• Journal of Food Hygiene and Safety
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• v.24 no.3
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• pp.232-237
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• 2009

This study was conducted to develop a model for describing the effect of storage temperature (4, 10, 15, 20, 25, 30 and $35^{\circ}C$) on the growth of Escherichia coli O157 : H7 in ready-to-eat (RTE) lettuce treated with or without (control) alkaline electrolyzed water (AIEW). The growth curves were well fitted with the Gompertz equation, which was used to determine the specific growth rate (SGR) and lag time (LT) of E. coli O157 : H7 ($R^2$ = 0.994). Results showed that the obtained SGR and LT were dependent on the storage temperature. The growth rate increased with increasing temperature from 4 to $35^{\circ}C$. The square root models were used to evaluate the effect of storage temperature on the growth of E. coli O157 : H7 in lettuce samples treated without or with AIEW. The coefficient of determination ($R^2$), adjusted determination coefficient ($R^2_{Adj}$), and mean square error (MSE) were employed to validate the established models. It showed that $R^2$ and $R^_{Adj}$ were close to 1 (> 0.93), and MSE calculated from models of untreated and treated lettuce were 0.031 and 0.025, respectively. The results demonstrated that the overall predictions of the growth of E. coli O157: H7 agreed with the observed data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.5
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• pp.107-114
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• 2012

In this paper, the concerned existing blind equalizer convergence rate and residual inter-symbol interference using constellation reduced and cost function by separation the real part and an imaginary part, the dual structure CR-CMA(constellation Reduction CMA). The CMA methed compensates amplitude but does no compensate phase, On the other hand, The CMA method compensates both the amplitude and the phase but it has the convergence rate problem, and the MCMA method is a way to solve the phase problem of CMA method compensates both the amplitude and the phase after respectively calculating the real part and imaginary part components. Proposal a new method that the dual structure of CR-CMA, the cost function and error function and respectively calculating the real part and imaginary part components can advantages by improving the CMA and the MCMA algorithms so that the amplitude and phase retrieval and constellation reduce the residual ISI and faster convergence rate and performance is good SER (Symbol Error Ratio) was confirmed by computer simulations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.4C
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• pp.414-423
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• 2009

This paper considers a linear precoding scheme that achieves near optimal sum rate. While the minimum mean square error (MMSE) precoding provides the better MSE performance at all signal-to-noise ratio (SNR) than the zero forcing (ZF) precoding, its sum rate shows superior performance to ZF precoding at low SNR but inferior performance to ZF precoding at high SNR, From this observation, we first propose a near optimal linear precoding scheme in terms of sum rate. The resulting precoding scheme regularizes ZF precoding to maximize the sum rate, resulting in better sum rate performance than both ZF precoding and MMSE precoding at all SNR ranges. To find regularization parameters, we propose a simple algorithm such that locally maximal sum rate is achieved. As a low complexity alternative, we also propose a simple power re-allocation scheme in the conventional regularized channel inversion scheme. Finally, the proposed scheme is tested under the presence of channel estimation error. By simulation, we show that the proposed scheme can maintain the performance gain in the presence of channel estimation error and is robust to the channel estimation error.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.51-61
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• 2023

Travel demand estimation of E-Scooter is the start point of solving the regional demand-supply imbalance problem and plays pivotal role in a linked transportation system such as Mobility-as-a-Service (a.k.a. MaaS). Most focuses on developing trip generation model of shared E-Scooter but it is no study on selection of an appropriate zone scale when it comes to estimating travel demand of E-Scooter. This paper aimed for selecting an optimal TAZ scale for developing trip distribution model for shared E-Scooter. The TAZ scale candidates were selected in 250m, 500m, 750m, 1,000m square grid. The shared E-Scooter usage historical data were utilized for calculating trip distance and time, and then applying to developing gravity model. Mean Squared Error (MSE) is applied for the verification step to select the best suitable gravity model by TAZ scale. As a result, 250m of TAZ scale is the best for describing practical trip distribution of shared E-Scooter among the candidates.

• Computers and Concrete
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• v.31 no.2
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• pp.111-121
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• 2023

The influence of material composition such as aggregate types, addition of supplementary cementitious materials as well as exposed temperature levels have significant impacts on concrete residual mechanical strength properties when exposed to elevated temperature. This study is based on data obtained from literature for fly ash blended concrete produced with natural and recycled concrete aggregates to efficiently develop prediction models for estimating its residual compressive strength after exposure to high temperatures. To achieve this, an extensive database that contains different mix proportions of fly ash blended concrete was gathered from published articles. The specific design variables considered were percentage replacement level of Recycled Concrete Aggregate (RCA) in the mix, fly ash content (FA), Water to Binder Ratio (W/B), and exposed Temperature level. Thereafter, a simplified mathematical equation for the prediction of concrete's residual compressive strength using Gene Expression Programming (GEP) was developed. The relative importance of each variable on the model outputs was also determined through global sensitivity analysis. The GEP model performance was validated using different statistical fitness formulas including R², MSE, RMSE, RAE, and MAE in which high R² values above 0.9 are obtained in both the training and validation phase. The low measured errors (e.g., mean square error and mean absolute error are in the range of 0.0160 - 0.0327 and 0.0912 - 0.1281 MPa, respectively) in the developed model also indicate high efficiency and accuracy of the model in predicting the residual compressive strength of fly ash blended concrete exposed to elevated temperatures.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.205-208
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• 2007

Due to the development and wide use of digital multimedia broadcasting (DMB) and Wireless Broadband Internet (WiBro), the digital contents industry using images has been progressed. Therefore, the image processing has been applied in a variety of fields and in order to transmit and conserve accurate information, the degradation phenomenon for images should be removed. As a representative cause of the degradation phenonenon, noise has become known and Gaussian noise occurs in the process of transmission. Diverse researches for Gaussian noise removal have been implemented and a great number of algorithms have been proposed until now. In this paper, for mage restoration an algorithm using the adaptive threshold value is proposed in Gaussian noise environment and the threshold value is established by using the histogram of edge image. And from simulation results, the noise removal performance of the proposed method is proven using mean square error (MSE) and peak signal to noise ratio (PSNR).

• Smart Structures and Systems
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• v.30 no.6
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• pp.673-686
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• 2022

Deep learning algorithms for Structural Health Monitoring (SHM) have been extracting the interest of researchers and engineers. These algorithms commonly used loss functions and evaluation indices like the mean square error (MSE) which were not originally designed for SHM problems. An updated loss function which was specifically constructed for deep-learning-based structural damage detection problems has been proposed in this study. By tuning the coefficients of the loss function, the weights for damage localization and quantification can be adapted to the real situation and the deep learning network can avoid unnecessary iterations on damage localization and focus on the damage severity identification. To prove efficiency of the proposed method, structural damage detection using convolutional neural networks (CNNs) was conducted on a truss bridge model. Results showed that the validation curve with the updated loss function converged faster than the traditional MSE. Data augmentation was conducted to improve the anti-noise ability of the proposed method. For reducing the training time, the normalized modal strain energy change (NMSEC) was extracted, and the principal component analysis (PCA) was adopted for dimension reduction. The results showed that the training time was reduced by 90% and the damage identification accuracy could also have a slight increase. Furthermore, the effect of different modes and elements on the training dataset was also analyzed. The proposed method could greatly improve the performance for structural damage detection on both the training time and detection accuracy.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Journal of the Korean Graphic Arts Communication Society
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• v.18 no.1
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• pp.25-33
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• 2000

In recent year, many imaging systems have been developed, and it became increasingly important to exchange image data through the computer network. Therefore, it is required to reproduce color image independently on each imaging device. However, even if the image are same, perceived color is not always same under different viewing conditions. On the other hand, even if the image are different, we want to perceive same color under different viewing conditions. Therefore we must know the spectral reflectance information of object. We measured many reflectance human skin can be estimate using only three principal component. For Munsell color patches, five principle components were necessary to estimate the reflectance spectra. For that purpose, we have developed color image acquisition system that is composed of five band filters and CCD camera. Improved spectral reflectance of object is predicted by five band images taken by color image acquisition system and then we take account of camera's noise and component of object image for predicting accurate spectral reflectance of object. In the results, we confirmed that color difference and MSE(Mean Square Error) between measured and predicted spectral reflectance of object decreased into 0.0071 and 7.72 respectively.

• Journal of Information Technology Applications and Management
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• v.21 no.4
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• pp.97-125
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• 2014

This study investigates the relationship between stock index and its associated nearby futures markets based on the cost-of-carry model. The purpose of this study is to explore the existence of mispriced futures contracts, and to test whether traders can earn trading profits in real financial market using the information about the mispriced futures contracts. This study suggests the concordance correlation coefficient to investigate the existence of mispriced futures contracts. The concordance correlation coefficient gives a desirable result for trading profits that results from a comparative analysis among profits from trading at the time to indicate trading opportunities determined by the degree of the difference between the observed market price and the theoretical price of a futures contract. In addition, this study also explains that the concordance correlation coefficient developed from the mean square error (MSE) has a statistically theoretical meaning. In conclusion, this study shows that the concordance correlation coefficient is appropriate for analyzing the relationship between the observed stock index futures market price and the theoretical stock index futures price derived from the cost-of-carry model.