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Scheduling space-ground communications for the Air Force Satellite Control Network

JOURNAL OF SCHEDULING 2004年第1期7卷 7-34页

作者： Barbulescu, L Watson, JP Whitley, LD Howe, AE Colorado State Univ Ft Collins CO 80523 USA

We present the first coupled formal and empirical analysis of the Satellite Range Scheduling application. We structure our study as a progression;we start by studying a simplified version of the problem in which only one resource is present. We show that the simplified version of the problem is equivalent to a well-known machine scheduling problem and use this result to prove that Satellite Range Scheduling is NP-complete. We also show that for the one-resource version of the problem, algorithms from the machine scheduling domain outperform a genetic algorithm previously identified as one of the best algorithms for Satellite Range Scheduling. Next, we investigate if these performance results generalize for the problem with multiple resources. We exploit two sources of data: actual request data from the U.S. Air Force Satellite Control Network (AFSCN) circa 1992 and data created by our problem generator, which is designed to produce problems similar to the ones currently solved by AFSCN. Three main results emerge from our empirical study of algorithm performance for multiple-resource problems. First, the performance results obtained for the single-resource version of the problem do not generalize: the algorithms from the machine scheduling domain perform poorly for the multiple-resource problems. Second, a simple heuristic is shown to perform well on the old problems from 1992;however it fails to scale to larger, more complex generated problems. Finally, a genetic algorithm is found to yield the best overall performance on the larger, more difficult problems produced by our generator.

关键词： satellite range scheduling algorithm performance genetic algorithm local search

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Finding Transition Models using Dimensional Analysis Gene Expression Programming

Finding Transition Models using Dimensional Analysis Gene Ex...

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AIAA SciTech Forum

作者： Bleh, A. Geiser, G. German Aerosp Ctr DLR Cologne Germany German Aerosp Ctr Inst Prop Technol D-51147 Cologne Germany

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

Data-driven turbulence modeling has become an emerging field, aiming to overcome the weaknesses of classical Reynolds Averaged Navier-Stokes (RANS) models. One branch is Gene Expression Programming (GEP), which tries to find symbolic expressions for unknown functional dependencies. As an evolutionary algorithm it typically relies on many function evaluations. To reduce the computational cost, prior knowledge should be included where possible. When modeling functional dependencies in a physical context, the classical GEP is unaware of the physical dimensions of the involved quantities. Nevertheless, the validity of an expression in terms of its dimensions is a valuable hint towards its suitability and may improve the algorithms' performance. Therefore, in this work, we propose a new approach to consider physical dimensions within GEP. The new algorithm is evaluated and compared against existing approaches and applied on well-described turbomachinery test cases at transitional flow conditions.

关键词： Reynolds Averaged Navier Stokes Turbulence Models Evolutionary algorithm Turbomachinery Design Transitional Flow algorithm performance CFD simulation Eddy Viscosity Python Convection Diffusion Equation

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Hybrid-Electric Aero-Propulsion Controls Testbed Results with Energy Storage

Hybrid-Electric Aero-Propulsion Controls Testbed Results wit...

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AIAA Aviation Forum

作者： Bianco, Santino J. Kratz, Jonathan L. Culley, Dennis E. Buescher, Halle E. Horning, Marcus A. Saus, Joseph R. Sachs-Wetstone, Jonah J. Connolly, Joseph W. NASA Glenn Res Ctr Intelligent Control & Auton Branch Cleveland OH 44135 USA HX5 LLC Brookpark OH 44142 USA

ISBN: (数字)9781624107047

ISBN: (纸本)9781624107047

Electrified aircraft propulsion (EAP) research is a priority of the National Aeronautics and Space Administration (NASA) for its potential to increase propulsion system efficiency, performance, and operability at the subsystem and vehicle levels while decreasing emissions. These EAP systems demand more advanced control algorithms due to increased complexity. NASA has developed a reconfigurable, hardware-in-the-loop rig to verify control algorithm performance using a sub-scale electro-mechanical system. A novel capability of this rig is the ability to test full scale EAP control algorithms on a sub-scale representation of the electromechanical system without turbomachinery/rotors. A novel feature is the use of a physical energy storage device within the electro-mechanical system. A dual spool, parallel hybridelectric turbofan architecture and energy management control system is tested with the goal of verifying the ability to obtain turbomachinery model operability benefits while controlling sub-scale electro-mechanical hardware. Pre-test predictions of the turbofan model, control, and rig performance were obtained through simulation using a software model of the rig. Theoretical results showing the true performance of the turbofan model were obtained through a software simulation using full-scale mechanical shaft models. The paper compares theoretical, predicted, and actual test results from the turbofan model, energy management control and rig perspectives. The results show that the presence of sub-scale electromechanical hardware did not inhibit the energy management algorithm from achieving turbomachinery operability benefits.

关键词： Energy Storage Turbomachinery Design Turbofan Engines algorithm performance Numerical Propulsion System Simulation Energy Management National Aeronautics and Space Administration Real Time System Propulsion Control Aircraft Propulsion

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Sa-Based Localization algorithm for Wireless Sensor Network

Sa-Based Localization Algorithm for Wireless Sensor Network

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IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Li Jie Wang Tian-zheng Zhao Ya-ning Equipment State Evaluat Ctr State Grid Shanxi Elect Power Res Inst Taiyuan Peoples R China

ISBN: (纸本)9781509054183

Positioning of wireless sensor network node is essentially an optimization problem of measurements based on different distance or path. In the discussion of simulated annealing algorithm and node location of wireless sensor networks, proposed localization algorithm and its realization based on simulated annealing combined with the objective function. Simulation results show that the algorithm achieved good results.

关键词： wireless sensor network simulated annealing algorithm algorithm performance node location

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An Efficient algorithm for Multiple-Pursuer-Multiple-Evader Pursuit/Evasion Game

An Efficient Algorithm for Multiple-Pursuer-Multiple-Evader ...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Bertram, Joshua R. Wei, Peng Iowa State Univ Ames IA 50011 USA George Washington Univ Washington DC 20052 USA

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

We present a method for pursuit/evasion from the Artificial Intelligence community that is highly efficient and and scales to large teams of aircraft. The underlying algorithm, FastMDP, is an efficient algorithm for solving Markov Decision Processes (MDPs) that supports fully continuous state spaces. We demonstrate the algorithm in a team pursuit/evasion setting in a 3D environment and study performance by varying sizes of teams up to 100 vs 100. We show that as the number of aircraft in the simulation grows, computational performance remains efficient and is suitable for real-time systems. To the authors knowledge this is the first approach for large scale pursuit-evasion problems that remains efficient enough to be deployed on embedded hardware. We use probability-to-win and survivability metrics that describe the teams' performance over multiple trials to show that the algorithm performs consistently. We provide numerical results showing control inputs for a typical 1v1 encounter and provide videos for 1v1, 2v2, 3v3, 4v4, and 10v10 contests to demonstrate the ability of the algorithm to adapt seamlessly to complex environments.

关键词： Markov Decision Process Artificial Intelligence Hamilton Jacobi Bellman (HJB) Equations Aircraft Dynamics Aeroplane Angle of Attack Minimum Safe Altitude algorithm performance Model Predictive Control Aircraft performance

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A Matrix-free GMRES algorithm on GPU Clusters for Implicit Large Eddy Simulation

A Matrix-free GMRES Algorithm on GPU Clusters for Implicit L...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Jourdan, Eduardo Wang, Z. J. Univ Kansas Dept Aerosp Engn 2120 Learned Hall Lawrence KS 66045 USA

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

This paper investigates the performance of the matrix-free GMRES algorithm without a preconditioner on GPU clusters for large eddy simulations (LES) with the high-order FR/CPR method. This implicit solution algorithm does not need to store the Jacobian matrix, which is often too large to store on GPUs. In addition to the residual operator, it is important to have an efficient global reduction operation. The present work considers various solution approximate orders on mixed unstructured meshes with four different element types: hexahedrons, prisms, pyramids and tetrahedrons. Issues related to their difference in speedup are discussed and compared with the explicit three-stage Runge-Kutta scheme on a GPU cluster. A strong scalability study is conducted on the Summit supercomputer with up to 1200 NVIDIA V100 GPUs. A LES benchmark case is used to evaluate different parameters and the convergence tolerance of the GMRES solver. Preliminary performance results are also described for a test case using the High Lift Common Research Model from the 4th AIAA High-Lift Prediction Workshop. In this case, a reduction of the simulation cost by a factor of 15 to 35 is obtained compared to an explicit scheme on a GPU cluster.

关键词： Supercomputers algorithm performance Multigrid Methods Free Stream Velocity Transitional Flow Vortex Breakdown Power Spectral Density Pressure Coefficient Low Pressure Turbine Lift Coefficient

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A Hybrid Ensemble Machine Learning Approach for Arrival Flight Delay Classification Prediction Using Voting Aggregation Technique

A Hybrid Ensemble Machine Learning Approach for Arrival Flig...

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AIAA Aviation Forum

作者： Bisandu, Desmond B. Moulitsas, Irene Cranfield Univ Ctr Computat Engn Sci Bedford MK43 0AL England Cranfield Univ Machine Learning & Data Analyt Lab Digital Aviat Res & Technol Ctr DARTeC Bedford MK43 0AL England

ISBN: (数字)9781624107047

ISBN: (纸本)9781624107047

The number of flights keeps increasing with the development of civil aviation, and flight delays have become a severe issue of concern to the aviation industry. The need for reliable flight delay classification and prediction cannot be overemphasised because of its importance. In this research, we classify arrival flight delay using a hybrid ensemble machine learning algorithm based on voting aggregation. Each ensemble's architecture consists of four supervised machine learning algorithms: Logistic Regression, Decision Tree, Support Vector Classifier and Random Forest. We conducted a comparative experiment using the United States Bureau of Statistics dataset to verify the efficacy of our proposed approach against other benchmark approaches. We employ multiple evaluation metrics to check the performance of our proposed model comprehensively. Accuracy, Recall, Precision, F1-Score, AUC Score, and ROC curve. Our experimental results show that our model outperforms the benchmark techniques on the binary classification of flight delays task. Hence, the hybrid ensemble model presented in this research can be used as a decision-support model to improve the flight scheduling management system design to assist the airline and airport operation managers and passengers with improved travel itineraries management.

关键词： Department of Transportation Airport Operations Civil Aviation Multilayer Perceptron Orlando International Airport Artificial Intelligence Traffic Flow Management Engineering Science algorithm performance Flight Operation

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Triad of Trust: Protocols for Credible Modeling, Simulation, and Analysis

Triad of Trust: Protocols for Credible Modeling, Simulation,...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Hurst, Terri N. Turner, Patricia L. Jones, Jaylan S. Schneider, Lawrence A. Raytheon Co Syst Design & Performance Bldg 805 M-S C1 Tucson AZ 85756 USA Raytheon Co Air & Land Warfare Syst Ctr Bldg 848 M-S C1 Tucson AZ 85756 USA Raytheon Co Air & Land Warfare Syst Ctr Bldg 250 M-S C1 Tucson AZ 85756 USA

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

By its nature, the practice of modeling, simulation, and analysis (MSA) is inherently multidisciplinary, requiring clarity of communication among stakeholders of varying backgrounds. Three standard protocols for assuring credible MSA are described. These protocols enable transparent, collective decision-making and have been shown to minimize wasted resources when developing, using, and analyzing results from MSA.

关键词： Bayesian Network Model High performance Computing algorithm performance GPS Receivers Probability Distribution Artificial Intelligence Sensitivity Analysis Brittleness

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Optimizing Recommendations for Clustering algorithms Using Meta-Learning

Optimizing Recommendations for Clustering Algorithms Using M...

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International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Jilling, Adam Alvarez, Marco Univ Rhode Isl Dept Comp Sci & Stat Kingston RI 02881 USA

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169262

The field of machine learning has seen explosive growth over the past decade, largely due to increases in technology and improvements of implementations. As powerful as machine learning solutions can be, they are still reliant on human input to select the optimal algorithms and parameters. Clustering algorithms, in particular, are typically chosen by trial and error, as researchers will select a number of algorithms and choose whichever provides the most desirable result. This study will use a process called meta-learning to evaluate and analyze datasets and extract a series of meta-features. These meta-features can then be used to intelligently recommend an optimal clustering algorithm without the cost of having to manually run the algorithm. To accomplish this, we will experiment using 135 datasets and determine their expected outcomes using only their meta-features. The outcomes being optimized are performance (accuracy) and runtime. Results are then ranked separately for performance and runtime and we can determine how accurately the learning model was able to choose the optimal algorithm for each objective. With respect to runtime, we are able to predict the top-performing algorithm 71.1% of the time, one of the top two algorithms 89.6% of the time, and an algorithm in the top three 93.3% of the time. performance is correctly predicted in the top two 50.4% of the time and in the top three at a rate of 63.7%.

关键词： clustering meta-learning algorithm performance

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Multi-Agent Search and Rescue Applied to a Swarm of Ground Vehicles

Multi-Agent Search and Rescue Applied to a Swarm of Ground V...

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AIAA SciTech Forum

作者： Cooper, John R. Vaughan, Matthew P. Kelley, Benjamin N. NASA Langley Res Ctr Autonomous Integrated Syst Res Branch Mail Stop 233 Hampton VA 23681 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

This paper presents an algorithm for efficient search and rescue using a multi-agent system of vehicles. The algorithm uses an artificial potential field combined with a time-varying reward function for visiting various points within the search area. The reward function is used as a weight for the attractiveness of these points in the potential field. The reward value increases while the point is not being observed, and decreases while the point is observed. Collision avoidance terms are used to repel vehicles from each other, which has the additional effect of reducing duplication of searching efforts. Gradient descent of the potential field results in persistent surveillance of the search area. The algorithm generates position commands in real-time based on communication with the other vehicles. This framework allows vehicles to react in a dynamic environment, which is a significant advantage to simply following a priori defined trajectories. The algorithm is applied to a swarm of ground robots, and experimental data is presented showing that the swarm effectively searches the entire area and self-allocates search regions to individual vehicles.

关键词： Robots Collision Avoidance Multi Robot Systems Artificial Potential Fields Guidance algorithms algorithm performance Stochastic Gradient Descent Yaw Adaptive Control Method Guidance System

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