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At least 145 records · Page 8

Reasoning about real-time systems with temporal interval logic constraints on multi-state automata

Models of real-time systems using a single paradigm often turn out to be inadequate, whether the paradigm is based on states, rules, event sequences, or logic. A model-based approach to reasoning about real-time systems is presented in which a temporal interval logic called TIL is employed to define constraints on a new type of high level automata. The combination, called hierarchical multi-state (HMS) machines, can be used to model formally a real-time system, a dynamic set of requirements, the environment, heuristic knowledge about planning-related problem solving, and the computational states of the reasoning mechanism. In this framework, mathematical techniques were developed for: (1) proving the correctness of a representation; (2) planning of concurrent tasks to achieve goals; and (3) scheduling of plans to satisfy complex temporal constraints. HMS machines allow reasoning about a real-time system from a model of how truth arises instead of merely depending of what is true in a system.

Gabrielian, Armen↗

STS-96 Crew Interview: Dan Barry

Live footage of a preflight interview with Mission Specialist Daniel T. Barry is seen. The interview addresses many different questions including why Barry became an astronaut, and the events that led to his interest. Other interesting information that this one-on-one interview discusses is the logistics and supply mission, why it is important to send equipment to the International Space Station (ISS), and the Integrated Cargo Carrier (ICC). Barry mentions Discovery's anticipated docking with the ISS, his scheduled space walk with Tamara E. Jernigan, plans for the supply and equipment transfers, and his responsibility during this transfer. A fly-around maneuver to take pictures of the ISS, and the deployment of the Student Tracked Atmospheric Research Satellite for Heuristic International Networking Equipment (STARSHINE) are also discussed.

Source record↗

Massively Parallel Dantzig-Wolfe Decomposition Applied to Traffic Flow Scheduling

Optimal scheduling of air traffic over the entire National Airspace System is a computationally difficult task. To speed computation, Dantzig-Wolfe decomposition is applied to a known linear integer programming approach for assigning delays to flights. The optimization model is proven to have the block-angular structure necessary for Dantzig-Wolfe decomposition. The subproblems for this decomposition are solved in parallel via independent computation threads. Experimental evidence suggests that as the number of subproblems/threads increases (and their respective sizes decrease), the solution quality, convergence, and runtime improve. A demonstration of this is provided by using one flight per subproblem, which is the finest possible decomposition. This results in thousands of subproblems and associated computation threads. This massively parallel approach is compared to one with few threads and to standard (non-decomposed) approaches in terms of solution quality and runtime. Since this method generally provides a non-integral (relaxed) solution to the original optimization problem, two heuristics are developed to generate an integral solution. Dantzig-Wolfe followed by these heuristics can provide a near-optimal (sometimes optimal) solution to the original problem hundreds of times faster than standard (non-decomposed) approaches. In addition, when massive decomposition is employed, the solution is shown to be more likely integral, which obviates the need for an integerization step. These results indicate that nationwide, real-time, high fidelity, optimal traffic flow scheduling is achievable for (at least) 3 hour planning horizons.

Rios, Joseph Lucio↗

Augmenting Conceptual Design Trajectory Tradespace Exploration with Graph Theory

Within conceptual design changes occur rapidly due to a combination of uncertainty and shifting requirements. To stay relevant in this fluid time, trade studies must also be performed rapidly. In order to drive down analysis time while improving the information gained by these studies, surrogate models can be created to represent the complex output of a tool or tools within a specified tradespace. In order to create this model however, a large amount of data must be collected in a short amount of time. By this method, the historical approach of relying on subject matter experts to generate the data required is schedule infeasible. However, by implementing automation and distributed analysis the required data can be generated in a fraction of the time. Previous work focused on setting up a tool called multiPOST capable of orchestrating many simultaneous runs of an analysis tool assessing these automated analyses utilizing heuristics gleaned from the best practices of current subject matter experts. In this update to the previous work, elements of graph theory are included to further drive down analysis time by leveraging data previously gathered. It is shown to outperform the previous method in both time required, and the quantity and quality of data produced.

Dees, Patrick D.↗

Augmenting Conceptual Design Trajectory Tradespace Exploration with Graph Theory

Within conceptual design changes occur rapidly due to a combination of uncertainty and shifting requirements. To stay relevant in this fluid time, trade studies must also be performed rapidly. In order to drive down analysis time while improving the information gained by these studies, surrogate models can be created to represent the complex output of a tool or tools within a specified tradespace. In order to create this model however, a large amount of data must be collected in a short amount of time. By this method, the historical approach of relying on subject matter experts to generate the data required is schedule infeasible. However, by implementing automation and distributed analysis the required data can be generated in a fraction of the time. Previous work focused on setting up a tool called multiPOST capable of orchestrating many simultaneous runs of an analysis tool assessing these automated analyses utilizing heuristics gleaned from the best practices of current subject matter experts. In this update to the previous work, elements of graph theory are included to further drive down analysis time by leveraging data previously gathered. It is shown to outperform the previous method in both time required, and the quantity and quality of data produced.

Dees, Patrick D.↗

SOFIA'S Challenge: Scheduling Airborne Astronomy Observations

The Stratospheric Observatory for Infrared Astronomy (SOFIA) is NASA's next generation airborne astronomical observatory, and will commence operations in 2005. The facility consists of a 747-SP modified to accommodate a 2.5 meter telescope. SOFIA is expected to fly an average of 140 science flights per year over its 20 year lifetime. Depending on the nature of the instrument used during flight, 5-15 observations per flight are expected. The SOFIA telescope is mounted aft of the wings on the port side of the aircraft and is articulated through a range of 20deg to 60deg of elevation. The telescope has minimal lateral flexibility; thus, the aircraft must turn constantly to maintain the telescope's focus on an object during observations. A significant problem in future SOFIA operations is that of scheduling flights in support of observations. Investigators are expected to propose small numbers of observations, and many observations must be grouped together to make up single flights. Flight planning for the previous generation airborne observatory, the Kuiper Airborne Observatory (KAO), was done by hand; planners had to choose takeoff time, observations to perform, and decide on setup-actions (called "dead-legs") to position the aircraft prior to observing. This task frequently required between 6-8 hours to plan one flight The scope of the flight planning problem for supporting GI observations with the anticipated flight rate for SOFIA makes the manual approach for flight planning daunting. In response, we have designed an Automated Flight Planner (AFP) that accepts as input a set of requested observations, designated flight days, weather predictions and fuel limitations, and searches automatically for high-quality flight plans that satisfy all relevant aircraft and astronomer specified constraints. The AFP can generate one candidate flight plan in 5-10 minutes, of computation time, a feat beyond the capabilities of human flight planners. The rate at which the AFP can generate flights enables humans to assess and analyze complex tradeoffs between fuel consumption, estimated science quality and the percentage of scheduled observations. Due to the changing nature of SOFIA scheduling problems, this functionality will play a crucial role in optimizing science and minimizing costs during operations. In the full paper, we will summarize the technical challenges that have been met in order to build this system. These include: design of the search algorithm, design of appropriate heuristics and approximations, and reduction in the size of the search space. We will also describe technical challenges that are currently being addressed, including the extension of the existing approach to handle new solution criteria. Finally, we will describe a variety of cultural challenges that the astronomical community must address in order to successfully use SOFIA, and describe how the AFT can be used to address some of these challenges. Specifically, many of the intended science users are accustomed to using ground-based or space-based observatories; we will identify some differences that arise due to the nature of airborne observatories, and how the AFT can be extended to provide useful services to ease these cultural differences.

Frank, Jeremy↗

Alternative mixed integer linear programming optimization for joint job scheduling and data allocation in grid computing

This paper presents a novel approach to the joint optimization of job scheduling and data allocation in grid computing environments. We formulate this joint optimization problem as a mixed integer quadratically constrained program. To tackle the nonlinearity in the constraint, we alternatively fix a subset of decision variables and optimize the remaining ones via Mixed Integer Linear Programming (MILP). We solve the MILP problem at each iteration via an off-the-shelf MILP solver. Our experimental results show that our method significantly outperforms existing heuristic methods, employing either independent optimization or joint optimization strategies. We have also verified the generalization ability of our method over grid environments with various sizes and its high robustness to the algorithm setting.

97 MATHEMATICS AND COMPUTING↗

Lunar Architecture Team - Phase 2 Habitat Volume Estimation: "Caution When Using Analogs"

The lunar surface habitat will serve as the astronauts' home on the moon, providing a pressurized facility for all crew living functions and serving as the primary location for a number of crew work functions. Adequate volume is required for each of these functions in addition to that devoted to housing the habitat systems and crew consumables. The time constraints of the LAT-2 schedule precluded the Habitation Team from conducting a complete "bottoms-up" design of a lunar surface habitation system from which to derive true volumetric requirements. The objective of this analysis was to quickly derive an estimated total pressurized volume and pressurized net habitable volume per crewmember for a lunar surface habitat, using a principled, methodical approach in the absence of a detailed design. Five "heuristic methods" were used: historical spacecraft volumes, human/spacecraft integration standards and design guidance, Earth-based analogs, parametric "sizing" tools, and conceptual point designs. Estimates for total pressurized volume, total habitable volume, and volume per crewmember were derived using these methods. All method were found to provide some basis for volume estimates, but values were highly variable across a wide range, with no obvious convergence of values. Best current assumptions for required crew volume were provided as a range. Results of these analyses and future work are discussed.

Rudisill, Marianne↗

Tuning Parameters in Heuristics by Using Design of Experiments Methods

With the growing complexity of today's large scale problems, it has become more difficult to find optimal solutions by using exact mathematical methods. The need to find near-optimal solutions in an acceptable time frame requires heuristic approaches. In many cases, however, most heuristics have several parameters that need to be "tuned" before they can reach good results. The problem then turns into "finding best parameter setting" for the heuristics to solve the problems efficiently and timely. One-Factor-At-a-Time (OFAT) approach for parameter tuning neglects the interactions between parameters. Design of Experiments (DOE) tools can be instead employed to tune the parameters more effectively. In this paper, we seek the best parameter setting for a Genetic Algorithm (GA) to solve the single machine total weighted tardiness problem in which n jobs must be scheduled on a single machine without preemption, and the objective is to minimize the total weighted tardiness. Benchmark instances for the problem are available in the literature. To fine tune the GA parameters in the most efficient way, we compare multiple DOE models including 2-level (2k ) full factorial design, orthogonal array design, central composite design, D-optimal design and signal-to-noise (SIN) ratios. In each DOE method, a mathematical model is created using regression analysis, and solved to obtain the best parameter setting. After verification runs using the tuned parameter setting, the preliminary results for optimal solutions of multiple instances were found efficiently.

Arin, Arif↗

Integrated Arrival and Departure Schedule Optimization Under Uncertainty

In terminal airspace, integrating arrivals and departures with shared waypoints provides the potential of improving operational efficiency by allowing direct routes when possible. Incorporating stochastic evaluation as a post-analysis process of deterministic optimization, and imposing a safety buffer in deterministic optimization, are two ways to learn and alleviate the impact of uncertainty and to avoid unexpected outcomes. This work presents a third and direct way to take uncertainty into consideration during the optimization. The impact of uncertainty was incorporated into cost evaluations when searching for the optimal solutions. The controller intervention count was computed using a heuristic model and served as another stochastic cost besides total delay. Costs under uncertainty were evaluated using Monte Carlo simulations. The Pareto fronts that contain a set of solutions were identified and the trade-off between delays and controller intervention count was shown. Solutions that shared similar delays but had different intervention counts were investigated. The results showed that optimization under uncertainty could identify compromise solutions on Pareto fonts, which is better than deterministic optimization with extra safety buffers. It helps decision-makers reduce controller intervention while achieving low delays.

stochastic optimization↗

Scalable Unit Commitment with Security Constrained AC Power Flow via ADMM and Hybrid Modeling Strategies

This research introduces a more efficient way to optimize power grid operations, breaking the problem into manageable steps and using advanced mathematical techniques to speed up calculations. By incorporating smart heuristics, improved preprocessing, and contingency analysis, the approach allows operators to make better decisions faster. These innovations enhance our understanding of how to optimize energy generation, making it possible to anticipate failures before they happen, reduce system costs, and improve overall grid performance. Ultimately, this research helps bridge the gap between theoretical models and real-world applications, paving the way for a smarter, more resilient power grid. This research directly benefits the public by making electricity more affordable, reliable, and sustainable. By improving how power grids schedule and distribute electricity, the project helps energy providers reduce operational costs, which can lead to lower electricity prices for consumers. Additionally, the ability to predict and prevent power system failures enhances grid reliability, reducing the likelihood of blackouts that can disrupt homes, businesses, and critical infrastructure such as hospitals. From an environmental perspective, optimizing power generation reduces energy waste and lowers carbon emissions, contributing to cleaner air and a more sustainable energy system. Furthermore, with extreme weather events becoming more frequent, these advancements make the power grid more resilient, ensuring communities are better prepared for emergencies and natural disasters. By strengthening the nation's energy infrastructure, this research plays a crucial role in improving economic stability, public safety, and environmental sustainability.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Resource constrained design of artificial neural networks using comparator neural network

We present a systematic design method executed under resource constraints for automating the design of artificial neural networks using the back error propagation algorithm. Our system aims at finding the best possible configuration for solving the given application with proper tradeoff between the training time and the network complexity. The design of such a system is hampered by three related problems. First, there are infinitely many possible network configurations, each may take an exceedingly long time to train; hence, it is impossible to enumerate and train all of them to completion within fixed time, space, and resource constraints. Second, expert knowledge on predicting good network configurations is heuristic in nature and is application dependent, rendering it difficult to characterize fully in the design process. A learning procedure that refines this knowledge based on examples on training neural networks for various applications is, therefore, essential. Third, the objective of the network to be designed is ill-defined, as it is based on a subjective tradeoff between the training time and the network cost. A design process that proposes alternate configurations under different cost-performance tradeoff is important. We have developed a Design System which schedules the available time, divided into quanta, for testing alternative network configurations. Its goal is to select/generate and test alternative network configurations in each quantum, and find the best network when time is expended. Since time is limited, a dynamic schedule that determines the network configuration to be tested in each quantum is developed. The schedule is based on relative comparison of predicted training times of alternative network configurations using comparator network paradigm. The comparator network has been trained to compare training times for a large variety of traces of TSSE-versus-time collected during back-propagation learning of various applications.

Wah, Benjamin W.↗

Assessing Relay Communications for Mars Sample Return Surface Mission Concepts

The Mars Sample Return (MSR) Campaign is a 3-mission campaign concept supported by NASA and ESA to return samples from the Mars surface. MSR will, for the firsttime ever, present a need to communicate with multiple surfaceassets that are co-located on Mars in a coordinated effort toaccomplish the unified objective of fetching, transporting, andreturning samples from Mars. Currently, Mars surface assetsrelay data to and from Earth using a number of orbiters inwhat’s known as the Mars Relay Network (MRN). This networkis characterized by a small number of surface assets distributedacross the Martian globe and a larger number of orbiters toprovide relay services. As of June 2020, there are two surfaceassets for which five orbiters are providing relay. During theMSR Campaign, there will be two rovers and a lander that allwill require relay communication from a small number of Marsorbiters to meet the aggressive MSR timeline. The inversion ofthe current MRN paradigm, a system of many surface assetsrequiring relay and few orbiters to provide relay, necessitatesthe unique challenge of optimally allocating relay passes tomaximize the operational capability of all assets. The allocationmust consider a large number of trade variables includingMars asset operational requirements and Earth ground systemconstraints, including staffing schedules, operations planningacross time zones, and more. To address these telecommunicationchallenges, the Mars Asset Relay Mission Link AllocationDesign Environment (MARMLADE) tool was developed. Itis a MATLAB-based tool to assign orbiter passes or Direct-From-Earth (DFE) links to each of the three surface assets andquantify the operational efficiency of each surface asset.MARMLADE uses a data set of simulated Mars relay orbitergeometry and telecommunication capabilities provided by JPL’sTelecom Orbit Analysis and Simulation Tool (TOAST) softwareto compute which asset should get each pass based on a seriesof heuristics and predictions of all assets’ states. WithinMARMLADE, the user can provide inputs including the optionfor time-based pass splitting, fixed FWD data rate capabilities,DFE communication capabilities, and link parameters allowingfor the assessment of complex operations and hardware tradesusing surface mission operational efficiency as a primary figureof merit. As the MSR mission concepts continue to mature,MARMLADE is being used to assess ability of all MSR elementsto meet the surface mission timeline requirements and to provide relay link allocations to each of the MSR surface assets.

Lee, Charles↗

Autonomously Calibrating a Quadrupole Mass Spectrometer

A computer program autonomously manages the calibration of a quadrupole ion mass spectrometer intended for use in monitoring concentrations and changes in concentrations of organic chemicals in the cabin air of the International Space Station. The instrument parameters calibrated include the voltage on a channel electron multiplier, a discriminator threshold, and an ionizer current. Calibration is achieved by analyzing the mass spectrum obtained while sweeping the parameter ranges in a heuristic procedure, developed by mass spectrometer experts, that involves detection of changes in signal trends that humans can easily recognize but cannot necessarily be straightforwardly codified in an algorithm. The procedure includes calculation of signal-to-noise ratios, signal-increase rates, and background-noise-increase rates; finding signal peaks; and identifying peak patterns. The software provides for several recovery-from-error scenarios and error-handling schemes. The software detects trace amounts of contaminant gases in the mass spectrometer and notifies associated command- and-data-handling software to schedule a cleaning. Furthermore, the software autonomously analyzes the mass spectrum to determine whether the parameters of a radio-frequency ramp waveform are set properly so that the peaks of the mass spectrum are at expected locations.

Lee, Seungwon↗

Choosing Objectives in Over-Subscription Planning

Many NASA planning problems are over-subscription problems - that is, there are a large number of possible goals of differing value, and the planning system must choose a subset &it car! be accomplished within the limited time and resources available. Examples include planning for telescopes like Hubble, SIRTF, and SOFIA; scheduling for the Deep Space Network; and planning science experiments for a Mars rover. Unfortunately, existing planning systems are not designed to deal with problems like this - they expect a well-defined conjunctive goal and terminate in failure unless the entire goal is achieved. In this paper we develop techniques for over-subscription problems that assist a classical planner in choosing which goals to achieve, and the order in which to achieve them. These techniques use plan graph cost-estimation techniques to construct an orienteering problem, which is then used to provide heuristic advice on the goals and goal order that should considered by a planner.

Smith, David E.↗

Assessing Relay Communications for Mars Sample Return Surface Mission Concepts

The Mars Sample Return (MSR) Campaign would be a 3-mission campaign concept supported by NASA and ESA to return samples from the Mars surface. MSR would, for the first time ever, present a need to communicate with multiple surface assets that are co-located on Mars in a coordinated effort to accomplish the unified objective of fetching, transporting, and returning samples from Mars. Currently, Mars surface assets relay data to and from Earth using a number of orbiters in what’s known as the Mars Relay Network (MRN). This network is characterized by a small number of surface assets distributed across the Martian globe and a larger number of orbiters to provide relay services. As of June 2020, there are two surface assets for which five orbiters are providing relay. During the MSR Campaign, there would be two rovers and a lander that all would require relay communication from a small number of Mars orbiters to meet the aggressive MSR timeline. The inversion of the current MRN paradigm, a system of many surface assets requiring relay and few orbiters to provide relay, necessitates the unique challenge of optimally allocating relay passes to maximize the operational capability of all assets. The allocation must consider a large number of trade variables including Mars asset operational requirements and Earth ground system constraints, including staffing schedules, operations planning across time zones, and more. To address these telecommunication challenges, the Mars Asset Relay Mission Link Allocation Design Environment (MARMLADE) tool was developed. It is a MATLAB-based tool to assign orbiter passes or Direct-From-Earth (DFE) links to each of the three surface assets and quantify the operational efficiency of each surface asset.MARMLADE uses a data set of simulated Mars relay orbiter geometry and telecommunication capabilities provided by JPL’s Telecom Orbit Analysis and Simulation Tool (TOAST) software to compute which asset should get each pass based on a series of heuristics and predictions of all assets’ states. Within MARMLADE, the user can provide inputs including the option for time-based pass splitting, fixed FWD data rate capabilities, DFE communication capabilities, and link parameters allowing for the assessment of complex operations and hardware trades using surface mission operational efficiency as a primary figure of merit. As the MSR mission concepts continue to mature, MARMLADE is being used to assess ability of all MSR elements to meet the surface mission timeline requirements and to provide relay link allocations to each of the MSR surface assets.This paper will describe the motivation and design of the MARMLADE tool and how it is being used to perform campaign and mission level trades, generate requirements, and support development of the MSR surface mission scenarios.

Lee, Charles↗

Real-time adaptive aircraft scheduling

One of the most important functions of any air traffic management system is the assignment of ground-holding times to flights, i.e., the determination of whether and by how much the take-off of a particular aircraft headed for a congested part of the air traffic control (ATC) system should be postponed in order to reduce the likelihood and extent of airborne delays. An analysis is presented for the fundamental case in which flights from many destinations must be scheduled for arrival at a single congested airport; the formulation is also useful in scheduling the landing of airborne flights within the extended terminal area. A set of approaches is described for addressing a deterministic and a probabilistic version of this problem. For the deterministic case, where airport capacities are known and fixed, several models were developed with associated low-order polynomial-time algorithms. For general delay cost functions, these algorithms find an optimal solution. Under a particular natural assumption regarding the delay cost function, an extremely fast (O(n ln n)) algorithm was developed. For the probabilistic case, using an estimated probability distribution of airport capacities, a model was developed with an associated low-order polynomial-time heuristic algorithm with useful properties.

Kolitz, Stephan E.↗

The Transition from Spacecraft Development Ot Flight Operation: Human Factor Considerations

In the field of aeronautics and astronautics, a paradigm shift has been witnessed by those in academia, research and development, and private industry. Long development life cycles and the budgets to support such programs and projects has given way to aggressive task schedules and leaner resources to draw from all the while challenging assigned individuals to create and produce improved products of processes. however, this "faster, better, cheaper" concept cannot merely be applied to the design, development, and test of complex systems such as earth-orbiting of interplanetary robotic spacecraft. Full advantage is not possible without due consideration and application to mission operations planning and flight operations, Equally as important as the flight system, the mission operations system consisting of qualified personnel, ground hardware and software tools, and verified and validated operational processes, should also be regarded as a complex system requiring personnel to draw upon formal education, training, related experiences, and heuristic reasoning in engineering an effective and efficient system. Unquestionably, qualified personnel are the most important elements of a mission operations system. This paper examines the experiences of the Deep Space I Project, the first in a series of new technology in-flight validation missions sponsored by the United States National Aeronautics and Space Administration (NASA), specifically, in developing a subsystems analysis and technology validation team comprised of former spacecraft development personnel. Human factor considerations are investigated from initial concept/vision formulation; through operational process development; personnel test and training; to initial uplink product development and test support. Emphasis has been placed on challenges and applied or recommended solutions, so as to provide opportunities for future programs and projects to address and disposition potential issues and concerns as early as possible to reap the benefits associated with learning from other's past experiences.

Basilio, Ralph R.↗