• Journal of the Earthquake Engineering Society of Korea
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• v.20 no.1
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• pp.55-62
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• 2016

It is important to select an accurate set of ground motions when conducting linear and nonlinear response history analyses of structures. This study proposes a method for selecting ground motions from a ground motion library with response spectra that match the target response spectrum mean, variance and correlation structures. This study also has addressed the determination of an appropriate value for the weight factor of a correlation structure. The proposed method is conceptually simple and straightforward, and does not involve a simulation algorithm. In this method, a desired number of ground motions are sequentially selected from first to last. The proposed method can be also used for selecting ground motions with response spectra that match the conditional spectrum. The accuracy and efficiency of the proposed procedure are verified with numerical examples.

• Structural Engineering and Mechanics
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• v.57 no.2
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• pp.369-387
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• 2016

In this work, random homogenization analysis for the effective thermal properties of a three-dimensional composite material with unidirectional fibers is presented by combining the equivalent inclusion method with Random Factor Method (RFM). The randomness of the micro-structural morphology and constituent material properties as well as the correlation among these random parameters are completely accounted for, and stochastic effective thermal properties as thermal expansion coefficients as well as their correlation are then sought. Results from the RFM and the Monte-Carlo Method (MCM) are compared. The impact of randomness and correlation of the micro-structural parameters on the random homogenized results is revealed by two methods simultaneously, and some important conclusions are obtained.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.8
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• pp.1722-1732
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• 1997

In this paper, we propose a coding method of remotely sensed satellite image data using region classification and interband correlation. This method classifies each pixel vector consider spectral characteristics. Then we perform the classified intraband VQ to remove spatial (intraband redundancy for a reference band image. To remove interband redundancy effectively, we perform the classified interband prediction for the band images that the high correlation spectrally and perform the classified interband VQ for the remaining band images. Experiments on LANDSAT TM image show that the coding efficiency of the proposed method is better than that of the conventional Gupta's method. Especially, this method removes redundancies effectively for satellite iamge including various geographical objects and for and images that have low interband correlation.

• Journal of the Korean Geosynthetics Society
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• v.13 no.2
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• pp.41-47
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• 2014

Various prediction methods for the bearing capacity of helical piles have been introduced with consideration of both the steel shaft and the helix plates attached to the shaft. In this paper, three representative methods, that is, individual bearing method, cylindrical shear method, and torque correlation method are discussed and compared to each other. The prediction methods were verified by comparing with a series of loading test results performed on moderate-size helical piles from the companion paper. As a result, the measured bearing capacity is greater than the bearing capacity predicted by the cylindrical shear method, but smaller than that of the individual bearing method. In addition, the bearing capacity predicted by the torque correlation method is in good agreement with the measured bearing capacity.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.551-554
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• 2005

The method of the reduction of the automotive induction noise can be classified by the method of passive control and the method of active control. However, the passive control method has a demerit to reduce the effect of noise reduction at low frequency (below 500Hz) range and to be limited by a space of the engine room. Whereas, the active control method can overcome the demerit of passive control method. The algorithm of active control is mostly used the LMS (Least-Mean-Square) algorithm because the LMS algorithm can easily obtain the complex transfer function in real-time. Especially, When the Filtered-X LMS (FXLMS) algorithm is applied to an ANC system. However, the convergence performance of LMS algorithm goes bad when the FXLMS algorithm is applied to an active control of the induction noise under rapidly accelerated driving conditions. Thus Normalized FXLMS algorithm was developed to improve the control performance under the rapid acceleration. The advantage of Normalized FXLMS algorithm is that the step size is no longer constant. Instead, it varies with time. But there is one additional practical difficulty that can arise when a nonstationary input is used. If the input is zero for consecutive samples, then the step size becomes unbounded. So, in order to solve this problem. the Correlation FXLMS algorithm was developed. The Correlation FXLMS algorithm is realized by using an estimate of the cross correlation between the adaptation error and the filtered input signal to control the step size. In this paper, the performance of the Correlation FXLMS Is presented in comparison with that of the other FXLMS algorithms based on computer simulations.

• The KIPS Transactions:PartB
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• v.17B no.1
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• pp.9-14
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• 2010

In this paper, we propose method of correlation coefficients between sensors by statistical analysis that selects optimal sensors in odor recognition system of selective multi-sensors. The proposed sensor decision method obtains odor data from Metal Oxide Semiconductor(MOS) sensor array and then, we decide optimal sensors based on correlation of obtained odors. First of all, we select total number of 16 sensors eliminated sensor of low response and low reaction rate response among similar sensors. We make up DB using 16 sensors from input odor and we select sensor of low correlation after calculated correlation coefficient of each sensor. Selected sensors eliminate similar sensors' response therefore proposed method are able to decide optimal sensors. We applied to floral scent recognition for performance evaluation of proposed sensors decision method. As a result, application of proposed method with floral scent recognition using correlation coefficient obtained recognition rate of 95.67% case of using 16 sensors while applied floral scent recognition system of proposed sensor decision method confirmed recognition rate of 94.67% using six sensors and 96% using only 8 sensors.

• Analytical Science and Technology
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• v.33 no.3
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• pp.143-150
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• 2020

In this study, crude protein was analyzed and compared using the Kjeldahl and Dumas method for cereals, meat, sea food, chemical samples and vegetable. The nine kinds of cereal, including white rice, were analyzed. In the result, the correlation coefficient of the Kjeldahl and the Dumas method indicated that there was no significant difference between them, showing 0.994 of it and 0.956 of p-value. Also, for the nine kinds of meat, five kinds of sea food, three kinds of chemical samples, four kinds of vegetable, there was little difference about the correlation coefficient of the Kjeldahl and the Dumas method, showing 0.9725, 0.9879, 0.9985 and 0.9873 of it and 0.947, 0.761, 0.997 and 0.727 of p-value, respectively. For the samples of meat, they were not fully homogenized, so the reproducibility of them was not good in the Dumas method, which is required to be analyzed in small size. However, when vegetables, which contain a lot of nitrates, are analyzed using Kjeldahl, they showed the lower reproducibility compared to the result of using Dumas because they are not completely decomposed in the Kjeldahl method. In the Dumas method, the samples should be homogenized because only 0.1 g sample is used. In short, neither of the Kjeldahl and Dumas methods are an accurate quantitative test because both of them do not directly analyze pure protein but measure the amount of protein based on analysis of nitrogen. Therefore, it is important of selecting the appropriate analysis method considering the characteristics of samples.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.15 no.2
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• pp.186-194
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• 2008

Purpose: The purpose of this studγ is to understand relations among perceived stress, stress coping method, self-rare behavior and Hemoglobin $A_1c$ of elderly diabetic mellitus patients. Method: Participants were 128 patients who were under medical treatment in outpatient department at C university hospital. The collected data were analyzed using technical statistics, frequency, percentage, T-test, ANOVA and person's correlation coefficient by SPSS 12.0. Result: There were positive correlation between self-care behavior and coping method based on emotions under the lower category of stress coping method. Hemoglobin $A_1c$ of patients had a negative correlation with physical exorcise of self coping method under the lower category. Perceived stress had also a negative correlation with emotions as coping method in the lower category and medication of self-care behavior. Conclusion: It was noted that elderly diabetic mellitus patients didn't much use a coping method based mainly problems and another method based emotions in case of perceived stress. The findings of this study will be useful for developing an nursing intervention program focused on elderly diabetic mellitus patients.

• ETRI Journal
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• v.36 no.1
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• pp.99-105
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• 2014

This paper presents a new locomotion mode recognition method based on a transformed correlation feature analysis using an electromyography (EMG) pattern. Each movement is recognized using six weighted subcorrelation filters, which are applied to the correlation feature analysis through the use of six time-domain features. The proposed method has a high recognition rate because it reflects the importance of the different features according to the movements and thereby enables one to recognize real-time EMG patterns, owing to the rapid execution of the correlation feature analysis. The experiment results show that the discriminating power of the proposed method is 85.89% (${\pm}2.5$) when walking on a level surface, 96.47% (${\pm}0.9$) when going up stairs, and 96.37% (${\pm}1.3$) when going down stairs for given normal movement data. This makes its accuracy and stability better than that found for the principal component analysis and linear discriminant analysis methods.

• Nuclear Engineering and Technology
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• v.44 no.8
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• pp.977-984
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• 2012

The cross-correlation technique has been widely used for leakage detection of buried pipes, and this technique can be successfully applied when the leakage signal has a high signal-to-noise ratio. In the case of a power plant, the measured leakage signals obtained from the sensors may contain background noise and mechanical noise generated by adjacent machinery. In such a case, the conventional method using the cross-correlation function may fail to estimate the leakage point. In order to enhance the leakage estimation capability of a buried pipe in a noisy environment, an improved cross-correlation technique is proposed. It uses a noise rejection technique in the frequency domain to effectively eliminate the tonal noise due to rotating machinery. Experiments were carried out to verify the validity of the proposed method. The results show that even in a tonal noisy environment, the proposed method can provide more reliable means for estimating the time delay of the leakage signals.