• Geophysics and Geophysical Exploration
• /
• v.6 no.1
• /
• pp.23-27
• /
• 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
• /
• v.28 no.1
• /
• pp.11-20
• /
• 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
• /
• v.18 no.2
• /
• pp.182-188
• /
• 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.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.49 no.4
• /
• pp.23-31
• /
• 2012

This paper proposes a motion estimation-based super resolution algorithm to restore input low-resolution images of large movement into a super-resolution image. It is difficult to find the sub-pixel motion estimation in images of large movement compared to typical experimental images. Also, it has disadvantage which have high computational complexity to find reference images and candidate images using general motion estimation method. In order to solve these problems for the traditional two-dimensional motion estimation using the proposed registration threshold that satisfy the conditions based on the reference image is determined. Candidate image with minimum weight among the best candidates for super resolution images, the restoration process to proceed with to find a new image registration algorithm is proposed. According to experimental results, the average PSNR of the proposed algorithm is 31.89dB and this is better than PSNR of traditional super-resolution algorithm and it also shows improvement of computational complexity.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.39 no.1
• /
• pp.25-30
• /
• 2016

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.52 no.1
• /
• pp.56-64
• /
• 2016

This study analyzed characteristics of officers' watch-keeping during fishing operation at the fisheries training ship KAYA (GT: 1,737 tons, Pukyong National University). It observed fishing works of three officers in wheel house of KAYA. The observations were carried out at the fishing ground 45 miles away from east of Jeju from 7 to 8 January 2010. The works and movements of the officers were recorded with three common video cameras and a 4-channel MPEG-4 Triplex DVR. Recorded data of the working circulation was analyzed by using the post-processing method. As a result of the traffic lines, the average (${\pm}S.D$) of working hour (min) and moving frequency (times), distance (m) and speed (m/min) during setting the net was 11.8 (0.9), 43.7 (8.1), 133.9 (35.8) and 10.5 (0.6), respectively. During trawling the net, it was 100, 241 (39.8), 615.7 (194.6) and 5.2 (1.6), respectively. During hauling the net, it was 17.6 (1.4), 41.0 (7.2), 196.9 (37.6) and 10.7 (0.8), respectively. In addition, it has a different tendency of the instrument usage frequency by the fishing works. During setting, the usage priority was CCTV, ECDIS, RPM and pitch controller, net monitor, GPS plotter, chart room, X-band radar, fish finder and public addressor. During trawling, it was CCTV, ECDIS, fish finder, X-band radar, net monitor, chart room, GPS plotter, RPM and pitch controller, auto pilot and steering, interphone, wind speed and direction indicator, No.1. VHF, navigation light control panel and public addressor. During hauling, it was CCTV, RPM and pitch controller, GPS plotter, public addressor, chart room, net monitor, X-band radar, auto pilot and steering and fish finder.

• Journal of Korea Water Resources Association
• /
• v.30 no.1
• /
• pp.45-54
• /
• 1997

The correct estimation of the daily or hourly urban water demand is required for the efficient management and operation of the water supply facilities. The prediction of water supply demand are regression model and time series method, the optimum ARIMA (Auto Regressive Integrated Moving Average) model was sought for the daily urban water demand estimation in this paper. The data used for this study were obtained from the city of Kwangju Korea. The raw data used in this study were rearranged 15, 30, 60, 90 days for the purpose of analysis. The statistical analysis was applied to the data to obtain the ARIMA model. As a result, the parameters determining the ARIMA model was obtained. The accuracy of the model was 2% of water supply. The developed model was found to be useful for the practical operation and management of the water supply facilities.

• Wind and Structures
• /
• v.31 no.3
• /
• pp.269-285
• /
• 2020

The statistical characteristics of typhoon wind speed records tend to have a considerable time-varying trend; thus, the stationary wind model may not be appropriate to estimate the wind characteristics of typhoon events. Several nonstationary wind speed models have been proposed by pioneers to characterize wind characteristics more accurately, but comparative studies on the applicability of the different wind models are still lacking. In this study, three landfall typhoons, Ampil, Jongdari, and Rumbia, recorded by ultrasonic anemometers atop the Shanghai World Financial Center (SWFC), are used for the comparative analysis of stationary and nonstationary wind characteristics. The time-varying mean is extracted with the discrete wavelet transform (DWT) method, and the time-varying standard deviation is calculated by the autoregressive moving average generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model. After extracting the time-varying trend, the longitudinal wind characteristics, e.g., the probability distribution, power spectral density (PSD), turbulence integral scale, turbulence intensity, gust factor, and peak factor, are comparatively analyzed based on the stationary wind speed model, time-varying mean wind speed model and time-varying standard deviation wind speed model. The comparative analysis of the different wind models emphasizes the significance of the nonstationary considerations in typhoon events. The time-varying standard deviation model can better identify the similarities among the different typhoons and appropriately describe the nonstationary wind characteristics of the typhoons.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.25 no.1A
• /
• pp.167-177
• /
• 2000

In this paper, a new algorithm to design a time-domain equalizer (TEQ) for an xDSL system employing the discrete multitone (DMT) modulation is proposed. The proposed algorithm, derived by neglecting the terms whichdo not affect the performance of a DMT system in ARMA modeling, is shown to have similar performance tothe previous TEQ algorithms such as matrix inverse algorithm, fast algorithm, iterative algorithm, and inversepower method, even with the significantly lower computational complexity. In addition, since the proposedalgorithm requires only the received signal, the information on the channel impulse response or training sequenceis not needed. It is also shown that for the case where bridged tap is not included, the number of TEQ tapsrequired can be reduced to half(from 16 to 8) without affecting the overall performance. The performances of theproposed and previous TEQ algorithms are compared by applying them to ADSL environment.

• Journal of Digital Contents Society
• /
• v.18 no.4
• /
• pp.747-751
• /
• 2017

The log has variable informations that are important and necessary to manage a network when accessed to network servers. These informations are used to reduce a cost and efficient manage a network through the meaningful prediction information extraction from the amount of user access. And, the network manager can instantly monitor the status of CPU, memory, disk usage ratio on network using the SNMP. In this paper, firstly, we have accumulated and analysed the 6 network logs and extracted the informations that used to predict the amount of user access. And then, we experimented the prediction simulation with the time series analysis such as moving average method and exponential smoothing. Secondly, we have simulated the usage ration of CPU, memory, and disk using Xian SNMP simulator and extracted the OID for the time series prediction of CPU, memory, and disk usage ration. And then, we presented the visual result of the variable experiments through the Excel and R programming language.