• IEIE Transactions on Smart Processing and Computing
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• v.2 no.2
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• pp.67-76
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• 2013

Under an imperfect channel estimation, a Device-to-Device (D2D) communication framework based on MIMO relay technology is presented. The model consists of a D2D pair equipped with M antennas and a relay equipped with N antennas as well as a cellular user equipped with M antennas. The outage probability under different relaying protocols (i.e. AF and DF protocol) with and without considering a direct link was derived. The actual and theoretical outage probability of the five links under different antenna numbers was emulated. The number of user antennas and channel estimation error was analyzed carefully to determine their impact on the outage probability of a system. The simulation verified the theory analyses and the results showed that MIMO relaying improves the D2D communication system performance.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1083-1096
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• 2021

Compressed sensing-based matching pursuit algorithms can estimate the sparse channel of massive multiple input multiple-output systems with short pilot sequences. Although they have the advantages of low computational complexity and low pilot overhead, their accuracy remains insufficient. Simply multiplying the weight value and the estimated channel obtained in different iterations can only improve the accuracy of channel estimation under conditions of low signal-to-noise ratio (SNR), whereas it degrades accuracy under conditions of high SNR. To address this issue, an improved weighted matching pursuit algorithm is proposed, which obtains a suitable weight value u_op by training the channel data. The step of the weight value increasing with successive iterations is calculated according to the sparsity of the channel and u_op. Adjusting the weight value adaptively over the iterations can further improve the accuracy of estimation. The results of simulations conducted to evaluate the proposed algorithm show that it exhibits improved performance in terms of accuracy compared to previous methods under conditions of both high and low SNR.

• Communications for Statistical Applications and Methods
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• v.27 no.4
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• pp.469-486
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• 2020

Entropy is an important term in statistical mechanics that was originally defined in the second law of thermodynamics. In this paper, we consider the maximum likelihood estimation (MLE), maximum product spacings estimation (MPSE) and Bayesian estimation of the entropy of an inverse Weibull distribution (InW) under a generalized type I progressive hybrid censoring scheme (GePH). The MLE and MPSE of the entropy cannot be obtained in closed form; therefore, we propose using the Newton-Raphson algorithm to solve it. Further, the Bayesian estimators for the entropy of InW based on squared error loss function (SqL), precautionary loss function (PrL), general entropy loss function (GeL) and linex loss function (LiL) are derived. In addition, we derive the Lindley's approximate method (LiA) of the Bayesian estimates. Monte Carlo simulations are conducted to compare the results among MLE, MPSE, and Bayesian estimators. A real data set based on the GePH is also analyzed for illustrative purposes.

• Communications for Statistical Applications and Methods
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• v.26 no.5
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• pp.473-495
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• 2019

In this paper, a new extension of Lindley distribution has been introduced. Certain characterizations based on truncated moments, hazard and reverse hazard function, conditional expectation of the proposed distribution are presented. Besides, these characterizations, other statistical/mathematical properties of the proposed model are also discussed. The estimation of the parameters is performed through different classical methods of estimation. Bayes estimation is computed under gamma informative prior under the squared error loss function. The performances of all estimation methods are studied via Monte Carlo simulations in mean square error sense. The potential of the proposed model is analyzed through two data sets. A modified goodness-of-fit test using the Nikulin-Rao-Robson statistic test is investigated via two examples and is observed that the new extension might be used as an alternative lifetime model.

• Journal of Ginseng Research
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• v.40 no.1
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• pp.90-96
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• 2016

Background: Knowledge on microclimate conditions under artificial shades in a ginseng field would facilitate climate-aware management of ginseng production. Methods: Weather data were measured under the shade and outside the shade at two fields located in Gochang-gun and Jeongeup-si, Korea, in 2011 and 2012 seasons to assess temperature and humidity conditions under the shade. An empirical approach was developed and validated for the estimation of leaf wetness duration (LWD) using weather measurements outside the shade as inputs to the model. Results: Air temperature and relative humidity were similar between under the shade and outside the shade. For example, temperature conditions favorable for ginseng growth, e.g., between $8^{\circ}C$ and $27^{\circ}C$, occurred slightly less frequently in hours during night times under the shade (91%) than outside (92%). Humidity conditions favorable for development of a foliar disease, e.g., relative humidity > 70%, occurred slightly more frequently under the shade (84%) than outside (82%). Effectiveness of correction schemes to an empirical LWD model differed by rainfall conditions for the estimation of LWD under the shade using weather measurements outside the shade as inputs to the model. During dew eligible days, a correction scheme to an empirical LWD model was slightly effective (10%) in reducing estimation errors under the shade. However, another correction approach during rainfall eligible days reduced errors of LWD estimation by 17%. Conclusion: Weather measurements outside the shade and LWD estimates derived from these measurements would be useful as inputs for decision support systems to predict ginseng growth and disease development.

• Journal of the Korean Statistical Society
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• v.29 no.4
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• pp.385-394
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• 2000

Jackknife variance estimation based on adjusted imputed values when nonresponse is nonrandom and follow-up data are available for a subsample of nonrespondents is provided. Both hot-deck and ratio imputation method are considered as imputation method. The performance of the proposed variance estimator under nonrandom response mechanism is investigated through numerical simulation.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.1009-1019
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• 2006

In this paper, we consider the simultaneous estimation problem for the normal means. We set up the model structure using the several different distributions of the errors for observing their effects of model perturbation for the error terms in obtaining the empirical Bayes and hierarchical Bayes estimators. We compare the performance of those estimators under model perturbation based on a simulation study.

• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.4
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• pp.699-707
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• 2015

The ice-resistance estimation technique for icebreaking ships had been studied intensively over recent years to meet the needs of designing Arctic vessels. Before testing in the ice model basin, the estimation of a ship's ice resistance with high reliability is very important to decide the delivered power necessary for level ice operation. The main idea of previous studies came from several empirical formulas, such as Poznyak and Ionov (1981), Enkvist (1972) and Shimansky (1938) methods, in which ice resistance components such as icebreaking, buoyancy and clearing resistances were represented by the integral equations along the Design Load Water Line (DLWL). The current study proposes a few modified methods not only considering the DLWL shape, but also the hull shape under the DLWL. In the proposed methodology, the DLWL shape for icebreaking resistance and the hull shape under the DLWL for buoyancy and clearing resistances can be directly considered in the calculation. Especially, when calculating clearing resistance, the flow pattern of ice particles under the DLWL of ship is assumed to be in accordance with the ice flow observed during ice model testing. This paper also deals with application examples for a few ship designs and its ice model testing programs at the AARC ice model basin. From the comparison of results of the model test and the estimation, the reliability of this estimation technique has been discussed.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.615-624
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• 2015

We consider the estimation of seasonal cointegration in the presence of conditional heteroskedasticity (CH) using a feasible generalized least squares method. We capture cointegrating relationships and time-varying volatility for long-run and short-run dynamics in the same model. This procedure can be easily implemented using common methods such as ordinary least squares and generalized least squares. The maximum likelihood (ML) estimation method is computationally difficult and may not be feasible for larger models. The simulation results indicate that the proposed method is superior to the ML method when CH exists. In order to illustrate the proposed method, an empirical example is presented to model a seasonally cointegrated times series under CH.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.10
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• pp.51-58
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• 1997

In this paper, a technique ot estimate center positions of the circular parts under noisy condition is presented. The circle chords are segmented from the circle with successively varying angle and weighted to reduce the center estimation errors effected by the orientations of the circle chords. The weighting factors for variable length chords are adaptively detemined according to the error contribution of each chord in center estimation. Robust estimation of the center positions of the circular parts are possible even though the edge informations are partially contaminated by the non-uniform lighting or the background textures. Computer simulations for several images which are obtained for same object under real environment y camera, show that the proposed techniqeu yields 1.85 and 2.77 of estimated error-distribution for center position and radius in mean square error, that the proposed has more robust estimation than those of the conventional methods.