• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.56-63
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• 2016

We consider a satellite mission scheduling problem, which is a promising problem in recent satellite industry. This problem has various considerations such as customer importance, due date, limited capacity of energy and memory, distance of the location of each mission, etc. Also we consider the objective of each satellite such as general purpose satellite, strategic mission and commercial satellite. And this problem can be modelled as a general knapsack problem, which is famous NP-hard problem, if the objective is defined as to maximize the total mission score performed. To solve this kind of problem, heuristic algorithm such as taboo and genetic algorithm are applied and their performance are acceptable in some extent. To propose more efficient algorithm than previous research, we applied a particle swarm optimization algorithm, which is the most promising method in optimization problem recently in this research. Owing to limitation of current study in obtaining real information and several assumptions, we generated 200 satellite missions with required information for each mission. Based on generated information, we compared the results by our approach algorithm with those of CPLEX. This comparison shows that our proposed approach give us almost accurate results as just less than 3% error rate, and computation time is just a little to be applied to real problem. Also this algorithm has enough scalability by innate characteristic of PSO. We also applied it to mission scheduling problem of various class of satellite. The results are quite reasonable enough to conclude that our proposed algorithm may work in satellite mission scheduling problem.

• Journal of Astronomy and Space Sciences
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• v.37 no.1
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• pp.29-34
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• 2020

On 21 August 2017, during 16:49 UT and 20:02 UT period, a total solar eclipse started. The totality shadow occurred over the United States in time between ~17:15 UT and ~18:47 UT. When the solar radiation is blocked by the moon, observations of the ionospheric parameters will be important in the space weather community. Fortunately, during this eclipse, two Swarm satellites (A and C) flied at about 445 km through lunar penumbra at local noon of United States in the upper ionosphere. In this work, we investigate the effect of the solar eclipse on electron density, slant total electron content (STEC) and electron temperature using data from Swarm mission over United States. We use calibrated measurements of plasma density and electron temperature. Our results indicate that: (1) the electron density and STEC have a significant depletion associated with the eclipse; which could be due to dominance of dissociative recombination over photoionization caused by the reduction of ionizing extreme ultraviolet (EUV) radiation during the eclipse time (2) the electron temperature decreases, compared with a reference day, by up to ~150 K; which could be due to the decrease in photoelectron heating from reduced photoionization.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.24 no.4
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• pp.12-19
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• 2016

In the case of hard problems to find solutions or complx combination problems, there are various optimization algorithms that are used to solve the problem. Among these optimization algorithms, the representative of the optimization algorithm created by imitating the behavior patterns of the organism is the PSO (Particle Swarm Optimization) algorithm. Since the PSO algorithm is easily implemented, and has superior performance, the PSO algorithm has been used in many fields, and has been applied. In particular, PSO-SAPARB (PSO with Swarm Arrangement, Parameter Adjustment and Reflective Boundary) algorithm is an advanced PSO algorithm created to complement the shortcomings of PSO algorithm. In this paper, this PSO-SAPARB algorithm was applied to the longitudinal controller design of a VTOL (Vertical Take-Off and Landing) aircraft that has the advantages of fixed-wing aircraft and rotorcraft among drones which has attracted attention in the field of UAVs. Also, through the introduction and performance of the Korean SBAS (Satellite Based Augmentation System) named KASS (Korea Augmentation Satellite System) which is being developed currently, this paper deals with the availability of algorithm such as the PSO-SAPARB.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2281-2297
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• 2024

Employing multiple drones as a swarm to complete missions can sharply improve the working efficiency and expand the scope of investigation. Remote UAV swarms utilize satellites as relays to forward investigation information. The increasing amount of data demands higher transmission rate and complex field network coding (CFNC) is deemed as an effective solution for data return. CFNC applied to UAV swarms enhances transmission efficiency by occupying only two time slots, which is less than other network coding schemes. However, conventional CFNC applied to UAVs is combined with constant coding and modulation scheme and results in a waste of spectrum resource when the channel conditions are better. In order to avoid the waste of power resources of the relay satellite and further improve spectral efficiency, a CFNC transmission scheme with MPSK modulation is proposed in this paper. For the proposed scheme, the satellite relay no longer directly forwards information, but transmits information after processing according to the current channel state. The proposed transmission scheme not only maintains throughput advantage of CFNC, but also enhances spectral efficiency, which obtains higher throughput performance. The symbol error probability (SEP) and throughput results corroborated by Monte Carlo simulation show that the proposed transmission scheme improves spectral efficiency in multiples compared to the conventional CFNC schemes. In addition, the proposed transmission scheme enhances the throughput performance for different topology structures while keeping SEP below a certain value.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.28-36
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• 2016

Recently, owing to the development of ICT industry and wide spread of smart phone, the number of people who use car sharing service are increased rapidly. Currently two-way car sharing system with same rental and return locations are mainly operated since this system can be easily implemented and maintained. Currently the demand of one-way car sharing service has increase explosively. But this system have several obstacle in operation, especially, vehicle stock imbalance issues which invoke vehicle relocation. Hence in this study, we present an optimization approach to depot location and relocation policy in one-way car sharing systems. At first, we modelled as mixed-integer programming models whose objective is to maximize the profits of a car sharing organization considering all the revenues and costs involved and several constraints of relocation policy. And to solve this problem efficiently, we proposed a new method based on particle swarm optimization, which is one of powerful meta-heuristic method. The practical usefulness of the approach is illustrated with a case study involving satellite cities in Seoul Metrolitan Area including several candidate area where this kind systems have not been installed yet and already operating area. Our proposed approach produced plausible solutions with rapid computational time and a little deviation from optimal solution obtained by CPLEX Optimizer. Also we can find that particle swarm optimization method can be used as efficient method with various constraints. Hence based on this results, we can grasp a clear insight into the impact of depot location and relocation policy schemes on the profitability of such systems.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.11
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• pp.973-982
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• 2007

In this paper, optimal design framework is developed by automatic mesh generation and PSO(Particle Swarm Optimization) algorithm based on parallel computing environment and applied to structural optimal design of satellite adapter module. By applying automatic mesh generation, it became possible to change the structural shape of adapter module. PSO algorithm was merged with parallel computing environment and for maximizing a computing performance, asynchronous PSO algorithm was developed and could reduce the computing time of optimization process. As constraint conditions, eigen-frequency and maximum stress was considered. Finally using optimal design framework, weight reduction of satellite adapter module is derived with satisfaction of structural safety.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.4
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• pp.363-369
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• 2021

Since the Global Navigation Satellite System (GNSS) signal received from the low Earth orbit (LEO) satellite is only affected by the upper ionosphere, the magnitude of the ionospheric delay of Global Positioning System (GPS) signal received from ground user is different. Therefore, the ground-based two-dimensional ionospheric model cannot be applied to LEO satellites. The NeQuick model used in Galileo provides the ionospheric delay according to the user's altitude, so it can be used in the ionospheric model of the LEO satellites. However, the NeQuick model is not suitable for space receivers because of the high computational cost. A simplified NeQuick model with reduced computing time was recently presented. In this study, the computing time of the NeQuick model and the simplified NeQuick model was analyzed based on the GPS Klobuchar model. The NeQuick and simplified NeQuick model were applied to the GNSS data from GRACE-B, Swarm-C, and GOCE satellites to analyze the performance of the ionospheric correction and positioning. The difference in computing time between the NeQuick and simplified NeQuick model was up to 90%, but the difference in ionospheric accuracy was not as large as within 4.5%.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.8
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• pp.854-867
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• 2014

This paper deals with the performance comparison of a PSO algorithm inspired in the process of simulating the behavior pattern of the organisms. The PSO algorithm finds the optimal solution (fitness value) of the objective function based on a stochastic process. Generally, the stochastic process, a random function, is used with the expression related to the velocity included in the PSO algorithm. In this case, the random function of the normal distribution (Gaussian) or uniform distribution are mainly used as the random function in a PSO algorithm. However, in this paper, because the probability distribution which is various with 2 shape parameters can be expressed, the performance comparison of a PSO algorithm using the beta probability distribution function, that is a random function which has a high degree of freedom, is introduced. For performance comparison, 3 functions (Rastrigin, Rosenbrock, Schwefel) were selected among the benchmark Set. And the convergence property was compared and analyzed using PSO-FIW to find the optimal solution.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.12
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• pp.1150-1159
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• 2011

Reliability is defined as a probability that a system will operate properly for a specified period of time under the design operating conditions without failure. Reliability-Redundancy Optimization Problem(RROP) involves selection of components with multiple choices, redundancy levels and redundancy strategy(Active or Standby) for maximizing system reliability with constraints such as cost, weight, etc. Based on the design configuration of Multi-Spectral Camera(MSC) system of KOMPSAT-2, the mathematical programming model for RROP is suggested in this study. Due to the nature of RROP, i.e. NP-hard problem, Parallel Particle Swarm Optimization(PPSO) algorithm is proposed to solve it. The result of the numerical experiment for RROP is presented as instance of recommended design configuration at some mission time.

• International Journal of Aeronautical and Space Sciences
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• v.6 no.2
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• pp.1-16
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• 2005

Adaptive control methods are studied for the Satellite to isolate vibration in spite of the nonlinear system dynamics and parameter uncertainties of disturbance. First, a centralized control scheme is developed based on the particle swarm optimization(PSO) algorithm and feedback theory to automatically tune controller gains. A simulation study of a 3 degree-of-freedom device was conducted to evaluate the performance of the proposed control scheme. Next, since a centralized control scheme is hard to construct model dynamics and not goad at performance when controller and systems environment are easily changed, a decentralized control scheme is presented to avoid these defects of the centralized control scheme from the point of view of production and maintenance. It is based on the adaptive control methodologies to find PID controller parameters. Experiment studies were conducted to apply the adaptive control scheme and evaluate the performance of the proposed control scheme with those of the conventional control schemes.