• Journal of the Speleological Society of Korea
• /
• v.14 no.15
• /
• pp.42-90
• /
• 1987

Lee, Moon Won reported by 63 kinds lescribing the petrography and bulk chemical Composition in Petrology of Cheju volcanic island. The total Chemical Composition data was analyzed by the program of FORTRAN77. First, the Conversition equations and the scatter diagram were examined to the analysis, by the least square method. Next, a statistical data requested a mean Value, maximum value, minimum value, the range, the standard deviation, the variance, the Standord Error and the Coefficient of variation. In the standard deviation, a small Composition is MnO and P$_2$O$\sub$5/, a large Composition is SiO$_2$, Mgo and FeO. The Standard error and the variance were the tandency looked like the Standard deviation well. However, the Coefficient Variation differs from the Standard deviation. Where, a large Coefficient of variation are H$_2$O$\^$-/ and H$_2$O$\^$+/, a small Coefficient of variation are Al$_2$O$_3$ and SiO$_2$. The Correlation of Coefficient Can be Calculated numerically from the relation between SiO$_2$, Al$_2$O$_3$ and TiO$_2$ to other Compositions.

• Bioinformatics and Biosystems
• /
• v.1 no.2
• /
• pp.115-122
• /
• 2006

A three-dimensional pharmacophore model was developed based on 24 currently available inhibitors, which were rationally selected from 472 compounds with diverse molecular structure and bioactivity, for generating pharmacophore of uPA (Urokinase Plasminogen Activator) inhibitors. The best hypothesis (Hypo1) comprised of five features, namely, one positive ionizable group, one hydrogen-bond acceptor group and three hydrophobic aromatic groups. The correlation coefficient, root mean square deviation and cost difference were 0.973, 0.695, and 94.291 respectively, suggesting that a highly predictive pharmacophore model was successfully obtained. The application of the model showed great success in predicting the activities of 251 known uPA inhibitors (test set) with a correlation coefficient of 0.837, and there was also none of the outcome hypotheses that had similar cost difference and RMS deviation (RMSD) with that of the initial hypothesis generated by Cat-Scramble validation test with 95% confidence level. Accordingly, our model should be reliable in identifying structurally diverse compounds with desired biological activity.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.27 no.11
• /
• pp.596-602
• /
• 2015

In this study, experiments using bioelectronic signals and questionnaire surveys were carried out in learning conditions when temperatures changed from low- and high-uncomfortable to comfortable. As a result, the stress factor Photoplethysmography (PPG) decreased, while the Root Mean Square of Standard Deviation (RMSSD) of PPG increased when the indoor temperature was changed from low- or high-uncomfortable to comfortable. Additionally, the absolute power of the ${\alpha}$-wave in the brain increased. According to the analysis of the association between the questionnaire and bioelectronic signals, the standard deviation of the stress factor as measured by pulse was closely related to the result of the thermal sensation questionnaire. In addition, it was found that the concentration on studying improved under comfortable temperatures when compared to uncomfortable temperatures.

• Nuclear Engineering and Technology
• /
• v.52 no.8
• /
• pp.1771-1776
• /
• 2020

Adverse effects in the measured gamma spectrum caused by radioactive statistical fluctuations, gamma ray scattering, and electronic noise can be reduced by energy spectrum denoising. Wavelet threshold denoising can be used to perform multi-scale and multi-resolution analysis on noisy signals with small root mean square errors and high signal-to-noise ratios. However, in traditional wavelet threshold denoising methods, there are signal oscillations in hard threshold denoising and constant deviations in soft threshold denoising. An improved wavelet threshold calculation method and threshold processing function are proposed in this paper. The improved threshold calculation method takes into account the influence of the number of wavelet decomposition layers and reduces the deviation caused by the inaccuracy of the threshold. The improved threshold processing function can be continuously guided, which solves the discontinuity of the traditional hard threshold function, avoids the constant deviation caused by the traditional soft threshold method. The examples show that the proposed method can accurately denoise and preserves the characteristic signals well in the gamma energy spectrum.

• Tunnel and Underground Space
• /
• v.12 no.4
• /
• pp.268-276
• /
• 2002

In this study, a 3D coordinate measurement system equipped with a laser displacement meter for digitizing rock joint surface was established and the digitized data were used to calculate several roughness parameters. The parameters used in this study were micro avenge inclination $angle(i_{ave})$, average slope of joint $asperity(SL_{ ave})$, root mean square of $i-angle(i_{rms})$, standard deviation of height(SDH), standard deviation of $i-angle(SD_i)$, roughness profile $index(R_P)$, and fractal dimension(D). The relationships between the roughness parameters based on the digitzation of the surface profile were analyzed. Since the measured value varied according to the degree of reflection and the variation of colors at the measuring point, rock joint surface was painted in white to minimize the influence of the surface conditions. The comparison of the measured values and roughness parameters before and after painting revealed the better consequence from measurement on the painted surfaces. Also, effect of measuring interval was studied. As measured interval was increased, roughness parameters were exponentially decreased. The incremental sequence of degree of decrease was $SDH\; i_{ave},\; i_{rms},\; SD_i,\;and\; R_ p-1$. As a result of comparison of parameters from pin-type measurement system and laser type measurement system, all value of parameters were higher when laser-type measurement system was used, except SDH.

• Communications for Statistical Applications and Methods
• /
• v.18 no.3
• /
• pp.287-294
• /
• 2011

Peer assessment is an educational valuation system that involves studying with a colleague and granting value to the progress made by the colleague. Although this method has many merits, there is also a drawback pertaining to calculating the mean of the scores that were granted to the levels of contribution. However, this has been improved upon by a diversified study. However, the concept of the chi-square test and p-value used in the preceding study is not easy individuals engaged in the industrial engineering field or education when using peer assessment. This study uses simple statistics like standard deviation, in addition to, investigating the availability of a suggested method as well as examples of utility and application. This study can contribute to increase the convenience of users through the use of convenience analysis and with this method.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.20 no.3
• /
• pp.262-276
• /
• 2018

Models to predict Leaf Wetness Duration (LWD) were evaluated using the observed meteorological and dew data at the 11 citrus orchards in Jeju, South Korea from 2016 to 2017. The sensitivity and the prediction accuracy were evaluated with four models (i.e., Number of Hours of Relative Humidity (NHRH), Classification And Regression Tree/Stepwise Linear Discriminant (CART/SLD), Penman-Monteith (PM), Deep-learning Neural Network (DNN)). The sensitivity of models was evaluated with rainfall and seasonal changes. When the data in rainy days were excluded from the whole data set, the LWD models had smaller average error (Root Mean Square Error (RMSE) about 1.5hours). The seasonal error of the DNN model had the similar magnitude (RMSE about 3 hours) among all seasons excluding winter. The other models had the greatest error in summer (RMSE about 9.6 hours) and the lowest error in winter (RMSE about 3.3 hours). These models were also evaluated by the statistical error analysis method and the regression analysis method of mean squared deviation. The DNN model had the best performance by statistical error whereas the CART/SLD model had the worst prediction accuracy. The Mean Square Deviation (MSD) is a method of analyzing the linearity of a model with three components: squared bias (SB), nonunity slope (NU), and lack of correlation (LC). Better model performance was determined by lower SB and LC and higher NU. The results of MSD analysis indicated that the DNN model would provide the best performance and followed by the PM, the NHRH and the CART/SLD in order. This result suggested that the machine learning model would be useful to improve the accuracy of agricultural information using meteorological data.

• Journal of the Earthquake Engineering Society of Korea
• /
• v.28 no.3
• /
• pp.121-128
• /
• 2024

There are growing concerns that the recently implemented Earthquake Early Warning service is overestimating the rapidly provided earthquake magnitudes (M). As a result, the predicted damages unnecessarily activate earthquake protection systems for critical facilities and lifeline infrastructures that are far away. This study is conducted to improve the estimation accuracy of M by incorporating the observed S-wave seismograms in the near source region after removing the site effects of the seismograms in real time by filtering in the time domain. The ensemble of horizontal S-wave spectra from at least five seismograms without site effects is calculated and normalized to a hypocentric target distance (21.54 km) by using the distance attenuation model of Q(f)=348f^0.52 and a cross-over distance of 50 km. The natural logarithmic mean of the S-wave ensemble spectra is then fitted to Brune's source spectrum to obtain the best estimates for M and stress drop (SD) with the fitting weight of 1/standard deviation. The proposed methodology was tested on the 18 recent inland earthquakes in South Korea, and the condition of at least five records for the near-source region is sufficiently fulfilled at an epicentral distance of 30 km. The natural logarithmic standard deviation of the observed S-wave spectra of the ensemble was calculated to be 0.53 using records near the source for 1~10 Hz, compared to 0.42 using whole records. The result shows that the root-mean-square error of M and ln(SD) is approximately 0.17 and 0.6, respectively. This accuracy can provide a confidence interval of 0.4~2.3 of Peak Ground Acceleration values in the distant range.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.12 no.2
• /
• pp.79-88
• /
• 2017

One of the important applications of activity sensing in the home is energy monitoring. Many previous methodologies for detecting and recognizing household appliances have been proposed. This paper presents an approach that uses an energy metering circuit (EMC) to classify and identify the various electrical devices in home based on root-mean-square (RMS) consumed current value. EMC gathers the RMS current values created by appliance state transition (e.g., on to off) and apparatus operating process. In this paper, an identification algorithm is proposed to detect a change in current levels using the standard deviation of current signals and their average values. In addition, characteristic of the appliance is extracted concerning four feature parameters concerning the number of current levels, the minimum level, the maximum level, and signal-to-noise ratio (SNR) of them. Experiment results validate the reliable performance of the proposed identification method for 11 representative appliances.

• Journal of Korean Academy of Nursing Administration
• /
• v.12 no.1
• /
• pp.23-31
• /
• 2006

Purpose: The purpose of this study was to test the empowerment structural model based on Kanter's work empowerment theory. Method: A predictive, nonexperimental design was used in a sample of 279 nurses from 3 university affiliated hospitals. Data were collected with self-administered questionnaires and analyzed using mean, standard deviation, pearson correlation coefficient and path analysis. Results: The results showed that the overall fitness of the hypothethical model to the data was good(chi-square=.7751, df=4, p=.942, GFI=.999, AGFI=.996, RMSEA=.000). Both formal power and informal power directly influenced on nurses' perceived empowerment level and empowerment directly influenced on nurses' organizational commitment and indirectly influenced on nurses' intent to stay. Conclusion: The results imply that hospital and nurse managers should provide the empowering working condition for nurses to be stayed in hospitals.