• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.9 s.240
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• pp.1217-1224
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• 2005

To improve the accuracy of a metamodel, additional sample points can be selected by using a specified criterion, which is often called sequential sampling approach. Sequential sampling approach requires small computational cost compared to one-stage optimal sampling. It is also capable of monitoring the process of metamodeling by means of identifying an important design region for approximation and further refining the fidelity in the region. However, the existing critertia such as mean squared error, entropy and maximin distance essentially depend on the distance between previous selected sample points. Therefore, although sufficient sample points are selected, these sequential sampling strategies cannot guarantee the accuracy of metamodel in the nearby optimum points. This is because criteria of the existing sequential sampling approaches are inefficient to approximate extremum and inflection points of original model. In this research, new sequential sampling approach using the sensitivity of metamodel is proposed to reflect the response. Various functions that can represent a variety of features of engineering problems are used to validate the sensitivity approach. In addition to both root mean squared error and maximum error, the error of metamodel at optimum points is tested to access the superiority of the proposed approach. That is, optimum solutions to minimization of metamodel obtained from the proposed approach are compared with those of true functions. For comparison, both mean squared error approach and maximin distance approach are also examined.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.48 no.9
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• pp.517-522
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• 1999

This paper addresses the issue of current sampling in a high performance AC drive system fed by a discontinuous PWM inverter. The effect of the sampling error due to the measurement delay produced by an input stage low pass filter and an A/D converter is described in the case of discontinuous PWM. To compensate for the sampling error, a method to estimate the delay time of the whole measurement system based on the measured current is proposed and its effectiveness is verified by experimental results. The proposed algorithm can automatically estimate the system delay introduced by the low pass filter and the A/D converter at the commissioning stage. By delaying the current sampling by the estimated value, experimental results indicate that more than 50% reduction of current ripple can be achieved.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.477-483
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• 2015

This study deals with the decision problem of sample size by the relative standard error of estimates derived from survey results in successive occasions. The population of the construction in business survey results is used to calculate quartile of the relative standard error of the 1,000 sample obtained from simple or stratified random sampling. The sample size at time t with a relative standard error of the point (t-1) in the successive occasions were calculated according to the sampling method. As a result, in terms of the sample size according to the size of the relative standard error of the (t-1), simple random sampling differs significantly from stratified sampling. In addition, we could see differences in sample size (depending on how the population is stratified) and that careful attention is required in the problem of sample size by the relative standard error of estimates derived from survey results in successive occasions.

• Journal of the Korean Statistical Society
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• v.35 no.2
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• pp.157-166
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• 2006

This research proposes an efficient Monte Carlo algorithm for computing error probability in high performance digital communication st stems. It characterizes special features of the problem and suggests an importance sampling algorithm specially designed to handle the problem. It uses a shifted exponential density as the importance sampling density, and shows an adaptive way of choosing the rate and the origin of the shifted exponential density. Instead of equal allocation, an intelligent allocation of the samples is proposed so that more samples are allocated to more important part of the error probability. The algorithm uses the nested feature of the error space and avoids redundancy in estimating the probability. The algorithm is applied to an example data set and shows a great improvement in accuracy of the error probability estimation.

• Journal of Power Electronics
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• v.13 no.3
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• pp.349-361
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• 2013

As a result of the advantages such as high efficiency, continuous current and high stability margin, push-pull converter with continuous current (PPCWCC) is competitive for battery discharge regulator (BDR) which plays an important role in power conditioning unit (PCU). Leakage inductance yields current spike in low-ripple current of PPCWCCs. The operating modes are added due to leakage inductance. Therefore the steady state performance is affected, which is embodied in the spike of low-ripple current. PPCWCCs which are suitable for BDR can be separated into three types by current spike characteristics. Three representative topologies IIs1, IIcb2 and Is3 are analyzed in order to investigate the factors on the magnitude and duration of spike. Equivalent current sampling method (ECSM) which eliminates the sampling time delay and achieves excellent dynamic performance is adopted to prevent the spike disturbance on current sampling. However, ECSM reduces the sampling accuracy and telemetry accuracy due to neglecting the spike. In this paper, ECSM used in PPCWCCs is summarized. The current sampling error is analyzed in quality and quantity, which provides the foundation for offsetting and enhancing the telemetry accuracy. Finally, current sampling error rate of three topologies is compared by experiment results, which verify the theoretical analysis.

• Proceedings of the Korea Water Resources Association Conference
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• 2001.05a
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• pp.7-14
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• 2001

In this study, we characterized the variation of sampling errors using the Waymire-Gupta-rodriguez-Iturbe multi-dimensional rainfall model (WGR model). The parameters used for this study are those derived by Jung et al. (2000) for the Han River Basin using a genetic algorithm technique. The sampling error problems considering in this study are those far using raingauge network, satellite observation and also for both combined. The characterization of sampling errors was done for each month and also for the downstream plain area and the upstream mountain area, separately. As results of the study we conclude: (1) The pattern of sampling errors estimated are obviously different from the seasonal pattern of mentally rainfall amounts. This result may be understood from the fact that the sampling error is estimated not simply by considering the rainfall amounts, but by considering all the mechanisms controlling the rainfall propagation along with its generation and decay. As the major mechanism of moisture source to the Korean Peninsula is obviously different each month, it seems rather norma1 to provide different pattern of sampling errors from that of monthly rainfall amounts. (2) The sampling errors estimated for the upstream mountain area is about twice higher than those for the down stream plain area. It is believed to be because of the higher variability of rainfall in the upstream mountain area than in the down stream plain area.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.286-289
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• 1996

Two servo control algorithms are suggested to reduce the tracking error of a computer hard disk drive. One is the repetitive control to reduce the repeatable tracking error which is not explicitly taken into account in the design of a conventional controller. This algorithm was successfully applied to a commercial disk using a fixed point DSP. The other is the multi-rate sampling control which generates the control output between each sampling times since the sampling time of hard disk drives is limited. These algorithms were shown effectively to reduce tracking errors.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.11
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• pp.1760-1767
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• 2004

Sequential sampling approaches of a metamodel that sampling points are updated sequentially become a significant consideration in metamodeling technique. Sequential sampling design is more effective than classical space filling design of all-at-once sampling because sequential sampling design is to add new sampling points by means of distance between sampling points or precdiction error obtained from metamodel. However, though the extremum points can strongly reflect the behaviors of responses, the existing sequential sampling designs are inefficient to approximate extremum points of original model. In this research, new sequential sampling approach using the sensitivity of Kriging model is proposed, so that new approach reflects the behaviors of response sequentially. Various sequential sampling designs are reviewed and the performances of the proposed approach are compared with those of existing sequential sampling approaches by using mean squared error. The accuracy of the proposed approach is investigated against optimization results of test problems so that superiority of the sensitivity approach is verified.

• Journal of Power System Engineering
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• v.22 no.1
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• pp.18-24
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• 2018

This paper focuses on systematic design about the membership ranges of the main design factors such as control error, control error rate, and sampling time for the fuzzy logic control of the variable speed drive refrigeration cycle. The upper and the lowest limit of the membership ranges are set up from the data of static characteristics obtained by experiments. Three kinds of membership ranges on the control error and the control error rate are tested by experiments. Especially, an effect of sampling time on control performance is also investigated in the same way. Experimental data showed the control error rate and the sampling time strongly effected on the control performance of the refrigeration cycle with a variable speed drive.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.11B
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• pp.1038-1049
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• 2003

Exactly measuring traffic load is the basis for efficient traffic engineering. However, precise traffic measurement involves inspecting every packet traversing a lint resulting in significant overhead on routers with high-speed links. Sampling techniques are proposed as an alternative way to reduce the measurement overhead. But, since sampling inevitably accompany with error, there should be a way to control, or at least limit, the error for traffic engineering applications to work correctly. In this paper, we address the problem of bounding sampling error within a pre-specified tolerance level. We derive a relationship between the number of samples, the accuracy of estimation and the squared coefficient of variation of packet size distribution. Based on this relationship, we propose an adaptive random sampling technique that determines the minimum sampling probability adaptively according to traffic dynamics. Using real network traffic traces, we show that the proposed adaptive random sampling technique indeed produces the desired accuracy, while also yielding significant reduction in the amount of traffic samples.