• International Journal of Automotive Technology
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• v.6 no.5
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• pp.475-481
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• 2005

This paper deals with the algorithm of estimating the longitudinal speed of a braking vehicle using measurements from an accelerometer and a standard wheel speed sensor. We evolve speed estimation algorithms of increasing complexity and accuracy on the basis of experimental tests. A final speed estimation algorithm based on a Kalman filtering is developed to reduce measurement noise of the wheel speed sensor, error of the tire radius, and accelerometer bias. This developed algorithm can give peak errors of less than 3 percent even when the accelerometer signal is significantly biased.

• Communications for Statistical Applications and Methods
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• v.17 no.2
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• pp.165-181
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• 2010

This paper presents some chain ratio-type estimators for estimating finite population variance using two auxiliary variables in two phase sampling set up. The expressions for biases and mean squared errors of the suggested c1asses of estimators are given. Asymptotic optimum estimators(AOE's) in each class are identified with their approximate mean squared error formulae. The theoretical and empirical properties of the suggested classes of estimators are investigated. In the simulation study, we took a real dataset related to pulmonary disease available on the CD with the book by Rosner, (2005).

• Communications for Statistical Applications and Methods
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• v.26 no.4
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• pp.347-357
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• 2019

We propose new classes of estimators of population mean under non-response using bivariate auxiliary information. Some improved regression (or difference) type estimators have been proposed in four different situations of non response along with their properties and the expressions for the bias and mean square errors of the proposed estimators are derived under double (two-stage) sampling scheme. The properties of the suggested class of estimators are studied and it is observed that the proposed estimators performed better when compared to conventional estimators proposed by Singh and Kumar (Journal of Statistical Planning and Inference, 140, 2536-2550, 2010b), Shabbir and Khan (Communications in Statistics - Theory and Methods, 42, 4127-4145, 2013) and Bhushan and Naqvi (Journal of Statistics and Management Systems, 18, 573-602, 2015). A comparative study is also conducted both theoretically as well as empirically in order to support the results.

• Journal of Internet Computing and Services
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• v.11 no.2
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• pp.61-72
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• 2010

This paper addresses the problem of moving object localization using Ultra-Wide-Band(UWB) range measurement and the method of location accuracy improvement of the indoor moving object. Unlike outdoor environment, it is difficult to track moving object position due to various noises in indoor. UWB is a radio technology that has attention for localization applications recently. UWB's ranging technique offer the cm accuracy. Its capabilities for data transmission, range accurate estimation and material penetration are suitable technology for indoor positioning application. This paper propose a positioning algorithm of an moving object using UWB ranging technique and particle filter. Existing positioning algorithms eliminate estimation errors and bias after location estimation of mobile object. But in this paper, the proposed algorithm is that eliminate predictable UWB range distance error first and then estimate the moving object's position. This paper shows that the proposed positioning algorithm is more accurate than existing location algorithms through experiments. In this study, the position of moving object is estimated after the triangulation and eliminating the bias and the ranging error from estimation range between three fixed known anchors and a mobile object using UWB. Finally, a particle filter is used to improve on accuracy of mobile object positioning. The results of experiment show that the proposed localization scheme is more precise under the indoor.

• Korea journal of population studies
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• v.31 no.1
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• pp.99-125
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• 2008

This article aimed to clear that the systematic bias according to the length of work time exists between estimated work time by self-reported questions used mainly in measuring and calculating the length of work time because of strong points of easy in measuring and so on and diary work time by time-diary method used because of the strong point that can measure more accurate lifetime by recording various activities of respondents during 24 hours in the stream. As the result that analyze the data from Lifetime Use Survey in 2004, the result like the contradiction came that the tendency of overstating work time is rising according as estimated work time increases via estimated work time, whereas the tendency of understating work time is rising according as diary work time increases via diary work time. The reason that the opposite results come despite the data from the same survey is that random errors act in the opposite directions by regression to the mean. Therefore, we cannot emphasize that a man working long hours tends to exaggerate his work hours by the result via estimated work time. That is, the fact that the systematic bias by the increase of work time does not exist is confirmed, and therefore, it is also impossible to raise questions about the accuracy of the data through estimated work time by self-reported questions from the evidence of the existence of that bias.

• Journal of The Korean Society of Integrative Medicine
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• v.9 no.1
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• pp.203-217
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• 2021

Purpose: The purpose of this meta-analysis was to examine the high-level evidence of the effects of manual therapy on musculoskeletal diseases. Methods: Domestic databases were searched for studies that conducted clinical trials associated with manual therapy on chronic musculoskeletal diseases. A total of 591 studies published between 2005 and 2018 were identified, with 18 studies satisfying the inclusion data. The studies were classified according to patient, intervention, comparison, and outcome (PICO). The search outcomes were items associated with pain and physical function. The 18 studies included in the study were evaluated by using the R meta-analysis (version 4.0). The quality of 18 randomized control trials was evaluated by using the Cochrane risk of bias (ROB). The effect sizes were computed as the corrected standardized mean difference (SMD). Subgroup and meta-regression analyses were also used. Egger's regression test was carried out in order to analyze the publication bias. Cumulative meta-analysis and sensitivity analysis were also conducted in order to analyze the data error. Results: The following factors showed the large effect size of manual therapy on chronic musculoskeletal diseases: pain (Hedges's g = 2.66; 95% CI = 1.47 ~ 3.85), and physical function (Hedges's g = 2.15; 95% CI: 1.22 ~ 3.08). The subgroup analysis only showed a statistical difference in the type of manual therapy (pain) and outcome (physical function). No statistically significant difference was found in the meta-regression analysis. Publication bias was found in the data, but the results of the trim-and-fill method showed that such bias did not largely affect the obtained data. Furthermore, there were no data errors in the cumulative meta-analysis and sensitivity analysis. Conclusion: This study provides evidence for the effectiveness of manual therapy on chronic musculoskeletal diseases in pain and physical function. Subgroup analysis suggests that only the type of manual therapy for pain and the type of outcome for physical function differed in effect size.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1121-1121
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• 2001

A previous study (Berzaghi et al., 2001) evaluated the performance of 3 calibration methods, modified partial least squares (MPLS), local PLS (LOCAL) and artificial neural networks (ANN) on the prediction of the 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 reference values for moisture, crude protein and neutral detergent fibre content. The spectra of the samples were collected using 10 different Foss NIR Systems instruments, only some of which had been standardized to one master instrument. The aim of the present study was to evaluate the behaviour of these different calibration methods when predicting the same samples measured on different instruments. Twenty-two sealed samples of different kind of forages were measured in duplicate on seven instruments (one master and six slaves). Three sets of near infrared spectra (1100 to 2500nm) were created. The first set consisted of the spectra in their original form (unstandardized); the second set was created using a single sample standardization (Clone1); the third was created using a multiple sample procedure (Clone6). WinISI software (Infrasoft International Inc., Port Mathilda, PA, USA) was used to perform both types of standardization, Clone1 is just a photometric offset between a “master” instrument and the “slave” instrument. Clone6 modifies both the X-axis through a wavelength adjustment and the Y-axis through a simple regression wavelength by wavelength. The Clone1 procedure used one sample spectrally close to the centre of the population. The six samples used in Clone 6 were selected to cover the range of spectral variation in the sample set. The remaining fifteen samples were used to evaluate the performances of the different models. The predicted values for dry matter, protein and neutral detergent fibre from the master Instrument were considered as “reference Y values” when computing the statistics RMSEP, SEPC, R, Bias, Slope, mean GH (global Mahalanobis distance) and mean NH (neighbourhood Mahalanobis distance) for the 6 slave instruments. From the results we conclude that i) all the calibration techniques gave satisfactory results after standardization. Without standardization the predicted data from the slaves would have required slope and bias correction to produce acceptable statistics. ii) Standardization reduced the errors for all calibration methods and parameters tested, reducing not only systematic biases but also random errors. iii) Standardization removed slope effects that were significantly different from 1.0 in most of the cases. iv) Clone1 and Clone6 gave similar results except for NDF where Clone6 gave better RMSEP values than Clone1. v) GH and NH were reduced by half even with very large data sets including unstandardized spectra.

• Korean Journal of Remote Sensing
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• v.35 no.6_4
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• pp.1373-1389
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• 2019

KOMPSAT-5 is the first satellite in Korea equipped with X-band Synthetic Aperture Radar (SAR) instrument and has been operated since August 2013. KOMPSAT-5 is used to monitor the global environment according to its observation purpose and the availability of KOMPSAT-5 is also highlighted as the need of high resolution wind data for investigating the coastal region. However, the previous study for the validation of wind derived from KOMPSAT-5 showed that the accuracy is lower than that of other SAR satellites. Therefore, in this study, we developed the correction equation of normalized radar cross section (NRCS or backscattering coefficient) for improvement of wind from the KOMPSAT-5 and validated the effect of the equation using the in-situ measurement of ocean buoys. Theoretical estimated NRCS and observed NRCS from KOMPSAT-5 showed linear relationship with incidence angle. Before applying the correction equation, the accuracy of the estimated wind speed showed the relatively high root-mean-square errors (RMSE) of 2.89 m s^-1 and bias of -0.55 m s^-1. Such high errors were significantly reduced to the RMSE of 1.60 m s^-1 and bias of -0.38 m s^-1 after applying the correction equation. The improvement effect of the correction equation showed dependency relying on the range of incidence angle.

• Journal of Environmental Health Sciences
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• v.45 no.1
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• pp.71-81
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• 2019

Objectives: The lung injuries by exposure to the humidifier disinfectants (HDs) were reported in 2011, Korea. For the HD victims, environmental exposure level and clinical diagnosis were conducted to determine the levels of damage by HDs. Methods: The exposure assessment to the HDs from 1st to 4th questionnaire surveys were carried out for 5,245 victims. And the affecting factors of exposure levels were analyzed by characterizing exposure and demographic information. By using of exposure concentration and cumulative time, exposure levels were classified and compared by percentage of clinical diagnosis classes. The high exposure and low clinical diagnosis rating groups, and low exposure and high clinical diagnosis rating groups were analyzed to overcome the limitation of past exposure assessment such as recall bias. Results: Among the all applicants damaged by the humidifier disinfectants, survivors were 4,028 and the dead were 1,217. And male and female were 2,675, and 2,547, respectively. In case of occurrence age of lung disease, under 10 years was majority age group (1,536) and followed by thirties (917). Pregnant women and fetuses were 339 and 439, respectively. And the damages by exposure to the HDs were concentrated on these susceptible populations in groups with low exposure and high clinical diagnosis rating. On the other hand, the groups classified by high exposure and low clinical diagnosis rating were shown different characterization. Conclusions: The questionnaire survey on past exposure may be uncertain due to recall bias. However, the relationship between classified exposure levels and clinical diagnosis ratings might be shown positive correlation if the exposure assessment errors were analyzed and controlled.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.1
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• pp.49-61
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• 2021

The repeated shipboard measurements that have been conducted by the National Institute of Fisheries Science (NIFS) for more than a half century, provide the valuable long-term hydrographic data with high spatial-temporal resolution. However, this unprecedent dataset has been rarely used for oceanic climate sciences because of its reliability issue. In this study, temporal variability of salinity error in the NIFS data was quantified by means of extremely small variability of salinity in the deep layer of the south-western East Sea, in order to contribute to studies on long-term variability of the East Sea. The NIFS salinity errors estimated on the isothermal surfaces of 1℃ have a remarkable temporal variation, such as ~0.160 g/kg in the year of 1961~1980, ~0.060 g/kg in 1981~1994,~0.020 g/kg in 1995~2002, and ~0.010 g/kg in 2003~2014 on average, which basically represent bias error. In the recent years, even though the quality of salinity has been improved, there still remain relatively large bias errors in salinity data presumably due to failure of salinity sensor managements, especially in 2011, 2013, and 2014. On the contrary, the salinity in the year of 2012 was very accurate and stable, whose error was estimated as about 0.001 g/kg comparable to the salinity sensor accuracy. Thus, as long as developing proper data quality control procedures and sensor management systems, I expect that the NIFS shipboard hydrographic data could have good enough quality to support various studies on ocean response to climate variabilities. Additionally, a few points to improve the current NIFS shipboard measurements were suggested in the discussion section.