• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.261-264
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• 2001

In this paper, we introduce a simple design method f9r mot-squared type raised cosine filter with equiripple characteristics. Through some design examples, we show that the proposed filter has much better performance in ripple than the conventional SRCF at the expense of small increasing of ISI. In addition, the proposed Inter is compatible with conventional SRCF. Finally, we designs the filter for W-CDMPI which uses RRC (Root Raised Cosine) with a=0.22, in 12bit finite precision.

• Journal of the Korean Statistical Society
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• v.32 no.3
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• pp.289-298
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• 2003

We consider the estimation problem for the scale parameter of the Rayleigh distribution using weighted balanced loss function (WBLF) which reflects both goodness of fit and precision. Under WBLF, we obtain the optimal estimator which creates a kind of balance between Bayesian and non-Bayesian estimation. We also deal with the estimation of R ＝ P(Y < X) when Y and X are two independent but not identically distributed Rayleigh distribution under squared error loss function.

• The Journal of Korean Physical Therapy
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• v.24 no.5
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• pp.325-333
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• 2012

Purpose: The purpose of this study was to determine the concurrent validity between Figure-of-8 Walking Test (F8W), Berg Balance Scale (BBS), Four Squared Step Test (FSST), and Timed UP and GO Test (TUG) in patients with stroke. Methods: Forty two participants (26 men, 16 women, $55.0{\pm}11.72$) with at least three months post stroke who were able to walk at least 10 m without walking aid participated in this study. Assessment of concurrent validity between the F8W (time and steps) and BBS was performed using Spearman rank order correlation and between the F8W (time and steps), FSST and TUG assessed using Pearson correlation. Results: The time of the F8W showed correlation with BBS (r=-0.46, p<0.01), FSST (r=0.64, p<0.01), and TUG (r=0.81, p<0.01), and steps of the F8W showed correlation with BBS (r=-0.43, p<0.01), FSST (r=0.47, p<0.01), and TUG (r=0.51, p<0.01). Conclusion: The F8W is a valid measure of balance and walking skill among patients with stroke and may provide complementary information with regard to dynamic balance and functional walking for the real life of stroke patients.

• Industrial Engineering and Management Systems
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• v.9 no.3
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• pp.262-274
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• 2010

This article investigates how to adaptively predict the time-varying metrology delay that could realistically occur in the semiconductor manufacturing practice. Metrology delays pose a great challenge for the existing run-to-run (R2R) controllers, driving the process output significantly away from target if not adequately predicted. First, the expected asymptotic double exponentially weighted moving average (DEWMA) control output, by using the EWMA and recursive least squares (RLS) prediction methods, is derived. It has been found that the relationships between the expected control output and target in both estimation methods are parallel, and six cases are addressed. Within the context of time-varying metrology delay, this paper presents a modified recursive least squares-linear trend (RLS-LT) controller, in combination with runs test. Simulated single input-single output (SISO) R2R processes subject to various time-varying metrology delay scenarios are used as a testbed to evaluate the proposed algorithms. The simulation results indicate that the modified RLS-LT controller can yield the process output more accurately on target with smaller mean squared error (MSE) than the original RLSLT controller that only deals with constant metrology delays.

• Journal of Environmental Science International
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• v.21 no.6
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• pp.695-704
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• 2012

The application of disinfection models on the plasma process was investigated. Nine empirical models were used to find an optimum model. The variation of parameters in model according to the operating conditions (first voltage, second voltage, air flow rate, pH) were investigated in order to explain the disinfection model. In this experiment, the DBD (dielectric barrier discharge) plasma reactor was used to inactivate Ralstonia Solanacearum which cause wilt in tomato plantation. Optimum disinfection models were chosen among the nine models by the application of statistical SSE (sum of squared error), RMSE (root mean sum of squared error), $r^2$ values on the experimental data using the GInaFiT software in Microsoft Excel. The optimum model was shown as Weibull+talil model followed by Log-linear+ Shoulder+Tail model. Two models were applied to the experimental data according to the variation of the operating conditions. In Weibull+talil model, Log10($N_o$), Log10($N_{res}$), ${\delta}$ and p values were examined. And in Log-linear+Shoulder+Tail model, the Log10($N_o$), Log10($N_{res}$), $k_{max}$, Sl values were calculated and examined.

• Journal of Korean Society for Atmospheric Environment
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• v.15 no.6
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• pp.779-789
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• 1999

To support daily ground-level $O_3$ forecasting in Seoul, a transfer function model(TFM) has been developed by using surface meteorological data and pollutant data(previous-day [$O_3$] and [$NO_2$]) from 1 May to 31 August in 1997. The forecast performance of the TFM was evaluated by statistical comparison with $O_3$ concentration observed during September it is shown that correlation coefficient(R), root mean squared error(RMSE), normalized mean squared error(NMSE) and mean relative error(MRE) were 0.73, 15.64, 0.006 and 0.101, respectively. The TFM appeared to have some difficulty forecasting very high $O_3$ concentrations. To compare with this model, multiple regression model(MRM) was developed for the same period. According to statistical comparison between the TFM and MRM. two models had similar predictive capability but TFM based on $O_3$ concentration higher than 60 ppb provided more accurate forecast than MRM. It was concluded that statistical model based on TFM can be useful for improving the accuracy of local $O_3$ forecast.

• Journal of Biosystems Engineering
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• v.43 no.4
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• pp.369-378
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• 2018

Purpose: The aim of this study was to construct a saponin content-predicting model using shortwave infrared imaging spectroscopy. Methods: The experiment used a shortwave imaging spectrometer and ENVI spectral acquisition software sampling a spectrum of 910 nm-2500 nm. The corresponding preprocessing and mathematical modeling analysis was performed by Unscrambler 9.7 software to establish a ginsenoside nondestructive spectral testing prediction model. Results: The optimal preprocessing method was determined to be a standard normal variable transformation combined with the second-order differential method. The coefficient of determination, $R^2$, of the mathematical model established by the partial least squares method was found to be 0.9999, while the root mean squared error of prediction, RMSEP, was found to be 0.0043, and root mean squared error of calibration, RMSEC, was 0.0041. The residuals of the majority of the samples used for the prediction were between ${\pm}1$. Conclusion: The experiment showed that the predicted model featured a high correlation with real values and a good prediction result, such that this technique can be appropriately applied for the nondestructive testing of ginseng quality.

• Advances in Computational Design
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• v.8 no.2
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• pp.133-145
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• 2023

Scaling and similitude large scale structural member to small scale model is considered the most important matter for the experimental tests because of the difficulty in controlling, lack of capacities and expenses, furthermore that most of MSc and PhD students suffering from choosing the suitable specimen before starting their experimental study. The current study adopts to take large scale slab with opening as a case study of structural member where the slab is squared with central squared opening, the boundary condition is fixed from all sides, the load represents by four concentrated force in four corners of opening, as well as, the study adopts Buckingham theorem which has been used for scaling, all the parameters of the problem have been formed in dimensionless groups, the main groups have been connected by a relations, those relations are represented by force, maximum stress and maximum displacement. Finite element method by ANSYS R18.1 has been used for analyzing and forming relations for the large scale member. Prediction analysis has been computed for three small scale models by depending on the formed relations of the large scale member. It is found that Buckingham theorem is considered suitable way for creating relations among the parameters for any structural problem then making similitude and scaling the large scale members to small scale members. Finally, verification between the prediction and theoretical results has been done, it is observed that the maximum deviation between them is not more than 2.4%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.179-190
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• 2020

This research analyses the financial characteristics of corporate R&D intensity in the Korean capital market. It is important to pay greater attention to this subject, given the current situation of the shortage of core components domestically in Korea. Three hypotheses are postulated to investigate the financial factors of R&D investments for KOSDAQ-listed firms during the post-era of the global financial turmoil. By applying a conditional quantile regression (CQR) model, three variables included R&D intensity in the previous year (Lag_RD), the squared term of Lag_RD, and interaction between the high-tech sector and Lag_Rd, reveal significant effects on the current R&D ratio. Whereas more than half of the total variables show variable impacts between firms with higher and lower R&D intensity, only Lag_RD and squared term of Lag_RD were found to be significant. It is expected that these results may contribute to being financial catalysts for an optimal level of R&D expenditures, thereby maximizing firm value for shareholders in KOSDAQ-listed firms.

• Journal of Korean Tunnelling and Underground Space Association
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• v.14 no.1
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• pp.37-53
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• 2012

There is considerable uncertainty in ground properties used in tunnel designs. In this study, a back analysis was performed to find optimal ground properties based on the artificial neural network facility of MATLAB program of using tunnel monitoring data. Total 81 data were constructed by changing elastic modulus and coefficient of lateral pressure which have great influence on tunnel convergence. A sensitivity analysis was conducted to establish an optimal training model by varying the number of hidden layers, the number of nodes, learning rate, and momentum. Meanwhile, the optimal training model was selected by comparing MSE (Mean Squared Error) and coefficient of determination ($R^2$) and was used to find the correct elastic moduli of layers and the coefficient of lateral pressure. In future, it is expected that the suggested method of this study can be applied to determine the optimum tunnel support pattern under given ground conditions.