The Earth observation (EO) market is rapidly growing due to technology miniaturization, cheaper launch opportunities, and a wider spectrum of EO applications. Along with an exponential growth of the ground-based users...
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The Earth observation (EO) market is rapidly growing due to technology miniaturization, cheaper launch opportunities, and a wider spectrum of EO applications. Along with an exponential growth of the ground-based users that can access low-Earth-orbit (LEO) spacecraft data, this growing community represents an important demand for data relay missions. LEO spacecraft have short visibility windows to the ground stations (GSs), which limits their throughput. Data relay missions comprise spacecraft at higher-altitude orbits (geostationary orbits) acting as relays of data among LEO spacecraft and GSs. Those missions are then able to offer more frequent data downlink opportunities to the LEO spacecraft, thus increasing the volume of the data reaching the ground and improving the responsiveness between users' requests and downlink operations. Ground-based mission planning systems (MPSs) are commonly managing such complex missions, representing a large operational cost. In this paper, the application of a swarm intelligence algorithm to the design of an automated MPS for data relay missions is proposed. Automated MPSs have the potential to save operational costs while leaving the high-level decisions to human operators. This paper represents the first time that an antcolonyoptimization (ACO) algorithm is applied to this type of scheduling problem. This family of algorithms is generally found to offer a good level of efficiency and scalability. In this work, an ACO approach is compared against an algorithm that is popular in the literature, called Squeaky Wheel optimization, outperforming it.
The stratospheric ballooning community has undergone a recent transformation in the scope and scale of high altitude vehicle platforms which requires a new approach to trajectory prediction methods. To address these n...
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ISBN:
(数字)9781624105890
ISBN:
(纸本)9781624105890
The stratospheric ballooning community has undergone a recent transformation in the scope and scale of high altitude vehicle platforms which requires a new approach to trajectory prediction methods. To address these new challenges, the Southwest Research Institute Lighter-Than-Air team has developed a new model for uncertainty quantification for the use of stochastic vehicle path optimization. This new model incorporates multiple wind data sources to generate wind vector fields with parameterized weighting to mitigate measurement and resolution errors. The model calculations generate a probability of occurrence coinciding with each trajectory to inform path optimizationalgorithms. The path optimizationalgorithms search parameters are defined as the launch location, launch time, and float altitude. These parameters can be modified based upon mission requirements. The optimality value to select the "best path" is calculated as a combination of mission objectives and trajectory probability. The inclusion of uncertainty quantification for trajectory prediction will help to reduce operational risk by providing for a more informed decision-making process both before and during flights.
The e-learning paradigm is becoming one of the most important educational methods, which is a decisive factor for learning and for making learning relevant. However, most existing e-learning platforms offer traditiona...
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The e-learning paradigm is becoming one of the most important educational methods, which is a decisive factor for learning and for making learning relevant. However, most existing e-learning platforms offer traditional e-learning system in order that learners access the same evaluation and learning content. In response, Big Data technology in the proposed adaptive e-learning model allowed to consider new approaches and new learning strategies. In this paper, we propose an adaptive e-learning model for providing the most suitable learning content for each learner. This model based on two levels of adaptive e-learning. The first level involves two steps: (1) determining the relevant future educational objectives through the adequate learner e-assessment method using MapReduce-based Genetic Algorithm, (2) generating adaptive learning path for each learner using the MapReduce-based antcolonyoptimization algorithm. In the second level, we propose MapReduce-based Social Networks Analysis for determining the learner motivation and social productivity in order to assign a specific learning rhythm to each learner. Finally, the experimental results show that the presented algorithms implemented on Big Data environment converge much better than those implementations with traditional concurrent works. Also, this work provides main benefit because it describes how Big Data technology transforms e-learning paradigm. (C) 2018 Elsevier B.V. All rights reserved.
As the number of vehicles increases, the problem of parking has become more and more highlighted. This paper proposes the practice of sharing private parking space when not in use, to meet the ever-increasing parking ...
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ISBN:
(纸本)9789811035517;9789811035500
As the number of vehicles increases, the problem of parking has become more and more highlighted. This paper proposes the practice of sharing private parking space when not in use, to meet the ever-increasing parking demand through maximizing utilization of private parking lots. Optimal route and optimal parking location are two main focuses in this study on private parking sharing selection. Optimal route is a decision-making process based on the shortest travel time, using improved ant colony optimization algorithms to determine the route and travel time from travel origin to a shared private parking space, and to prepare for the quantification of the evaluation index for travel time in the shared parking space choice model, which first determines the quantified evaluation indexes according to the factors of interest when a person chooses a private parking space. Optimal parking location is determined by factoring in all the evaluation indexes and using the weighted TOPSIS model.
The Earth Observation market is growing rapidly, along with the missions’ complexity. Therefore, automated Mission Planning systems are being designed, allowing for operators to simply specify their intentions on a h...
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Mission design for multiple debris removal is performed by selecting the most favorable sequences of objects to be removed. Debris items among a population with similar inclination values are considered. The chaser re...
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Mission design for multiple debris removal is performed by selecting the most favorable sequences of objects to be removed. Debris items among a population with similar inclination values are considered. The chaser rendezvouses with the objects and attaches a removal kit. An approximate analysis, based on the use of the J2 effect to minimize propellant consumption, provides estimations of transfer times and Delta V between any object pair in order to evaluate the costs of any possible sequence. Estimations, which are verified by using an evolutionary optimization of four-impulse transfers that take J2 perturbation into account, are fast and accurate;and they permit evaluation of all available sequences when the number of objects to be removed is limited. The object sequence determination problem is converted to a traveling salesman problem, and an antcolonyoptimization algorithm is introduced to analyze longer sequences. The mass of the removal kit for any debris item is then evaluated, depending on the selected removal method. The overall mission mass budget is finally computed, and the best opportunities in terms of the mass and mission time are selected. Different removal techniques exploiting chemical and electric propulsion are compared. The results prove that removal of four to eight objects in less than a year is feasible with current technologies.
This Paper proposes a two-step binary linear programming formulation for task scheduling of a constellation of low-Earth-orbit satellites and demonstrates its applicability and scalability to obtain high-quality solut...
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This Paper proposes a two-step binary linear programming formulation for task scheduling of a constellation of low-Earth-orbit satellites and demonstrates its applicability and scalability to obtain high-quality solutions using a standard mixed-integer linear programming solver. In this instance, the goal of satellite constellation task scheduling is to allocate each task for the satellites and to determine the task starting times in order to maximize the overall mission performance metric. The scheduling problem is formulated to find the solution by first finding a set of candidate communication time intervals for each satellite/ground-station pair as one of the key constraints and time tabling the observation task to acquire the user-requested data, with the incorporation of key constraints for satellite constellation operation. Numerical experiments are designed for investigating the trends, sensitivity, and characteristics of scheduling outputs based on multiple representative instances. The performance of the scheduling solutions by the proposed two-step binary linear programming method exhibits significant improvement of up to 35% in the number of assignments and the sum of profits over the general greedy algorithm.
作者:
Stuart, Jeffrey R.Howell, Kathleen C.Wilson, Roby S.Purdue Univ
Sch Aeronaut & Astronaut 701 West Stadium Ave W Lafayette IN 47907 USA Purdue Univ
Sch Aeronaut & Astronaut Aeronaut & Astronaut 701 West Stadium Ave W Lafayette IN 47907 USA CALTECH
Jet Prop Lab Mission Design & Nav Sect Nav & Mission Design Syst Engn Grp 4800 Oak Grove Dr Pasadena CA 91109 USA CALTECH
Jet Prop Lab Mission Design & Nav Sect Inner Planet Mission Anal Grp 4800 Oak Grove Dr Pasadena CA 91109 USA
The sun-Jupiter Trojan asteroids are celestial bodies of great scientific interest as well as potential natural assets offering mineral resources for long-term human exploration of the Solar System. Previous investiga...
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The sun-Jupiter Trojan asteroids are celestial bodies of great scientific interest as well as potential natural assets offering mineral resources for long-term human exploration of the Solar System. Previous investigations have addressed the automated design of tours within the asteroid swarm and the transition of prospective tours to higher-fidelity, end-to-end trajectories. The current development incorporates the route-finding antcolonyoptimization algorithm into the automated tour-generation procedure. Furthermore, the potential scientific merit of the destination asteroids is incorporated such that encounters with higher-value asteroids are preferentially incorporated during sequence creation.
Garbage recycling and collection problem is an interesting problem that researchers are applying swarm intelligence algorithms to solve. Some previous approaches used particle swarm optimization, immune systems and an...
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ISBN:
(纸本)9781479967117
Garbage recycling and collection problem is an interesting problem that researchers are applying swarm intelligence algorithms to solve. Some previous approaches used particle swarm optimization, immune systems and ant colony optimization algorithms and achieved good results. antcolonyoptimization is a well-known swarm intelligence algorithm that is normally used to solve computational problems which can be reduced to finding good paths in graphs. A multi-robotic system can be applied to solve this problem but it will need a control algorithm to accomplish the task. Applying the regular antcolonyoptimization algorithm to control the multi-robotic system is not a trivial task due to the graph representation needed. This work proposes modifications in the antcolonyoptimization algorithm that uses grid representation and applies the modified algorithm to solve this problem. The results showed a decrease of one order of magnitude in the number of iterations needed to solve the problem compared to the previous version of the algorithm. Considering the results the proposed algorithm showed to be able to control a multi-robotic system for the chosen problem.
antcolonyoptimization (ACO) has been successfully applied to a wide number of complex and real domains. From classical optimization problems to video games, these kind of swarm-based approaches have been adapted, to...
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ISBN:
(数字)9783662455234
ISBN:
(纸本)9783662455234;9783662455227
antcolonyoptimization (ACO) has been successfully applied to a wide number of complex and real domains. From classical optimization problems to video games, these kind of swarm-based approaches have been adapted, to be later used, to search for new metaheuristic based solutions. This paper presents a simple ACO algorithm that uses a specifically designed heuristic, called common-sense, which has been applied in the classical video game Lemmings. In this game a set of lemmings must reach the exit point of each level, using a subset of finite number of skills, taking into account the contextual information given from the level. The paper describes both the graph model and the context-based heuristic, designed to implement our ACO approach. Afterwards, two different kind of simulations have been carried out to analyse the behaviour of the ACO algorithm. On the one hand, a micro simulation, where each ant is used to model a lemming, and a macro simulation where a swarm of lemmings is represented using only one ant. Using both kind of simulations, a complete experimental comparison based on the number and quality of solutions found and the levels solved, is carried out to study the behaviour of the algorithm under different game configurations.
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