Multi-day evaluation of space domain awareness architectures via decision analysis and multi-objective optimization

2022 ◽  
pp. 154851292110677
Author(s):  
Albert R Vasso ◽  
Richard G Cobb ◽  
John M Colombi ◽  
Bryan D Little ◽  
David W Meyer
Keyword(s):  
Decision Analysis ◽  
Performance Measures ◽  
Research Effort ◽  
National Policy ◽  
Space Environment ◽  
Multi Objective Optimization ◽  
Space Object ◽  
Multi Objective ◽  
Average Maximum ◽  
Us Government

The US Government is the world’s de facto provider of space object cataloging data, but it is challenged to maintain pace in an increasingly complex space environment. This work advances a multi-disciplinary approach to better understand and evaluate an underexplored solution recommended by national policy in which current collection capabilities are augmented with non-traditional sensors. System architecting techniques and extant literature identified likely needs, performance measures, and potential contributors to a conceptualized Augmented Network (AN). Multiple hypothetical architectures of ground- and space-based telescopes with representative capabilities were modeled and simulated on four separate days throughout the year, then evaluated against performance measures and constraints using Multi-Objective Optimization. Decision analysis and Pareto optimality identified a small, diverse set of high-performing architectures while preserving design flexibility. Should decision-makers adopt the AN approach, this research effort indicates (1) a threefold increase in average capacity, (2) a 55% improvement in coverage, and (3) a 2.5-h decrease in the average maximum time a space object goes unobserved.

scholarly journals Augmenting the space domain awareness ground architecture via decision analysis and multi-objective optimization

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Albert Vasso ◽  
Richard Cobb ◽  
John Colombi ◽  
Bryan Little ◽  
David Meyer
Keyword(s):  
Decision Analysis ◽  
Performance Measures ◽  
Low Cost ◽  
Fold Increase ◽  
Multi Objective Optimization ◽  
Space Object ◽  
Content Type ◽  
Multi Objective ◽  
Maximum Time ◽  
Average Maximum

Purpose The US Government is challenged to maintain pace as the world’s de facto provider of space object cataloging data. Augmenting capabilities with nontraditional sensors present an expeditious and low-cost improvement. However, the large tradespace and unexplored system of systems performance requirements pose a challenge to successful capitalization. This paper aims to better define and assess the utility of augmentation via a multi-disiplinary study. Design/methodology/approach Hypothetical telescope architectures are modeled and simulated on two separate days, then evaluated against performance measures and constraints using multi-objective optimization in a heuristic algorithm. Decision analysis and Pareto optimality identifies a set of high-performing architectures while preserving decision-maker design flexibility. Findings Capacity, coverage and maximum time unobserved are recommended as key performance measures. A total of 187 out of 1017 architectures were identified as top performers. A total of 29% of the sensors considered are found in over 80% of the top architectures. Additional considerations further reduce the tradespace to 19 best choices which collect an average of 49–51 observations per space object with a 595–630 min average maximum time unobserved, providing redundant coverage of the Geosynchronous Orbit belt. This represents a three-fold increase in capacity and coverage and a 2 h (16%) decrease in the maximum time unobserved compared to the baseline government-only architecture as-modeled. Originality/value This study validates the utility of an augmented network concept using a physics-based model and modern analytical techniques. It objectively responds to policy mandating cataloging improvements without relying solely on expert-derived point solutions.

Multi-objective optimization-based method for kinematic posture prediction: development and validation

Robotica ◽  
2010 ◽  
Vol 29 (2) ◽  
pp. 245-253 ◽  
Author(s):  
Jingzhou (James) Yang ◽  
Tim Marler ◽  
Salam Rahmatalla
Keyword(s):  
Performance Measures ◽  
Human Performance ◽  
Design Tool ◽  
Upper Body ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Validation Criteria ◽  
Posture Prediction ◽  
Development And Validation ◽  
Digital Human

SUMMARYPosture prediction plays an important role in product design and manufacturing. There is a need to develop a more efficient method for predicting realistic human posture. This paper presents a method based on multi-objective optimization (MOO) for kinematic posture prediction and experimental validation. The predicted posture is formulated as a multi-objective optimization problem. The hypothesis is that human performance measures (cost functions) govern how humans move. Twelve subjects, divided into four groups according to different percentiles, participated in the experiment. Four realistic in-vehicle tasks requiring both simple and complex functionality of the human simulations were chosen. The subjects were asked to reach the four target points, and the joint centers for the wrist, elbow, and shoulder and the joint angle of the elbow were recorded using a motion capture system. We used these data to validate our model. The validation criteria comprise R-square and confidence intervals. Various physics factors were included in human performance measures. The weighted sum of different human performance measures was used as the objective function for posture prediction. A two-domain approach was also investigated to validate the simulated postures. The coefficients of determinant for both within-percentiles and cross-percentiles are larger than 0.70. The MOO-based approach can predict realistic upper body postures in real time and can easily incorporate different scenarios in the formulation. This validated method can be deployed in the digital human package as a design tool.

The Efficient Multi-Objective Optimization of Finite Element Analysis Model Using ModelCenter

2016 ◽  
Author(s):  
Lyu Wang ◽  
Yuan Yun ◽  
Bin Zhang ◽  
Tao Zhang
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Frequency Response ◽  
Space Environment ◽  
Multi Objective Optimization ◽  
Analysis Model ◽  
Element Analysis ◽  
Finite Element Analysis Model ◽  
Multi Objective ◽  
Software Analysis

The multi-objective optimization for a nested flying vehicle (NFV) of space science experiments is carried out aiming at the launch weight, frequency response and vacuum effect. The parametric model and finite element analysis are adopted to implement the structural analysis. The NFV is optimized to enhance the performance in the space environment where the lunch weight and structural strength are key constraints to concern about. The CAX software, analysis models and algorithms are integrated based on ModelCenter framework which makes modeling, analyzing and optimization more convenient and efficient. The optimizer of ModelCenter is chosen to optimize the structural performance of NFV, including the total mass, maximum deformation caused by vacuum environment and frequency response. As to validate the results, both weighting method with gradient optimization algorithm and Genetic Algorithm (GA) for multi-objective optimization are used. The optimization results of NFV verify the approaches proposed in this paper can improve the performance of NFV and apply to the finite element analysis model.

scholarly journals Multi-Objective Optimization Method for Posture Prediction of Symmetric Static Lifting Using a Three-Dimensional Human Model

2020 ◽  
Vol 18 (2) ◽  
Author(s):  
Sirous Azizi ◽  
Afsaneh Dadarkhah ◽  
Alireza Asgharpour Masouleh
Keyword(s):  
Performance Measures ◽  
Three Dimensional ◽  
Human Model ◽  
Virtual Human ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Biomechanical Models ◽  
Human Models ◽  
Posture Prediction ◽  
Lifting Task

Background: The development of virtual human models has recently gained considerable attention in biomechanical studies intending to design for ergonomics. The computer-based simulations of virtual human models can reduce the time and cost of the design cycle. There is an increasing interest in finding the realistic posture of the human body with applications in prototype design and reduction of injuries in the workplace. Objectives: This paper presents a generic method based on a multi-objective optimization (MOO) for posture prediction of a sagittal-plane lifting task. Methods: Improved biomechanical models are used to formulate the predicted posture as a MOO problem. The lifting task has been defined by seven performance measures that are mathematically represented by the weighted sum of cost functions. Specific weights are assigned for each cost function to predict both stoop and squat type postures. Some inequality constraints have been used to ensure that the virtual human does not assume a completely unrealistic configuration. Results: The method can predict the hand configuration effectively. Simulations reveal that predicting a squat posture requires the minimization of certain objective functions, while these measures are less significant for the prediction of a stooped posture. Conclusions: In this study, a MOO-based posture prediction model with a validation process is presented. We employed a three-dimensional model to evaluate the applicability of using a combination of seven performance measures to the posture prediction of symmetric lifting tasks. Results have been compared with the available empirical data to validate the simulated postures. Furthermore, the assigned weights are obtained for a range of percentiles from 50% male to 90% female according to the postures obtained by 3D SSPPTM software.

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