• Journal of the Korean Magnetic Resonance Society
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• v.10 no.1
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• pp.1-37
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• 2006

General expressions for solid state NMR lines are described for transitions under static, magic angle spinning, and variable angle spinning conditions in the case where the principal axis system for the anisotropic chemical shift tensor is noncoincident with that of the quadrupole coupling tensor. It is demonstrated that solid state NMR powder pattern simulation program VMAS based on the conventional grid point method of integrating over the Euler angle space is fast enough in comparison with the POWDER simulation package and Gauss-point method.

• Journal of Korean Institute of Industrial Engineers
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• v.8 no.2
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• pp.15-21
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• 1982

This paper offers an alternative to the common probability generating function approach to the solution of steady state equations when a Markovian queue has a multivariate state space. Identifying states and substates and grouping them into vectors appropriately, we formulate a two - dimensional Markovian queue as a Markov chain. Solving the resulting matrix equations the transition point steady state probabilities (SSPs) are obtained. These are then converted into arbitrary time SSPs. The procedure uses only probabilistic arguments and thus avoids a large and cumbersome state space which often poses difficulties in the solution of steady state equations. For the purpose of numerical illustration of the approach we solve a Markovian queue with one server and two classes of customers.

• 한국정보디스플레이학회:학술대회논문집
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• 2008.10a
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• pp.1138-1141
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• 2008

By defining a uniform reference point array corresponding to the 3D voxel array and abandoning voxels whose deviations from their respective reference points exceed a given tolerance, the distribution of voxels within the volumetric 3D image space gets uniform, effects of non-uniform distribution upon the image reconstructing are eased.

• Proceedings of the KIEE Conference
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• summer
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• pp.342-344
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• 2004

In conventional small signal stability analysis, system is assumed to be invariant and the state space equations are used to calculate the eigenvalues of state matrix. However, when a system contains switching elements such as FACTS devices, it becomes non-continuous system. In this case, a mathematically rigorous approach to system small signal stability analysis is by means of eigenvalue analysis of the system periodic transition matrix based on discrete system analysis method. In this research, RCF(Resistive Companion Form) method is used to analyse small signal stability of a non-continuous system including switching elements'. Applying the RCF method to the differential and integral equations of power system, generator, controllers and FACTS devices including switching elements should be modeled in the form of state transition matrix. From this state transition matrix eigenvalues which are mapped to unit circle can be calculated.

• Proceedings of the KIEE Conference
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• 1996.07a
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• pp.431-433
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• 1996

It is expected that utility interactive small scale dispersed PV system will be widely diffused in the future. This paper discussed the design and control method of single phase PV inverter system with compensation capability of reactive power including harmonic distortion, based on state space modelling. As the results, compensation effects were suggested by simulation and experiment.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.2
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• pp.178-186
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• 1996

This paper proposes a new design technique that can be used for BSB (brain-state-in-a-box) neural networks to realize autoassociative memories. The proposed method is based on the parametrization of solution space and optimization using genetic algorithm. The applicability of the established technique is demonstrated by means of a simulation example, which illustrates its strengths.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.76.5-76
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• 2001

This paper considers the parameter estimation for the state-space model based time-delay systems in the case that the Lyapunov stability of the system is guaranteed. In order to estimate the parameters, two estimation methods can be proposed which are known as the parallel model and the series parallel model. It is shown that the parameters can be estimated using each method, and also certied that the results are correct by simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.10
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• pp.832-835
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• 2003

The objective of this study is to investigate the possibility of chemical process synthesis purely based on mathematical programming when given an objective, feed conditions, product specifications, and model equations for available process units. A method based on a state space approach is proposed, and applied to an example problem with a reactor, a heat exchanger, and a separator. The results indicate that a computer can automatically synthesize an optimal process without any heuristics or expertise in process design provided that global optimization techniques are improved to be suitable for large problems.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1870-1873
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• 2004

The objective of this study is to investigate the possibility of chemical process synthesis purely based on mathematical programming when given an objective, feed conditions, product specifications, and model equations for available process units. A method based on a state space approach is proposed, and applied to an example problem with a reactor, a heat exchanger, and a separator. The results indicate that a computer can automatically synthesize an optimal process without any heuristics or expertise in process design provided that global optimization techniques are improved to be suitable for large problems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.270-275
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• 2016

We present an approach for acceleration actor-critic algorithm for reinforcement learning with continuous action space. Actor-critic algorithm has already proved its robustness to the infinitely large action spaces in various high dimensional environments. Despite that success, the main problem of the actor-critic algorithm remains the same-speed of convergence to the optimal policy. In high dimensional state and action space, a searching for the correct action in each state takes enormously long time. Therefore, in this paper we suggest a search accelerating function that allows to leverage speed of algorithm convergence and reach optimal policy faster. In our method, we assume that actions may have their own distribution of preference, that independent on the state. Since in the beginning of learning agent act randomly in the environment, it would be more efficient if actions were taken according to the some heuristic function. We demonstrate that heuristically-accelerated actor-critic algorithm learns optimal policy faster, using Educational Process Mining dataset with records of students' course learning process and their grades.