• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.3
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• pp.41-46
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• 2011

The tire damage classification method is researched by periodicity detection of ultrasonic envelope signals to occur at the driving vehicle tire. Because periodic signals is generated by rotations of the damaged tire, it should convert to pulse for using the density function. After time intervals of pulses are represented by the density function, the dominant periodicity is detected. The threshold to make a pulse is calculated by moving average of envelope signals. The result of time density function in case of one damage material, the first peak's time is equals to tire's rotation period, 162ms and 102ms, about the speed of 50km/h and 80km/h. In case of more than one damage material, the sum of each peak's time is equals to tire's rotation period about the speed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.2
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• pp.284-294
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• 2018

Statistical methods have been widely used to estimate the remaining battery runtime of mobile smart devices, such as smart phones, smart gears, tablets, and etc. However, existing work available in the literature only considers a particular statistical method. Thus, it is difficult to determine whether statistical methods are applicable to estimating thr remaining battery runtime of mobile devices or not. In this paper, we evaluated the performance of statistical methods applicable to estimating the remaining battery runtime of mobile smart devices. The statistical estimation methods evaluated in this paper are as follows: simple and moving average, linear regression, multivariate adaptive regression splines, auto regressive, polynomial curve fitting, and double and triple exponential smoothing methods. Research results presented in this paper give valuable data of insight to IT engineers who are willing to deploy statistical methods on estimating the remaining battery runtime of mobile smart devices.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.227-230
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• 2002

The Loose Part Monitoring System(LPMS) has been designed to detect, locate and evaluate detached or loosened parts and foreign objects in the reactor coolant system. In this paper, at first, we presents an application of the back propagation neural network. At the preprocessing step, the moving window average filter is adopted to reject the low frequency background noise components. And then, extracting the acoustic signature such as Starting point of impact signal, Rising time, Half period, and Global time, they are used as the inputs to neural network. Secondly, we applied the neural network algorithm to LPMS in order to estimate the mass of loose parts. We trained the impact test data of YGN3 using the backpropagation method. The input parameter for training is Rising Time, Half Period, Maximum amplitude. The result showed that the neural network would be applied to LPMS. Also, applying the neural network to the Practical false alarm data during startup and impact test signal at nuclear power Plant, the false alarms are reduced effectively. 1.

• Microbiology and Biotechnology Letters
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• v.21 no.4
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• pp.354-365
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• 1993

A novel control method with automatic tuning of PID controller parameters has been developed for efficient regulation of dissolved oxygen concentration in fed-batch fermentations of Escherichia coli. Agitation speed and oxygen partial pressure in the inlet gas stream were chosen to be the manipulated variables. A heuristic reasoning allowed improved tuning decisions from the supervision of control performance indices and it coule obviate the needs for process assumptions or disturbance patterns. The control input consisted of feedback and feedforword parts. The feedback part was determined by PID control and the feedforward part is determined from the feed rate. The proportional gain was updated on-line by a set of heuristics rules based on the supervision of three performance indices. These indices were output error covariance, the average value of output error, and input covariance, which were calculated on-line using a moving window. The integral and derivative time constants were determined from the period of output response. The specific growth rate was maintained at a low level to avoid acetic acid accumulation and thus to achieve a high cell density. The specific growthe rate was estimated from the carbon dioxide evolution rate. In fed-batch fermentation, the simutaneous control of dissolved oxygen concentration (at 0.2; fraction of saturated value) and specific growth rate (at 0.25$hr^{-1}$) was satisfactory for the entire culture period in spite of the changes in the feed rate and the switching of control input.

• Journal of the Korean Geotechnical Society
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• v.27 no.2
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• pp.63-72
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• 2011

The measured ground water behavior by TDR (time domain reflectometer) sensors were analyzed by the data filtering technique such as moving average method and Fourier transform, and the ground water level and unsaturated zone were tried to be determined numerically. At first, the variation of TDR data according to the saturation degree was measured by lab test, which is translated as a function of saturation degree. Then, changes of ground water level and lateral seepage in field conditions were simulated using acrylic pipe, and the measured data were analyzed to make calibration curve. Furthermore, TDR sensors were installed into the in-situ embankment to insure the field application. The saturation degree, unsaturated and dried zones were determined from the measured data.

• Journal of Korean Public Health Nursing
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• v.28 no.1
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• pp.114-126
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• 2014

Purpose: The purpose of this study was to determine the accuracy and satisfaction with IVIC300 (Intravenous infusion controller). Method: The subjects consisted of 90 nurses and 100 patients. Data collected during July 2013 were analyzed using descriptive statistics, t-test, ANOVA, and $X^2$-test (Chi-Square). Result: The error value of IVIC300 was less than that of Dosi-flow. Differences of error values according to variable of fluid infusion (needle's gauge, patient's moving, patient's calls, and nurse's arabitary calls) were not significant. The mean scores for satisfaction with IVIC300 of nurses ($3.67{\pm}.76$), patients ($3.75{\pm}.63$) were above average. Satisfaction with IVIC300 of nurses differed significantly according to age, work unit, and clinical career. Satisfaction with IVIC300 of patients was not significantly different according to general characteristics. Conclusion: These findings indicate that it is proven the accuracy of IVIC300, and is considered individual characteristics in use IVIC300.

• Journal of Korea Water Resources Association
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• v.42 no.10
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• pp.809-824
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• 2009

Hydrological system forecasting, which is the short term runoff historical data during the limited period in dam site, is a conditional precedent of hydrological persistence by stochastic analysis. We have forecasted the monthly hydrological system from Andong dam basin data that is the rainfall, evaporation, and runoff, using the seasonal ARIMA (autoregressive integrated moving average) model. Also we have conducted long term runoff simulations through the forecasted results of TANK model and ARIMA+TANK model. The results of analysis have been concurred to the observation data, and it has been considered for application to possibility on the stochastic model for dam inflow forecasting. Thus, the method presented in this study suggests a help to water resource mid- and long-term strategy establishment to application for runoff simulations through the forecasting variables of hydrological time series on the relatively short holding runoff data in an object basins.

• Geophysics and Geophysical Exploration
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• v.6 no.1
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• pp.23-27
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• 2003

A magnetic survey on the lake war carried out to explore the possible UXO (unexploded ordnance) under the water. A magnetic gradiometer with 2 magnetometer sensors was used, which measures total magnetic intensity. For the positioning of the measurement points on the water, RTK (real time kinematic) survey system was used. The theoretical responses were calculated assumming the dimension and the material of the UXO so that the detectability could be investigated. Since the areal size of the survey vessel was rather small, the influence from the magnetic material of the vessel and the other equipments such as a laptop computer was not negligible, and the influence did not remain constant during the survey due to the change of survey direction. These effects were reduced remarkably using moving average technique. The result reveals the lineament of a pipe line laid on the bottom of the lake, which can be regarded as an indirect proof of detectability of the method.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.11-20
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• 2017

In this paper, we introduced linear spatial time series (space-time autoregressive and moving average model) and nonlinear spatial time series (space-time bilinear model). Also we estimated the parameters by Kalman Filter method and made comparative studies of power of forecast in the final model. We proposed several weight matrices such as equal proportion allocation, reciprocal proportion between distances, and proportion of population sizes. For applications, we collected Mumps data at Korea Center for Disease Control and Prevention from January 2001 until August 2008. We compared three approaches of weight matrices using the Mumps data. Finally, we also decided the most effective model based on sum of square forecast error.

• Journal of Gastric Cancer
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• v.18 no.2
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• pp.182-188
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• 2018

Purpose: Despite the fact that there are several reports of single-port laparoscopic distal gastrectomy (SPDG), no analysis of its learning curve has been described in the literature. The aim of this study was to investigate the favorable factors for SPDG and to analyze the learning curve of SPDG. Materials and Methods: A total of 125 cases of SPDG performed from November 2011 to December 2015 were enrolled. All operations were performed by 2 surgeons (surgeon A and surgeon B). The moving average method was used for defining the learning curve. All cases were divided into 10 cases in a sequence, and the mean operative time and estimated blood loss data were extracted from each group. Results: Surgeon A performed 68 cases (female-to-male sex ratio, 91.1%:8.82%), and surgeon B performed 57 cases (female-to-male sex ratio, 61.4%:38.5%). The operative time of surgeon B significantly decreased after 30 cases ($157.8{\pm}38.4$ minutes vs. $118.1{\pm}34.5$ minutes, P=0.003); that of surgeon A did not significantly decrease before and after around 30 cases ($160.8{\pm}51.6$ minutes vs. $173.3{\pm}35.2$ minutes, P=0.6). The subgroup analysis showed that the operative time significantly decreased in the patients with body mass index (BMI) of <$25kg/m^2$ (<$25kg/m^2$:${\geq}25kg/m^2$, $159.3{\pm}41.7$ minutes: $194.25{\pm}81.1$ minutes; P=0.001). Conclusions: Although there was no significant decrease in the operative time for surgeon A, surgeon B reached the learning curve upon conducting 30 cases of SPDG. BMI of <$25kg/m^2$ was found to be a favorable factor for SPDG.