• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.154-154
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• 2019

Characterization of reliable field-scale root-zone soil moisture (RZSM) variability contribute to effective hydro-meterological monitoring. Although a promising cosmic-ray neutron probe (CRNP) holds the pontential for field-scale RZSM measurement, it is often restricted at deeper depths due to the non-unique sensitivity of CRNP-measured fast neutron signal to other hydrogen pools. In this study, a merging framework relied on coupling cosmic-ray soil moisture with a representative additional RZSM, was introduced to scale shallower CRNP effective depth to represent root-zone layer. We tested our proposed framework over a densely vegetated region in South Korea covering a network of one CRNP and nine in-situ point measurements. In particular, cosmic-ray soil moisture and ancillary RZSM retrieved from the most time stable location were considered as input datasets; whereas the remaining point locations were used to generate a reference RZSM product. The errors between these two input datasets and the reference were forecasted by a linear autoregressive model. A linear combination of forecasts was then employed to compute a suitable weight for merging two input products from the predicted errors. The performance of merging framework was evaluated against reference RZSM in comparison to the two original products and a commonly used exponential filter technique. The results of this study showed that merging framework outperformed other products, demonstrating its robustness in improving field-scale RZSM. Moreover, a strong relationship between the quality of input data and the performance merging framework in light of CRNP effective depth variation has been also underlined via the merging framework.

• Journal of Biomedical Engineering Research
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• v.43 no.4
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• pp.185-192
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• 2022

Early detection of mild cognitive impairment can help prevent the progression of dementia. The purpose of this study was to design and validate a machine learning model that automatically differential diagnosed patients with mild cognitive impairment and identified cognitive decline characteristics compared to a control group with normal cognition using resting-state quantitative electroencephalogram (qEEG) with eyes closed. In the first step, a rectified signal was obtained through a preprocessing process that receives a quantitative EEG signal as an input and removes noise through a filter and independent component analysis (ICA). Frequency analysis and non-linear features were extracted from the rectified signal, and the 3067 extracted features were used as input of a linear support vector machine (SVM), a representative algorithm among machine learning algorithms, and classified into mild cognitive impairment patients and normal cognitive adults. As a result of classification analysis of 58 normal cognitive group and 80 patients in mild cognitive impairment, the accuracy of SVM was 86.2%. In patients with mild cognitive impairment, alpha band power was decreased in the frontal lobe, and high beta band power was increased in the frontal lobe compared to the normal cognitive group. Also, the gamma band power of the occipital-parietal lobe was decreased in mild cognitive impairment. These results represented that quantitative EEG can be used as a meaningful biomarker to discriminate cognitive decline.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.469-474
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• 2011

This paper presents the intelligent tracking method for maneuvering target using the positional error compensation of the maneuvering target. The difference between measured point and predict point is separated into acceleration and noise. K-means clustering and TS fuzzy system are used to get the optimal acceleration value. The membership function is determined for acceleration and noise which are divided by K-means clustering and the characteristics of the maneuvering target is figured out. Divided acceleration and noise are used in the tracking algorithm to compensate computational error. While calculating expected value, the non-linearity of the maneuvering target is recognized as linear one by dividing acceleration and the capability of Kalman filter is kept in the filtering process. The error for the non-linearity is compensated by approximated acceleration. The proposed system improves the adaptiveness and the robustness by adjusting the parameters in the membership function of fuzzy system. Procedures of the proposed algorithm can be implemented as an on-line system. Finally, some examples are provided to show the effectiveness of the proposed algorithm.

• Journal of Korean Society on Water Environment
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• v.35 no.5
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• pp.409-417
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• 2019

In this study, a $480m^3/day$ pilot device was constructed through laboratory experiments based on the Ministry of Environment manual. The purpose of this study was to analyze the characteristics of CSO treatment and backwashing characteristics by applying the pilot device to the field. The purpose of this study was to acquire the basic data necessary for the design and operation management of the real scale filtration type non-point pollution control system. The filtration was conducted while maintaining the linear velocity of 20m/hour. The CSO treatment efficiencies of the pilot devices were 0.4-76.1%(mean 49.0 %), SS 51.4-91.6%(mean 77.8%), COD 22.2-59.4% (mean 38.3%) and TP 14.5-52.6%(mean 38.1%),respectively. The correlation coefficient between SS and the turbidity of influent water was 0.90, higher than that of CSO. To operate the treatment system effectively, the turbidity can be easily measured in real time as the monitoring item is the most appropriate because SS is the main target substance of the non-point source. As a result of analyzing the adsorbent treatment characteristics of PP filter material applied to this pilot device, the average particle diameter range of influent was $4.6-40.1{\mu}m$(mean $21.2{\mu}m$) and the treated water was $0.9-24.5{\mu}m$(mean $6.4{\mu}m$), respectively. Particles of approximately 10m or less are leached out, and so it is necessary to compensate for the raw water containing micro particulate matter.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.2A
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• pp.89-96
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• 2011

The damping ratio as an index of bridge vibration could be considered as one of the important dynamic characteristics of a suspension bridge. But estimating of damping ratio on an existing suspension bridge under ambient vibration condition could be a laborious task. Moreover, it is not simple to directly distinguish aerodynamic damping and friction damping from apparent damping. According to previous studies, the aerodynamic damping properties can be linearly affected by wind speed level, and apparent damping ratio can be affected by amplitude of vibration. Therefore, in this article, the relationships among damping ratio, wind speed level and amplitude of acceleration were studied for separating extract aerodynamic damping and friction damping from apparent damping. Damping ratios on Sorok Bridge, a suspension bridge which is a located in Go-Heung, Korea, were estimated by two different methods as using Hilbert transform and extended Kalman filter which were well known as effective estimation methods for non-linear state. It was possible to distinguish aerodynamic damping and friction damping from apparent damping using averaged normal components of wind speed, RMQ values of acceleration, and estimated damping ratios from wind-induced vibration responses and vehicle loading responses.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.2
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• pp.307-316
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• 1994

The rainfall forecasting model of the short term is improved at the point where meterological data is not gaged. In this study, the adopted model is based on the assumptions for simulation model of rainfall process, meteorological homogeneousness, prediction and estimation of meteorological data. A Kalman Filter technique is used for rainfall forecasting. In the existing models, the equation of the model is non-linear type with regard to rainfall rate, because hydrometer size distribution (HSD) depends on rainfall intensity. The equation is linearized about rainfall rate as HSD is formulated by the function of the water storage in the cloud. And meteorological input variables are predicted by emprical model. It is applied to the storm events over Taech'ong Dam area. The results show that root mean square error between the forecasted and the observed rainfall intensity is varing from 0.3 to 1.01 mm/hr. It is suggested that the assumptions of this study be reasonable and our model is useful for the short term rainfall forecasting at the ungaged point of the meteorological data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.10
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• pp.1383-1389
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• 2013

There were telecommunication noise and malfunctions of the electronic devices occurred over a wide area due to the high harmonic voltage and/or current levels of the Back-to-back converter in the DFIG wind power system even though the magnitude of all harmonics is within the international standards. The triangular carrier signals of the PWM used in the power converter system is related to the telecommunication noise because they are in the range of audible frequencies and amplified by a variety of the standing waves that were excited by harmonic voltage sources in the weak grid system such as a long distance distribution transmission lines. This paper describes the characteristics of the harmonics in the wind turbine-generator, numerical analysis and simulation of the harmonics resonance phenomena in the distribution lines as well as measuring induced voltage of the telecommunication lines in parallel with power lines in order to verify the root cause of the telecommunication noise. These noise problems can occur in a wind turbine power system with a non-linear converter at any time, as well as photovoltaic power system. So, the preliminary review of suitable filter devices and switching frequencies of the PWM have to be required by considering the stability of the controller at the design stage but as part of the measures the effect of the telecommunication cable shields was analyzed by comparing the measured data between multi-conductor with/without shields so as to attenuate the sources of the harmonics voltage induced into the telecommunication lines and to apply the most cost-effective measures in the field.

• Journal of Broadcast Engineering
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• v.21 no.6
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• pp.920-928
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• 2016

Recently high dynamic range imaging technique is hot issue in computer graphic area. We present a progressive tone mapping algorithm, which is based on weighted least squares optimization framework. Our approach combines weighted least squares filtering with iCAM06 model. To show more perceptual high dynamic range images in conventional display, we decompose high dynamic range image into base layers and detail layers. The base layers are obtained by using weighted least squares filter. Then, we adopt chromatic adaption function and non-linear compression function to deal with base layers. Only the base layers reduce contrast, and preserving detail. The image quality assessment shows that our tone mapped image is more similar to original high dynamic range image. Moreover, the subjective result shows our algorithm produces more reliable and pleasing image.

• The Transactions of the Korean Institute of Power Electronics
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• v.10 no.5
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• pp.508-517
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• 2005

This paper describes a high performance voltage controller for 3-phase 4-wire UPS (Uninterruptible Power Supply) system, and proposes a new scheme of synchronous reference frame controller in order to compensate for the voltage distortions due to unbalanced and nonlinear loads. Proposed scheme can eliminate the negative sequence voltage component due to unbalanced loads and also reduce the harmonic voltage component due to non-linear loads, even when the bandwidth of voltage control loop is a very low. In order to compensate for the effects of unbalanced loads, the synchronous reference frame controller with the positive and negative sequence computation block is proposed, and the synchronous frame controller with a bandpass filter is proposed to compensate for the selected harmonic frequency of output voltage. The effectiveness of the proposed scheme has been investigated and verified through computer simulations and experiments by a 30kVA UPS.