anas Essop

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U.S. Army and WSU to 3D print Black Hawk helicopter components

The U.S. Army Aviation and Missile Command (AMCOM) will be partnering up with Wichita State University’s National Institute for Aviation Research (NIAR) to create a comprehensive virtual 3D model of a UH-60L Black Hawk helicopter. By completely disassembling the airframe and components of the helicopter, the researchers will be able to 3D scan each structural […]

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Author: Kubi Sertoglu