• Structural Engineering and Mechanics
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• v.75 no.4
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• pp.425-434
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• 2020

Practical non-synoptic fluctuating wind often exhibits nonstationary features and should be modeled as nonstationary random processes. Generally, the coherence function of the fluctuating wind field has time-varying characteristics. Some studies have shown that there is a big difference between the fluctuating wind field of the coherent function model with and without time variability. Therefore, it is of significance to simulate nonstationary fluctuating wind field with time-varying coherent function. However, current studies on the numerical simulation of nonstationary fluctuating wind field with time-varying coherence are very limited, and the proposed approaches are usually based on the traditional spectral representation method with low simulation efficiency. Especially, for the simulation of multi-variable wind field of large span structures such as transmission tower-line, not only the simulation is inefficient but also the matrix decomposition may have singularity problem. In this paper, it is proposed to conduct the numerical simulation of nonstationary fluctuating wind field in one-spatial dimension with time-varying coherence based on the wavenumber-frequency spectrum. The simulated multivariable nonstationary wind field with time-varying coherence is transformed into one-dimensional nonstationary random waves in the simulated spatial domain, and the simulation by wavenumber frequency spectrum is derived. So, the proposed simulation method can avoid the complicated Cholesky decomposition. Then, the proper orthogonal decomposition is employed to decompose the time-space dependent evolutionary power spectral density and the Fourier transform of time-varying coherent function, simultaneously, so that the two-dimensional Fast Fourier transform can be applied to further improve the simulation efficiency. Finally, the proposed method is applied to simulate the longitudinal nonstationary fluctuating wind velocity field along the transmission line to illustrate its performances.

• Journal of KIISE:Databases
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• v.33 no.4
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• pp.396-408
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• 2006

Moving object databases manage a large amount of past location data which are accumulated as the time goes. To retrieve fast the past location of moving objects, we need index structures which consider features of moving objects. The KDB-tree has a good performance in processing range queries. Although we use the KDB-tree as an index structure for moving object databases, there has an over-split problem in the spatial domain since the feature of moving object databases is to increase the time domain. Because the over-split problem reduces spatial regions in the MBR of nodes inverse proportion to the number of splits, there has a problem that the cost for processing spatial-temporal range queries is increased. In this paper, we propose the dynamic split strategy of the KDB-tree to process efficiently the spatial-temporal range queries. The dynamic split strategy uses the space priority splitting method for choosing the split domain, the recent time splitting policy for splitting a point page to maximize the space utilization, and the last division policy for splitting a region page. We compare the performance of proposed dynamic split strategy with the 3DR-tree, the MV3R-tree, and the KDB-tree. In our performance study for range queries, the number of node access in the MKDB-tree is average 30% less than compared index structures.

• Journal of Power Electronics
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• v.16 no.4
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• pp.1526-1540
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• 2016

This work mainly discusses an accurate and fast islanding detection based on fractional wavelet packet transform (FRWPT)for multibus microgrid systems. The proposed protection scheme uses combined desirable features retrieved from discrete fractional Fourier transform (FRFT) and wavelet packet transform (WPT) techniques, which provides precise time-frequency information on minute perturbation signals introduced in the system. Moreover, this study focuses on the design of decoupling control with a distributed controller based on state feedback for the efficient operation of microgrid systems that are transitioning from the grid-connected mode to the islanded mode. An IEEE 9-bus test system with inverter based distributed generation (DG) units is considered for islanding assessment and smooth operation. Finally, tracking errors are greatly reduced with stability improvement based on the proposed controller. FRWPT based islanding detection is demonstrated via a time domain simulation of the system. Simulated results show an improvement in system stability with the application of the proposed controller and accurate islanding detection based on the FRWPT technique in comparison with the results obtained by applying the wavelet transform (WT) and WPT.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2277-2287
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• 2015

In recent times, multilevel inverters are given high priority in many large industrial drive applications. However, the reliability of multilevel inverters are mainly affected by the failure of power electronic switches. In this paper, open-switch and short-switch failure of multilevel inverters and its identification using a high performance diagnostic system is discussed. Experimental and simulation studies were carried out on five level cascaded H-Bridge multilevel inverter and its output voltage waveforms were analyzed at different switch fault cases and at different modulation index values. Salient frequency domain features of the output voltage signal were extracted using the discrete wavelet transform multi resolution signal decomposition technique. Real time application of the proposed fault diagnostic system was implemented through the LabVIEW software. Artificial neural network was trained offline using the Matlab software and the resultant network parameters were transferred to LabVIEW real time system. In the proposed system, it is possible to precisely identify the individual faulty switch (may be due to open-switch (or) short-switch failure) of multilevel inverters.

• Journal of Mechanical Science and Technology
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• v.20 no.8
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• pp.1139-1148
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• 2006

This paper addresses the analytical modeling and dynamic response of the advanced composite rotating blade modeled as thin-walled beams and incorporating viscoelastic material. The blade model incorporates non-classical features such as anisotropy, transverse shear, rotary inertia and includes the centrifugal and coriolis force fields. The dual technology including structural tailoring and passive damping technology is implemented in order to enhance the vibrational characteristics of the blade. Whereas structural tailoring methodology uses the directionality properties of advanced composite materials, the passive material technology exploits the damping capabilities of viscoelastic material (VEM) embedded into the host structure. The VEM layer damping treatment is modeled by using the Golla-Hughes-McTavish (GHM) method, which is employed to account for the frequency-dependent characteristics of the VEM. The case of VEM spread over the entire span of the structure is considered. The displayed numerical results provide a comprehensive picture of the synergistic implications of both techniques, namely, the tailoring and damping technology on the dynamic response of a rotating thin-walled b ε am exposed to external time-dependent excitations.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.3
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• pp.136-142
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• 2014

Distinct features in heart rate signals during the driver's wake and sleep states could provide an initiative for the development of a safe driving systems such as drowsiness detecting sensor in a smart wheel. We measured ECG from health subjects ($23.5{\pm}2.5$ in age) during the wake and drowsiness states. The proposed method is able to detect R waves and R-R interval calculation in the ECG even when the signal includes in abnormal signals. Heart rate variability(HRV) was investigated for the time domain and frequency domains. The STD HR(0.029), NN50(0.044) and VLF power(0.0018) of the RR interval series of the subjects were significantly different from those of the control group (p < 0.05). In conclusion, there are changes in heart rate from wake to drowsiness that are potentially to be detected. The results in our study could be useful for the development of drowsiness detection sensors for effective real-time monitoring.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.556-564
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• 2021

Numerous spatial and transform-domain-based conventional denoising algorithms struggle to keep critical and minute structural features of the image, especially at high noise levels. Although neural network approaches are effective, they are not always reliable since they demand a large quantity of training data, are computationally complicated, and take a long time to construct the model. A new framework of enhanced hybrid filtering is developed for denoising color images tainted by additive white Gaussian Noise with the goal of reducing algorithmic complexity and improving performance. In the first stage of the proposed approach, the noisy image is refined using a high-dimensional non-local means filter based on Principal Component Analysis, followed by the extraction of the method noise. The wavelet transform and SURE Shrink techniques are used to further culture this method noise. The final denoised image is created by combining the results of these two steps. Experiments were carried out on a set of standard color images corrupted by Gaussian noise with multiple standard deviations. Comparative analysis of empirical outcome indicates that the proposed method outperforms leading-edge denoising strategies in terms of consistency and performance while maintaining the visual quality. This algorithm ensures homogeneous noise reduction, which is almost independent of noise variations. The power of both the spatial and transform domains is harnessed in this multi realm consolidation technique. Rather than processing individual colors, it works directly on the multispectral image. Uses minimal resources and produces superior quality output in the optimal execution time.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.25-33
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• 2021

With the development of machine learning techniques, various types of data such as vibration, temperature, and flow rate can be used to detect and diagnose abnormalities in machine conditions. In particular, in the field of the state monitoring of rotating machines, the fault diagnosis of machines using vibration data has long been carried out, and the methods are also very diverse. In this study, an experiment was conducted to collect vibration data from normal and abnormal compressors by installing accelerometers directly on rotary compressors used in household air conditioners. Data segmentation was performed to solve the data shortage problem, and the main features for the fault classification model were extracted through the chi-square test after statistical and physical features were extracted from the vibration data in the time domain. The support vector machine (SVM) model was developed to classify the normal or abnormal conditions of compressors and improve the classification accuracy through the hyperparameter optimization of the SVM.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.4
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• pp.181-188
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• 2006

The purpose of this study is to predict the defibrillation success of a ventricular Fibrillation ECG signal using time-frequency analysis. During CPR, coronary perfusion pressure and electrocardiogram were measured. Parameters extracted from time-frequency domain were served as predictor of resuscitation success. Time frequency distribution(TFD) of ECG signals was estimated from the smoothed pseudo Wigner-Ville distribution(SPWVD). Median frequency, peak frequency, 1/f slope, frequency band ratios$(2{\sim}4Hz,\;4{\sim}6Hz,\;6{\sim}8Hz,\;8{\sim}10Hz,\;10{\sim}12Hz,\;12{\sim}15Hz)$ were extracted from each TFD as function of time. Paired t-test was used to determine the differences in ROSC and non-ROSC groups. In the statistical results, we selected four significant parameters - median frequency, 1/f slope, $2{\sim}4Hz$ band ratio, $8{\sim}10Hz$ band ratio. We made an attempt to predict defibrillation success by combining features extracted from time frequency distribution. Independent t-test was used to determine the differences ROSC and non-ROSC groups. Consequently, we selected four significant parameters-median frequency, 1/f slope, $2{\sim}4Hz$ band ratio, $8{\sim}10Hz$ band ratio. The relationship between coronary perfusion pressure and ECG parameters was analyzed with linear regression analysis. R-square value was 55%. 1/f slope and $8{\sim}10Hz$ band ratio had the significant relationship with coronary perfusion pressure.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.28 no.1
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• pp.27-37
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• 2010

This study tries to illuminate the features and values of the Buddhist temple Palkyung by closely examining the forms, structures, and meanings of Tongdo-palkyung(通度八景) handed down at Tongdosa Temple, the best among Korea's Buddhist temples with its three treasures of Buddha, law of Buddha and Buddhist monks. The findings of this study can be summarized as the following. First of all, it reveals the meaning of the geographical name Yeongchuksan(靈鷲山), located to the west of Tongdosa, and a spectacular sight spread like an eagle's spread wings, as well as its location and spatial features. In particular, the arrangement features of a number of attached hermitages clearly show Yeongchuksan's world as being a temple with buddhist treasures. The multi-layered unfolding and centripetal intention of the scenery can be perceived through the shape of the Sshangryongnongju(雙龍弄珠形), around Tongdosa and the feature of the enclosed landscape encircling the steps of Hyeolcheo(穴處) Geumganggyedan. The substances and components of Tongdopalkyung include sound-based spectacles derived from Beoneumgu(梵音具) creating sounds related to religious rituals to enlighten and redeem mankind, such as Yeongji(影池: a holy pond with shadow reflections), drum sounds, and bell sounds along with physical features like pine trees, Dae(臺), waterfalls, Dongcheon (洞天), and a glow in the sky. On the other hand, Palkyung's geographical arrangements exhibit a circular spatial formation based on the main motif as Buddhist symbolism, beginning with the 'Gukjangsangseokpyo(國長生石標)' awakening the territoriality of Tongdosa and locating the first scene 'Mupunghansong(舞風寒松)' in its introductory area, with the features of water, bridge, pine grove, and Iljumun(gate) to stand for the influx. Six other scenes including 'Anyangdongdae(安養東臺)' are placed in the sacred precincts around Daeungjeon and Geumganggyedan while the glow of sunset at 'Danjoseong' just outside the domain closes the symbolic circular formation of the Tongdopalkyung, which coincides with the development of the Mandala figure symbolizing 'Gusanpalhae(九山八海)' centered in Sumisan(須彌山). What is more, Tongdopalkyung, while excluding primary scenic elements inside the temple, maximizes the domain of the mountain's entrance and the effects of the multi-layered mountain, mountain upon mountain, by intensifying the influx and centripetal qualities. The Tongdopalkyung analysis reveals the antithesis of four-coupled scenes conveying buddhist principles and thoughts on the basis of seasons, directions, space and time to display a narrative structural landscape when viewed from the temple's territoriality. Likewise, the characteristics and porch structures of Tongdopalkyung are tools and language of symbols to both externally strengthen the temple's territoriality and to internally, maximize the desires to the Land of Happiness as well as intensify religious wishes and the Mandala's multi-layered qualities through the meanings of time and space.