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AIAA Aviation Forum and Exposition

作者： Chaffee, Ethan A. Ellis, Patrick B. Reguero-Inserni, Jan Paul Southwest Res Inst Space Sci & Engn Div 6220 Culebra Rd San Antonio TX 78238 USA Southwest Res Inst Def & Intelligence Solut Div 6220 Culebra Rd San Antonio TX 78238 USA

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 optimization algorithms. The path optimization algorithms 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.

关键词： High Altitude Balloon Trajectory optimization ant colony optimization algorithms Particle Swarm optimization Flight Operation Path Planning Stratosphere Flight Data Monte Carlo Simulation Statistical Analysis

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A novel adaptive e-learning model based on Big Data by using competence-based knowledge and social learner activities

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APPLIED SOFT COMPUTING 2018年 69卷 14-32页

作者： Birjali, Marouane Beni-Hssane, Abderrahim Erritali, Mohammed Univ Chouaib Doukkali Dept Comp Sci LAROSERI Lab Fac Sci El Jadida Morocco Univ Sultan Moulay Slimane Fac Sci & Technol Dept Comp Sci TIAD Lab Beni Mellal Morocco

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 ant colony optimization 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.

关键词： Adaptive e-learning Big data MapReduce Genetic algorithm Personalized learning path ant colony optimization algorithms Social networks analysis Motivation and productivity Learning content

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optimization of Active Debris Removal Missions with Multiple Targets

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JOURNAL OF SPACECRAFT AND ROCKETS 2018年第1期55卷 181-189页

作者： Shen, Hong-Xin Zhang, Tian-Jiao Casalino, Lorenzo Pastrone, Dario Xian Satellite Control Ctr State Key Lab Astronaut Dynam Xian 710043 Shaanxi Peoples R China Peking Univ Dept Mech & Engn Sci Beijing 100871 Peoples R China Xi An Jiao Tong Univ Dept Elect & Informat Engn Xian 710049 Shaanxi Peoples R China Polytech Univ Turin Dipartimento Ingn Meccan & Aerospaziale Corso Duca Abruzzi 24 I-10126 Turin Italy

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 ant colony optimization 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.

关键词： Hybrid Propellants Electric Propulsion ant colony optimization algorithms Earth Periapsis Chemical Propulsion Orbital Elements Hall Thruster Rockets Propulsion System

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ant-Based Automated Mission Planning for Data Relay Space Missions

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2019年第7期16卷 249-262页

作者： Ntagiou, Evridiki, V Armellin, Roberto Iacopino, Claudio Policella, Nicola Donati, Alessandro Univ Surrey Surrey Space Ctr Guildford GU2 7XH Surrey England Surrey Satellite Technol Ltd Ground Software Team Guildford GU2 7YE Surrey England ESA Adv Mission Concepts & Technol European Space Operat Ctr Robert Bosch Str 5 D-64293 Darmstadt Germany ESA Adv Mission Concepts & Technol Robert Bosch Str 5 D-64293 Darmstadt Germany

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 ant colony optimization (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.

关键词： Space Operations Earth Ground Station Central Processing Unit Greedy Algorithm ant colony optimization algorithms Constellations Satellites Air Force Satellite Control Network Algorithm Performance

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optimization-Based Scheduling Method for Agile Earth-Observing Satellite Constellation

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2018年第11期15卷 611-626页

作者： Cho, Doo-Hyun Kim, Jun-Hong Choi, Han-Lim Ahn, Jaemyung Korea Adv Inst Sci & Technol Dept Aerosp Engn Daejeon 34141 South Korea

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.

关键词： Low Earth Orbit Satellites Earth Branch and Bound Algorithm Constellations ant colony optimization algorithms Mixed Integer Linear Programming Satellite Ground Station Ground Station EOS Data Store

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Research on Parking Choice Model Based on Shared Private Parking Space 7th

Research on Parking Choice Model Based on Shared Private Par...

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7th International Conference on Green Intelligent Transportation System and Safety (GITSS)

作者： Ding, Tongqiang Wang, Bo Zheng, Lili Xi, Jianfeng Wang, Shengli Xu, Shuangshan Jilin Univ Coll Traff Changchun 130022 Jilin Peoples R China

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.

关键词： Shared parking Parking choice Route guidance ant colony optimization algorithms TOPSIS

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ant-based mission planning: Two examples 15th

Ant-based mission planning: Two examples

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15th International Conference on Space Operations, SpaceOps 2018

作者： Ntagiou, Evridiki V. Armellin, Roberto Iacopino, Claudio Policella, Nicola Donati, Alessandro Surrey Space Centre University of Surrey GuildfordGU2 7XH United Kingdom Surrey Satellite Technology GuildfordGU2 7YE United Kingdom ESOC European Space Agency Robert-Bosch-Strasse 5 Darmstadt64293 Germany

ISBN: (数字)9781624105623

ISBN: (纸本)9781624105623

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 high level. In this paper, we propose an automated Mission Planning System based on the ants’ foraging mechanism and apply it to two different mission planning problems, from an Earth Imaging and a Data Relay mission, investigating the system’s ability to be generalised. We compare the planning process for the two problems and generalise on the type of planning problems the system can address. © 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Earth Satellites Tracking Data and Relay Satellite System Disaster Monitoring Constellation Ground Station ant colony optimization algorithms Sun Synchronous Orbit Satellite Technology Space Operations Constellation Mission

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Fuzzy Dynamic Parameter Adaptation in ACO and PSO for Designing Fuzzy Controllers: The Cases of Water Level and Temperature Control

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ADVANCES IN FUZZY SYSTEMS 2018年第1期2018卷

作者： Valdez, Fevrier Carlos Vazquez, Juan Gaxiola, Fernando Tijuana Inst Technol Tijuana BC Mexico Autonomous Univ Chihuahua Chihuahua Chih Mexico

A novel approach applied to Particle Swarm optimization (PSO) and ant colony optimization is presented. The main contribution of this work is the use of fuzzy systems to dynamically update the parameters for the ACO and PSO algorithms. In the case of ACO, two fuzzy systems are designed for the ant colony System (ACS) algorithm variant. The first system adjusts the value for the pheromone evaporation parameter from the global pheromone trail update equation and the second system adjusts the values for the pheromone evaporation parameter from the local pheromone trail update equation. In the case of PSO, a fuzzy system is designed to find the values for the inertia weight parameter from the velocity equation. Fuzzy logic controllers (FLCs) are optimized with ACO and PSO, respectively, to prove the performance of the proposed approach. The particular benchmark problems considered to test the proposed methods are the water level control in a tank and temperature control in a shower. Therefore, PSO and ACO algorithms are applied in the optimization of the parameters of the FLCs. The achievement of the proposed fuzzy ACO and PSO algorithms is compared with the original results of each benchmark control problem.

关键词： Navigability Particle swarm optimization ant colony optimization algorithms Temperature control

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Operating Room Surgery Scheduling with Fuzzy Surgery Durations Using a Metaheuristic Approach

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ADVANCES IN OPERATIONS RESEARCH 2018年第1期2018卷

作者： Nazif, Habibeh Payeme Noor Univ Dept Math Tehran Iran

Operating room (OR) surgery scheduling is a challenging combinatorial optimization problem that determines the operation start time of every surgery to be performed in different surgical groups, as well as the resources assigned to each surgery over a schedule period. One of the main challenges in health care systems is to deliver the highest quality of care at the lowest cost. In real-life situations, there is significant uncertainty in several of the activities involved in the delivery of surgical care, including the duration of the surgical procedures. This paper tackles the operating room surgery scheduling problem with uncertain surgery durations, where uncertainty in surgery durations is represented by means of fuzzy numbers. The problem can be considered as a Fuzzy Flexible Job-shop Scheduling Problem (FFJSP) due to similarities between operating room surgery scheduling with uncertain surgery durations and a multi-resource constraint flexible job-shop scheduling problem with uncertain processing times. This research handles both the advanced and allocation scheduling problems simultaneously and provides an ant colony optimization (ACO) metaheuristic algorithm which utilized a two-level ant graph to integrate sequencing jobs and allocating resources at the same time. To assess the performance of the proposed method, a computational study on five test surgery cases is presented, considering both deterministic and fuzzy surgery durations to enhance the significance of the study. The results of this experiment demonstrated the effectiveness of the proposed metaheuristic algorithm.

关键词： Metaheuristic ant colony optimization algorithms Schedule Operating theater

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Design of End-to-End Trojan Asteroid Rendezvous Tours Incorporating Scientific Value

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JOURNAL OF SPACECRAFT AND ROCKETS 2016年第2期53卷 278-288页

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 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 ant colony optimization 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.

关键词： Asteroid Collisions Solar System ant colony optimization algorithms Propellant Astrodynamics Saturn Search Algorithm Sample Return Missions Astronomical Unit Plasma Propulsion System

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