• Korean Journal of Organic Agriculture
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• v.25 no.2
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• pp.279-293
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• 2017

This study suggests the yield forecast models for autumn chinese cabbage and radish using crop growth and development information. For this, we construct 24 alternative yield forecast models and compare the predictive power using root mean square percentage errors. The results shows that the predictive power of model including crop growth and development informations is better than model which does not include those informations. But the forecast errors of best forecast models exceeds 5%. Thus it is important to establish reliable data and improve forecast models.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1475-1478
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• 2002

This paper presents a performance analysis of an improved adaptive algorithm proposed by the authors recently. It is based on the normalized least mean square (NLMS) algorithm, which Is one of the major techniques to adapt the cofficients of a transversal filter. Generally, the performance of an adaptive algorithm is often discussed by investigating the mis-adjustment. In this paper, unlike these approaches, a novel analytical method is considered. letting the parameters so that the residual mean square error (MSE) after the convergence of the algorithm is equal to that of the NLMS algorithm, the MSE level is compared. It is shown that the theoretical analysis is agreed with the simulation results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.315-318
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• 2001

We propose two new blind LMS and MMSE algorithms called projection-based least mean square (PB-LMS) and projection-based minimum mean square error (PB-MMSE) for smart antennas. Both algorithms employ the finite constellation property of digital signal to transform the conventional LMS and MMSE algorithms into blind algorithms. Computer simulations were carried out in the AWGN channel and Rayleigh fading channel with AWGN in CDMA environment to verify the performance of the two proposed algorithms.

• ETRI Journal
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• v.31 no.2
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• pp.162-172
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• 2009

A linear-prediction-based blind equalization algorithm for single-input single-output (SISO) finite impulse response/infinite impulse response (FIR/IIR) channels is proposed. The new algorithm is based on second-order statistics, and it does not require channel order estimation. By oversampling the channel output, the SISO channel model is converted to a special single-input multiple-output (SIMO) model. Two forward linear predictors with consecutive prediction delays are applied to the subchannel outputs of the SIMO model. It is demonstrated that the partial parameters of the SIMO model can be estimated from the difference between the prediction errors when the length of the predictors is sufficiently large. The sufficient filter length for achieving the optimal prediction is also derived. Based on the estimated parameters, both batch and adaptive minimum-mean-square-error equalizers are developed. The performance of the proposed equalizers is evaluated by computer simulations and compared with existing algorithms.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.4
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• pp.133-142
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• 2014

The least mean square(LMS) algorithm has been popular owing to its simplicity, stability, and availability to implement. But it inherently has a problem of slow convergence speed, and the presence of a transfer function in the secondary path following the adaptive controller and the error path has been shown to generally degrade the stability and the performance of the LMS algorithm in applications of acoustical noise control. In general, in order to solve these problems, the filtered-x LMS (FX-LMS) type algorithms can be used and the bi-directional Filtered-x LMS(BFXLMS) algorithm is very attractive among them, which increase the convergence speed and the performance of the controller with nearly equivalent computation complexity. In this paper, a mathematical analysis for the BFXLMS algorithm is presented. In terms of view points of time domain, frequency domain, and stochastic domain, the characteristics and stabilities of algorithm is accurately analyzed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.3
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• pp.17-22
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• 2011

In this paper, we analyze co-channel interference cancellation performance to be generated in multi-hop military communication system. First, remove interference using zero-forcing (ZF) and minimum mean square error (MMSE) scheme as interference cancellation methods, and then obtain additional diversity gain and improve interference cancellation performance by applying successive interference cancellation (SIC). We consider Rayleigh fading channel and system performance is analyzed as respect of bit error probability. From simulation results, we confirm MMSE improves significantly BER performance than ZF in multi-hop wireless network environment. It is also confirmed ZF and MMSE schemes applying SIC algorithm have better performance comparing to the existing schemes. Therefore, MMSE-SIC method can provide more reliable signal transmission in the multi-hop military communication system.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.269-274
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• 2004

In this paper, proposed is the dual-mode algorithm of blind decision feedback equalizer (DFE) for digital terrestrial television signals. According to channel impairments, the proposed dual-mode algorithm for blind DFE operates in decision-directed mode or in blind mode of operation. The error signals being used in tap update of the equalizer are generated in the best mode of operations, so that the confidence of equalizer tap coefficient update is more accurate. As a result, it is possible to track the channel characteristics variations by automatic switching over between two modes of operations. For 8-level vestigial sideband modulated digital television signals, the mean square errors and symbol error rates of the proposed algorithm are compared with those of conventional methods. And the usability of the proposed scheme is assessed by computer simulations under various static and dynamic multipath channel environments.

• The Journal of the Acoustical Society of Korea
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• v.33 no.2
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• pp.146-151
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• 2014

This paper analyzes the previous Anjok model of the Gayageum and describes a method to improve the frequency modeling based on previous model. In the previous work, relation between the fundamental frequency and Anjok's location on the body is assumed as an exponential function and these frequencies are integrated by a first-order leaky integrator. Finally, a parameter of the formula to calculate the fundamental frequency is obtained by applying integrated frequencies to the linear regression. This model shows 2.5 Hz absolute deviation on average and has maximum error 7.75 Hz for the low fundamental frequencies. In order to overcome this problem, this paper proposes that the Anjok's locations are grouped according to the rate of error increase and linear regression is applied to each group. To find the optimal parameter, the RMSE(Root Mean Square Error) between measured and calculated fundamental frequencies is used. The proposed model shows substantial reduction in errors, especially maximum three times.

• Computers and Concrete
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• v.31 no.3
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• pp.253-265
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• 2023

In this research, the gene expression programming (GEP) technique was employed to provide a new model for predicting the maximum loading capacity of concrete-encased steel (CES) columns. This model was developed based on 96 CES column specimens available in the literature. The six main parameters used in the model were the compressive strength of concrete (f_c), yield stress of structural steel (f_ys), yield stress of steel rebar (f_yr), and cross-sectional areas of concrete, structural steel, and steel rebar (A_c, A_s and A_r respectively). The performance of the prediction model for the ultimate load-carrying capacity was investigated using different statistical indicators such as root mean square error (RMSE), correlation coefficient (R), mean absolute error (MAE), and relative square error (RSE), the corresponding values of which for the proposed model were 620.28, 0.99, 411.8, and 0.01, respectively. Here, the predictions of the model and those of available codes including ACI ITG, AS 3600, CSA-A23, EN 1994, JGJ 138, and NZS 3101 were compared for further model assessment. The obtained results showed that the proposed model had the highest correlation with the experimental data and the lowest error. In addition, to see if the developed model matched engineering realities and corresponded to the previously developed models, a parametric study and sensitivity analysis were carried out. The sensitivity analysis results indicated that the concrete cross-sectional area (A_c) has the greatest effect on the model, while parameter (f_yr) has a negligible effect.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1151-1156
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• 2023

In this paper, we propose a deep learning model that utilizes charge/discharge data from initial lithium-ion batteries to predict the remaining useful life of lithium-ion batteries. We build the DMP using the PNP model. To demonstrate the performance of DMP, we organize DML using the LSTM model and compare the remaining useful life prediction performance of lithium-ion batteries between DMP and DML. We utilize the RMSE and RMSPE error measurement methods to evaluate the performance of DMP and DML models using test data. The results reveal that the RMSE difference between DMP and DML is 144.62 [Cycle], and the RMSPE difference is 3.37 [%]. These results indicate that the DMP model has a lower error rate than DML. Based on the results of our analysis, we have showcased the superior performance of DMP over DML. This demonstrates that in the field of lithium-ion batteries, the PNP model outperforms the LSTM model.