• Proceedings of the Korea Society for Simulation Conference
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• 1999.04a
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• pp.70-79
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• 1999

Importance Sampling (IS) has been applied to accelerate the occurrence of rare events. However, it has a drawback of effective biasing scheme to make the estimator from IS unbiased. Adaptive Importance Sampling (AIS) employs an estimated sampling distribution of IS to the systems of interest during the course of simulation. We propose Nonparametric Adaptive Importance Sampling (NAIS) technique which is nonparametrically modified version of AIS and test it to estimate a probability of rare event in M/M/1 queueing model. Comparing with classical Monte Carlo simulation, the computational efficiency and variance reductions gained via NAIS are substantial. A possible extension of NAIS regarding with random number generation is also discussed.

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.3
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• pp.328-336
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• 2008

This research proposes a method for economic-statistical design of adaptive moving average (A-MA) charts. The basic idea of the A-MA chart is to accumulate previous samples selectively in order to increase the sensitivity. The A-MA chart is a kind of adaptive chart such as the variable sampling size (VSS) chart. A major advantage of the A-MA chart over the VSS chart is that it is easy to maintain rational subgroups by using the fixed sampling size. A steady state cost rate function is constructed based on Lorenzen and Vance (1986) model. The cost rate function is optimized with respect to five design parameters. Computational experiments show that the A-MA chart is superior to the VSS chart as well as to the Shewhart $\bar{X}$ chart in the economic-statistical sense.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.623-627
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• 2009

Recently, we have many kinds of picture format and display, and resizing (scaling) of picture becomes important. In this processing, quality of picture depends on re-sizing method. For this, some methods to improve the PSNR have been proposed. However, subjective picture quality is more important. Especially, degradation caused by re-sizing, such as jaggy (aliasing) and ringing, should be reduced. To solve them, we have proposed the method using directional adaptive interpolation. To improve the performance of this method, we consider the shape analysis this time. In the proposed method, directional adaptive processing is applied for pure edge only. In the texture area and flat area, 8 tap re-sampling filter is used. As the results of processing, the reductions of jaggy and incorrect interpolated pixels are recognized. The subjective picture quality of proposed method is significantly better than 8-tap re-sampling which gives good PSNR.

• Journal of Korean Society for Quality Management
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• v.36 no.1
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• pp.7-19
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• 2008

During the past two decades, a huge amount of research on adaptive control charts has been accomplished. Especially, variable sampling interval (VSI), variable sample size (VSS), and variable sample size and sampling interval (VSSI) charts have been focused by many researchers due to their simplicity and efficiency. On the other hand, the difference among notations, assumptions, methodologies may cause confusions in per forming further studies or practical implementations. This research analyses and compares diverse models so as to provide a unified view on statistical and economical characteristics. As a result, we perform comparative study on economical design models of VSI, VSS, and VSSI charts, respectively, We also present practical guidelines to utilize those adaptive control charts.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.04a
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• pp.131-136
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• 1997

This paper represent the variable structure adaptive mode control technique which is new approach to implement the robust control of industrial robot manipulator with external disturbances and parameter uncertainties. Sliding mode control is a well-known technique for robust control of uncertain nonlinear systems. The robustness of sliding model controllers can be shown in contiuous time, but digital implementation may not preserve robustness properties because the sampling process limits the existence of a true sliding mode. the sampling process often forces the trajectory to oscillate in the neighborhood of the sliding surface. Adaptive control technique is particularly well-suited to robot manipulators where dynamic model is highly complex and may contain unknown parameters. Adaptive control algorithm is designed by using the principle of the model reference adaptive control method based upon the hyperstability theory. The proposed control scheme has a simple sturcture is computationally fast and does not require knowledge of the complex dynamic model or the parameter values of the manipulator or the payload. Simulation results show that the proposed method not only improves the performance of the system but also reduces the chattering problem of sliding mode control, Consequently, it is expected that the new adaptive sliding mode control algorithm will be suited for various practical applications of industrial robot control system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.4
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• pp.149-154
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• 2017

Performance of the mobile devices, such as Smartphones, is sensible by the early-stage of the execution of the applications. To addressing this issue, the dynamic frequency scaling by the ondemand governor has an inherent weakness by the sampling period that may induces some delay in the execution time of the applications. In this paper, we propose an adaptive sampling method that varying the sampling period of the ondemand governor in accordance with the execution of the applications. By the experiment result, the proposed method outperforms 3.34% in early-stage of the execution time that impacts the sensible performance, and exhibits negligible differences in terms of the energy consumption.

• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.479-485
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• 2009

When a part of data is unobserved the marginal likelihood of parameters given the observed data often involves analytically intractable high dimensional integral and hence it is hard to find the maximum likelihood estimate of the parameters. Simulated maximum likelihood(SML) method which estimates the marginal likelihood via Monte Carlo importance sampling and optimize the estimated marginal likelihood has been used in many applications. A key issue in SML is to find a good proposal density from which Monte Carlo samples are generated. The optimal proposal density is the conditional density of the unobserved data given the parameters and the observed data, and attempts have been given to find a good approximation to the optimal proposal density. Algorithms which adaptively improve the proposal density have been widely used due to its simplicity and efficiency. In this paper, we describe a fully adaptive algorithm which has been used by some practitioners but has not been well recognized in statistical literature, and evaluate its estimation performance and robustness via a simulation study. The simulation study shows a great improvement in the order of magnitudes in the mean squared error, compared to non-adaptive or partially adaptive SML methods. Also, it is shown that the fully adaptive SML is robust in a sense that it is insensitive to the starting points in the optimization routine.

• Journal of Power Electronics
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• v.4 no.2
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• pp.102-111
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• 2004

This paper is concerned with a deadbeat current control implementation of shunt-type three-phase active power filter (APF). Although the one-dimensional deadbeat control method can attain time-optimal response of APF compensating current, one sampling period is actually required fur its settling time. This delay is a serious drawback for this control technique. To cancel such a delay and one more delay caused by DSP execution time, the desired APF compensating current has to be predicted two sampling periods ahead. Therefore an adaptive predictor is adopted for the purpose of both predicting the control error of two sampling periods ahead and bringing the robustness to the deadbeat current control system. By adding the adaptive predictor output as an adjustment term to the reference value of half a source voltage period before, settling time is made short in a transient state. On the other hand, in a steady state, THD (total harmonic distortion) of the utility grid side AC source current can be reduced as much as possible, compared to the case that ideal identification of controlled system could be made.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.12
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• pp.70-79
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• 2020

This paper describes a comparative study of characteristics of probabilistic design using various reliability analysis methods in the structure design of an automatic salt collector. The thickness sizing variables of the main structural member were considered to be random variables, including the uncertainty of corrosion, which would be an inevitable hazard in the work environment of the automatic salt collector. Probabilistic performance functions were selected from the strength performances of the automatic salt collector structure. First-order reliability method, second-order reliability method, mean value reliability method, and adaptive importance sampling method were applied during the reliability analyses. The probabilistic design performances such as reliability probability and numerical costs based on the reliability analysis methods were compared to the Monte Carlo simulation results. The adaptive importance sampling method showed the most rational results for the probabilistic structure design of the automatic salt collector.

• Journal of Sensor Science and Technology
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• v.1 no.2
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• pp.155-163
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• 1992

The phased array antenna has the ability to perform adaptive sampling by directing the radar beam without inertia in any direction. The adaptive sampling capability of the phased array antenna allows each sampling time interval to be varied for each target, depending on the acceleration of each target at any time. In this paper we design a three-dimensional adaptive tracking algorithm for the phased array radar system with a given set of measurement parameters. The tracking algorithm avoids taking unnecessarily frequent samples, while keeping the angular prediction error within a fraction of antenna beamwidth so that the probability of detection will not be degraded during a track update illuminations. In our algorithm, the target model and the sampling rate are selected depending on the target range and the target maneuver status which is determined by a maneuver detector. A detailed simulation is conducted to test the validity of our tracking algorithm for encounter geometries under various conditions of maneuver.