• JSTS:Journal of Semiconductor Technology and Science
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• v.12 no.2
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• pp.230-239
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• 2012

In this paper, we present the radio-frequency (RF) modeling for gate-all-around (GAA) junctionless (JL) MOSFETs with 30-nm channel length. The presented non-quasi-static (NQS) model has included the gate-bias-dependent components of the source and drain (S/D) resistances. RF characteristics of GAA junctionless MOSFETs have been obtained by 3-dimensional (3D) device simulation up to 1 THz. The modeling results were verified under bias conditions of linear region (VGS = 1 V, VDS = 0.5 V) and saturation region (VGS = VDS = 1 V). Under these conditions, the root-mean-square (RMS) modeling error of $Y_{22}$-parameters was calculated to be below 2.4%, which was reduced from a previous NQS modeling error of 10.2%.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.51-55
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• 2020

In this paper, an improved FAM is proposed by adopting similarity learning in the existing FAM (Fuzzy Associative Memory) used in image restoration. Image restoration refers to the recovery of the latent clean image from its noise-corrupted version. In serious application like face recognition, this process should be noise-tolerant, robust, fast, and scalable. The existing FAM is a simple single layered neural network that can be applied to this domain with its robust fuzzy control but has low capacity problem in real world applications. That similarity measure is implied to the connection strength of the FAM structure to minimize the root mean square error between the recovered and the original image. The efficacy of the proposed algorithm is verified with significant low error magnitude from random noise in our experiment.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.6
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• pp.126-131
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• 2012

Tire force is one of important parameters which determine vehicle dynamics. However, it is hard to measure tire force directly through sensors. Not only the sensor is expensive but also installation of sensors on harsh environments is difficult. Therefore, estimation algorithms based on vehicle dynamic models are introduced to estimate the tire forces indirectly. In this paper, an estimation system for estimating lateral force and states is suggested. The state-space equation is constructed based on the 3-DOF bicycle model. Extended Kalman Filter, Unscented Kalman Filter and Ensemble Kalman Filter are used for estimating states on the nonlinear system. Performance of each algorithm is evaluated in terms of RMSE (Root Mean Square Error) and maximum error.

• Journal of Biosystems Engineering
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• v.40 no.4
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• pp.327-334
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• 2015

Purpose: A falling-type weight sorter equipped with a load cell was developed to sort lightweight dried persimmons. The performance of the sorter was also evaluated. Methods: The electronic weight sorter for dried persimmon comprises a feeder part, a weight-measurement part, an indicator part, a carrier cup, a discharging part, and a driving part. The weight setting and zero-point adjustment are performed digitally for the convenience of users. For the experimental trials, 228 rubber-clay specimens (representative of dried persimmons) in the weight range of 24.73~99.56 g were manufactured for use in experiments to evaluate the performance of the sorter. Results: The average error of the weight measurements from three experimental trials was 1.655%, with a bias of -0.492 g, a root-mean-square error (RMSE) of ${\pm}0.808g$, and a coefficient of determination ($R^2$ ) of 0.997. Conclusions: The load-cell-based electronic dried-persimmon weight sorter developed in this study facilitates effective, precise, and convenient sorting of dried persimmons.

• Journal of Korean Society of Water and Wastewater
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• v.28 no.4
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• pp.377-389
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• 2014

In recent years, Smart Water Grid (SWG) concept has globally emerged over the last decade and also gained significant recognition in South Korea. Especially, there has been growing interest in water demand forecast and this has led to various studies regarding energy saving and improvement of water supply reliability. In this regard, this study aims to develop a nonlinear ensemble model for hourly water demand forecasting which allow us to estimate uncertainties across different model classes. The concepts was demonstrated through application to observed from water plant (A) in the South Korea. Various statistics (e.g. the efficiency coefficient, the correlation coefficient, the root mean square error, and a maximum error rate) were evaluated to investigate model efficiency. The ensemble based model with an cross-validate prediction procedure showed better predictability for water demand forecasting at different temporal resolutions. In particular, the performance of the ensemble model on hourly water demand data showed promising results against other individual prediction schemes.

• Atmosphere
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• v.17 no.4
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• pp.327-337
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• 2007

In this study, we examined the new ensemble training approach to reduce the systematic error and improve prediction skill of wind by using the Short-range Ensemble prediction system (SENSE), which is the mesoscale multi-model ensemble prediction system. The SENSE has 16 ensemble members based on the MM5, WRF ARW, and WRF NMM. We evaluated the skill of surface wind prediction compared with AWS (Automatic Weather Station) observation during the summer season (June - August, 2006). At first stage, the correction of initial state for each member was performed with respect to the observed values, and the corrected members get the training stage to find out an adaptive weight function, which is formulated by Root Mean Square Vector Error (RMSVE). It was found that the optimal training period was 1-day through the experiments of sensitivity to the training interval. We obtained the weighted ensemble average which reveals smaller errors of the spatial and temporal pattern of wind speed than those of the simple ensemble average.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2154-2161
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• 2014

Growth analysis and selection of sea cucumbers (Stichopus japonicas) is typically performed through length or weight measurements. However, because sea cucumbers continuously change shape depending on the external environment, weight measurement has been the preferred approach. Weight measurements require extensive time and labor, moreover it is often difficult to accurately weigh sea cucumbers because of their wet surface. The present study measured sea cucumber features, including the body length, width, and thickness, by using a vision system and regression analysis to generate $R^2$ values that were used to develop a weight estimation algorithm. The $R^2$ value between the actual volume and weight of the sea cucumbers was 0.999, which was relatively high. Evaluation of the performance of this algorithm using cross-validation showed that the root mean square error and worst-case prediction error were 1.434 g and ${\pm}5.879g$, respectively. In addition, the present study confirmed that the proposed weight estimation algorithm and single slide rail device for weight measurement can measure weights at approximately 4,500 sea cucumbers per hour.

• Earthquakes and Structures
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• v.14 no.4
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• pp.305-312
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• 2018

Significant research efforts were undertaken to evaluate seismic performance of vertically irregular buildings on flat ground. However, there is scarcity of study on seismic performance of buildings on hill slopes. The present study attempts to investigate seismic behaviour of reinforced concrete irregular stepback building frames with different configurations on sloping ground. Based on extensive regression study of free vibration results of four hundred seventeen frames with varying ground slope, number of story and span number, a modification is proposed to the code based empirical fundamental time period estimation formula. The modification to the fundamental time period estimation formula is a simplified function of ground slope and a newly introduced equivalent height parameter to reflect the effect of stiffness and mass irregularity. The derived empirical formula is successfully validated with various combinations of slope and framing configurations of buildings. The correlation between the predicted and the actual time period obtained from the free vibration analysis results are in good agreement. The various statistical parameters e.g., the root mean square error, coefficient of determination, standard average error generally used for validation of such regression equations also ensure the prediction capability of the proposed empirical relation with reasonable accuracy.

• Computers and Concrete
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• v.26 no.3
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• pp.293-307
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• 2020

The aim of this paper is to develop design-oriented models for the prediction of the ultimate strength and ultimate axial strain for concrete confined with glass fiber-reinforced polymer (GFRP) wraps. Twenty of most used and recent design-oriented models developed to predict the strength and strain of GFRP-confined concrete in circular sections are selected and evaluated basing on a database of 163 test results of concrete cylinders confined with GFRP wraps subjected to uniaxial compression. The evaluation of these models is performed using three statistical indices namely the coefficient of the determination (R²), the root mean square error (RMSE), and the average absolute error (AAE). Based on this study, new strength and strain models for GFRP-wrapped concrete are developed using regression analysis. The obtained results show that the proposed models exhibit better performance and provide accurate predictions over the existing models.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.189-204
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• 2018

This study is on forecasting the electricity consumption of an industrial manufacturing building called GGM from January 2014 to April 2017. We fitted models using SARIMA, SARIMA + GARCH, Holt-Winters method and ARIMA with Fourier transformation. We also forecasted electricity consumption for one month ahead and compared the predicted root mean square error as well as the predicted error rate of each model. The electricity consumption of GGM fluctuates weekly and annually; therefore, SARIMA + GARCH model considering both volatility and seasonality, shows the best fit and prediction.