• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4A
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• pp.507-515
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• 2008

In this study, a method predicting the displacement response of structures from the measured dynamic strain signal is proposed by using mode decomposition technique. Evaluation of bridge stability is normally focused on the bridge completed. However, dynamic loadings including wind and seismic loadings could be exerted to the bridge under construction. In order to examine the bridge stability against these dynamic loadings, the prediction of displacement response is very important to evaluate bridge stability. Because it may be not easy for the displacement response to be acquired directly on site, an indirect method to predict the displacement response is needed. Thus, as an alternative for predicting the displacement response indirectly, the conversion of the measured strain signal into the displacement response is suggested, while the measured strain signal can be obtained using fiber optic Bragg-grating (FBG) sensors. As previous studies on the prediction of displacement response by using the FBG sensors, the static displacement has been mainly predicted. For predicting the dynamic displacement, it has been known that the measured strain signal includes higher modes and then the predicted dynamic displacement can be inherently contaminated by broad-band noises. To overcome such problem, a mode decomposition technique was used. Mode decomposition technique estimates the displacement response of each mode with mode shape estimated to use POD from strain signal and with the measured strain signal decomposed into mode by EMD. This is a method estimating the total displacement response combined with the each displacement response about the major mode of the structure. In order to examine the mode decomposition technique suggested in this study model experiment was performed.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2009.04a
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• pp.205-208
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• 2009

POD(proper orthogonal decomposition)는 가해지는 하중(입력)의 계측없이 출력(응답)만으로 구조물의 동적특성을 파악할 수 있는 기법이다. 하지만 실제의 경우 측정데이터에 노이즈가 포함되어 있으면 분해가 완전하게 일어나지 않아 동적특성(특히 감쇠비)을 완벽히 파악하기 힘들다. 본 연구에서는 이러한 문제점을 보완하기 위해서 POD기법으로 추출된 각 모드의 자유진동파형에 RD(random decrement)법을 적용하여 노이즈에 의한 영향을 제거하는 방법을 제안하였다. 본 논문에서는 먼저 수치모델을 사용하여 계측노이즈가 있을 경우 제안된 방법을 사용하면 노이즈의 영향을 감소시킬 수 있음을 검증한 후 실험실 규모의 구조물모형에서 얻은 자유진동계측치에 제안된 기법을 적용하여 시스템식별을 수행하여 동특성을 파악하였다.

• Journal of the Korean Society of Propulsion Engineers
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• v.24 no.4
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• pp.25-32
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• 2020

A numerical study on combustion instabilities in a model combustor was conducted with various momentum flux ratios. Five ratios are calculated based on an actual operating condition of rocket engine. As momentum flux ratio increases, the spreading angle on the injector outlet decreases. And, as increase of axial momentum flux, pressure fluctuation decreases inside the combustor. By using dynamic mode decomposition method, the acoustic modes inside the combustor are identified. Combustion stabilities are analyzed by comparing the damping coefficient of the 2^nd longitudinal mode.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2017.05a
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• pp.59-60
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• 2017

Combustion instability of gas turbine is performed by adopting dynamic mode decomposition (DMD). The unstable frequencies are calculated and compared with FFT results. The damping coefficient derived from the DMD technique and FFT results were compared and analyzed. OH radical is measured by experimental work and fluctuation field is extracted and FTF was calculated at various points with DMD. The gains of FTF are changed depending on the extraction position of the heat release fluctuation field.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.2A
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• pp.97-108
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• 2012

Dynamic responses were measured using long-term monitoring system for Uldolmok tidal current pilot power plant which is one of jacket-type offshore structures. Among the dynamic quantities, the tilt angle was chosen because the low frequency response components can be precisely measured by dynamic tiltmeter, and the natural frequencies and modal damping ratio were successfully identified using proposed LS-FDD (least squared frequency domain decomposition) method. And the effects of tidal height and tidal current velocity on the variation of natural frequencies and modal damping ratios were investigated in time and frequency domain. Also the non-parametric models were tested to model the relationship between tidal conditions and modal properties such as natural frequencies and damping ratios.

• Journal of the Korea institute for structural maintenance and inspection
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• v.21 no.3
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• pp.60-68
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• 2017

Since the bridge is the main facility of the road that is the core of the civil infrastructure, the bridge is constructed to ensure stability and serviceability during the traffic use. In order to secure the safety of bridges, evaluating the integrity of bridges at present is an important task in the maintenance work of bridges. In general, to evaluate the load carrying capacity of bridges, it is possible to confirm the superimposed behavior and symmetric behavior of bridges by estimating the lateral load distribution factor of the bridges through vehicle loading tests. However, in order to measure the lateral load distribution factor of a commonly used bridge, a static loading test is performed. There is a difficulty in traffic control. Therefore, in this study, the static displacement component of the bridge measured in the dynamic loading test and the ambient vibration test was extracted by using empirical mode decomposition technique. The lateral load distribution was estimated using the extracted static displacement component and compared with the lateral load distribution factor measured in the static loading test.

• Aerospace Engineering and Technology
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• v.1 no.1
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• pp.1-7
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• 2002

This paper describes the analysis method about the dynamic characteristics and vibratory load through HDS(Helicopter Design Study). To analyze the dynamic characteristics of helicopter rotor blade, the natural frequencies and modes are calculated according to rotor operational speed(Ω). Generally the proximity of rotor natural frequency and N times of rotor operational speed is a dominant component to determine the helicopter vibration. Also we can predict the airframe vibration by calculating the airload of rotating blade exactly. We expect to establish the design procedure of rotor dynamics by describing the two major analysis methods necessary to rotor design.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.579-586
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• 2018

Short-term prediction of travel speed has been widely studied using data-driven non-parametric techniques. There is, however, a lack of research on the prediction aimed at urban areas due to their complex dynamics stemming from traffic signals and intersections. The purpose of this study is to develop a hybrid approach combining ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) for predicting urban travel speed. The EEMD decomposes the time-series data of travel speed into intrinsic mode functions (IMFs) and residue. The decomposed IMFs represent local characteristics of time-scale components and they are predicted using an ANN, respectively. The IMFs can be predicted more accurately than their original travel speed since they mitigate the complexity of the original data such as non-linearity, non-stationarity, and oscillation. The predicted IMFs are summed up to represent the predicted travel speed. To evaluate the proposed method, the travel speed data from the dedicated short range communication (DSRC) in Daegu City are used. Performance evaluations are conducted targeting on the links that are particularly hard to predict. The results show the developed model has the mean absolute error rate of 10.41% in the normal condition and 25.35% in the break down for the 15-min-ahead prediction, respectively, and it outperforms the simple ANN model. The developed model contributes to the provision of the reliable traffic information in urban transportation management systems.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.156-159
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• 2008

Proper orthogonal decomposition is a statistical pattern analysis technique for finding the dominant components, called the proper orthogonal modes, in ensembles of spatially distributed data. We present recent ideas based on proper orthogonal decomposition (POD) and detailed experiments that yield new perspectives into the microscale structures. The linearized modeling technique based on POD is very useful to show the principal characteristics of the complex dynamic responses.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.11
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• pp.369-382
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• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(quality of service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to activate only the minimum number of servers needed to handle current user requests. Previous studies on energy aware server cluster put efforts to reduce power consumption or heat dissipation, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management method to improve not only performance per watt but also QoS of the existing server power mode control method based on autonomous learning. Our proposed method is to adjust server power mode based on a hybrid approach of autonomous learning method with multi level thresholds and power consumption prediction method. Autonomous learning method with multi level thresholds is applied under normal load situation whereas power consumption prediction method is applied under abnormal load situation. The decision on whether current load is normal or abnormal depends on the ratio of the number of current user requests over the average number of user requests during recent past few minutes. Also, a dynamic shutdown method is additionally applied to shorten the time delay to make servers off. We performed experiments with a cluster of 16 servers using three different kinds of load patterns. The multi-threshold based learning method with prediction and dynamic shutdown shows the best result in terms of normalized QoS and performance per watt (valid responses). For banking load pattern, real load pattern, and virtual load pattern, the numbers of good response per watt in the proposed method increase by 1.66%, 2.9% and 3.84%, respectively, whereas QoS in the proposed method increase by 0.45%, 1.33% and 8.82%, respectively, compared to those in the existing autonomous learning method with single level threshold.