• Journal of Advanced Marine Engineering and Technology
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• v.27 no.5
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• pp.641-647
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• 2003

The use of infrared range-finder sensor as the environment recognition system for mobile robot have the advantage of low sensing cost compared with the use of other vision sensor such as laser finder CCD camera. However, it is not easy to find the previous works on the use of infrared range-finder sensor for a mobile robot because of the non-linear characteristic of that. This paper describes the error due to non-linearity of a sensor and the correction of it using neural network. The neural network consists of multi-layer perception and Levenberg-Marquardt algorithm is applied to learning it. The effectiveness of the proposed algorithm is verified from experiment.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.327-336
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• 2001

This work concerns estimating a regression function, which is not linear, using aggregate data. In much of the empirical research, data are aggregated for various reasons before statistical analysis. In a traditional parametric approach, a linear estimation of the non-linear function with aggregate data can result in unstable estimators of the parameters. More serious consequence is the bias in the estimation of the non-linear function. The approach we employ is the kernel regression smoothing. We describe the conditions when the aggregate data can be used to estimate the regression function efficiently. Numerical examples will illustrate our findings.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.8
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• pp.1075-1079
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• 2014

In this paper, we describe the analysis and the compensation method of the g-sensitivity error for MEMS vibratory gyroscopes. Usually, the g-sensitivity error has been ignored in the commercial MEMS gyroscope, but it deserves our attention to apply for the missile application as a tactical grade performance. Thus, it is necessary to compensate for the g-sensitivity error to reach a tactical grade performance. Generally, the g-sensitivity error seems intuitively to be a gyroscope bias error proportional to the linear acceleration. However, we assert that the g-sensitivity error mainly causes not a bias error but a scale-factor error. And we verify that the g-sensitivity scale-factor error occurs due to the non-linearity of parallel plate electrodes. Therefore, we propose the compensation method to remove the g-sensitivity scale-factor error. The experimental result showed that a proposed compensation method improved successfully the performance of the MEMS vibratory gyroscope.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.11-23
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• 2020

Sampling design should provide statistics to meet a given accuracy while saving cost and time. However, a large number of non-responses are occurring due to the deterioration of survey circumstances, which significantly reduces the accuracy of the survey results. Non-responses occur for a variety of reasons. Chung and Shin (2017, 2019) and Min and Shin (2018) found that the accuracy of estimation is improved by removing the bias caused by non-response when the response rate is an exponential or linear function of variable of interests. For that case they assumed that the error of the super population model follows normal distribution. In this study, we proposed a non-response bias adjusted estimator in the case where the error of a super population model follows the gamma distribution or the log-normal distribution in a business survey. We confirmed the superiority of the proposed estimator through simulation studies.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.631-642
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• 2019

Many methods have been developed to solve problems found in sample surveys involving a large number of item non-responses that cause inaccuracies in estimation. However, the non-response adjustment method used under the assumption of random non-response generates a bias in cases where the response rate is affected by the variable of interest. Chung and Shin (2017) and Min and Shin (2018) proposed a method to improve the accuracy of estimation by appropriately adjusting a bias generated when the response rate is a function of the variables of interest. In this study, we studied a case where the response rate function is linear and the error of the super population model follows normal distribution. We also examined the effect of the number of stratum population on bias adjustment. The performance of the proposed estimator was examined through simulation studies and confirmed through actual data analysis.

• Asian-Australasian Journal of Animal Sciences
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• v.8 no.4
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• pp.365-368
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• 1995

Lactation curves of dairy cows were generated using three models, namely; incomplete gamma function (model 1), polynomial inverse function (model 2) and non-linear regression (model 3). Secondary milk yield data of 27 cows which had completed 6 lactations were used in this study. Milk yield records (once a week) throughout the lactation and from the first three months of lactation were fitted to the models. Estimation of total milk yield by model 3 using the data once a week throughout the lactation resulted in smaller % bias and standard error than those generated from model 1 and 2. But, model 2 was more accurate in predicting the 305-day milk yield equivalent closer to actual yields with smaller bias % and error using partial records up to 3 months. Also, model 2 was able to estimate the time to reach peak yield close to the actual data using partial records and model 2 could be used as a tool to advise farmers on appropriate feeding and management practices to be adopted.

• 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 Institute of Electronics Engineers of Korea SD
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• v.40 no.11
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• pp.83-94
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• 2003

In this paper. a highly linear and low glitch CMOS current mode digital-to-analog converter (DAC) by self calibration bias circuit is proposed. The architecture of the DAC is based on a current steering 6＋4 segmented type and new switching scheme for the current cell matrix, which reduced non-linearity error and graded error. In order to achieve a high performance DAC . novel current cell with a low spurious deglitching circuit and a new inverse thermometer decoder are proposed. The prototype DAC was implemented in a 0.35${\mu}{\textrm}{m}$ n-well CMOS technology. Experimental result show that SFDR is 60 ㏈ when sampling frequency is 32MHz and DAC output frequency is 7.92MHz. The DAC dissipates 46 mW at a 3.3 Volt single power supply and occupies a chip area of 1350${\mu}{\textrm}{m}$ ${\times}$750${\mu}{\textrm}{m}$.

• Atmosphere
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• v.33 no.4
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• pp.423-440
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• 2023

The updated version of Global Ocean Data Assimilation and Prediction System (GODAPS) in the NIMS/KMA (National Institute of Meteorological Sciences/Korea Meteorological Administration), which has been in operation since December 2021, is being introduced. This technical note on GODAPS2 describes main progress and updates to the previous version of GODAPS, a software tool for the operating system, and its improvements. GODAPS2 is based on Forecasting Ocean Assimilation Model (FOAM) vn14.1, instead of previous version, FOAM vn13. The southern limit of the model domain has been extended from 77°S to 85°S, allowing the modelling of the circulation under ice shelves in Antarctica. The adoption of non-linear free surface and variable volume layers, the update of vertical mixing parameterization, and the adjustment of isopycnal diffusion coefficient for the ocean model decrease the model biases. For the sea-ice model, four vertical ice layers and an additional snow layer on top of the ice layers are being used instead of previous single ice and snow layers. The changes for data assimilation include the updated treatment for background error covariance, a newly added bias scheme combined with observation bias, the application of a new bias correction for sea level anomaly, an extension of the assimilation window from 1 day to 2 days, and separate assimilations for ocean and sea-ice. For comparison, we present the difference between GODAPS and GODAPS2. The verification results show that GODAPS2 yields an overall improved simulation compared to GODAPS.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1141-1141
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• 2001

The aim of the study was to evaluate the performance of 3 calibration methods, modified partial least squares (MPLS), local PLS (LOCAL) and artificial neural network (ANN) on the prediction of chemical composition of forages, using a large NIR database. The study used forage samples (n=25,977) from Australia, Europe (Belgium, Germany, Italy and Sweden) and North America (Canada and U.S.A) with information relative to moisture, crude protein and neutral detergent fibre content. The spectra of the samples were collected with 10 different Foss NIR Systems instruments, which were either standardized or not standardized to one master instrument. The spectra were trimmed to a wavelength range between 1100 and 2498 nm. Two data sets, one standardized (IVAL) and the other not standardized (SVAL) were used as independent validation sets, but 10% of both sets were omitted and kept for later expansion of the calibration database. The remaining samples were combined into one database (n=21,696), which was split into 75% calibration (CALBASE) and 25% validation (VALBASE). The chemical components in the 3 validation data sets were predicted with each model derived from CALBASE using the calibration database before and after it was expanded with 10% of the samples from IVAL and SVAL data sets. Calibration performance was evaluated using standard error of prediction corrected for bias (SEP(C)), bias, slope and R2. None of the models appeared to be consistently better across all validation sets. VALBASE was predicted well by all models, with smaller SEP(C) and bias values than for IVAL and SVAL. This was not surprising as VALBASE was selected from the calibration database and it had a sample population similar to CALBASE, whereas IVAL and SVAL were completely independent validation sets. In most cases, Local and ANN models, but not modified PLS, showed considerable improvement in the prediction of IVAL and SVAL after the calibration database had been expanded with the 10% samples of IVAL and SVAL reserved for calibration expansion. The effects of sample processing, instrument standardization and differences in reference procedure were partially confounded in the validation sets, so it was not possible to determine which factors were most important. Further work on the development of large databases must address the problems of standardization of instruments, harmonization and standardization of laboratory procedures and even more importantly, the definition of the database population.