• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1257-1262
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• 2016

The purpose of this study is to evaluate whether to take a medicine based on a measuring data using textile proximity sensor. We developed a proximity sensor of ring type using conductive textile, and acquired a data in accordance with the quantity of each pills. To evaluate our approach, we designed an experimental protocol that is counting pills subtracting the one which contains range of 0 T(Tablet, 4,100mg) from 20 T. And, The experiments were performed a nine times in the same way. In order to remove a noise and smoothen data, data preprocessing were performed using resampling method and moving average filter which has ten points. Then, we calculated a linear trend line equation, and analyzed a correlation between pill quantity and trend line equation. As a results, correlation coefficient was shown at 0.833 through using a Spearman's correlation method and we could be determined that data was continuos decreases when take a medicine.

• Smart Structures and Systems
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• v.21 no.5
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• pp.571-582
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• 2018

Track irregularities of high-speed Maglev lines have significant influence on ride comfort. Their adjustment is of key importance in the daily maintenance of these lines. In this study, an adjustment method is proposed and track irregularities analysis is performed. This study considers two modules: an inspection module and a vehicle-guideway coupling vibration analysis module. In the inspection module, an inertial reference method is employed for field-measurements of the Shanghai high-speed Maglev demonstration line. Then, a partial filtering, integration method, resampling method, and designed elliptic filter are employed to analyze the detection data, which reveals the required track irregularities. In the analysis module, a vehicle-guideway interaction model and an electromagnetic interaction model were developed. The influence of the measured line irregularities is considered for the calculations of the electromagnetic force. Numerical integration method was employed for the calculations. Based on the actual field detection results and analysis using the numerical model, a threshold analysis method is developed. Several irregularities modalities with different girder end's deviations were considered in the simulations. The inspection results indicated that long-wavelength irregularities with larger girder end's deviations were the dominant irregularities. In addition, the threshold analysis of the girder end's deviation shows that irregularities that have a deviation amplitude larger than 6 mm and certain modalities (e.g., M- and N-shape) are unfavorable. These types of irregularities should be adjusted during the daily maintenance.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.2
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• pp.381-389
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• 2012

This paper proposes a method which corrects location data of GPS for navigation of outdoor mobile robot. The method uses a Bayesian filter approach called the particle filter(PF). The method iterates two procedures: prediction and correction. The prediction procedure calculates robot location based on translational and rotational velocity data given by the robot command. It incorporates uncertainty into the predicted robot location by adding uncertainty to translational and rotational velocity command. Using the sensor characteristics of the GPS, the belief that a particle assumes true location of the robot is calculated. The resampling from the particles based on the belief constitutes the correction procedure. Since usual GPS data includes abrupt and random noise, the robot motion command based on the GPS data suffers from sudden and unexpected change, resulting in jerky robot motion. The PF reduces corruption on the GPS data and prevents unexpected location error. The proposed method is used for navigation of a mobile robot in the 2011 Robot Outdoor Navigation Competition, which was held at Gwangju on the 16-th August 2011. The method restricted the robot location error below 0.5m along the navigation of 300m length.

• Korean journal of applied entomology
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• v.54 no.3
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• pp.159-167
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• 2015

In order to establish B. tabaci control in paprika greenhouses a fixed-precision-level sampling plan was developed. The sampling plan consisted of spatial distribution analysis, a sampling stop line, and decision making. Sampling was conducted simultaneously in two independent greenhouses (GH 1, GH 2). GH 1 and 2 were surveyed every week for 22 consecutive weeks, using 19 sampling locations in GH 1 and 9 sampling locations in GH 2. The plant in both greenhouses were divided into top (180-220 cm from the ground), middle (80-120 cm from the ground) and bottom (30-70 cm from the ground) sections and B. tabaci adults and pupae were observed on three paprika leaves at each position and recorded separately. GH 2 data were used to validate the fixed-precision sampling plan, which was developed using GH 1 data. In this study, spatial distribution analysis was performed using Taylor's power law with the pooled data of the top and bottom position (B. tabaci adults), and the middle and bottom positions (B. tabaci pupae), based on a 1-leaf sampling unit. Decision making was undertaken using the maximum of action threshold in accordance with previously published method, and the value was decided by the price of the plants. Using the results obtained in the greenhouse, simulated validation of the developed sampling plan by RVSP (Resampling Validation for Sampling Plan) indicated a reasonable level of precision.

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.207-216
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• 2011

Bootstrap is a resampling technique to find an estimate of parameters or to evaluate the estimate. This technique has been used in estimating parameters in linear model(LM) and generalized linear model(GLM). In this paper, we explore the possibility of applying Bootstrapping Residuals, Pairs, and an Estimating Equation that are most widely used in LM and GLM to the generalized estimating equation(GEE) algorithm for modelling repeatedly measured regression data sets. We compared three bootstrapping methods with coefficient and standard error estimates of GEE models from one simulated and one real data set. Overall, the estimates obtained from bootstrap methods are quite comparable, except that estimates from bootstrapping pairs are somewhat different from others. We conjecture that the strange behavior of estimates from bootstrapping pairs comes from the inconsistency of those estimates. However, we need a more thorough simulation study to generalize it since those results are coming from only two small data sets.

• Journal of KIISE
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• v.42 no.6
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• pp.769-780
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• 2015

Smoothing a digital curve by averaging its connected points is widely employed to minimize sharp changes of the curve that are generally introduced by noise. An appropriate degree of smoothing is critical since the area or features of the original shape can be distorted at a higher degree while the noise is insufficiently removed at a lower degree. In this paper, we provide a mathematical relationship between the parameters, such as the number of iterations, average distance between neighboring points, weighting factors for averaging and the moving distance of the point on the curve after smoothing. Based on these findings, we propose to control the smoothed curve such that its deviation is bounded particular error level as well as to significantly expedite smoothing for a pixel-based digital curve.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.72-74
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• 1997

We have developed a 3-D image processing and display technique that include image resampling, modification of MIP, and fusion of MIP image and volumetric rendered image. This technique facilitates the visualization of the three-dimensional spatial relationship between vasculature and surrounding organs by overlapping the MIP image on the volumetric rendered image of the organ. We applied this technique to a MR brain image data to produce an MRI angiogram that is overlapped with 3-D volume rendered image of brain. MIP technique was used to visualize the vasculature of brain, and volume rendering was used to visualize the other structures of brain. The two images are fused after adjustment of contrast and brightness levels of each image in such a way that both the vasculature and brain structure are well visualized either by selecting the maximum value of each image or by assigning different color table to each image. The resultant image with this technique visualizes both the brain structure and vasculature simultaneously, allowing the physicians to inspect their relationship more easily. The presented technique will be useful for surgical planning for neurosurgery.

• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.489-498
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• 2004

In this paper, a new form of linear models referred to as generalized weighted linear models is proposed. The proposed models assume that the relationship between the response variable and explanatory variables can be modelled by a distribution function of the response mean and a weighted linear combination of distribution functions of covariates. This form addresses a structural problem of the link function in the generalized linear models in which the parameter space may not be consistent with the space derived from linear predictors. The maximum likelihood estimation with Lagrange's undetermined multipliers is used to estimate the parameters and resampling method is applied to compute confidence intervals and to test hypotheses.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2005.11a
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• pp.169-172
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• 2005

크기조절 트랜스코딩에서 움직임 벡터 재 예측 과정은 많은 연산량을 필요로 하기 때문에, 실시간 처리를 위해서는 이 과정의 연산량을 줄이는 것이 필요하다. 본 논문에서는 여러 영상에 대해 예측 움직임 벡터를 산출하는 방법을 적응적으로 수행함으로써, 기존 방법에 비해 화질열화 없이 연산량을 줄이는 방법을 제안한다. 전체 움직임의 크기와 움직임 벡터들의 균일성(homogeneity)을 이용하여 움직임이 작을 때는 움직임 벡터 재산출 과정 없이 예측 움직임 벡터 성분을 0으로, 움직임이 크면 움직임 벡터들의 균일성의 정도에 따라 평균값 또는 중간값을 예측 움직임 벡터 성분으로 적응적으로 선택하였다. 그리고 좀 더 효율적인 움직임 벡터 수행을 위해 제안된 과정을 수평, 수직 성분에 각각 따로 적용하였다. 가중치를 부여하여 평균값을 취하는 가중평균 방법과 비효 실험한 결과, 같은 PSNR을 유지하는 조건에서 움직임 벡터 재산출 과정의 덧셈과 곱셈 연산의 수가 평균적으로 각각 96%, 42% 정도 감소하였다.

• The Journal of Asian Finance, Economics and Business
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• v.5 no.1
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• pp.11-16
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• 2018

This research examines the alternative ways of estimating the coefficient of non-diversifiable risk, namely beta coefficient, in Capital Asset Pricing Model (CAPM) introduced by Sharpe (1964) that is an essential element of assessing the value of diverse assets. The non-parametric methods used in this research are the robust Least Trimmed Square (LTS) and Maximum likelihood type of M-estimator (MM-estimator). The Jackknife, the resampling technique, is also employed to validate the results. According to finance literature and common practices, these coecients have often been estimated using Ordinary Least Square (LS) regression method and monthly return data set. The empirical results of this research pointed out that the robust Least Trimmed Square (LTS) and Maximum likelihood type of M-estimator (MM-estimator) performed much better than Ordinary Least Square (LS) in terms of eciency for large-cap stocks trading actively in the United States markets. Interestingly, the empirical results also showed that daily return data would give more accurate estimation than monthly return data in both Ordinary Least Square (LS) and robust Least Trimmed Square (LTS) and Maximum likelihood type of M-estimator (MM-estimator) regressions.