• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.109-121
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• 2024

In various manufacturing processes such as textiles and automobiles, when equipment breaks down or stops, the machines do not work, which leads to time and financial losses for the company. Therefore, it is important to detect equipment abnormalities in advance so that equipment failures can be predicted and repaired before they occur. Most equipment failures are caused by bearing failures, which are essential parts of equipment, and detection bearing anomaly is the essence of PHM(Prognostics and Health Management) research. In this paper, we propose a preprocessing algorithm called SWT-SVD, which analyzes vibration signals from bearings and apply it to an anomaly transformer, one of the time series anomaly detection model networks, to implement bearing anomaly detection model. Vibration signals from the bearing manufacturing process contain noise due to the real-time generation of sensor values. To reduce noise in vibration signals, we use the Stationary Wavelet Transform to extract frequency components and perform preprocessing to extract meaningful features through the Singular Value Decomposition algorithm. For experimental validation of the proposed SWT-SVD preprocessing method in the bearing anomaly detection model, we utilize the PHM-2012-Challenge dataset provided by the IEEE PHM Conference. The experimental results demonstrate significant performance with an accuracy of 0.98 and an F1-Score of 0.97. Additionally, to substantiate performance improvement, we conduct a comparative analysis with previous studies, confirming that the proposed preprocessing method outperforms previous preprocessing methods in terms of performance.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.761-774
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• 2022

Accurate hydrologic prediction is essential to analyze the effects of drought, flood, and climate change on flow rates, water quality, and ecosystems. Disentangling the uncertainty of the hydrological model is one of the important issues in hydrology and water resources research. Hydrologic data assimilation (DA), a technique that updates the status or parameters of a hydrological model to produce the most likely estimates of the initial conditions of the model, is one of the ways to minimize uncertainty in hydrological simulations and improve predictive accuracy. In this study, the two ensemble-based sequential DA techniques, ensemble Kalman filter, and particle filter are comparatively analyzed for the daily discharge simulation at the Yongdam catchment using airGRdatassim. The results showed that the values of Kling-Gupta efficiency (KGE) were improved from 0.799 in the open loop simulation to 0.826 in the ensemble Kalman filter and to 0.933 in the particle filter. In addition, we analyzed the effects of hyper-parameters related to the data assimilation methods such as precipitation and potential evaporation forcing error parameters and selection of perturbed and updated states. For the case of forcing error conditions, the particle filter was superior to the ensemble in terms of the KGE index. The size of the optimal forcing noise was relatively smaller in the particle filter compared to the ensemble Kalman filter. In addition, with more state variables included in the updating step, performance of data assimilation improved, implicating that adequate selection of updating states can be considered as a hyper-parameter. The simulation experiments in this study implied that DA hyper-parameters needed to be carefully optimized to exploit the potential of DA methods.

• Journal of the Earthquake Engineering Society of Korea
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• v.10 no.6 s.52
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• pp.137-145
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• 2006

Recently, the interests in structural monitoring of civil infrastructures are increased. Especially, as the civil infrastructures such as bridges, tunnels and buildings become large-scale, it is necessary to monitor and maintain the safety state of the structures, which requires smart systems that can supply long-term monitoring during the service time of the structures. In this paper, we investigated the possibilities of fiber optic sensor application to the various structures. We investigate the possibility of using fiber optic Bragg grating sensors to joint structure. The sensors show good response to the structural behavior of the joint while electric gauges lack of sensitivity, durability and long term stability for continuous monitoring. We also apply fiber optic structural monitoring to the composite repaired concrete beam structure. Peel-out effects is detected with optical fiber Bragg grating sensors and the strain difference between main structure and repaired carbon sheets is observed when they separate each other. The real field test was performed to verify the behaviors of fiber Bragg grating sensors attached to the containment structure in Uljin nuclear power plant in Korea as a part of structural integrity test which demonstrates that the structural response of the non-prototype primary containment structures. The optical fiber Bragg grating sensor smart system which is the probable means for long term assessments can be applicable to monitoring of structural members in various civil infrastructures.

• Journal of the Korea Society of Computer and Information
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• v.18 no.7
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• pp.23-35
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• 2013

This paper proposes an effective technique that is used to automatically extract feature vectors from vibration signals for fault classification systems. Conventional mel-frequency cepstral coefficients (MFCCs) are sensitive to noise of vibration signals, degrading classification accuracy. To solve this problem, this paper proposes spectral envelope cepstral coefficients (SECC) analysis, where a 4-step filter bank based on spectral envelopes of vibration signals is used: (1) a linear predictive coding (LPC) algorithm is used to specify spectral envelopes of all faulty vibration signals, (2) all envelopes are averaged to get general spectral shape, (3) a gradient descent method is used to find extremes of the average envelope and its frequencies, (4) a non-overlapped filter is used to have centers calculated from distances between valley frequencies of the envelope. This 4-step filter bank is then used in cepstral coefficients computation to extract feature vectors. Finally, a multi-layer support vector machine (MLSVM) with various sigma values uses these special parameters to identify faulty types of induction motors. Experimental results indicate that the proposed extraction method outperforms other feature extraction algorithms, yielding more than about 99.65% of classification accuracy.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.102-109
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• 2014

A particle system is used for modeling the physical phenomenon. There are many traditional ways for simulation modeling which can be well suited for application including the landscapes of branches, clouds, waves, fog, rain, snow and fireworks in the three-dimensional space. In this paper, we present a new fireworks modeling technique for modeling 3D firework based on Firework Particle Tracking (FPT) using the particle system. Our method can track and recognize the launched and exploded particle of fireworks, and extracts relatively accurate 3D positions of the particles using 3D depth values. It can realize 3D simulation by using tracking information such as position, speed, color and life time of the firework particle. We exploit Region of Interest (ROI) for fast particle extraction and the prevention of false particle extraction caused by noise. Moreover, Kalman filter is used to enhance the robustness in launch step. We propose a new fireworks particle tracking method for the efficient tracking of particles by considering maximum moving range and moving direction of particles, and shall show that the 3D speeds of particles can be obtained by finding the rotation angles of fireworks. Also, we carry out the performance evaluation of particle tracking: tracking speed and accuracy for tracking, classification, rotation angle respectively with respect to four types of fireworks: sphere, circle, chrysanthemum and heart.

• Journal of the Korea Society of Computer and Information
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• v.15 no.4
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• pp.83-90
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• 2010

In wireless sensor networks, the data transmitted from the sensor nodes are susceptible to corruption by errors which caused of noisy channels and other factors. In view of the severe energy constraint in Sensor Networks, it is important to use the error control scheme of the energy efficiently. In this paper, we presented RS (Reed-Solomon) codes in terms of their BER performance and power consumption. RS codes work by adding extra redundancy to the data. The encoded data can be stored or transmitted. It could have errors introduced, when the encoded data is recovered. The added redundancy allows a decoder to detect which parts of the received data is corrupted, and corrects them. The number of errors which are able to be corrected by RS code can determine by added redundancy. The results of experiment validate the performance of proposed method to provide high degree of reliability in low-power communication. We could predict the lifetime of RS codes which transmitted at 32 byte a 1 minutes. RS(15, 13), RS(31, 27), RS(63, 57), RS(127,115), and RS(255,239) can keep the days of 173.7, 169.1, 163.9, 150.7, and 149.7 respectively. The evaluation based on packet reception ratio (PRR) indicates that the RS(255,239) extends a sensor node's communication range by up about 3 miters.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.24 no.7
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• pp.710-721
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• 2013

The noise prediction of motor driving system is one of the most important parts in EV/HEV, as the number of power electronic devices increases. This paper describes the mechanism of noise making process and proposes a simulation model of motor driving system for the prediction of the conducted noise. Theoretical calculations and model based simulations were carried out. DOD-dependent-battery parameters were extracted by AC analysis, and an inverter model including dynamic diode was used. Furthermore, 2-D EM tool was used for the motor modeling and was combined with the circuit models of battery and inverter. The simulated voltages, currents and spectrums in the motor driving system showed qualitatively meaningful results, suggesting the validness of the suggested modeling methods.

• Journal of Korea Entertainment Industry Association
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• v.14 no.8
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• pp.513-523
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• 2020

This study has derived the patents of the technology that have been filed and registered so far to investigate the trends of virtual reality(VR) experience contents technology, and analyzed them focusing on core patent technologies. The patents of Korea, USA, Japan, Europe and PCT, which were released until June 2020, were targeted, and patent search was conducted using WISDOMAIN search DB. The keywords for patent search were related to experience technology using VR, and a total of 1,013 data were obtained after creating a search formula by combining the derived keywords. Among them, a total of 65 data were extracted from the result of selecting valid patents, and a political analysis was conducted on them. Looking at the overall application trend, most of Korean patent applications accounted for, and noise patents are system-related devices to implement VR technology. The United States and Europe are focused on developing augmented reality(AR) technology, the study found. The technology of VR experience has increased rapidly since 2017, and the technology growth stage is the period from the beginning to the growth stage. As a result of examining the valid patents related to VR experience, technology was searched in various fields such as rural tour, exhibition, education, and performance, and patents for contents writing and general virtual experience related technology were also searched. If we predict the possibility of development of VR industry in the future, it is necessary to respond to preemption of intellectual property rights by proceeding technology development and patent application for more diverse fields.