• Journal of Advanced Navigation Technology
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• v.17 no.5
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• pp.484-490
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• 2013

With the rapid development of wireless communication at VHF and UHF bands, there is an increasing need for the reliable propagation prediction tools. Therefore, the different propagation models that have been developed in many countries as well as korea has been trying to secure a model suitable for their geographical area but then it is giving us a different result when we compared it to measured values. In this paper, based on the measurements of DTV broadcasting services in domestic urban area, analysis and comparison of ITU-R P.1546 and BCAST models provide errors between measured and predicted values, and some points for improving SMI system has been proposed. As a result, P.1546 model provides the valid predicted data similar to measured data, but BCAST model has some problems of large deviation and higher prediction to measured data. In future, these problems and diffractions due to high buildings need to be studied further.

• Korean Journal of Remote Sensing
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• v.21 no.1
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• pp.73-81
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• 2005

We construct improved geocentric digital elevation model (DEM), estimate tidal dynamics and ice stream velocity over Sulzberger Ice Shelf, West Antarctica employing differential interferograms from 12 ERS tandem mission Synthetic Aperture Radar (SAR) images acquired in austral fall of 1996. Ice, Cloud, and land Elevation Satellite (ICESat) laser altimetry profiles acquired in the same season as the SAR scenes in 2004 are used as ground control points (GCPs) for Interferometric SAR (InSAR) DEM generation. 20 additional ICESat profiles acquired in 2003-2004 are then used to assess the accuracy of the DEM. The vertical accuracy of the OEM is estimated by comparing elevations with laser altimetry data from ICESat. The mean height difference between all ICESat data and DEM is -0.57m with a standard deviation of 5.88m. We demonstrate that ICESat elevations can be successfully used as GCPs to improve the accuracy of an InSAR derived DEM. In addition, the magnitude and the direction of tidal changes estimated from interferogram are compared with those predicted tidal differences from four ocean tide models. Tidal deformation measured in InSAR is -16.7cm and it agrees well within 3cm with predicted ones from tide models. Lastly, ice surface velocity is estimated by combining speckle matching technique and InSAR line-of-sight measurement. This study shows that the maximum speed and mean speed are 509 m/yr and 131 m/yr, respectively. Our results can be useful for the mass balance study in this area and sea level change.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.75-80
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• 2021

Positioning technology is performing important functions in augmented reality, smart factory, and autonomous driving. Among the positioning techniques, the positioning method using beacons has been considered a challenging task due to the deviation of the RSSI value. In this study, the position of a moving object is predicted by training a neural network that takes the RSSI value of the receiver as an input and the distance as the target value. To do this, the measured distance versus RSSI was collected. A neural network was introduced to create synthetic data from the collected actual data. Based on this neural network, the RSSI value versus distance was predicted. The real value of RSSI was obtained as a neural network for generating synthetic data, and based on this value, the coordinates of the object were estimated by learning a neural network that tracks the location of a terminal in a virtual environment.

• Korean Circulation Journal
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• v.53 no.7
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• pp.472-479
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• 2023

Background and Objectives: The association between bilirubin and atrial fibrillation (AF) has been evaluated previously in observational studies but with contradictory results. This study evaluated the causal association between serum bilirubin level and AF using Mendelian randomization (MR) analysis. Methods: This cross-sectional study includes 8,977 participants from the Dong-gu Study. In the observational analysis, multivariate logistic regression was performed to evaluate the association between bilirubin and prevalent AF. To evaluate the causal association between bilirubin and AF, MR analysis was conducted by using the UGT1A1 rs11891311 and rs4148323 polymorphisms as instrumental variables. Results: Elevated serum bilirubin levels were associated with an increased risk for AF in observational analysis (total bilirubin: odds ratio [OR], 1.31; 95% confidence interval [95% CI], 1.15-1.48 per 1 standard deviation [SD]; direct bilirubin: OR, 1.31; 95% CI, 1.18-1.46 per 1 SD), whereas the genetically predicted serum bilirubin levels in MR analysis did not show this association (total bilirubin: OR, 1.02; 95% CI, 0.67-1.53 per 1 SD; direct bilirubin: OR, 1.03; 95% CI, 0.61-1.73 per 1 SD). Conclusions: Genetically predicted bilirubin levels were not associated with prevalent AF. Thus, the observational association between serum bilirubin levels and AF may be noncausal and affected by reverse causality or unmeasured confounding.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.27 no.4
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• pp.293-302
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• 1991

The anomalous sea level deviation or storm surge caused by the typhoon Thelma in 1987 are studied analysing tidal observation data at 7 stations in the south coast of Korean peninsula. The surges are calculated by subtracting the predicted tidal height from the observed tidal record. The tidal deviation at these stations along the coast are discussed in association with meteorological data. The sea level anomalies are studied by means of the empirical orthogonal function (EOF) analysis and the fast fourier transform (FFT) method. The results of analysis suggest that the peak value of surges are higher at the tidal stations in semi-enclosed bay and in long narrow channel than at the ones facing with the open sea. From the result of EOF analysis, the temporal and spatial fluctuations of storm surge can be described by the first EOF mode, which explains 63% of the total variances during the passage of typhoon Thelma. The deviation of storm surge in the studied areas indicates bi-modal peak during the passage of typhoon Thelma. From the results of FFT spectrum analysis, the peak of energy of autospectrum for surge, atmospheric pressure, and wind stress appeared at low frequency fluctuations band of 0.008-0.076 cph over the 4 stations. Auto-correlation function of surge showed periodicity, while that of atmospheric pressure and wind stress indicates no periodicity. The result of FFT analysis shows that the typhoon surges are related chiefly with the change of atmospheric pressure in an open bay (Cheju Harbor), but with the wind stress in a semi-enclosed bay (Yeosu Harbor).

• Korean Circulation Journal
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• v.48 no.11
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• pp.1002-1011
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• 2018

Background and Objectives: The prognostic impact of left axis deviation (LAD) on clinical outcomes in acute heart failure syndrome (AHFS) with left bundle branch block (LBBB) is unknown. The aim of this study was to determine the prognostic significance of axis deviation in acute heart failure patients with LBBB. Methods: Between March 2011 and February 2014, 292 consecutive AHFS patients with LBBB were recruited from 10 tertiary university hospitals. They were divided into groups with no LAD (n=189) or with LAD (n=103) groups according to QRS axis <-30 degree. The primary outcome was all-cause mortality. Results: The median follow-up duration was 24 months. On multivariate analysis, the rate of all-cause death did not significantly differ between the normal axis and LAD groups (39.7% vs. 46.6%, adjusted hazard ratio, 1.01; 95% confidence interval, 0.66, 1.53; p=0.97). However, on the multiple linear regression analysis to evaluate the predictors of the left ventricular ejection fraction (LVEF), presence of LAD significantly predicted a worse LVEF (adjusted beta, -3.25; 95% confidence interval, -5.82, -0.67; p=0.01). Right ventricle (RV) dilatation was defined as at least 2 of 3 electrocardiographic criteria (late R in lead aVR, low voltages in limb leads, and R/S ratio <1 in lead V5) and was more frequent in the LAD group than in the normal axis group (p<0.001). Conclusions: Among the AHFS with LBBB patients, LAD did not predict mortality, but it could be used as a significant predictor of worse LVEF and RV dilatation (Trial registry at KorAHF registry, ClinicalTrial.gov, NCT01389843).

• Wind and Structures
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• v.36 no.4
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• pp.237-247
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• 2023

The emergence of high-rise buildings has necessitated frequent structural health monitoring and maintenance for safety reasons. Wind causes damage and structural changes on tall structures; thus, safe structures should be designed. The pressure developed on tall buildings has been utilized in previous research studies to assess the impacts of wind on structures. The wind tunnel test is a primary research method commonly used to quantify the aerodynamic characteristics of high-rise buildings. Wind pressure is measured by placing pressure sensor taps at different locations on tall buildings, and the collected data are used for analysis. However, sensors may malfunction and produce erroneous data; these data losses make it difficult to analyze aerodynamic properties. Therefore, it is essential to generate missing data relative to the original data obtained from neighboring pressure sensor taps at various intervals. This study proposes a deep learning-based, deep convolutional generative adversarial network (DCGAN) to restore missing data associated with faulty pressure sensors installed on high-rise buildings. The performance of the proposed DCGAN is validated by using a standard imputation model known as the generative adversarial imputation network (GAIN). The average mean-square error (AMSE) and average R-squared (ARSE) are used as performance metrics. The calculated ARSE values by DCGAN on the building model's front, backside, left, and right sides are 0.970, 0.972, 0.984 and 0.978, respectively. The AMSE produced by DCGAN on four sides of the building model is 0.008, 0.010, 0.015 and 0.014. The average standard deviation of the actual measures of the pressure sensors on four sides of the model were 0.1738, 0.1758, 0.2234 and 0.2278. The average standard deviation of the pressure values generated by the proposed DCGAN imputation model was closer to that of the measured actual with values of 0.1736,0.1746,0.2191, and 0.2239 on four sides, respectively. In comparison, the standard deviation of the values predicted by GAIN are 0.1726,0.1735,0.2161, and 0.2209, which is far from actual values. The results demonstrate that DCGAN model fits better for data imputation than the GAIN model with improved accuracy and fewer error rates. Additionally, the DCGAN is utilized to estimate the wind pressure in regions of buildings where no pressure sensor taps are available; the model yielded greater prediction accuracy than GAIN.

• Fibers and Polymers
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• v.7 no.4
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• pp.404-413
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• 2006

This study examines multiple quality optimization of the injection molding for Polyether Ether Ketone (PEEK). It also looks into the dimensional deviation and strength of screws that are reduced and improved for the molding quality, respectively. This study applies the Taguchi method to cut down on the number of experiments and combines grey relational analysis to determine the optimal processing parameters for multiple quality characteristics. The quality characteristics of this experiment are the screws' outer diameter, tensile strength and twisting strength. First, one should determine the processing parameters that may affect the injection molding with the $L_{18}(2^1{\times}3^7)$ orthogonal, including mold temperature, pre-plasticity amount, injection pressure, injection speed, screw speed, packing pressure, packing time and cooling time. Then, the grey relational analysis, whose response table and response graph indicate the optimum processing parameters for multiple quality characteristics, is applied to resolve this drawback. The Taguchi method only takes a single quality characteristic into consideration. Finally, a processing parameter prediction system is established by using the back-propagation neural network. The percentage errors all fall within 2%, between the predicted values and the target values. This reveals that the prediction system established in this study produces excellent results.

• Clean Technology
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• v.7 no.4
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• pp.243-250
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• 2001

The fuel cell has been continuously studied as environment-compatible alternative energy technology. Lately the basic techniques about stacking and widening are considered to be important for practical use. Although phosphoric acid fuel cell (PAFC) is the most progressed one in the fuel cell technologies, few studies about temperature profile of the stack which can be the basic data for the fuel cell design have been reported yet. In this study, the temperature profile of PAFC stack was simulated. The temperature profiles of stack were obtained at various operating conditions, and when stack is operated the proper position to measure the temperature could be predicted. Also we can propose more effective cooling design. The standard deviation of the temperature profile of the proposed design was is about 50% smaller.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.10
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• pp.497-503
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• 2017

In recent years, studies on the prediction of building load using Artificial Neural Network (ANN) models have been actively conducted in the field of building energy In general, building loads predicted by ANN models show a sharp deviation unless large data sets are used for learning. On the other hands, some of the input data are hard to be acquired by common measuring devices. In this work, we estimate daily building loads with a limited number of input data and fewer pastdatasets (3 to 10 days). The proposed model with fewer input data gave satisfactory results as regards to the ASHRAE Guide Line showing 21% in CVRMSE and -3.23% in MBE. However, the level of accuracy cannot be enhanced since data used for learning are insufficient and the typical ANN models cannot account for thermal capacity effects of the building. An attempt proposed in this work is that learning procersses are sequenced frequrently and past data are accumulated for performance improvement. As a result, the model met the guidelines provided by ASHRAE, DOE, and IPMVP with by 17%, -1.4% in CVRMSE and MBE, respectively.