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All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Study of EVA operations associated with satellite services

Extravehicular mobility unit (EMU) factors associated with satellite servicing activities are identified and the EMU improvements necessary to enhance satellite servicing operations are outlined. Areas of EMU capabilities, equipment and structural interfaces, time lines, EMU modifications for satellite servicing, environmental hazards, and crew training are vital to manned Eva/satellite services and as such are detailed. Evaluation of EMU capabilities indicates that the EMU can be used in performing near term, basic satellite servicing tasks; however, satellite servicing is greatly enhanced by incorporating key modifications into the EMU. The servicing missions involved in contamination sensitive payload repair are illustrated. EVA procedures and equipment can be standardized, reducing both crew training time and in orbit operations time. By standardizing and coordinating procedures, mission cumulative time lines fall well within the EMU capability.

Nash, J. O.↗
NASA NTRS1997

The Design of Model-Based Training Programs

This paper proposes a model-based training program for the skills necessary to operate advance avionics systems that incorporate advanced autopilots and fight management systems. The training model is based on a formalism, the operational procedure model, that represents the mission model, the rules, and the functions of a modem avionics system. This formalism has been defined such that it can be understood and shared by pilots, the avionics software, and design engineers. Each element of the software is defined in terms of its intent (What?), the rationale (Why?), and the resulting behavior (How?). The Advanced Computer Tutoring project at Carnegie Mellon University has developed a type of model-based, computer aided instructional technology called cognitive tutors. They summarize numerous studies showing that training times to a specified level of competence can be achieved in one third the time of conventional class room instruction. We are developing a similar model-based training program for the skills necessary to operation the avionics. The model underlying the instructional program and that simulates the effects of pilots entries and the behavior of the avionics is based on the operational procedure model. Pilots are given a series of vertical flightpath management problems. Entries that result in violations, such as failure to make a crossing restriction or violating the speed limits, result in error messages with instruction. At any time, the flightcrew can request suggestions on the appropriate set of actions. A similar and successful training program for basic skills for the FMS on the Boeing 737-300 was developed and evaluated. The results strongly support the claim that the training methodology can be adapted to the cockpit.

Polson, Peter↗
NASA NTRS2004

Accelerated Training at Mach 20: A Brief Communication Submitted from the International Space Station

The performance of complex tasks on the International Space Station (ISS) requires significant preflight crew training commitments and frequent skill and knowledge refreshment. This report documents a recently developed just-in-time training methodology, which integrates preflight hardware familiarization and procedure training with an on-orbit CD-ROM-based skill enhancement. This just-in-time concept was used to support real-time remote expert guidance to complete medical examinations using the ISS Human Research Facility (HRF). An American and Russian ISS crewmember received 2-hours of hands on ultrasound training 8 months prior to the on-orbit ultrasound exam. A CD-ROM-based Onboard Proficiency Enhancement (OPE) interactive multimedia program consisting of memory enhancing tutorials, and skill testing exercises, was completed by the crewmember six days prior to the on-orbit ultrasound exam. The crewmember was then remotely guided through a thoracic, vascular, and echocardiographic examination by ultrasound imaging experts. Results of the CD ROM based OPE session were used to modify the instructions during a complete 35 minute real-time thoracic, cardiac, and carotid/jugular ultrasound study. Following commands from the ground-based expert, the crewmember acquired all target views and images without difficulty. The anatomical content and fidelity of ultrasound video were excellent and adequate for clinical decision-making. Complex ultrasound experiments with expert guidance were performed with high accuracy following limited pre-flight training and CD-ROM-based in-flight review, despite a 2-second communication latency. In-flight application of multimedia proficiency enhancement software, coupled with real-time remote expert guidance, can facilitate the performance of complex demanding tasks.

Foale, C. Michael↗
NASA NTRS2006

Human Space Flight

The performance of complex tasks on the International Space Station (ISS) requires significant preflight crew training commitments and frequent skill and knowledge refreshment. This report documents a recently developed just-in-time training methodology, which integrates preflight hardware familiarization and procedure training with an on-orbit CD-ROM-based skill enhancement. This just-in-time concept was used to support real-time remote expert guidance to complete medical examinations using the ISS Human Research Facility (HRF). An American md Russian ISS crewmember received 2-hours of hands on ultrasound training 8 months prior to the on-orbit ultrasound exam. A CD-ROM-based Onboard Proficiency Enhancement (OPE) interactive multimedia program consisting of memory enhancing tutorials, and skill testing exercises, was completed by the crewmember six days prior to the on-orbit ultrasound exam. The crewmember was then remotely guided through a thoracic, vascular, and echocardiographic examination by ultrasound imaging experts. Results of the CD ROM based OPE session were used to modify the instructions during a complete 35 minute real-time thoracic, cardiac, and carotid/jugular ultrasound study. Following commands from the ground-based expert, the crewmember acquired all target views and images without difficulty. The anatomical content and fidelity of ultrasound video were excellent and adequate for clinical decision-making. Complex ultrasound experiments with expert guidance were performed with high accuracy following limited pre-flight training and CD-ROM-based in-flight review, despite a 2-second communication latency.

Woolford, Barbara↗
NASA NTRS2015

Robonaut 2 - Building a Robot on the International Space Station

In 2010, the Robonaut Project embarked on a multi‐phase mission to perform technology demonstrations on‐board the International Space Station (ISS), showcasing state of the art robotics technologies through the use of Robonaut 2 (R2). This phased approach implements a strategy that allows for the use of ISS as a test bed during early development to both demonstrate capability and test technology while still making advancements in the earth based laboratories for future testing and operations in space. While R2 was performing experimental trials onboard the ISS during the first phase, engineers were actively designing for Phase 2, Intra‐Vehicular Activity (IVA) Mobility, that utilizes a set of zero‐g climbing legs outfitted with grippers to grasp handrails and seat tracks. In addition to affixing the new climbing legs to the existing R2 torso, it became clear that upgrades to the torso to both physically accommodate the climbing legs and to expand processing power and capabilities of the robot were required. In addition to these upgrades, a new safety architecture was also implemented in order to account for the expanded capabilities of the robot. The IVA climbing legs not only needed to attach structurally to the R2 torso on ISS, but also required power and data connections that did not exist in the upper body. The climbing legs were outfitted with a blind mate adapter and coarse alignment guides for easy installation, but the upper body required extensive rewiring to accommodate the power and data connections. This was achieved by mounting a custom adapter plate to the torso and routing the additional wiring through the waist joint to connect to the new set of processors. In addition to the power and data channels, the integrated unit also required updated electronics boards, additional sensors and updated processors to accommodate a new operating system, software platform, and custom control system. In order to perform the unprecedented task of building a robot in space, extensive practice sessions and meticulous procedures were required. Since crew training time is at a premium, the R2 team took a skills‐based training approach to ensure the astronauts were proficient with a basic skill set while refining the detailed procedures over several practice sessions and simulations. In addition to the crew activities, meticulous ground procedures were required in order to upgrade firmware on the upper body motor drivers. The new firmware for the IVA mobility unit needed to be deployed using the old software system. This also provided an opportunity to upgrade the upper body joints with new software and allowed for limited insight into the success of the updates. Complete verification that the updated firmware was successfully loaded was not confirmed until the rewiring of the upper body torso was complete.

Diftler, Myron↗
NASA NTRS2023

Earth Independent Medical Operations (EIMO) Training Technical Interchange Meeting, 26th October 2023: Background and Summary of Discussion

On 26th October 2023, ExMC convened a panel of Subject Matter Experts (SMEs) from NASA, broadly representing Headquarters, the Human Research Program, Medical Operations, the Human Health and Performance Directorate at JSC, the Health and Medical Technical Authority, the Flight Operations Directorate, and representation from other Centers including: Ames Research Center (ARC), and Glenn Research Center (GRC). Representatives from the Canadian Space Agency also participated in this TIM. In addition, SMEs from industry included representation from the following entities: Axiom Space, Space Exploration Technologies Corporation, Level Ex, Shiny Box Interactive. Additional SMEs from academia included: University of Houston, Touro University, Weill Cornell Medical College and the Translational Research Institute for Space Health at Baylor College of Medicine. This group of stakeholders discussed the training issues related to facilitating EIMO. Sub-topics for this discussion included the following: - Pre-launch medical curriculum development - Ground-based training on medical hardware - Dental health - Behavioral health - Telemedicine - Just-in-time training - Simulation-based training (extended reality)

CMO Training↗
NASA NTRS1976

Development and analysis of a modular approach to payload specialist training

A modular training approach for Spacelab payload crews is described. Representative missions are defined for training requirements analysis, training hardware, and simulations. Training times are projected for each experiment of each representative flight. A parametric analysis of the various flights defines resource requirements for a modular training facility at different flight frequencies. The modular approach is believed to be more flexible, time saving, and economical than previous single high fidelity trainer concepts. Block diagrams of training programs are shown.

Watters, H.↗
NASA NTRS1993

Self-attitude awareness training: An aid to effective performance in microgravity and virtual environments

This paper describes ongoing development of training procedures to enhance self-attitude awareness in astronaut trainees. The procedures are based on observations regarding self-attitude (perceived self-orientation and self-motion) reported by astronauts. Self-attitude awareness training is implemented on a personal computer system and consists of lesson stacks programmed using Hypertalk with Macromind Director movie imports. Training evaluation will be accomplished by an active search task using the virtual Spacelab environment produced by the Device for Orientation and Motion Environments Preflight Adaptation Trainer (DOME-PAT) as well as by assessment of astronauts' performance and sense of well-being during orbital flight. The general purpose of self-attitude awareness training is to use as efficiently as possible the limited DOME-PAT training time available to astronauts prior to a space mission. We suggest that similar training procedures may enhance the performance of virtual environment operators.

Parker, Donald E.↗
NASA NTRSDate not supplied

Summary of Technical Interchange Meetings (TIMs) Designed to Enable Earth Independent Medical Operations (EIMO)

The Exploration Medical Capability Element (ExMC) in NASA’s Human Research Program hosted a series of TIMs in 2023-2024 designed to stimulate discussion around specific topics with the goal of enabling EIMO. In context of the thematic constituent elements of EIMO, namely pre-mission planning, acute/emergent/prolonged medical decision making, supply/resource management and task load management, subject matter experts from industry, academia and government (NASA and other Agencies) provided valuable and actionable guidance and recommendations. Earth-based medical experts will remain indispensable for pre-mission planning, however, management of acute/emergent medical contingencies will require a gradual transition of medical care and decision making from terrestrial to space-based assets to enable support of astronaut health and performance and reduce overall mission risk. Key to achieving these enhancements is providing an integrated data system platform capable of utilizing multiple data streams in concert with a variety of on-board databases and passive monitoring of video and wearable sensors to enable a multi-modal, agentic AI-based clinical decision support system (CDSS) to support crew medical officer (CMO) medical decision-making. The EIMO series of TIMs (I-V) have proven to be instructive and portend a significant paradigm shift will be necessary to maintain crew health and performance on exploration class missions. Importantly, since the expected paradigm shift will be significantly different from the methods of operation that have been employed for the majority of missions from the inception of human spaceflight to date, any proposed methods must be deployed in the setting of ongoing operations early and be “tested, reviewed and practiced” while reliable back-up is available to facilitate an Enterprise-wide level of comfort and acceptance. Serious constraints on data transmission coupled with a large and expanding universe of on-board medical informatics data streams will necessitate implementation of a CDSS to supplant the current reliance on support provided by ground-based SMEs. Establishment of trust in the system by CMO/crew and the ground-based medical support team will be essential. Co-development of a CDSS with industry partners will assure that state of the art tools can be employed, and industry efficiencies can be leveraged. Training regimens, materials and tools must evolve to be responsive (just-in-time training) and facilitate autonomous execution of procedures. Proficiency metrics should be established and be based on validated competencies or milestones as opposed to a prescribed number of training hours. Training should be prioritized for broad, translatable skills that have universal application across a variety of medical conditions. Repetition was deemed to be the key to achieving proficiency and emphasis should lie in procedural training which is known to extinguish more rapidly than diagnostic skills. Advanced tools, e.g., extended reality, can provide more realistic and effective training. Use of advanced probabilistic risk assessment tools will be essential to optimize the medical system capability while carefully balancing risk relative to mass/power/volume limitations. Importance of factoring use-life of medical supplies and maintaining awareness of redundancy and opportunity to re-purpose under off nominal situations was emphasized. Consideration of adopting optimized performance standards vs. “good-enough” performance thresholds is warranted. The use of legacy systems as opposed to creating new systems may be preferable. Managing task load and associated cognitive load will be essential to maintain operational safety and behavioral health. ExMC aspires to create a shared EIMO paradigm and strategic vision for advancing medical system design through novel technologies, training, protocols, and support capabilities, built upon the spirit of successful strategies and innovations over the past six decades of space medicine operations.

Jay Lemery↗
NASA NTRS2024

Sensor Fault Detection in Smart Extraterrestrial Habitats Using Unsupervised Learning

Various types of sensors are needed to monitor the health state of smart deep-space habitats. However, measured data can be affected by sensor faults, which influence the health management system and consequently the decision-making. In this paper, an unsupervised learning approach based on convolutional autoencoders (CAEs) is developed to detect anomalies in temperature and pressure sensors. The proposed method is systematically investigated using a habitat simulator (HabSim). Several illustrative examples are demonstrated in the nominal and hazardous states of the habitat, including micrometeorite impact and fire scenarios. The performance of the proposed method using CAEs is compared with that of existing methods using auto-associative neural networks (AANNs) and variational autoencoders. This comparison is based on typical evaluation metrics, including precision, recall, F1 score, training time, and testing time. The effect of temperature–pressure coupling on the detection performance of CAEs and AANNs is explored by training different data-driven models, including one with temperature sensors, one with pressure sensors, and one with both temperature and pressure sensors. The effect of the number of faulty sensors on the performance of CAEs is studied, as with an increase in the number of faulty sensors, redundant information among the sensors is reduced. The capability of CAEs to change the number of sensors without redesigning the network architecture and retraining the neural network is investigated and demonstrated. The capabilities and limitations of the proposed solution are discussed.

Zixin Wang↗
NASA NTRS1991

Real Time Data System (RTDS)

Information is given in viewgraph form on the Real Time Data System (RTDS). The goals are to increase the quality of flight decision making, reduce and enhance flight controller training time, and serve as a near-operations technology test-bed. Information is given on the growth of RTDS; flight control disciplines; RTDS technology deployment in 1987-1989 and 1990-91; a functionality comparison of mainframes and workstations; and technology transfer activities.

Heindel, Troy A.↗
NASA NTRSDate not supplied

Manual Crew Override of Vehicle Landings Following G-Transitions

BACKGROUND Manual control during exploration spaceflight consists of both planned automated supervisory control and unplanned crew override. This crew override capability is critical to enable overall mission success during landing contingencies. However, the introduction of manual override capabilities must be implemented to enable crews to mitigate risks introduced by human error. Adaptive changes in the sensorimotor system can manifest during g-transitions as spatial disorientation. While training and landing aids enable successful landing through disorientation, these adaptive changes may increase cognitive demand that needs to be accounted for in the manual control strategy. It is important to characterize these effects as soon as possible following the G-transition to develop appropriate countermeasures. METHODS The following study seeks to inform the risk associated with altered sensorimotor and vestibular function impacting critical mission tasks. We aim to characterize the effects of short and long-duration weightlessness on manual control following G-transitions using simulated lunar landing on a six-degree-of-freedom (6DOF) motion base, a fixed base simulation, and a supervisory control tablet task. The primary goal is to understand the impact of spaceflight on crew ability to perform manual crew override and supervisory control. This aim will be assessed by comparing pre- versus postflight simulation performance in crewmembers assigned to either short duration (< 30 day) or long duration (~6- month) missions to the International Space Station (ISS). We hypothesize there will be postflight increases in the percent time that pilots are outside of the acceptable range for recommended vehicle state parameters and the reaction time for secondary cognitive tasks. Ground-based control subjects, who are demographically matched to the crew considering age (± 5 years) and gender, will undergo the same testing schedule as the crew to examine the effects of flight phase independent of microgravity exposure. The second aim is to examine how adaptive changes in vestibular and cognitive function relate to changes in manual crew override proficiency. Crew performance for a sensorimotor perceptual test battery will evaluate motion perception tracking, roll nulling, and/or vection sensitivity using the 6DOF motion base. We hypothesize that a higher severity of vestibular alterations will be associated with increased percent time outside of guidance limits. Motion sickness severity and sleepiness will also be evaluated. To determine the impact of “just-in-time” training, the third aim seeks to compare performance during on-board lunar landing tasks conducted late in-flight to early postflight. We hypothesize that proficiency on the “just-in-time” laptop trainer late in mission will be positively correlated with early postflight proficiency on the same task. The final aim will establish assessments of performance, training protocols, and the learning progression in a ground-control cohort of first-time users. RESULTS The assessment of the learning progression associated with the piloting task on the motion base system with thirty ground subjects will be reported. Learning curves will be established across four distinct sessions and within session considering trial difficulty. The difficulty of the landing task can be modulated with the landing divert distance and cross or downrange difficulty. Results may include changes in performance across multiple trials of a multi-attribute lunar tablet supervisory control task. Preliminary investigations of eighteen subjects who completed vestibular threshold and motion perception tasks offer expected performance ranges for upcoming preflight crew evaluations. The results yielded an average roll threshold of 0.46 ± 0.30 deg/s and an average roll nulling root mean square error performance of 2.52 ± 0.52 deg/s. RELEVANCE This project will deliver an operational demonstration of crew monitoring capability following spaceflight and identify potential deficits that may require remediation. Comparison of individual vestibular and cognitive changes with crew performance will help better characterize the manual control risks associated with sensorimotor alterations. Ground testing will evaluate learning progression, refine training protocols, and serve as a control cohort for comparisons to crew performance. ACKNOWLEDGEMENTS: The authors acknowledge contributions from Draper, the Dynamic Skills Trainer (DST) Lab, and the Software, Robotics, and Simulation Division toward the development of the lunar landing simulation platforms. This project is funded by the Human Health Countermeasures Element.

Hannah M. Weiss↗
NASA NTRS1991

A neural network for the identification of measured helicopter noise

The results of a preliminary study of the components of a novel acoustic helicopter identification system are described. The identification system uses the relationship between the amplitudes of the first eight harmonics in the main rotor noise spectrum to distinguish between helicopter types. Two classification algorithms are tested; a statistically optimal Bayes classifier, and a neural network adaptive classifier. The performance of these classifiers is tested using measured noise of three helicopters. The statistical classifier can correctly identify the helicopter an average of 67 percent of the time, while the neural network is correct an average of 65 percent of the time. These results indicate the need for additional study of the envelope of harmonic amplitudes as a component of a helicopter identification system. Issues concerning the implementation of the neural network classifier, such as training time and structure of the network, are discussed.

Cabell, R. H.↗
NASA NTRS2019

Psychophysiological Monitoring of Aerospace Crew State

As next-generation space exploration missions necessitate increasingly autonomous systems, there is a critical need to better detect and anticipate crewmember interactions with these systems. The success of present and future autonomous technology in exploration spaceflight is ultimately dependent upon safe and efficient interaction with the human operator. Optimal interaction is particularly important for surface missions during highly coordinated extravehicular activity (EVA), which consists of high physical and cognitive demands with limited ground support. Crew functional state may be affected by a number of variables including workload, stress, and motivation. Real-time assessments of crew state that do not require a crewmember’s time and attention to complete will be especially important to assess operational performance and behavioral health during flight. In response to the need for objective, passive assessment of crew state, the aim of this work is to develop an accurate and precise prediction model of human functional state for surface EVA using multi-modal psychophysiological sensing. The psychophysiological monitoring approach relies on extracting a set of features from physiological signals and using these features to classify an operator’s cognitive state. This work aims to compile a non-invasive sensor suite to collect physiological data in real-time. Training data during cognitive and more complex functional tasks will be used to develop a classifier to discriminate high and low cognitive workload crew states. The classifier will then be tested in an operationally relevant EVA simulation to predict cognitive workload over time. Once a crew state is determined, further research into specific countermeasures, such as decision support systems, would be necessary to optimize the automation and improve crew state and operational performance.

Wusk, Grace C.↗
NASA NTRS2017

Evaluation of the Next-Gen Exercise Software Interface in the NEEMO Analog

NSBRI (National Space Biomedical Research Institute) funded research grant to develop the 'NextGen' exercise software for the NEEMO (NASA Extreme Environment Mission Operations) analog. Develop a software architecture to integrate instructional, motivational and socialization techniques into a common portal to enhance exercise countermeasures in remote environments. Increase user efficiency and satisfaction, and institute commonality across multiple exercise systems. Utilized GUI (Graphical User Interface) design principals focused on intuitive ease of use to minimize training time and realize early user efficiency. Project requirement to test the software in an analog environment. Top Level Project Aims: 1) Improve the usability of crew interface software to exercise CMS (Crew Management System) through common app-like interfaces. 2) Introduce virtual instructional motion training. 3) Use virtual environment to provide remote socialization with family and friends, improve exercise technique, adherence, motivation and ultimately performance outcomes.

Hanson, Andrea↗
NASA NTRS2018

Evaluation of Crew-Centric Onboard Mission Operations Planning and Execution Tool: Year 2

Currently, mission planning for the International Space Station (ISS) is largely affected by ground operators in mission control. The task of creating a week-long mission plan for ISS crew takes dozens of people multiple days to complete, and is often created far in advance of its execution. As such, re-planning or adapting to changing real-time constraints or emergent issues is similarly taxing. As we design for future mission operations concepts to other planets or areas with limited connectivity to Earth, more of these ground-based tasks will need to be handled autonomously by the crew onboard.There is a need for a highly usable (including low training time) tool that enables efficient self-scheduling and execution within a single package. The ISS Program has identified Playbook as a potential option. It already has high crew acceptance as a plan viewer from previous analogs and can now support a crew self-scheduling assessment on ISS or on another mission. The goals of this work, a collaboration between the Human Research Program and the ISS Program, are to inform the design of systems for more autonomous crew operations and provide a platform for research on crew autonomy for future deep space missions. Our second year of the research effort have included new insights on the crew self-scheduling sessions performed by the crew through use on the HERA (Human Exploration Research Analog) and NEEMO (NASA Extreme Environment Mission Operations) analogs. Use on the NEEMO analog involved two self-scheduling strategies where the crew planned and executed two days of EVAs (Extra-Vehicular Activities). On HERA year two represented the first HERA campaign where we were able to perform research tasks. This involved selected flexible activities that the crew could schedule, mock timelines where the crew completed more complex planning exercises, usability evaluation of the crew self-scheduling features, and more insights into the limit of plan complexity that the crew could effectively self-schedule. In parallel we have added in new features and functionality in the Playbook tool based off of our insights from crew self-scheduling in the NASA analogs. In particular this year we have added in the ability for the crew to add, edit, and remove their own activities in the Playbook tool, expanding the type of planning and re-planning possible in the tool and opening up the ability for more free form plan creation. The ability to group and manipulate groups of activities from the plan task list was also added, allowing crew members to add predefined sets of activities onto their mission timeline. In addition we also added a way for crew members to roll back changes in their plan, in order to allow an undo like capability. These features expand and complement the initial self-scheduling features added in year one with the goal of making crew autonomous planning more efficient. As part of this work we have also finished developing the first version of our Playbook Data Analysis Tool, a research tool built to interpret and analyze the unobtrusively collected data obtained during the NASA analog missions through Playbook. This data which includes user click interaction as well as plan change information, through the Playbook Data Analysis Tool, allows us to playback this information as if a video camera was mounted over the crewmember's tablet. While the primary purpose of this tool is to allow usability analysis of crew self-scheduling sessions used on the NASA analog, since the data collected is structured, the tool can automatically derive metrics that would be traditionally tedious to achieve without manual analysis of video playback. We will demonstrate and discuss the ability for future derived metrics to be added to the tool. In addition to the current data and results gathered in year two we will also discuss the preparation and goals of our International Space Station (ISS) onboard technology demonstration with Playbook. This technology demonstration will be preformed as part of the CAST payload starting in late 2016.

onboard planning↗
NASA NTRS2010

Multivariate Methods for Prediction of Geologic Sample Composition with Laser-Induced Breakdown Spectroscopy

Laser-induced breakdown spectroscopy (LIBS) uses pulses of laser light to ablate a material from the surface of a sample and produce an expanding plasma. The optical emission from the plasma produces a spectrum which can be used to classify target materials and estimate their composition. The ChemCam instrument on the Mars Science Laboratory (MSL) mission will use LIBS to rapidly analyze targets remotely, allowing more resource- and time-intensive in-situ analyses to be reserved for targets of particular interest. ChemCam will also be used to analyze samples that are not reachable by the rover's in-situ instruments. Due to these tactical and scientific roles, it is important that ChemCam-derived sample compositions are as accurate as possible. We have compared the results of partial least squares (PLS), multilayer perceptron (MLP) artificial neural networks (ANNs), and cascade correlation (CC) ANNs to determine which technique yields better estimates of quantitative element abundances in rock and mineral samples. The number of hidden nodes in the MLP ANNs was optimized using a genetic algorithm. The influence of two data preprocessing techniques were also investigated: genetic algorithm feature selection and averaging the spectra for each training sample prior to training the PLS and ANN algorithms. We used a ChemCam-like laboratory stand-off LIBS system to collect spectra of 30 pressed powder geostandards and a diverse suite of 196 geologic slab samples of known bulk composition. We tested the performance of PLS and ANNs on a subset of these samples, choosing to focus on silicate rocks and minerals with a loss on ignition of less than 2 percent. This resulted in a set of 22 pressed powder geostandards and 80 geologic samples. Four of the geostandards were used as a validation set and 18 were used as the training set for the algorithms. We found that PLS typically resulted in the lowest average absolute error in its predictions, but that the optimized MLP ANN and the CC ANN often gave results comparable to PLS. Averaging the spectra for each training sample and/or using feature selection to choose a small subset of wavelengths to use for predictions gave mixed results, with degraded performance in some cases and similar or slightly improved performance in other cases. However, training time was significantly reduced for both PLS and ANN methods by implementing feature selection, making this a potentially appealing method for initial, rapid-turn-around analyses necessary for Chemcam's tactical role on MSL. Choice of training samples has a strong influence on the accuracy of predictions. We are currently investigating the use of clustering algorithms (e.g. k-means, neural gas, etc.) to identify training sets that are spectrally similar to the unknown samples that are being predicted, and therefore result in improved predictions

Morris, Richard↗
NASA NTRS2011

Sparse Solutions for Single Class SVMs: A Bi-Criterion Approach

In this paper we propose an innovative learning algorithm - a variation of One-class nu Support Vector Machines (SVMs) learning algorithm to produce sparser solutions with much reduced computational complexities. The proposed technique returns an approximate solution, nearly as good as the solution set obtained by the classical approach, by minimizing the original risk function along with a regularization term. We introduce a bi-criterion optimization that helps guide the search towards the optimal set in much reduced time. The outcome of the proposed learning technique was compared with the benchmark one-class Support Vector machines algorithm which more often leads to solutions with redundant support vectors. Through out the analysis, the problem size for both optimization routines was kept consistent. We have tested the proposed algorithm on a variety of data sources under different conditions to demonstrate the effectiveness. In all cases the proposed algorithm closely preserves the accuracy of standard one-class nu SVMs while reducing both training time and test time by several factors.

Das, Santanu↗