• Analytical Science and Technology
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• v.11 no.1
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• pp.47-53
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• 1998

This study is to improve the diagnosis of diabetes mellitus and the self-monitoring of blood glucose in people with diabetes by providing a non-invasive method of monitoring blood glucose. A near-infrared (NIR) spectrophotometer was used to measure absorption spectra of 80 glucose samples ranges from 1 mg/dL to 200 mg/dL, and shows the standard error of prediction 1.8 mg/dL. Also, to investigate the effect of interference in blood, NaCl and sand were added in glucose and found the standard error of prediction of 2.8 mg/dL and 3.8 mg/dL, respectively. A new and more accurate calibration system for the spectrophotometer was developed from systematic study of light scattering, which cause nonlinear spectrophotometer response.

• Smart Structures and Systems
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• v.6 no.9
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• pp.1057-1077
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• 2010

An early detection of structural damages is critical for the decision making of repair and replacement maintenance in order to guarantee a specified structural reliability. Consequently, the structural damage detection, based on vibration data measured from the structural health monitoring (SHM) system, has received considerable attention recently. The traditional time-domain analysis techniques, such as the least square estimation (LSE) method and the extended Kalman filter (EKF) approach, require that all the external excitations (inputs) be available, which may not be the case for some SHM systems. Recently, these two approaches have been extended to cover the general case where some of the external excitations (inputs) are not measured, referred to as the adaptive LSE with unknown inputs (ALSE-UI) and the adaptive EKF with unknown inputs (AEKF-UI). Also, new analysis methods, referred to as the adaptive sequential non-linear least-square estimation with unknown inputs and unknown outputs (ASNLSE-UI-UO) and the adaptive quadratic sum-squares error with unknown inputs (AQSSE-UI), have been proposed for the damage tracking of structures when some of the acceleration responses are not measured and the external excitations are not available. In this paper, these newly proposed analysis methods will be compared in terms of accuracy, convergence and efficiency, for damage identification of structures based on experimental data obtained through a series of laboratory tests using a scaled 3-story building model with white noise excitations. The capability of the ALSE-UI, AEKF-UI, ASNLSE-UI-UO and AQSSE-UI approaches in tracking the structural damages will be demonstrated and compared.

• The Transactions of the Korean Institute of Power Electronics
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• v.14 no.5
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• pp.372-378
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• 2009

With the increase in capacity of photovoltaic generation systems, studies are being actively conducted to improve system efficiency. In order to develop the high performance photovoltaic power system it is required to understand the physical characteristics of the solar cell. However, solar cell models have a non-linear form with many parameters entangled and conventional methods suggested to extract the parameters of the solar cell model require some kind of assumptions, which accompanies the calculation errors, thereby lowering the accuracy of the model. Therefore, in this paper a novel method is proposed to calculate the ideality factor and reverse saturation current of the solar cell from the I-V curve measured and announced by solar cell manufacturers, derive the ideal I-V curve, and then extract the series and shunt resistances value from the difference between the ideal and measured I-V curve. Also, validity of the proposed method is demonstrated by calculating the correlation between I-V curve based on modeling parameters and I-V curve actually measured through least squares method.

• Korean Journal of Agricultural Science
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• v.47 no.3
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• pp.645-655
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• 2020

Bacteria are a very common cause of food poisoning. Moreover, bacteria form biofilms to protect themselves from harsh environments. Conventional detection methods for foodborne bacterial pathogens including the plate count method, enzyme-linked immunosorbent assays (ELISA), and polymerase chain reaction (PCR) assays require a lot of time and effort. Hyperspectral imaging has been used for food safety because of its non-destructive and real-time detection capability. This study assessed the feasibility of using hyperspectral imaging and machine learning techniques to detect biofilms formed by Escherichia coli. E. coli was cultured on a high-density polyethylene (HDPE) coupon, which is a main material of food processing facilities. Hyperspectral fluorescence images were acquired from 420 to 730 nm and analyzed by a single wavelength method and machine learning techniques to determine whether an E. coli culture was present. The prediction accuracy of a biofilm by the single wavelength method was 84.69%. The prediction accuracy by the machine learning techniques were 87.49, 91.16, 86.61, and 86.80% for decision tree (DT), k-nearest neighbor (k-NN), linear discriminant analysis (LDA), and partial least squares-discriminant analysis (PLS-DA), respectively. This result shows the possibility of using machine learning techniques, especially the k-NN model, to effectively detect bacterial pathogens and confirm food poisoning through hyperspectral images.

• Journal of the Korea Computer Graphics Society
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• v.3 no.2
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• pp.27-34
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• 1997

3-D image modeling is in high demand for automated visual inspection and non-destructive testing. It also can be useful in biomedical research, medical therapy, surgery planning, and simulation of critical surgery (i.e. cranio-facial). Image processing and image analysis are used to enhance and classify medical volumetric data. Analyzing medical volumetric data is very difficult In this paper, we propose a new image modeling method based on tetrahedrization to improve the visualization of three-dimensional medical volumetric data. In this method, the trivariate piecewise linear interpolation is applied through the constructed tetrahedral domain. Also, visualization methods including iso-surface, color contouring, and slicing are discussed. This method can be useful to the correct and speedy analysis of medical volumetric data, because it doesn't have the ambiguity problem of Marching Cubes algorithm and achieves the data reduction. We expect to compensate the degradation of an accuracy by using an adaptive sub-division of tetrahedrization based on least squares fitting.

• Journal of Digital Convergence
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• v.14 no.1
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• pp.203-209
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• 2016

In this paper, we introduce a fuzzy inference systems for nonlinear inference using fuzzy cluster. Typically, the generation of fuzzy rules for nonlinear inference causes the problem that the number of fuzzy rules increases exponentially if the input vectors increase. To handle this problem, the fuzzy rules of fuzzy model are designed by dividing the input vector space in the scatter form using fuzzy clustering algorithm which expresses fuzzy cluster. From this method, complex nonlinear process can be modeled. The premise part of the fuzzy rules is determined by means of FCM clustering algorithm with fuzzy clusters. The consequence part of the fuzzy rules have four kinds of polynomial functions and the coefficient parameters of each rule are estimated by using the standard least-squares method. And we use the data widely used in nonlinear process for the performance and the nonlinear characteristics of the nonlinear process. Experimental results show that the non-linear inference is possible.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.489-496
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• 2019

This paper is a study on the evaluation of loss factor of damping treatment materials to reduce the noise and vibration for panels of railway vehicles and automobiles. In order to determine the modal parameters of damping materials, beam excitation tests were carried out using different type PVC coated aluminum and steel base beam specimens. The specimens were excited from 10 Hz to 1000 Hz frequency range using sinusoidal force, and transfer mobility data were measured by using an accelerometer. The loss factors were determined by using integrated program, based on theories of Half Power Method, Minimum Tangent Error Method, Minimum Angle Error Method and Phase Change Method, which enable to evaluate the parameters using modal circle fit and least squares error method. In the case of lower loss factor and data of linear characteristics, any method could be applied for evaluation of parameters, however the case of higher loss factor or data including non-linear characteristics, the minimum angle error method could reduce the loss factor evaluation. The obtained dynamic properties of the coating material could be used for application of Finite Element Method analyzing the noise control effects of complex structures such as carbody or under-floor boxes of rolling stock. The damping material will be very useful to control the structural noise, because the obtained modal loss factors of each mode show very good effect on over $2^{nd}$ mode frequency range.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.353-356
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• 2005

In this paper, the proposed $gamma({\gamma})$ line system is developed for reducing the error between non-linear gamma curve produced by a formula and result produced by hardware implementation. The proposed algorithm and system is based on the specific gamma value 2.2, namely the formula is represented by {0,1}$^{2.2}$ and the bit width of input and out data is 10bit. In order to reduce the error, the system is using least squares polynomial of the numerical method which is calculating the best fitting polynomial through a set of points. The proposed gamma line is consisting of nine kinds of quadratic equations, each with their own overlap sections to get more precise. Based on the algorithm verified by $MATLAB^{TM}$ 7.0, the proposed system is implemented by using Verilog-HDL. The proposed system has 2 clock latency; 1 result per clock. The error range (LSB) is -4 and +3. Its standard deviation is 1.287956238. The total gate count of system is 2,083 gates and the maximum timing is 15.56[ns].

• Journal of Gastric Cancer
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• v.15 no.1
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• pp.29-38
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• 2015

Purpose: Before expanding our indications for laparoscopic gastrectomy to advanced gastric cancer and adopting reduced port laparoscopic gastrectomy, we analyzed and audited the outcomes of laparoscopy-assisted distal gastrectomy (LADG) for adenocarcinoma; this was done during the adoptive period at our institution through the comparative analysis of short-term surgical outcomes and learning curves (LCs) of two surgeons with different careers. Materials and Methods: A detailed comparative analysis of the LCs and surgical outcomes was done for the respective first 95 and 111 LADGs performed by two surgeons between July, 2006 and June, 2011. The LCs were fitted by using the non-linear ordinary least squares estimation method. Results: The postoperative morbidity and mortality rates were 14.6% and 0.0%, respectively, and there was no significant difference in the morbidity rates (12.6% vs. 16.2%, P=0.467). More than 25 lymph nodes were retrieved by each surgeon during LADG procedures. The LCs of both surgeons were distinct. In this study, a stable plateau of the LC was not achieved by both surgeons even after performing 90 LADGs. Conclusions: Regardless of the experience with gastrectomy or laparoscopic surgery for other organs, or the age of surgeon, the outcome was quite acceptable; the learning process differ according to the surgeon's experience and individual characteristics.

• Journal of Korea Water Resources Association
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• v.45 no.8
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• pp.815-826
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• 2012

The underestimating trend using existing ordinary regression (OR) based trend analysis has been a well-known problem. The existing OR method based on least squares approximate the conditional mean of the response variable given certain values of the time t, and the usual assumption of the OR method is normality, that is the distribution of data are not dissimilar form a normal distribution. In this regard, this study proposed a quantile regression that aims at estimating either the conditional median or other quantiles of the response variable. This study assess trend in annual daily maximum rainfall series over 64 weather stations through both in OR and QR approach. The QR method indicates that 47 stations out of 67 weather stations are a strong upward trend at 5% significance level while OR method identifies a significant trend only at 13 stations. This is mainly because the OR method is estimating the condition mean of the response variable. Unlike the OR method, the QR method allows us flexibly to detect the trends since the OR is designed to estimate conditional quantiles of the response variable. The proposed QR method can be effectively applied to estimate hydrologic trend for either non-normal data or skewed data.