• Nuclear Engineering and Technology
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• v.55 no.4
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• pp.1310-1323
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• 2023

A reactor coolant pump (RCP) is essential for transporting coolant in the primary loop of pressurized water reactors. In the advanced passive reactor, the absence of a long pipeline between the steam generator and RCP serves as a transition section, resulting in a non-uniform flow field at the pump inlet. Therefore, the characteristics of the pump should be investigated under non-uniform flow to determine its influence on the pump. In this study, the pressure pulsation characteristics were examined in the time and frequency domains, and the sources of low-frequency and high-amplitude signals were analyzed using wavelet coherence analysis and numerical simulation. From computational fluid dynamics (CFD) results, non-uniform inflow has a great effect on the flow structures in the pump's inlet. The pressure pulsation in the pump at the rated flow increased by 78-128.7% under the non-uniform inflow condition in comparison with that observed under the uniform inflow condition. Furthermore, a low-frequency signal with a high amplitude was observed, whose energy increased significantly under non-uniform flow. The wavelet coherence and CFD analysis verified that the source of this signal was the low-frequency pulsating vortex under the steam generator.

• Nuclear Engineering and Technology
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• v.56 no.10
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• pp.4390-4396
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• 2024

The response of an anti-Compton phoswich detector to gamma rays was investigated using Monte-Carlo method, and the pulses from different crystal cases, including gamma deposition only in the LaBr3(Ce) or CsI(Tl) crystal and coincidence in both crystals, were analyzed. A novel pulse discrimination method for gamma deposition events based on wavelet transform analysis, called SSD (Scale Shape Discrimination), was developed in this study. Compared to the traditional PSD (Pulse Shape Discrimination) method, SSD has the advantage of transforming one-dimensional pulses in the time-domain into two-dimensional time-frequency spectra, providing the more useful features for pulse discrimination. The performances of the Compton suppression and Full-energy peak loss using PSD and SSD methods was studied. The results show that the Compton suppression factor I_PSD = 5.12 and I_SSD = 5.32, and FEP loss factor PL_PSD = 0.0554 and PL_SSD = 0.0587. Meanwhile, the influences of the cutoff values for pulse discrimination on the results of I and PL with different method were analyzed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.4
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• pp.1272-1277
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• 2010

Target detection and tracking wireless sensor network must have various signal processing ability. Wireless sensor nodes need to light weight signal processing algorithm because of energy constraints and communication bandwidth constraints. General signal processing algorithm of wireless sensor node consists of de-noising, received signal strength computation, feature extraction and signal compression. Wireless sensor network life-time and performance of target detection and classification depend on sensor node signal processing. In this paper, we propose energy efficient signal processing algorithm using wavelet transform. The proposed method estimates exact target position.

• Journal of Broadcast Engineering
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• v.26 no.2
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• pp.143-154
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• 2021

In this paper, we propose a method of compressing digital hologram standardized data provided by JPEG Pleno. In numerical reconstruction of digital holograms, the addition of random phases for visualization reduces speckle noise due to interference and doubles the compression efficiency of holograms. Holograms are composed of completely complex floating point data, and due to ultra-high resolution and speckle noise, it is essential to develop a compression technology tailored to the characteristics of the hologram. First, frequency characteristics of hologram data are analyzed using various wavelet filters to analyze energy concentration according to filter types. Second, we introduce the subband selection algorithm using energy concentration. Finally, the JPEG2000, SPIHT, H.264 results using the Daubechies 9/7 wavelet filter of JPEG2000 and the proposed method are used to compress and restore, and the efficiency is analyzed through quantitative quality evaluation compared to the compression rate.

• Journal of Korea Water Resources Association
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• v.31 no.3
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• pp.317-326
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• 1998

Water consumption characteristics on the northern part of Seoul were analyzed using wavelet transform with a base function of Coiflets 5. It turns out that long term evolution mode detected at 212 scale in 1995 was in a shape of hyperbolic tangent over the entire period due to the development of Sanggae resident site. Furthermore, there was seasonal water demand having something to do with economic cycle which reached its peak at the ends of June and December. The amount of this additional consumption was about $1,700\;\textrm{cm}^3/hr$ on June and $500\;\textrm{cm}^3/hr$ on December. It was also shown that the periods of energy containing sinusoidal component were 3.13 day, 33.33 hr, 23.98 hr and 12 hr, respectively, and the amplitude of 23.98 hr component was the most humongous. The components of relatively short frequency detected at $2^i$[i = 1,2,…12] scale were following Gaussian PDF. The most reliable predictive models are multiple AR[32,16,23] and ARMA[20, 16, 10, 23] which the input of temperature from the view point of minimized predictive error, mutual independence or residuals and the availableness of reliable meteorological data. The predicted values of water supply were quite consistent with the measured data which cast a possibility of the deployment of the predictive model developed in this study for the optimal management of water supply facilities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.907-923
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• 2020

For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).

• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.2
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• pp.79-86
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• 2020

This study examines the power generation characteristics of a vehicle rooftop photovoltaic module with urban driving conditions. Actual test data with an illuminometer and a thermometer were used to analyze the power generation characteristics of the vehicle rooftop photovoltaic module. In addition, the power generation characteristics were analyzed in terms of urban driving conditions, irradiance, ambient temperature, and photovoltaic module temperature. This study also analyzes the power generation characteristics of the vehicle rooftop photovoltaic module with urban driving conditions through a wavelet transform filtering method. The power generation characteristics of the vehicle rooftop photovoltaic module with urban driving conditions depend on the change in irradiance rather than that in photovoltaic module temperature.

• Earthquakes and Structures
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• v.16 no.4
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• pp.415-423
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• 2019

Near-fault ground motions can impose particularly high seismic demands on structures due to the pulses that are typically observed in the velocity time-histories. In this study it is empirically found that the critical response can be estimated from the directions corresponding to the maximum (max) or minimum (min) pulse-energy. Determination of the pulse-energy requires removing of the high-frequency content. For achieving this, the wavelet analysis and the least-square-fitting (LSF) algorithm are adopted. Results obtained by the two strategies are compared and differences between them are analyzed. Finally, the relationship between the critical response and the response derived from directions having the max or min pulse-energy confirms that using the pulse-energy for deriving the critical response of the building structures is reasonable.

• Smart Structures and Systems
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• v.19 no.2
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• pp.181-194
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• 2017

In order to investigate the interface debonding defects detection mechanism between steel tube and concrete core of concrete-filled steel tubes (CFSTs), multi-physical fields coupling finite element models constituted of a surface mounted Piezoceramic Lead Zirconate Titanate (PZT) actuator, an embedded PZT sensor and a circular cross section of CFST column are established. The stress wave initiation and propagation induced by the PZT actuator under sinusoidal and sweep frequency excitations are simulated with a two dimensional (2D) plain strain analysis and the difference of stress wave fields close to the interface debonding defect and within the cross section of the CFST members without and with debonding defects are compared in time domain. The linearity and stability of the embedded PZT response under sinusoidal signals with different frequencies and amplitudes are validated. The relationship between the amplitudes of stress wave and the measurement distances in a healthy CFST cross section is also studied. Meanwhile, the responses of PZT sensor under both sinusoidal and sweep frequency excitations are compared and the influence of debonding defect depth and length on the output voltage is also illustrated. The results show the output voltage signal amplitude and head wave arriving time are affected significantly by debonding defects. Moreover, the measurement of PZT sensor is sensitive to the initiation of interface debonding defects. Furthermore, wavelet packet analysis on the voltage signal under sweep frequency excitations is carried out and a normalized wavelet packet energy index (NWPEI) is defined to identify the interfacial debonding. The value of NWPEI attenuates with the increase in the dimension of debonding defects. The results help understand the debonding defects detection mechanism for circular CFST members with PZT technique.

• The Journal of the Acoustical Society of Korea
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• v.14 no.3
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• pp.71-81
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• 1995

In this paper, feature vector extraction methods and classification algorithms for the automatic classification of transient signals in underwater are discussed. A feature vector extraction method using wavelet transform, which shows good performance with small number of coefficients, is proposed and compared with the existing classical methods. For the automatic classification, artificial neural networks such as multilayer perceptron (MLP), radial basis function (RBF), and MLP-Class are utilized, where those neural networks as well as extracted feature vectors are combined to improve the performance and reliability of the proposed algorithm. It is confirmed by computer simulation with Traco's standard transient data set I and simulated data that the proposed feature vector extraction method and classification algorithm perform well, assuming that the energy of a given transient signal is sufficiently larger than that of a ambient noise, that there are the finite number of noise sources, and that there does not exist noise sources more than two simultaneously.