• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.4B no.3
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• pp.108-113
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• 2004

This paper presents methods to improve performance of the power supply system in a NASA deep space explorer. In the Stirling engine driven reciprocating Brushless DC (BLDC) generator, the accurate position information of the prime mover is important to diagnose the performance of the engine and prevent distortion of the output power. Since sensors to detect the position are fragile and unreliable, and conventional sensorless techniques have drawbacks in the low speed region, a novel sensorless position detection technique for the prime mover has been proposed and verified. Another major issue of the generator for the spacecraft is power density maximization. The mass of the power system is important to the mass of the satellite. Therefore, the components of the spacecraft should be lightweight. Conventional rectification methods cannot achieve the maximum power possible due to non-optimal current waveforms. The optimal current waveform for maximizing power density and minimizing machine size and weight in a nonsinusoidal power supply system has been derived, incorporated in a control system, and verified by simulation work.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.3
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• pp.87-93
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• 2017

A maximum likelihood (ML) approach is presented for estimating the mean of radar cross section (RCS) of a Swerling III target and its numerical solution methods are discussed. The solution methods are based on an approximate expression for implementing the expectation maximization (EM) algorithm. The methods are evaluated and compared through Monte Carlo simulations in terms of estimation accuracy and computational efficiency to obtain a most efficient method for both Swerling I and Swerling III targets. The methods are also compared with a previously reported method based on heuristics.

• Communications for Statistical Applications and Methods
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• v.15 no.4
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• pp.633-642
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• 2008

Variable selection algorithm for partial least square regression using penalty function is proposed. We use the fact that usual partial least square regression problem can be expressed as a maximization problem with appropriate constraints and we will add penalty function to this maximization problem. Then simulated annealing algorithm can be used in searching for optimal solutions of above maximization problem with penalty functions added. The HARD penalty function would be suggested as the best in several aspects. Illustrations with real and simulated examples are provided.

• International Journal of Highway Engineering
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• v.17 no.5
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• pp.93-103
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• 2015

PURPOSES : The purpose of this study is to present a linear programing optimization model for the design of lane-based lane-uses and signal timings for an isolated intersection. METHODS: For the optimization model, a set of constraints for lane-uses and signal settings are identified to ensure feasibility and safety of traffic flow. Three types of objective functions are introduced for optimizing lane-uses and signal operation, including 1) flow ratio minimization of a dual-ring signal control system, 2) cycle length minimization, and 3) capacity maximization. RESULTS : The three types of model were evaluated in terms of minimizing delay time. From the experimental results, the flow ratio minimization model proved to be more effective in reducing delay time than cycle length minimization and capacity maximization models and provided reasonable cycle lengths located between those of other two models. CONCLUSIONS : It was concluded that the flow ratio minimization objective function is the proper one to implement for lane-uses and signal settings optimization to reduce delay time for signalized intersections.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2299-2318
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• 2019

Location-aware networks are of great importance for both civil lives and military applications. Methods based on line-of-sight (LOS) measurements suffer sever performance loss in harsh environments such as indoor scenarios, where sensors can receive both LOS and non-line-of-sight (NLOS) measurements. In this paper, we propose a data association (DA) process based on the expectation maximization (EM) algorithm, which enables us to exploit multipath components (MPCs). By setting the mapping relationship between the measurements and scatters as a latent variable, coefficients of the Gaussian mixture model are estimated. Moreover, considering the misalignment of sensor position, we propose a space-alternating generalized expectation maximization (SAGE)-based algorithms to jointly update the target localization and sensor position information. A two dimensional (2-D) circularly symmetric Gaussian distribution is employed to approximate the probability density function of the sensor's position uncertainty via the minimization of the Kullback-Leibler divergence (KLD), which enables us to calculate the expectation step with low computational complexity. Moreover, a distributed implementation is derived based on the average consensus method to improve the scalability of the proposed algorithm. Simulation results demonstrate that the proposed centralized and distributed algorithms can perform close to the Monte Carlo-based method with much lower communication overhead and computational complexity.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2371-2376
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• 2021

As the γ-ray energy increases, a reconstructed image becomes noisy and blurred due to the penetration of the γ-ray through the coded mask. Therefore, the thickness of the coded mask was increased for high energy regions, resulting in severely decreased the performance of the detection efficiency due to self-collimation by the mask. In order to overcome the limitation, a modified uniformly redundant array γ-ray imaging system using dual anti-mask method was developed, and its performance was compared and evaluated in high-energy radiation region. In the dual anti-mask method, the two shadow images, including the subtraction of background events, can simultaneously contribute to the reconstructed image. Moreover, the reconstructed images using each shadow image were integrated using a hybrid update maximum likelihood expectation maximization (h-MLEM). Using the quantitative evaluation method, the performance of the dual anti-mask method was compared with the previously developed collimation methods. As the shadow image which was subtracted the background events leads to a higher-quality reconstructed image, the reconstructed image of the dual anti-mask method showed high performance among the three collimation methods. Finally, the quantitative evaluation method proves that the performance of the dual anti-mask method was better than that of the previously reconstruction methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.10
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• pp.1292-1297
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• 2018

In this paper, efficiency optimization design of 600W class IPMSM was performed by using Quasi-Newton method. The output was limited to 600W to meet the same output as the basic model. The behavior of each variable as the design progressed was judged on the efficiency, which is the target value through correlation analysis. The design variables were set as the width of the stator, the position of the permanent magnet from the end of the rotor, the thickness of the permanent magnet, and the width of the permanent magnet.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.719-729
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• 2001

Overlap Maximization is a sampling technique to reduce survey costs and costs associated with the survey. It was first studied by Keyfitz(1951). Ernst(1998) presented a remarkable procedure for maximizing the overlap when the sampling units can be selected for two identical stratified designs simultaneously, But the approach involves mimicking the behaviour of nonlinear function by linear function and so it is less direct, even though the stratification problem for the overlap corresponds directly to the linear programming problem. furthermore, it uses the controlled selection algorithm that repeatedly needs zero-restricted controlled roundings, which are solutions of capacitated transportation problems. In this paper we suggest a comparatively simple procedure to use linear programming in order to maximize the overlap. We show how this procedure can be implemented practically.

• Journal of applied mathematics & informatics
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• v.36 no.3_4
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• pp.301-306
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• 2018

I obtain the optimal portfolio and consumption strategies of an investor who have a Cobb-Douglas utility function. And I assume that there is negative wealth constraints. This constraints mean that the investor can borrow partially against her future labor income.

• East Asian mathematical journal
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• v.34 no.3
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• pp.331-338
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• 2018

We investigate the optimal consumption and portfolio strategies of an agent who has a constant elasticity of substitution (CES) utility function under the negative wealth constraint. We use the martingale method to derive the closed-form solution, and we give some numerical implications.