• Biotechnology and Bioprocess Engineering:BBE
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• v.7 no.5
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• pp.275-280
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• 2002

In order to control glucose concentration during fed-batch culture for antibiotic production, we applied so called “software sensor” which estimates unmeasured variable of interest from measured process variables using software. All data for analysis were collected from industrial scale cultures in a pharmaceutical company. First, we constructed an estimation model for glucose feed rate to keep glucose concentration at target value. In actual fed-batch culture, glucose concentration was kept at relatively high and measured once a day, and the glucose feed rate until the next measurement time was determined by an expert worker based on the actual consumption rate. Fuzzy neural network (FNN) was applied to construct the estimation model. From the simulation results using this model, the average error for glucose concentration was 0.88 g/L. The FNN model was also applied for a special culture to keep glucose concentration at low level. Selecting the optimal input variables, it was possible to simulate the culture with a low glucose concentration from the data sets of relatively high glucose concentration. Next, a simulation model to estimate time course of glucose concentration during one day was constructed using the on-line measurable process variables, since glucose concentration was only measured off-line once a day. Here, the recursive fuzzy neural network (RFNN) was applied for the simulation model. As the result of the simulation, average error of RFNN model was 0.91 g/L and this model was found to be useful to supervise the fed-batch culture.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.345-358
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• 2012

In a parametric sample selection model, the distribution assumption is critical to obtain consistent estimates. Conventionally, the normality assumption has been adopted for both error terms in selection and main equations of the model. The normality assumption, however, may excessively restrict the true underlying distribution of the model. This study introduces the $S_U$-normal distribution into the error distribution of a sample selection model. The $S_U$-normal distribution can accommodate a wide range of skewness and kurtosis compared to the normal distribution. It also includes the normal distribution as a limiting distribution. Moreover, the $S_U$-normal distribution can be easily extended to multivariate dimensions. We provide the log-likelihood function and expected value formula based on a bivariate $S_U$-normal distribution in a sample selection model. The results of simulations indicate the $S_U$-normal model outperforms the normal model for the consistency of estimators. As an empirical application, we provide the sample selection model for car ownership and a car expense relationship.

• Journal of Ecology and Environment
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• v.38 no.4
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• pp.485-491
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• 2015

This study was conducted to develop stem volume models for the volume estimation of Quercus glauca Thunb. in Jeju Island, Republic of Korea. Furthermore, this study validated the developed stem volume models using an independent dataset. A total of 167 trees were measured for their diameter at breast height (DBH), total height and stem volume using non-destructive sampling methods. Eighty percent of the dataset was used for the initial model development while the remaining 20% was used for model validation. The performance of the different models was evaluated using the following fit statistics: standard error of estimate (SEE), mean bias absolute mean deviation (AMD), coefficient of determination (R²), and root mean square error (RMSE). The AMD of the five models from the different DBH classes were determined using the validation dataset. Model 5 (V = aD^bH^c), which estimates volume using DBH and total height as predicting variables, had the best SEE (0.02745), AMD (0.01538), R² (0.97603) and RMSE (0.02746). Overall, volume models with two independent variables (DBH and total height) performed better than those with only one (DBH) based on the model evaluation and validation. The models developed in this study can provide forest managers with accurate estimations for the stem volumes of Quercus glauca in the subtropical forests of Jeju Island, Korea.

• Journal of Broadcast Engineering
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• v.20 no.2
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• pp.248-256
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• 2015

In this paper, we model the interference plus noise signal for terrestrial cloud transmission systems and present bit error rate (BER) performances. Since terrestrial cloud transmission systems experience co-channel interference from one or more transmitters, they have to operate under a negative signal-to-interference plus noise ratio (SINR) region. The interference plus noise signal can be modeled as Gaussian random variable under the required SINR region and we observe the BER performance of the cloud transmission system using the derived model. Also, we propose an improved channel estimation scheme by averaging the channel estimates based on least square based interpolation scheme. Simulation results show that the cloud transmission system can operate under negative SINR region using the proposed channel estimation scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.11
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• pp.1549-1556
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• 2016

By increased moving speed of aircraft, estimating location of itself becomes more important than ever. This requirement is satisfied by appearance of GPS, however it is useless when signal reception from satellite is not good enough by interruption, for example, traffic jamming. Applying link for communication to additional positioning system is capable of providing relative position of aircraft. Estimating location with link for communication is done without additional equipment but with signal processing based on correlation of received signal. ESPRIT is one of the representative algorithm among them. Estimating correlation matrix is possible to have error since it includes average operation needs enough number of samples not impractical. Therefore we propose algorithm that defines, estimates and removes error matrix of correlation. Proposing algorithm shows better performance than previous one when transmitters are close.

• Journal of the Korea Computer Graphics Society
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• v.17 no.1
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• pp.17-24
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• 2011

In Computer Graphics, interaction between a particle-based fluid and a rigid body is important. In General, this interaction has been simulated in a discrete environment. As a result, there have been lots of errors. The larger the time step is used, the bigger the error is. This paper describes how to minimize the error in a discrete environment. To be specific, the collision handling method is that estimates particle collision using a signed distance function increases continuously according to space. At the time a fluid particle and a rigid body model collide, the exact collision time and the position is estimated. Through this, we propose the method how to be simulated the interaction between a fluid and a rigid body model as a continuous environment.

• Fire Science and Engineering
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• v.27 no.6
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• pp.38-43
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• 2013

As a preliminary study to evaluate the reliability of the calculation method of fire load for residential furniture combustibles, the present study estimates the fire load considering the volume data obtained by the 3D geometrical information of combustibles and material properties based on the literature survey and sample burning test. A kitchen sink cabinet, couch and workstation were investigated for estimating its fire load and real fire test have been performed to measure total energy released from the combustibles. Based on total energy measured from real fire test, the relative error of the estimated fire load due to literature survey and measured material properties showed 6~120% and less than 20%, respectively. It shows that the estimation error of fire load are greatly affected by its material properties as well as geometrical information of combustibles and the present study will be able to contribute to accurate estimation of fire load.

• Journal of Wetlands Research
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• v.21 no.spc
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• pp.51-60
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• 2019

In this study, we performed At-site Frequency Analysis(AFA) and Regional Frequency Analysis(RFA) using the observed and climate change scenario data, and the relative root mean squared error(RMMSE) was compared and analyzed for both approaches through Monte Carlo simulation. To evaluate the rainfall quantile, the daily rainfall data were extracted for 615 points in Korea from HadGEM3-RA(12.5km) climate model data, one of the RCM(Regional Climate Model) data provided by the Korea Meteorological Administration(KMA). Quantile mapping(QM) and inverse distance squared methods(IDSM) were applied for bias correction and spatial disaggregation. As a result, it is shown that the RFA estimates more accurate rainfall quantile than AFA, and it is expected that the RFA could be reasonable when estimating the rainfall quantile based on climate change scenarios.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.12
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• pp.1287-1296
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• 2009

It is very important to estimate the Signal to Noise Ratio(SNR) of received signal in time varying channel state. Most SNR estimation techniques derive the SNR estimates solely from the samples of the received signal after the matched filter. In the severe distorted wireless channel, the performance of these estimators become unstable and degraded. LP-based SNR estimator which can operate on data samples collected at the front-end of a receiver shows more stable performance than other SNR estimator. In this paper, we study an efficient SNR estimation algorithm based on LP and propose a new estimation method to decrease the computation complexity. Proposed algorithm accomplishes the SNR estimation process efficiently because it uses the forward prediction error and its conjugate value during the linear prediction error update. Via the computer simulation, the performance of this proposed estimation method is compared and discussed with other conventional SNR estimators in digital communication channels.

• Journal of Advanced Navigation Technology
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• v.2 no.2
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• pp.75-83
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• 1998

This paper presents a low cost and high accuracy integrated Global Positioning System (GPS)/dead reckoning (DR) system. The integrated GPS/DR system is capable of providing highly accurate position data in real-time or in post processing. Based on the analysis of the main error source affecting the DR measurements, an eight-state mathematical model for the integrated system has been developed to represent these errors. This eight-state model has been used to build a nonlinear filter for the estimation of the state vector at every epoch when DR measurements are available. The accuracy of the system has been evaluated using 1Hz DR measurements and 3Hz continuous GPS position estimates. Through numerical simulation the system performance during periods with GPS outage has been investigated by comparing two different noise models. While one model is the position estimation filter containing a single noise model, the other filter includes two-level noise model. The simulation results have shown that the estimation filter containing two-level noise model for computing the position error of the integrated GPS/DR system yields better performance than that the filter including the single-level noise model does.