• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.955-958
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• 2005

In this study, the wind load characteristics for a transmission tower is investigated considering the effect of the transmission lines through stochastic analysis. The assemblage of the transmission line and insulator are modeled as a double pendulum system connected to the SDOF model of the tower It is observed that the background component of the overturing moment induced by the wind response of the transmission line has considerable portion in the total overturning moment. Based on this result, a rotational viscoelastic damper (VED) is proposed for the mitigation of the transmission line reactions, which act as wind load transferred to the tower. To verify the effectiveness of the proposed strategy, time history analysis is conducted for different wind velocities and VED damping constants. From the analysis, the proposed VED is proved to be effective for mitigation of the background component rather than the resonance component of the transmission line reaction.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.21-26
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• 2018

As the large amount of data can be efficiently stored, the methods extracting meaningful features from big data has become important. Especially, the techniques of converting high- to low-dimensional data are crucial for the 'Data visualization'. In this study, principal component analysis (PCA; linear dimensionality reduction technique) and Isomap (non-linear dimensionality reduction technique) are introduced and applied to neural big data obtained by the functional magnetic resonance imaging (fMRI). First, we investigate how much the physical properties of stimuli are maintained after the dimensionality reduction processes. We moreover compared the amount of residual variance to quantitatively compare the amount of information that was not explained. As result, the dimensionality reduction using Isomap contains more information than the principal component analysis. Our results demonstrate that it is necessary to consider not only linear but also nonlinear characteristics in the big data analysis.

• The Transactions of the Korean Institute of Power Electronics
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• v.18 no.1
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• pp.10-17
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• 2013

In this paper, a new active damping method of LCL filter without capacitor voltage sensors is proposed for 3 phase PWM Inverter. Normally, L filter or LCL filter is used as an output filter of grid connected PWM inverter. An LCL filter has more excellent performance than L filter to reduce harmonic current, so the small inductance value can be used. However, the resonance problem in LCL filter is happen due to the zero impedance by the addition of LC branch. To solve the resonance problem, the various active damping method has been proposed so far. Generally, the virtual resistor active damping methods is required to additional hardware sensors for measurement of capacitor voltage and current. In this paper, the new active damping method is proposed without any capacitor voltage or current sensors. In the proposed method, the resonance component of the capacitor voltage of LCL filter can be observed by a simple MRAS(Model Reference Adaptive System) observer without additional hardware sensors, and this component is suppressed by feedforward compensation. The validity of the proposed method is proven by simulation and experiment on the 3-phase PWM inverter system.

• The Transactions of the Korean Institute of Power Electronics
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• v.19 no.1
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• pp.64-70
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• 2014

In this paper, the new LLCL filter is proposed for grid connected three-phase PWM inverter for passive damping. LLCL filter inserts a small inductor in the branch of the capacitor of the traditional LCL filter to compose a series resonant circuit to reduce the switching-frequency component on grid current. Using LLCL filter, the switching-frequency current ripple components can be attenuated much better than the LCL filter, leading to a decrease in the total inductance. However, the resonance phenomena caused by zero impedance from the addition of LC branch in LLCL filter can be a big problem. Resonance phenomena of LLCL filter can be a source of grid system instability, so proper damping methods are required. However, it is difficult to apply a passive damping method in the conventional LLCL filter, because the damping resistor increase impedance of the LC branch. Therefore, switching frequency component of grid current can not much attenuated by low Q of LC series resonance effect. In this paper, a new LLCL filter is proposed to overcome the conventional LLCL filter with passive damping. The validity of the proposed method is proven by simulation and experimental result.

• Wind and Structures
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• v.38 no.1
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• pp.59-74
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• 2024

Large Eddy Simulation (LES) is used to explore the influence of vibration frequency and amplitude on the aerodynamic performance of a rectangular cylinder with an aspect ratio of B/D=5 (B: breadth; D: depth of cylinder) at a Reynolds number of 22,000 near resonance frequency. In smooth flow conditions, the research employs a sequence of three-dimensional simulations under forced vibration with diverse frequency ratios f_e / f_o = 0.8-1.2 (f_e : oscillation frequency; f_o : Strouhal frequency when the rectangular cylinder is stationary ) and oscillation amplitudes A_h/D = 0.05 - 0.3. The individual influences of f_e / f_o and A_h/D on the characteristics of integrated and distributed aerodynamic forces are the focal points of discussion. For the integrated aerodynamic force, particular emphasis is placed on the analysis of the dependence of velocity-proportional component C₁ and displacement-proportional component C₂ of unsteady aerodynamic force on amplitude and frequency ratio. Near the resonance frequency, the dependencies of C₁ and C₂ on amplitude are stronger than that of frequency ratio. For the distributed aerodynamic force, the increase in frequency and amplitude promotes the position of the main vortex core and reattachment to the leading edge in the streamwise direction. In the spanwise direction, vibration enhances the spanwise correlation of aerodynamic force to weaken the three-dimensional effect of the flow field, and a lower frequency ratio and larger amplitude amplify this effect.

• Journal of Biomedical Engineering Research
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• v.30 no.5
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• pp.373-380
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• 2009

In general, Independent component analysis (ICA) is a statistical blind source separation technique, used either in spatial or temporal domain. The spatial or temporal ICAs are designed to extract maximally independent sources in respective domains. The underlying sources for spatiotemporal data (sequence of images) can not always be guaranteed to be independent, therefore spatial ICA extracts the maximally independent spatial sources, deteriorating the temporal sources and vice versa. For such data types, spatiotemporal ICA tries to create a balance by simultaneous optimization in both the domains. However, the spatiotemporal ICA suffers the problem of source ambiguity. Recently, constrained ICA (c-ICA) has been proposed which incorporates a priori information to extract the desired source. In this study, we have extended the c-ICA for better analysis of spatiotemporal data. The proposed algorithm, i.e., constrained spatiotemporal ICA (constrained st-ICA), tries to find the desired independent sources in spatial and temporal domains with no source ambiguity. The performance of the proposed algorithm is tested against the conventional spatial and temporal ICAs using simulated data. Furthermore, its performance for the real spatiotemporal data, functional magnetic resonance images (fMRI), is compared with the SPM (conventional fMRI data analysis tool). The functional maps obtained with the proposed algorithm reveal more activity as compared to SPM.

• Journal of Biomedical Engineering Research
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• v.26 no.4
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• pp.223-230
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• 2005

This paper presents a new approach to investigate spatial correlation between independent components of brain alpha activity in functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). To avoid potential problems of simultaneous fMRI and EEG acquisitions in imaging pure alpha activity, data from each modality were acquired separately under a 'three conditions' setup where one of the conditions involved closing eyes and relaxing, thus making it conducive to generation of alpha activity. The other two conditions -- eyes open in a lighted room or engaged in a mental arithmetic task, were designed to attenuate alpha activity. Using a Mixture Density Independent Component Analysis (MD-ICA) that incorporates flexible non-linearity functions into the conventional ICA framework, we could identify the spatiotemporal components of fMRI activations and EEG activities associated with the alpha rhythm. Then, the sources of the individual EEG alpha activity component were localized by a Maximum Entropy (ME) method that is specially designed to find the most probable dipole distribution minimizing the localization error in sense of LMSE. The resulting active dipoles were spatially transformed to 3D MRls of the subject and compared to fMRI alpha activity maps. A good spatial correlation was found in the spatial distribution of alpha sources derived independently from fMRI and EEG, suggesting the proposed method can localize the cortical areas responsible for generating alpha activity successfully in either fMRI or EEG. Finally a functional connectivity analysis was applied to show that alpha activity sources of both modalities were also functionally connected to each other, implying that they are involved in performing a common function: 'the generation of alpha rhythms'.

• Journal of Korea Multimedia Society
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• v.24 no.4
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• pp.583-592
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• 2021

Obsessive-compulsive disorder (OCD) is a mental disorder in which a person repeated a particular thought or feels. The domain of beliefs and guilt predicted OCD symptoms. Although there were some neuroimaging studies investigating OCD symptoms, resting-state functional magnetic resonance imaging (rs-fMRI) study investigating intra-network functional connectivity associated with guilt for OCD is not reported yet. Therefore, in the current study, we assessed the differences between intra-network functional connectivity of healthy control group and OCD group using independent component analysis (ICA) method. In addition, we also aimed to investigate the correlation between changed functional connectivity and guilt score in OCD. Total 86 participants, which consisted of 42 healthy control volunteers and 44 OCD patients, acquired rs-fMRI data using the 3T MRI. After preprocessing the fMRI data, a functional connectivity was used for group independent component analysis. The results showed that OCD patients had higher score in emotion state in beliefs and lower functional connectivity in fronto-parietal network (FPN) than control group. A decrease of functional connectivity in FPN was negatively correlated with feelings of guilt in OCD. Our results suggest excessive increase in guilt negatively affect to process emotional state and behavior or cognitive processing by influencing intrinsic brain activity.

• Journal of Korea Multimedia Society
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• v.24 no.5
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• pp.684-694
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• 2021

Resting-state Functional Magnetic Resonance Imaging(fMRI) data detects the temporal correlations in Blood Oxygen Level Dependent(BOLD) signal and these temporal correlations are regarded to reflect intrinsic cortical connectivity, which is deactivated during attention demanding, non-self referential tasks, called Default Mode Network(DMN). The relationship between fMRI and anatomical connectivity has not been studied in detail, however, the preceded studies have tried to clarify this relationship using Diffusion Tensor Imaging(DTI) and fMRI. These studies use method that fMRI data assists DTI data or vice versa and it is used as guider to perform DTI tractography on the brain image. In this study, we hypothesized that functional connectivity in resting state would reflect anatomical connectivity of DMN and the combined images include information of fMRI and DTI showed visible connection between brain regions related in DMN. In the previous study, functional connectivity was determined by subjective region of interest method. However, in this study, functional connectivity was determined by objective and advanced method through Independent Component Analysis. There was a stronger connection between Posterior Congulate Cortex(PCC) and PHG(Parahippocampa Gyrus) than Anterior Cingulate Cortex(ACC) and PCC. This technique might be used in several clinical field and will be the basis for future studies related to aging and the brain diseases, which are needed to be translated not only functional connectivity, but structural connectivity.

• Journal of Sensor Science and Technology
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• v.27 no.2
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• pp.112-117
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• 2018

In this study, a bender-type piezoelectric energy harvester was fabricated and evaluated to compensate for the disadvantages of high-power generation only in the resonance frequency range of a piezoelectric harvester using a piezoelectric cantilever. The generated power was investigated according to various changes in the vibration environment. Compared with the piezoelectric cantilever module, the bender-type piezoelectric module showed a larger number of peak voltages. The primary peak voltage shifted toward the low frequency when the spring was coupled to the bender-type piezoelectric module. The harvester of the three bender-type modules had a vibration frequency exceeding 1 mW in the 34-45 Hz range and generated 3.112 mW of power at the vibration frequency of 38 Hz. The harvester of the six bender-type modules had a vibration frequency exceeding 1 mW in the 31-45 Hz range and generated 3.081 mW of power at the vibration frequency of 35 Hz.