• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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• pp.244-245
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• 2011

In this paper, the algorithm for detecting load shedding based on Wavelet Singular Value Decomposition(WSVD) is proposed. WSVD is method of signal processing which combine Wavelet Transform(WT) and Singular Value Decomposition(SVD) to analyze transients in power system. 345kV Busan transmission system is modeled by EMTP-RV and simulations according to successive change of load capability are conducted. This paper analyzes characteristics of WSVD by using simulation results and proposes algorithm for detecting load shedding.

• 청정기술
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• 제24권3호
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• pp.206-211
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• 2018

Lead molybdate ($PbMoO_4$) were successfully synthesized using a facile surfactant-assisted hydrothermal process and characterized by XRD, Raman, PL, BET and DRS. We also investigated the photocatalytic activity of these materials for the decomposition of Rhodamine B under UV-light irradiation. From XRD and Raman results, well-crystallized $PbMoO_4$ crystals have been successfully synthesized with a facile surfactant-assisted hydrothermal process and had 52-69 nm particle size. The $PbMoO_4$ catalysts prepared at $160^{\circ}C$ showed the highest photocatalytic activity. The PL peak was appeared at about 540 nm at all catalysts and it was also shown that the excitonic PL signal was proportional to the photocatalytic activity for the decomposition of Rhodamine B.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2001년도 추계학술대회논문집 II
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• pp.1323-1328
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• 2001

The theory of NAH is based on the assumption of reflection free. However, it is not always possible to meet this condition in many practical cases. Thus, a decomposition of direct and reflected fields is needed to apply NAH to reflective condition for noise problems. In addition, the decomposition of direct and reflected field can give acoustic characteristics of reflecting surfaces. This paper presents that in this condition the decomposition can also be successfully done by MUSIC(Multiple Signal Classification) power method and beamforming method, and that numerical simulation and real experiments verify its performance.

• Journal of Information Processing Systems
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• 제15권5호
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• pp.1068-1081
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• 2019

Signal complexity is one point of view to analyze the biological signal. It arises as a result of the physiological signal produced by biological systems. Signal complexity can be used as a method in extracting the feature for a biological signal to differentiate a pathological signal from a normal signal. In this research, Hjorth descriptors, one of the signal complexity measurement techniques, were measured on signal sub-band as the features for lung sounds classification. Lung sound signal was decomposed using two wavelet analyses: discrete wavelet transform (DWT) and wavelet packet decomposition (WPD). Meanwhile, multi-layer perceptron and N-fold cross-validation were used in the classification stage. Using DWT, the highest accuracy was obtained at 97.98%, while using WPD, the highest one was found at 98.99%. This result was found better than the multi-scale Hjorth descriptor as in previous studies.

• 융합신호처리학회논문지
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• 제2권2호
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• pp.95-102
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• 2001

일반적으로 피지시스템은 compact한 공간에 대한 어떠한 비선형 함수도 일정오차 이내에서 근사할 수 있다. 그러나 퍼지시스템의 응용은 퍼지규칙의 수가 많아지는 경우, 특히 고차의 비선형 시스템에 대하여는 사용되기 어렵다는 단점을 가지고 있다. 본 논문에서는 근사하고자 하는 비선형 함수의 분해를 이용한, 병렬형과 종속형의 두 가지 형태의 퍼지시스템 재구성 방식을 제안한다. 이 두 가지 형태의 재구성을 적절히 이용하여 퍼지규칙의 수를 기하급수적으로 줄일 수 있다. 제안된 알고리즘은 적응구조를 가진 퍼지시스템에 대하여 응용 가능하며 두 가지 적웅 퍼지 슬라이딩제어 예를 통하여 그 타당성을 보인다.

• Nuclear Engineering and Technology
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• 제55권4호
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• pp.1507-1517
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• 2023

Centrifugal pumps are a crucial part of nuclear power plants, and their dependable and safe operation is crucial to the security of the entire facility. Cavitation will cause the centrifugal pump to violently vibration with the large number of vacuoles generated, which not only affect the hydraulic performance of the centrifugal pump but also cause structural damage to the impeller, seriously affecting the operational safety of nuclear power plants. A closed cavitation test bench of a centrifugal pump is constructed, and a method for precisely identifying the cavitation state is proposed based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Deep Residual Shrinkage Network (DRSN). First, we compared the cavitation sensitivity of pressure fluctuation, vibration, and liquid-borne noise and decomposed the liquid-borne noise by CEEMD to capture cavitation characteristics. The decomposition results are sent into a 12-layer deep residual shrinkage network (DRSN) for cavitation identification training. The results demonstrate that the liquid-borne noise signal is the most cavitation-sensitive signal, and the accuracy of CEEMD-DRSN to identify cavitation at different stages of centrifugal pumps arrives at 94.61%

• 한국데이타베이스학회:학술대회논문집
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• 한국데이타베이스학회 1999년도 춘계공동학술대회: 지식경영과 지식공학
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 춘계공동학술대회-지식경영과 지식공학
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• 응용통계연구
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• 제26권2호
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• pp.335-347
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• 2013

다양한 분야에서 시그널(signal) 형태로 자료들이 표현된다. 예를 들면 심전도(electrocardiogram)는 심근에서 발생하는 활동 전류를 나타내는데, 심장의 박동에 따라 수축과 이완을 반복하는 과정을 시간에 따른 활동 전류량의 변동으로 나타낸다. 현실세계에서 측정하거나 관찰되는 시그널에는 다양한 형태의 시그널들이 혼합되어 있는 경우가 흔하다. 예를 들어 오케스트라 연주의 아름다운 선율은 고유한 주파수(frequency)를 지닌 악기들의 다양한 소리로 구성되어 있으며, 각기 다른 음조(note)가 하나로 모여 완벽한 하모니를 형성하게 된다. 시그널이 정상인(stationary) 경우에 혼합된 시그널들을 분해하여 분석하는 방법에 대해 현재까지 다양하게 연구되어 왔다. 자료가 비정상(non-stationary)일 경우에는 기존의 방법론들을 적용시키기에는 한계가 있다. 비정상성 자료를 다루기 위해 Huang 등 (1998)은 경험적 모드분해법(empirical mode decomposition)이라는 방법을 제안하였다. 자료에 내포되어 있는 국소적인 파동(oscillation)을 국소 극값들(local extrema)을 식별하여 자료 적응적으로 추출한다. 경험적 모드분해법은 잡음(error)에 의해 자료가 오염되어 있는 경우에는 국소 극값들을 통하여 국소적인 파동을 추정하기 어려우며, 자료의 크기가 커짐에 따라 계산량도 크게 늘어나는 단점 등이 있다. 본 연구에서는 이차 미분을 이용하여 국소적인 파동을 식별하고 추정하는 새로운 방법론을 제시하고자 한다.

• 한국항해항만학회지
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• 제28권4호
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• pp.305-309
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• 2004

The problem for parameters estimation of the received signals impinging on array sensors has long been of great research Interest in a great variety of applications, such as radar, sonar, and land mobile communications systems. Conventional subspace-based algorithms, such as MUSIC and ESPRIT, require an extensive computation of inverse matrix and eigen-decomposition In this paper, we propose a new parameters estimation algorithm via nonlinear minimization, which is simplified computationally and estimates signal parameters simultaneously.