• Progress in Superconductivity and Cryogenics
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• v.4 no.1
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• pp.114-118
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• 2002

We introduce a design technique for homogeneous magnets using evolution strategy. The method has several advantages over existing techniques including: it allows complete flexibility in geometric constraints on the shape of both the coil and the homogeneous volume; it guarantees a globally optimal solution, and it automatically searches the minimum number of coils that satisfies given constraints.

• Kyungpook Mathematical Journal
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• v.47 no.1
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• pp.31-56
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• 2007

In this paper, we consider a delay differential equation model of two competing species with harvesting of the mature and immature members of each species. The time delay in the model represents the time from birth to maturity of that species, which appears in the adults recruitment terms. We study the dynamics of our model analytically and we present results on positivity and boundedness of the solution, conditions for the existence and globally asymptotically stable of equilibria, a threshold of harvesting, and the optimal harvesting of the mature populations of each species.

• Journal of Radiation Protection and Research
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• v.42 no.4
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• pp.212-221
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• 2017

Background: This study aims to calculate detector positions as a design of a radioactive source localizing radiation portal monitor (RPM) system using an improved genetic algorithm. Materials and Methods: To calculate of detector positions for a source localizing RPM system optimization problem is defined. To solve the problem, a modified iterative genetic algorithm (MIGA) is developed. In general, a genetic algorithm (GA) finds a globally optimal solution with a high probability, but it is not perfect at all times. To increase the probability to find globally optimal solution rather, a MIGA is designed by supplementing the iteration, competition, and verification with GA. For an optimization problem that is defined to find detector positions that maximizes differences of detector signals, a localization method is derived by modifying the inverse radiation transport model, and realistic parameter information is suggested. Results and Discussion: To compare the MIGA and GA, both algorithms are implemented in a MATLAB environment. The performance of the GA and MIGA and that of the procedures supplemented in the MIGA are analyzed by computer simulations. The results show that the iteration, competition, and verification procedures help to search for globally optimal solutions. Further, the MIGA is more robust against falling into local minima and finds a more reliably optimal result than the GA. Conclusion: The positions of the detectors on an RPM for radioactive source localization are optimized using the MIGA. To increase the contrast of the measurements from each detector, a relationship between the source and the detectors is derived by modifying the inverse transport model. Realistic parameters are utilized for accurate simulations. Furthermore, the MIGA is developed to achieve a reliable solution. By utilizing results of this study, an RPM for radioactive source localization has been designed and will be fabricated soon.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.4
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• pp.729-739
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• 2017

The objective of this study was to optimize minimum-energy impulsive spacecraft intercept using genetic algorithms. A mathematical model was established on two-body system based on f and g solution and universal variable to address spacecraft intercept problem for non-coplanar elliptical orbits. This nonlinear problem includes many local optima due to discontinuity and strong nonlinearity. In addition, since it does not provide a closed-form solution, it must be solved using a numerical method. Therefore, the initial guess is that a very sensitive factor is needed to obtain globally optimal values. Genetic algorithms are effective for solving these kinds of optimization problems due to inherent properties of random search algorithms. The main goal of this paper was to find minimum energy solution for orbit transfer problem. The numerical solution using initial values evaluated by the genetic algorithm matched with results of Hohmann transfer. Such optimal solution for unrestricted arbitrary elliptic orbits using universal variables provides flexibility to solve orbit transfer problems.

• International Journal of Control, Automation, and Systems
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• v.3 no.spc2
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• pp.334-340
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• 2005

Based on the stability margin provided by the EEAC, the unstable contingencies can be classified into sets according to their unstable modes. This two-part paper develops a globally optimal algorithm for transient stability control to coordinate preventive actions and emergency actions. In the first part, an algorithm is proposed for a set of contingencies having identical unstable modes. Instead of iterations between discrete emergency actions and continuous preventive actions, the algorithm straightforwardly searches for a globally optimal solution. The procedure includes assessing a set of insufficient emergency schemes identified by the EEAC; calculating the related preventive actions needed for stabilizing the system; and selecting the scheme with the minimum overall costs. Simulations on a Chinese power system highlight its excellent performance. The positive results obtained are explained by analogizing settlements for 0-1 knapsack problems using the multi-points greedy algorithm.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.6
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• pp.325-332
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• 2003

The loss minimization is one of the most important problems to save the operational cost in distribution systems. This paper presents an efficient method for optimal feeder reconfiguration of distribution systems. Chaos search algorithm (CSA) is used to reconfigure distribution systems so that active power losses are globally minimized with turning on/off sectionalizing switches. In optimization problem, the CSA searches the global optimal solution on the basis of regularity in chaotic motions and easily escapes from local or near optimal solution. The CSA is tested on 15 buses and 32 buses distribution systems, and the results indicate that it is able to determine appropriate switching options for global optimum reconfiguration.

• Journal of Communications and Networks
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• v.16 no.1
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• pp.36-44
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• 2014

Exploiting the energy-delay tradeoff for energy saving is critical for developing green wireless communication systems. In this paper, we investigate the delay-constrained energy-efficient packet transmission. We aim to minimize the energy consumption of multiple randomly arrived packets in an additive white Gaussian noise channel subject to individual packet delay constraints, by taking into account the practical on-off circuit power consumption at the transmitter. First, we consider the offline case, by assuming that the full packet arrival information is known a priori at the transmitter, and formulate the energy minimization problem as a non-convex optimization problem. By exploiting the specific problem structure, we propose an efficient scheduling algorithm to obtain the globally optimal solution. It is shown that the optimal solution consists of two types of scheduling intervals, namely "selected-off" and "always-on" intervals, which correspond to bits-per-joule energy efficiency maximization and "lazy scheduling" rate allocation, respectively. Next, we consider the practical online case where only causal packet arrival information is available. Inspired by the optimal offline solution, we propose a new online scheme. It is shown by simulations that the proposed online scheme has a comparable performance with the optimal offline one and outperforms the design without considering on-off circuit power as well as the other heuristically designed online schemes.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1344-1346
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• 1996

In this paper, we discuss an informal analysis of diffusion, global optimization properties of Langevine competitive learning neural network. In the view of the stochastic process, it is important that competitive learning gurantee an optimal solution for pattern recognition. We show that the binary reinforcement function in Langevine competitive learning is a brownian motion as Gaussian process, and construct the Fokker-Plank equation for the proposed neural network. Finally, we show that the informal analysis of the proposed algorithm has a possiblity of globally optimal. solution with the proper initial condition.

• Journal of the Korean Society of Marine Environment & Safety
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• v.7 no.2
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• pp.67-87
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• 2001

ICZM(integrated coastal zone management) is recognized globally as an optimal solution on coastal issues and problems because it aims integration of all the different programs and stakeholders on coastal interests. Evaluation of ICZM program is also recognized globally for the success of ICZM because we can get many useful experiences and learning through evaluation. There are many issues and problems in coastal zone in Korea such as coastal development, wetland loss, deterioration of water quality, decreasing of fisheries stocks, limit of public access, etc. Recently highly centralized ICfM has been introduced in Korea but there are so high barrier between ICZM program and other relevant sectors. This paper tries to fond factors to evaluate and further evolution of ICZM in Korea which is very centralized system.

• Proceedings of the KIEE Conference
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• 2001.07b
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• pp.590-592
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• 2001

We introduce a technique for designing homogeneous magnets using linear programming. The method has several advantages over existing techniques including: it allows complete flexibility in arbitrary geometric constraints on both the coil locations and the shape of the homogeneous volume; it guarantees a globally optimal solution, and it automatically choose the minimum number of coils necessary for the constraints.