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By NASA
Download PDF: Statistical Analysis Using Random Forest Algorithm Provides Key Insights into Parachute Energy Modulator System
Energy modulators (EM), also known as energy absorbers, are safety-critical components that are used to control shocks and impulses in a load path. EMs are textile devices typically manufactured out of nylon, Kevlar® and other materials, and control loads by breaking rows of stitches that bind a strong base webbing together as shown in Figure 1. A familiar EM application is a fall-protection harness used by workers to prevent injury from shock loads when the harness arrests a fall. EMs are also widely used in parachute systems to control shock loads experienced during the various stages of parachute system deployment.
Random forest is an innovative algorithm for data classification used in statistics and machine learning. It is an easy to use and highly flexible ensemble learning method. The random forest algorithm is capable of modeling both categorical and continuous data and can handle large datasets, making it applicable in many situations. It also makes it easy to evaluate the relative importance of variables and maintains accuracy even when a dataset has missing values.
Random forests model the relationship between a response variable and a set of predictor or independent variables by creating a collection of decision trees. Each decision tree is built from a random sample of the data. The individual trees are then combined through methods such as averaging or voting to determine the final prediction (Figure 2). A decision tree is a non-parametric supervised learning algorithm that partitions the data using a series of branching binary decisions. Decision trees inherently identify key features of the data and provide a ranking of the contribution of each feature based on when it becomes relevant. This capability can be used to determine the relative importance of the input variables (Figure 3). Decision trees are useful for exploring relationships but can have poor accuracy unless they are combined into random forests or other tree-based models.
The performance of a random forest can be evaluated using out-of-bag error and cross-validation techniques. Random forests often use random sampling with replacement from the original dataset to create each decision tree. This is also known as bootstrap sampling and forms a bootstrap forest. The data included in the bootstrap sample are referred to as in-the-bag, while the data not selected are out-of-bag. Since the out-of-bag data were not used to generate the decision tree, they can be used as an internal measure of the accuracy of the model. Cross-validation can be used to assess how well the results of a random forest model will generalize to an independent dataset. In this approach, the data are split into a training dataset used to generate the decision trees and build the model and a validation dataset used to evaluate the model’s performance. Evaluating the model on the independent validation dataset provides an estimate of how accurately the model will perform in practice and helps avoid problems such as overfitting or sampling bias. A good model performs well on
both the training data and the validation data.
The complex nature of the EM system made it difficult for the team to identify how various parameters influenced EM behavior. A bootstrap forest analysis was applied to the test dataset and was able to identify five key variables associated with higher probability of damage and/or anomalous behavior. The identified key variables provided a basis for further testing and redesign of the EM system. These results also provided essential insight to the investigation and aided in development of flight rationale for future use cases.
For information, contact Dr. Sara R. Wilson. sara.r.wilson@nasa.gov
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By NASA
3 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
Dr. Rainee Simons (right) and Dr. Félix Miranda work together to create technology supporting heart health at NASA’s Glenn Research Center in Cleveland.Credit: NASA Prioritizing health is important on Earth, and it’s even more important in space. Exploring beyond the Earth’s surface exposes humans to conditions that can impact blood pressure, bone density, immune health, and much more. With this in mind, two NASA inventors joined forces 20 years ago to create a way to someday monitor astronaut heart health on long-duration spaceflight missions. This technology is now being used to monitor the health of patients with heart failure on Earth through a commercial product that is slated to launch in late 2024.
NASA inventors Dr. Rainee Simons, senior microwave communications engineer, and Dr. Félix Miranda, deputy chief of the Communications and Intelligent Systems Division, applied their expertise in radio frequency integrated circuits and antennas to create a miniature implantable sensor system to keep track of astronaut health in space. The technology, which was created at NASA’s Glenn Research Center in Cleveland with seed funds from the agency’s Technology Transfer Office, consists of a small bio-implanted sensor that can transmit a person’s health status from a sensor to a handheld device. The sensor is battery-less and wireless.
“You’re able to insert the sensor and bring it up to the heart or the aorta like a stent – the same process as in a stent implant,” Simons said. “No major surgery is needed for implantation, and operating the external handheld device, by the patient, is simple and easy.”
After Glenn patented the invention, Dr. Anthony Nunez, a heart surgeon, and Harry Rowland, a mechanical engineer, licensed the technology and founded a digital health medical technology company in 2007 called Endotronix, now an Edwards Lifesciences company. The company focuses on enabling proactive heart failure management with data-driven patient-to-physician solutions that detect dangers, based on the Glenn technology. The Endotronix primary monitoring system is called the Cordella Pulmonary Artery (PA) Sensor System. Dr. Nunez became aware of the technology while reading a technical journal that featured the concept, and he saw parallels that could be used in the medical technology industry.
The concept has proven to be an aid for heart failure management through several clinical trials, and patients have experienced improvements in their quality of life. Based on the outcome of Endotronix’s clinical testing to demonstrate safety and effectiveness, in June 2024 the U.S. Food and Drug Administration granted premarket approval to the Cordella PA Sensor System. The system is meant to help clinicians remotely assess, treat, and manage heart failure in patients at home with the goal of reducing hospitalizations.
“If you look at the statistics of how many people have congestive heart failure, high blood pressure… it’s a lot of people,” Miranda said. “To have the medical community saying we have a device that started from NASA’s intellectual property – and it could help people worldwide to be healthy, to enjoy life, to go about their business – is highly gratifying, and it’s very consistent with NASA’s mission to do work for the benefit of all.”
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By NASA
5 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
Pacific Island nations such as Kiribati — a low-lying country in the southern Pacific Ocean — are preparing now for a future of higher sea levels.NASA Earth Observatory Climate change is rapidly reshaping a region of the world that’s home to millions of people.
In the next 30 years, Pacific Island nations such as Tuvalu, Kiribati, and Fiji will experience at least 8 inches (15 centimeters) of sea level rise, according to an analysis by NASA’s sea level change science team. This amount of rise will occur regardless of whether greenhouse gas emissions change in the coming years.
The sea level change team undertook the analysis of this region at the request of several Pacific Island nations, including Tuvalu and Kiribati, and in close coordination with the U.S. Department of State.
In addition to the overall analysis, the agency’s sea level team produced high-resolution maps showing which areas of different Pacific Island nations will be vulnerable to high-tide flooding — otherwise known as nuisance flooding or sunny day flooding — by the 2050s. Released on Sept. 23, the maps outline flooding potential in a range of emissions scenarios, from best-case to business-as-usual to worst-case.
“Sea level will continue to rise for centuries, causing more frequent flooding,” said Nadya Vinogradova Shiffer, who directs ocean physics programs for NASA’s Earth Science Division. “NASA’s new flood tool tells you what the potential increase in flooding frequency and severity look like in the next decades for the coastal communities of the Pacific Island nations.”
Team members, led by researchers at the University of Hawaii and in collaboration with scientists at the University of Colorado and Virginia Tech, started with flood maps of Kiribati, Tuvalu, Fiji, Nauru, and Niue. They plan to build high-resolution maps for other Pacific Island nations in the near future. The maps can assist Pacific Island nations in deciding where to focus mitigation efforts.
“Science and data can help the community of Tuvalu in relaying accurate sea level rise projections,” said Grace Malie, a youth leader from Tuvalu who is involved with the Rising Nations Initiative, a United Nations-supported program led by Pacific Island nations to help preserve their statehood and protect the rights and heritage of populations affected by climate change. “This will also help with early warning systems, which is something that our country is focusing on at the moment.”
Future Flooding
The analysis by the sea level change team also found that the number of high-tide flooding days in an average year will increase by an order of magnitude for nearly all Pacific Island nations by the 2050s. Portions of the NASA team’s analysis were included in a sea level rise report published by the United Nations in August 2024.
Areas of Tuvalu that currently see less than five high-tide flood days a year could average 25 flood days annually by the 2050s. Regions of Kiribati that see fewer than five flood days a year today will experience an average of 65 flood days annually by the 2050s.
“I am living the reality of climate change,” said Malie. “Everyone (in Tuvalu) lives by the coast or along the coastline, so everyone gets heavily affected by this.”
Flooding on island nations can come from the ocean inundating land during storms or during exceptionally high tides, called king tides. But it can also result when saltwater intrudes into underground areas and pushes the water table to the surface. “There are points on the island where we will see seawater bubbling from beneath the surface and heavily flooding the area,” Malie added.
Matter of Location
Sea level rise doesn’t occur uniformly around the world. A combination of global and local conditions, such as the topography of a coastline and how glacial meltwater is distributed in the ocean, affects the amount of rise a particular region will experience.
“We’re always focused on the differences in sea level rise from one region to another, but in the Pacific, the numbers are surprisingly consistent,” said Ben Hamlington, a sea level researcher at NASA’s Jet Propulsion Laboratory in Southern California and the agency’s sea level change science team lead.
The impacts of 8 inches (15 centimeters) of sea level rise will vary from country to country. For instance, some nations could experience nuisance flooding several times a year at their airport, while others might face frequent neighborhood flooding equivalent to being inundated for nearly half the year.
Researchers would like to combine satellite data on ocean levels with ground-based measurements of sea levels at specific points, as well as with better land elevation information. “But there’s a real lack of on-the-ground data in these countries,” said Hamlington. The combination of space-based and ground-based measurements can yield more precise sea level rise projections and improved understanding of the impacts to countries in the Pacific.
“The future of the young people of Tuvalu is already at stake,” said Malie. “Climate change is more than an environmental crisis. It is about justice, survival for nations like Tuvalu, and global responsibility.”
To explore the high-tide flooding maps for Pacific Island nations, go to:
https://sealevel.nasa.gov
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Jane J. Lee / Andrew Wang
Jet Propulsion Laboratory, Pasadena, Calif.
818-354-0307 / 626-379-6874
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Last Updated Sep 25, 2024 Related Terms
Climate Change Earth Earth Science Jet Propulsion Laboratory Oceans Sea Level Rise Explore More
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By NASA
In the heart of NASA’s Johnson Space Center in Houston, a team of photographers, imagery acquisition specialists, analytic scientists, and graphic designers work together to create visual narratives that capture the defining moments of space exploration with creativity and precision.
From the Apollo missions to the Artemis campaign, these images, videos, and graphics chronicle NASA’s rich history and the people behind its monumental missions.
Official portrait of the Artemis II crew.NASA/Josh Valcarcel Each team at Johnson within Mission Imagery, the ISAG (Image Science and Analysis Group), and NASA’s OCOMM (Office of Communications) plays a role in this effort, ensuring the accuracy and artistry of visual narratives that have inspired generations.
“Behind every great leap for mankind, there is the courage, determination, and teamwork of people committed to pushing the boundaries of what’s possible,” said NASA photographer Josh Valcarcel.
Space Shuttle Enterprise atop the Shuttle Carrier Aircraft as it flies over New York City on April 27, 2012. NASA/Robert Markowitz “We consider ourselves exceptionally fortunate to contribute our passion to an esteemed agency, aiming to evoke joy and enduring memories through our imagery,” said NASA photographer Robert Markowitz.
Operating eight camera systems, the imagery acquisition group captures a range of visuals, from HD video and high-speed digital motion pictures to spherical 360 panoramas. These visuals document everything from engineering tests to astronaut training and mission control operations. The team is certified to fly on parabolic flights, T-38 jets, and helicopters, capturing pivotal moments in space exploration history.
“The duty to bear witness to events or conversations and preserve these moments in time – not only for those who cannot, but for the record books – is a noble cause,” said NASA photographer Helen Arase Vargas.
After capturing the imagery, the photo operations team processes these visuals using advanced software to enhance quality, perform color correction, and ensure they meet NASA’s high standards. Every frame is meticulously archived, including photos taken by astronauts aboard the International Space Station, preserving them for future generations.
“None of what we deliver would be possible without the work of the photo laboratory,” said Mark Sowa, the imagery acquisition group lead who brings over three decades of experience in scientific photography to his role.
The team also manages the care and handling of original Apollo mission films, which are preserved in a specially built cold storage vault. The goal is to preserve Apollo era spaceflight films – in both the digital and physical formats – for generations to come.
The cold storage film vault at NASA’s Johnson Space Center in Houston.NASA/Robert Markowitz The ISAG is charged with a different but equally critical mission. This team of scientists performs complex and in-depth analysis of engineering imagery. Their work involves evaluating space vehicle performance, dynamic events, and anomalies by measuring distances, sizes, motion, and hardware conditions to uncover crucial mission insights.
Their data visualization techniques bring these analyses to life, contributing to successful mission execution.
“At NASA we often say ‘the camera is the mission’ because in every image, there’s a story to be told – whether it’s one of engineering analysis or human inspiration,” said Dr. Kenton Fisher, the ISAG lead. “Our work helps ensure crew safety and provides insights that drive the next giant leap in space exploration.”
The Artemis I test flight marks the safe return of the Orion spacecraft to Earth.NASA/Josh Valcarcel NASA’s Orion spacecraft for Artemis I after splashdown in the Pacific Ocean on December 11, 2022.NASA/James Blair NASA’s OCOMM graphics team works closely with the imagery acquisition group, astronauts, and subject matter experts to create visuals that symbolize NASA’s missions and values.
From patches to educational infographics, their art reaches museums and schools nationwide, inspiring future generations and showcasing NASA’s commitment to exploration, innovation, and education.
A compilation of NASA’s graphics team highlights from 2023. “Every design we create is a piece of a larger narrative, helping to tell the story of space exploration in a way that’s engaging and accessible to everyone,” said Sean Collins, Johnson’s lead graphic designer.
The collaborative efforts of these teams ensure that NASA’s achievements are not just recorded but celebrated worldwide.
NASA team members participate in the National Collegiate Athletic Association Championship Game opening flag ceremonies on January 8, 2024, at NRG Stadium. NASA/Helen Arase Vargas NASA photographer Bill Stafford recalls a moment of awe when capturing the Moon juxtaposed with the U.S. flag above the Mission Control Center, a symbol of America’s space achievements.
“I feel a weight because my job is important,” he said. “I want people to look at my pictures and see what I was able to see.”
The Moon juxtaposed with the U.S. flag above the Mission Control Center at NASA’s Johnson Space Center in Houston. NASA/Bill Stafford A T-38 formation flyover as NASA’s Space Launch System rocket sits on the launch pad at Kennedy Space Center in Florida.NASA/Josh Valcarcel Space Shuttle Endeavour is ferried by NASA’s Shuttle Carrier Aircraft over Ellington Field on September 20, 2012.NASA/Bill Stafford Neil Armstrong speaks at the Rotary National Award for Space Achievement dinner in Houston, Texas. NASA/Bill Stafford Expedition 1 crew members (from left) William Shepherd, Yuri Gudzenko and Sergei Krikalev train in the building 9 shuttle Crew Compartment Trainer on May 12, 2000. NASA/James Blair NASA T-38 aircraft are parked on the flight line at Ellington Field during sunrise, May 7, 2005.NASA/James Blair A NASA engineer installs VIPER’s (Volatiles Investigating Polar Exploration Rover) starboard radiator in Johnson’s clean room. NASA/Helen Arase Vargas Engineers work in the VIPER (Volatiles Investigating Polar Exploration Rover) clean room at Johnson Space Center. NASA/Helen Arase Vargas The cast members from the Apollo 13 movie in zero gravity aboard NASA’s KC-135 aircraft.NASA/Robert Markowitz NASA astronaut John Glenn on his second spaceflight as part of the STS-95 crew.NASA/Robert Markowitz View the full article
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By NASA
6 Min Read NASA Trains Machine Learning Algorithm for Mars Sample Analysis
The Mars Organic Molecule Analyzer, aboard the ExoMars mission's Rosalind Franklin rover, will employ a machine learning algorithm to speed up specimen analysis. Credits: ESA When the ESA (European Space Agency)-led Rosalind Franklin rover heads to Mars no earlier than 2028, a NASA machine learning algorithm gets its first chance to shine after more than a decade of data training in the lab. The Mars Organic Molecule Analyzer (MOMA), a mass spectrometer instrument aboard the rover, will analyze samples collected by a coring drill and send the results back to Earth, where they will be fed into the algorithm to identify organic compounds found in the samples. If any organic compounds are detected by the rover, the algorithm could greatly speed up the process of identifying them, saving scientists time as they decide the most efficient uses of the rover’s time on the Red Planet. When a robotic rover lands on another world, scientists have a limited amount of time to collect data from the troves of explorable material, because of short mission durations and the length of time to complete complex experiments.
That’s why researchers at NASA’s Goddard Space Flight Center in Greenbelt, Maryland, are investigating the use of machine learning to assist in the rapid analysis of data from rover samples and help scientists back on Earth strategize the most efficient use of a rover’s time on a planet.
“This machine learning algorithm can help us by quickly filtering the data and pointing out which data are likely to be the most interesting or important for us to examine,” said Xiang “Shawn” Li, a mass spectrometry scientist in the Planetary Environments lab at NASA Goddard.
The algorithm will first be put to the test with data from Mars, by operating on an Earth-bound computer using data collected by the Mars Organic Molecule Analyzer (MOMA) instrument.
The analyzer is one of the main science instruments on the upcoming ExoMars mission Rosalind Franklin Rover, led by ESA (European Space Agency). The rover, which is scheduled to launch no earlier than 2028, seeks to determine if life ever existed on the Red Planet.
Related: NASA, ESA to Land Europe’s Rover on Mars After Rosalind Franklin collects a sample and analyzes it with MOMA, data will be sent back to Earth, where scientists will use the findings to decide the best next course of action.
“For example, if we measure a sample that shows signs of large, complex organic compounds mixed into particular minerals, we may want to do more analysis on that sample, or even recommend that the rover collect another sample with its coring drill,” Li said.
Algorithm May Help Identify Chemical Composition Beneath Surface of Mars
In artificial intelligence, machine learning is a way that computers learn from data — lots of data — to identify patterns and make decisions or draw conclusions.
This automated process can be powerful when the patterns might not be obvious to human researchers looking at the same data, which is typical for large, complex data sets such as those involved in imaging and spectral analysis.
In MOMA’s case, researchers have been collecting laboratory data for more than a decade, according to Victoria Da Poian, a data scientist at NASA Goddard who co-leads development of the machine learning algorithm. The scientists train the algorithm by feeding it examples of substances that may be found on Mars and labeling what they are. The algorithm will then use the MOMA data as input and output predictions of the chemical composition of the studied sample, based on its training.
NASA data scientist Victoria Da Poian presents on the MOMA’s machine learning algorithm at the Supercomputing 2023 conference in Denver, Colorado.NASA/Donovan Mathias “The more we do to optimize the data analysis, the more information and time scientists will have to interpret the data,” Da Poian said. “This way, we can react quickly to results and plan next steps as if we are there with the rover, much faster than we previously would have.”
The MOMA employs laser desorption to identify specimens, while preserving larger molecules that may be broken down by gas chromatography.
Credit: NASA’s Goddard Space Flight Center/Conceptual Image Lab
Download this video and related multimedia in HD formats Drilling Down for Signs of Past Life
What makes the Rosalind Franklin rover unique — and what scientists hope will lead to new discoveries — is that it will be able to drill down about 6.6 feet (2 meters) into the surface of Mars. Previous rovers have only reached about 2.8 inches (7 centimeters) below the surface.
“Organic materials on Mars’ surface are more likely to be destroyed by exposure to the radiation at the surface and cosmic rays that penetrate into the subsurface,” said Li, “but two meters of depth should be enough to shield most organic matter. MOMA therefore has the potential to detect preserved ancient organics, which would be an important step in looking for past life.”
Future Explorations Across the Solar System Could be More Autonomous
Searching for signs of life, past or present, on worlds beyond Earth is a major effort for NASA and the greater scientific community. Li and Da Poian see potential for their algorithm as an asset for future exploration of tantalizing targets like Saturn’s moons Titan and Enceladus, and Jupiter’s moon Europa.
Li and Da Poian’s long-term goal is to achieve even more powerful “science autonomy,” where the mass spectrometer will analyze its own data and even help make operational decisions autonomously, dramatically increasing science and mission efficiency.
This will be crucial as space exploration missions target more distant planetary bodies. Science autonomy would help prioritize data collection and communication, ultimately achieving much more science than currently possible on such remote missions.
“The long-term dream is a highly autonomous mission,” said Da Poian. “For now, MOMA’s machine learning algorithm is a tool to help scientists on Earth more easily study these crucial data.”
The MOMA project is led by the Max Planck Institute for Solar System Research (MPS) in Germany, with principal investigator Dr. Fred Goesmann. NASA Goddard developed and built the MOMA mass spectrometer subsystem, which will measure the molecular weights of chemical compounds in collected Martian samples.
Development of the machine learning algorithm was funded by NASA Goddard’s Internal Research and Development program.
By Matthew Kaufman
NASA’s Goddard Space Flight Center, Greenbelt, Md.
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Last Updated Aug 05, 2024 EditorRob GarnerContactRob Garnerrob.garner@nasa.govLocationGoddard Space Flight Center Related Terms
Technology Artificial Intelligence (AI) ExoMars Goddard Space Flight Center Goddard Technology Mars Planetary Science The Solar System Explore More
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