• 대한원격탐사학회지
• /
• 제21권1호
• /
• pp.73-81
• /
• 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.

• 한국정보통신학회논문지
• /
• 제25권1호
• /
• pp.75-80
• /
• 2021

측위 기술은 증강현실, 스마트 팩토리, 자율주행 등에서 중요한 기능을 수행하고 있다. 측위 기술 중에서 비콘을 이용한 측위 방법은 RSSI(Receiver Signal Strength Indicator) 값의 편차로 인하여 도전적인 과제로 여겨져 왔다. 본 논문에서는 수신기의 RSSI 값을 입력으로 하고 거리를 목표 값으로 하는 신경망을 학습시켜서 이동하는 객체에 대한 위치를 예측하였다. 이를 수행하기 위해 RSSI 대비 거리 실측값을 수집하였다. 수집한 데이터로 합성 데이터를 만들기 위한 신경망을 도입하였다. 이 신경망을 바탕으로 거리 대비 RSSI 값을 예측하였다. 합성 데이터를 바탕으로 가상으로 좌표계를 구성하여 객체의 위치를 예측하였다. 합성 데이터를 생성하기 위한 신경망으로 RSSI의 표준편차는 구하였고 이 값을 기반으로 가상환경에서 단말의 위치를 추적하는 신경망을 학습시켜 객체의 좌표를 추정하였다.

• 수산해양기술연구
• /
• 제27권4호
• /
• pp.293-302
• /
• 1991

태풍해일의 변동양상과 특성을 파악하기 위하여, 한국 남해안의 7개 검조소의 조석관측자료와 기상자료를 사용하여 태풍 Thelma 통과기간 중 각 항에서의 추산조위와 해일을 추정하여, 시간영역별 기상 및 해면변동에 관한 EOF분석을 하고 태풍해일, 기압, 바람응력의 spectrum분석을 실시하였다. 반폐쇄성 만(여수)인 경우 바람응력이 기압보다 해일발생에 큰 영향을 미치며, 개방된 항만(제주)은 기압이 해일에 큰 역할을 하는 것으로 나타났다. 해일의 값은 13.1~91.7cm 범위로서 여수에서 최고치를 보였다. 태풍 Thelma 통과시 EOF 제 1 모드는 전체 해일변동의 63%를 차지하였고, 제 1 모드에 수반된 해일의 시간변동은 18 시간 동안 쌍봉의 peak를 가진 변동을 보였다. 제 1 모드에 대한 남해안 7개 지점에서의 해일의 공간적 변동은 여수를 중심으로 동시에 해면이 상승하였다. 남해안 4개항(부산, 충무, 여수, 제주)의 해일, 기압 및 바람응력 spectrum의 peak 에너지는 0.008-0.076cph(약 3-10시간)의 저주파수대에 밀집되어 있고, 해일의 경우 여수와 제주에서 에너지 밀도가 크게 나타났다. 기압의 에너지 변동은 탁월하지 않았으며, 바람응력은 부산, 여수, 제주에서 에너지 밀도가 잘 나타났다. 또한, 세 변동성분의 자기상관은 해일의 경우 주기적 변동을 나타내었고, 기압과 바람응력은 모두 불규칙적인 상관을 보였다.

• Korean Circulation Journal
• /
• 제48권11호
• /
• pp.1002-1011
• /
• 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
• /
• 제36권4호
• /
• pp.237-247
• /
• 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
• /
• 제7권4호
• /
• pp.404-413
• /
• 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.

• 청정기술
• /
• 제7권4호
• /
• pp.243-250
• /
• 2001

연료전지는 환경 친화적 대체에너지로 지속적인 연구가 이루어지고 있다. 최근에는 연료전지의 실용화를 위해 적층, 대면적화에 대한 기본 기술이 중요시되고 있다. 그러나 연료전지중 가장 많은 기술적 발전을 이룬 인산형 연료전지에 관해서도 연료전지 설계의 기초자료가 되는 스택의 온도 분포에 대한 연구는 거의 발표되지 않았다. 본 연구에서는 인산형 연료전지 스택의 온도 분포를 전산모사하였다. 이를 통하여 여러 작동 조건에서 스택의 온도 분포를 알아내었으며, 스택 운전시 적절한 온도 측정 위치를 예측할 수 있었다. 또한 냉각단의 유로를 변경하여 전산모사를 수행한 결과 스택 내부의 온도 분포의 표준 편차를 약 50% 감소시키는 효과적인 냉각 디자인을 제안할 수 있었다.

• 설비공학논문집
• /
• 제29권10호
• /
• pp.497-503
• /
• 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.

• 대한기계학회논문집
• /
• 제17권5호
• /
• pp.1193-1201
• /
• 1993

본 연구에서는 통계학적 이론 및 희귀분석에 근거한 보정함수를 모델 수정에 도입하여 조업조건별로 예측오차 요인들을 제거함으로써 예측 모델의 정도를 향상시키 고자 하였다. 이를 위해 일반강에 비해 압연하중 모델의 예측정도가 상대적으로 낮은 극저탄소강을 대상으로 하여 압연조업에 따른 압연하중 예측모델의 오차요인을 조업인 자별로 분석하였고 적용시켜 모델의 적중도를 향상시켰다.

• International Journal of Fluid Machinery and Systems
• /
• 제2권1호
• /
• pp.80-91
• /
• 2009

In this study, a prediction theory for specific noise that is the overall characteristic of the fan has been proposed. This theory is based on total pressure prediction and broadband noise prediction. The specific noises of two forward curved fans with different number of blades were predicted. The flow around the impeller having 120 blades (MF120) was more biased at a certain positions than the impeller with 40 blades (MF40). An effective domain of the energy conversion of MF40 has extended overall than MF120. The total pressure was affected by the slip factor and pressure loss caused by the vortex flow. The suppression of a major pressure drop by the vortex flow and expansion of the effective domain for energy conversion contributed to an increase in the total pressure of MF40 at the design point. The position of maximum relative velocity was different for each fan. The relative velocity of MF120 was less than that of MF40 due to the deviation angle. The specific noise of MF120 was 2.7 dB less than that of MF40 due to the difference in internal flow. It has been quantitatively estimated that the deceleration in the relative velocity contributed to the improvement in the overall performance.