• Resources Recycling
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• v.29 no.5
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• pp.64-72
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• 2020

When the multi-stage combustion process is applied to the cement kiln to reduce nitrogen oxide emissions in the cement industry, oxidation/reduction section that can increase combustion efficiency by reducing NO_x to NO and completely burning unburned materials is essential In this study, when applied the oxidation/reduction system of the cement kiln preheater and calciner, the optimal oxidation/reduction calcination crisis that can secure the quality stability of the final product, cement clinker, was to be observed macroscopically, and the mass change of raw materials according to the burning conditions, decarbonation rate, and calcination rate were investigated. The results showed that the thermal decomposition of raw materials tends to be promoted in the oxidation condition rather than in the reduction condition, and that the thermal decomposition of limestone, which has a relatively high CaO content, is carried out later than that of cement raw meal, which is thought to be caused by the CO₂ fractionation in the kiln. The thermal decomposition properties of raw materials according to oxidation/reducing burning condition showed a relatively large difference in temperature range lower than normal limestone themal decomposition temperature, which is thought to be expected to improve the thermal efficiency of raw materials according to the formation of oxidation condition in the section 750℃ of burning temperature. However, for this study, lab scale. Because there is a difference from the field process as a scale study, it is deemed necessary to verify the actual test results of the pilot scale.

• Tribology and Lubricants
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• v.24 no.6
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• pp.320-325
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• 2008

The recent industrial application requires technical methods to get the cutting fluid droplet surfaces in particular from the viewpoint of topography and micro texture. To characterize the surface topography of droplet, the combination of the confocal laser scanning microscope (CLSM) and wavelet filtering is well suited for obtaining the droplet geometry encountered in tribological research. This technique indicates a better agreement in obtaining an appropriate droplet surface obtained by the CLSM over a detail range of surface accuracy (resolution: $2{\mu}m$). And the results allow an excellent accuracy in a measurement of a droplet surface. The combination of extended focal depth measurement configured and multi-scale wavelet filtering has proven that it can construct a droplet surface in a successive and accurate way. A multi-scale approach of wavelet filtering was developed based on the decomposition and reconstruction of droplet surface by 2D wavelet transform using db9 (a mother wavelet of daubechies). Also this technique can be extended to characterize the quantification of droplet properties and other field in a wide range of scales. Finally this method is verified to be a better droplet surface modeling in a micro scale arising in a mist machining.

• Atmosphere
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• v.21 no.2
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• pp.143-149
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• 2011

A new multi-timescale analysis method, Ensemble Empirical Mode Decomposition (EEMD), is used to diagnose the variation of the MJO activity determined by 850hPa and 200hPa zonal winds from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis data for the 56-yr period from 1950 to 2005. The results show that MJO activity can be decomposed into 9 quasi-periodic oscillations and a trend. With each level of contribution of the quasi-periodic oscillation discussed, the bi-seasonal oscillation, the interannual oscillation and the trend of the MJO activity are the most prominent features. The trend increases almost linearly, so that prior to around 1978 the activity of the MJO is lower than that during the latter part. This may be related to the tropical sea surface temperature(SST). It is speculated that the interdecadal change in the MJO activity appeared in around 1978 is related to the warmer SST in the equatorial warm pool, especially over the Indian Ocean.

• Atmosphere
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• v.24 no.3
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• pp.317-329
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• 2014

Eddy covariance data have been analyzed to investigate the influence of surface heterogeneity on turbulent transfer over farmland and industrial sites near Nakdong river, Korea, where both large and small scale heterogeneities co-exist. For this purpose, basic turbulent statistics, quadrant analysis and multi-resolution decomposition have been analyzed during the daytime. Basic turbulent statistics were compared with typical turbulent statistics in the surface layer. Such comparisons were in close agreement for momentum and heat at both sites but not for water vapor at industrial site. The correlation coefficient between water vapor and vertical velocity ($r_{wq}$) is relatively low and skewness of water vapor ($sk_q$) is very low at industrial site, possibly due to limited water source. For heat at both sites and water vapor at farmland, the quadrant analysis show similar behavior to that over homogeneous site but for water vapor at industrial site, the presence of river and limited water source at industrial site seems to influence on water vapor transfer by coherent eddy motion by increasing sweep contribution and decreasing ejection contribution. Multi-resolution decomposition analysis shows that large scale heterogeneity leads to low $r_{Tq}$ at large averaging time regardless of season at both sites and there are seasonal changes of $r_{Tq}$ in mid-averaging times at industrial site, possibly due to seasonal change of trees and grasses near the site.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1068-1081
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• 2019

Signal complexity is one point of view to analyze the biological signal. It arises as a result of the physiological signal produced by biological systems. Signal complexity can be used as a method in extracting the feature for a biological signal to differentiate a pathological signal from a normal signal. In this research, Hjorth descriptors, one of the signal complexity measurement techniques, were measured on signal sub-band as the features for lung sounds classification. Lung sound signal was decomposed using two wavelet analyses: discrete wavelet transform (DWT) and wavelet packet decomposition (WPD). Meanwhile, multi-layer perceptron and N-fold cross-validation were used in the classification stage. Using DWT, the highest accuracy was obtained at 97.98%, while using WPD, the highest one was found at 98.99%. This result was found better than the multi-scale Hjorth descriptor as in previous studies.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.1465-1469
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• 2006

수문순환 과정은 기상현상과 밀접한 관련을 가지고 서로 연관되어 있다. 이러한 연관성을 규명하여 수자원관리에 위험도를 감소시키려는 노력은 많은 분야에서 이루어지고 있으며, 주요 연구 주제가 되고 있다. 이러한 기상현상 중에서 가뭄은 여러 가지 요소가 복합되어 발생되는 것으로 알려지고 있으나 이를 설명하기에는 여전히 부족한 면이 존재한다. 가뭄을 발생시키는 몇 가지 가능한 원인으로는 E1 Nino-Southern Oscillation(ENSO)현상으로 잘 알려져 있는 비정상적인 해수면 온도의 변화나 기후 시스템의 비선형적 거동을 들 수 있다. 특히, 기후 시스템은 대개 경년 변화(inter-annual variability) 및 10년 이상의 주기(decadal variability) 특성을 가지고 있으며 가뭄 또한 경년변화의 주기 특성을 나타내고 있는 것으로 알려지고 있다. 이러한 관점에서 수문시계열을 특정 주파수(frequency)에서 고립시킨 후, 분석이 가능한 분해방법(decomposition method)을 통해 보다 해석적으로 접근하는 것이 가능하다. 이를 위해 본 연구에서는 Wavelet Transform분석을 도입하였으며 통계적으로 유의한 성분을 시계열로부터 추출하여 가뭄과 기상인자와의 변동성 분석을 실시하였다.

• Journal of Sensor Science and Technology
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• v.33 no.5
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• pp.298-304
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• 2024

In Korea, fires are the second most common type of disaster, causing large-scale damages. The installation of fire detectors is legislated to prevent fires and minimize damage. Conventional fire detectors have limitations in initial suppression of failures because they detect fires when large amounts of smoke and heat are generated. Additionally, frequent malfunctions in fire detectors may cause users to turn them off. To address these issues, recent studies focus on accurately detecting even small-scale fires using multi-sensor and deep-learning technologies. They also aim at quick fire detection and thermal decomposition using gas. However, these studies are not practical because they overlook the heavy computations involved. Therefore, we propose a fast and accurate fire detection system based on multi-sensor and deep-learning technologies. In addition, we propose a computation-reduction method for selecting sensors suitable for detection using the Pearson correlation coefficient. Specifically, we use a moving average to handle outliers and two-stage labeling to reduce false detections during preprocessing. Subsequently, a deep-learning model is selected as LSTM for analyzing the temporal sequence. Then, we analyze the data using a correlation analysis. Consequently, the model using a small data group with low correlation achieves an accuracy of 99.88% and a false detection rate of 0.12%.

• Journal of Korea Multimedia Society
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• v.22 no.10
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• pp.1160-1167
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• 2019

Because of the dynamic range limitation of digital equipment, it is impossible to obtain dark and bright areas at the same time with one shot. In order to solve this problem, an exposure fusion technique for fusing a plurality of images photographed at different exposure amounts into one is being studied. Among them, Laplacian pyramid decomposition based fusion method can generate natural HDR image by fusing images of various scales. But this requires a lot of computation time. Therefore, in this paper, we propose an approximation technique that achieves similar performance and greatly shortens computation time. The concept of vanishing point image for approximation is introduced, and the validity of the proposed approach is verified by comparing the computation time with the resultant image.

• Wind and Structures
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• v.37 no.6
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• pp.445-460
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• 2023

Accurate estimation of modal parameters (i.e., natural frequency, damping ratio) of tall buildings is of great importance to their structural design, structural health monitoring, vibration control, and state assessment. Based on the combination of variational mode decomposition, smoothed discrete energy separation algorithm-1, and Half-cycle energy operator (VMD-SH), this paper presents a method for structural modal parameter estimation. The variational mode decomposition is proved to be effective and reliable for decomposing the mixed-signal with low frequencies and damping ratios, and the validity of both smoothed discrete energy separation algorithm-1 and Half-cycle energy operator in the modal identification of a single modal system is verified. By incorporating these techniques, the VMD-SH method is able to accurately identify and extract the various modes present in a signal, providing improved insights into its underlying structure and behavior. Subsequently, a numerical study of a four-story frame structure is conducted using the Newmark-β method, and it is found that the relative errors of natural frequency and damping ratio estimated by the presented method are much smaller than those by traditional methods, validating the effectiveness and accuracy of the combined method for the modal identification of the multi-modal system. Furthermore, the presented method is employed to estimate modal parameters of a full-scale tall building utilizing acceleration responses. The identified results verify the applicability and accuracy of the presented VMD-SH method in field measurements. The study demonstrates the effectiveness and robustness of the proposed VMD-SH method in accurately estimating modal parameters of tall buildings from acceleration response data.

• Journal of Electrochemical Science and Technology
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• v.2 no.3
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• pp.157-162
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• 2011

We report a simple route for synthesizing multi-dimensional structured silicon anode materials from commercially available bulk silicon powders via metal-assisted chemical etching process. In the first step, silver catalyst was deposited onto the surface of bulk silicon via a galvanic displacement reaction. Next, the silver-decorated silicon particles were chemically etched in a mixture of hydrofluoric acid and hydrogen peroxide to make multi-dimensional silicon consisting of one-dimensional silicon nanowires and micro-scale silicon cores. As-synthesized silicon particles were coated with a carbon via thermal decomposition of acetylene gas. The carbon-coated multi-dimensional silicon anodes exhibited excellent electrochemical properties, including a high specific capacity (1800 mAh/g), a stable cycling retention (cycling retention of 89% after 20 cycles), and a high rate capability (71% at 3 C rate, compared to 0.1 C rate). This process is a simple and mass-productive (yield of 40-50%), thus opens up an effective route to make a high-performance silicon anode materials for lithiumion batteries.