• 전자공학회논문지A
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• 제31A권2호
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• pp.67-74
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• 1994

Effects of the GaAs semiconductor particles on electrophysical phenomena at the Pt electrode/10S0-3TM KCl aqueous electrolyte interfaces have been studied using voltammetric time based and electrochemical impedance techniques. The anodic decomposition effect f the GaAs semiconductor particles on electrophysical phenomena was significantly observed during the positive potential scan (0 to 1.0 V vs. SCE). On the other hand, the cathodic decomposition effect of the GaAs semiconductor particles was negligible during thenegative potential scan (0 to -1.0 V vs. SCE). The GaAs semiconductor particles act as current activators or mediators during the anodic process and act as charge screens during the cathodic process. The electrolyte resistance and related impedance was increased due to the presence of the GaAs semiconductor particles. The anodic decomposition effect of the GaAs semiconductor particles can directly be applied to activate the hydrogen evolution.

• Journal of Information Processing Systems
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• 제15권6호
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• pp.1296-1305
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• 2019

To solve the problem of poor noise suppression capability and frequent loss of edge contour and detailed information in current fusion methods, an infrared and visible light image fusion method based on variational multiscale decomposition is proposed. Firstly, the fused images are separately processed through variational multiscale decomposition to obtain texture components and structural components. The method of guided filter is used to carry out the fusion of the texture components of the fused image. In the structural component fusion, a method is proposed to measure the fused weights with phase consistency, sharpness, and brightness comprehensive information. Finally, the texture components of the two images are fused. The structure components are added to obtain the final fused image. The experimental results show that the proposed method displays very good noise robustness, and it also helps realize better fusion quality.

• ETRI Journal
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• 제45권4호
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• pp.704-712
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• 2023

This paper presents a flexible finite-difference technique for analyzing the nonuniform guiding structures. Because the voltage and current variations along the nonuniform structure differ for each segment, this work considers the adaptable discretization steps. This technique increases the accuracy of the final response. Moreover, by applying the singular value decomposition and discarding the nonprincipal singular values, an optimal lower rank approximation of the discretization matrix is obtained. The computational cost of the introduced method is significantly reduced using the optimal discretization matrix. Also, the proposed method can be extended to the nonuniform waveguides. The technique is verified by analyzing several practical transmission lines and waveguides with nonuniform profiles.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2000년도 전력전자학술대회 논문집
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• pp.111-114
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• 2000

Harmonics and fundamental reactive power of nonlinear loads in serious unbalanced power condition are compensated by current synchronous detection(CSD) theory which is also acceptable for single phase power system, but the CSD theory is not suitable any more in case of controlled independently harmonics and reactive component. Therefore a new algorithm the extended current synchronous detection (ECSD)theory for a three phase active power filter based on decomposition of fundamental reactive distorted components is proposed in this paper. The proposed ECSD theory is simulated and tested comparison with a few power theories under asymmtrical condition in power system.

• Journal of Electrical Engineering and Technology
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• 제12권2호
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• pp.559-568
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• 2017

Harmonic/inter-harmonic detection and analysis is an important issue in power system signal processing. This paper proposes a fast algorithm based on matching pursuit (MP) sparse signal decomposition, which can be employed to extract the harmonic or inter-harmonic components of a distorted electric voltage/current signal. In the MP iterations, the method extracts harmonic/inter-harmonic components in order according to the spectrum peak. The Fast Fourier Transform (FFT) and nonlinear optimization techniques are used in the decomposition to realize fast and accurate estimation of the parameters. First, the frequency estimation value corresponding to the maxim spectrum peak in the present residual is obtained, and the phase corresponding to this frequency is searched in discrete sinusoids dictionary. Then the frequency and phase estimations are taken as initial values of the unknown parameters for Nelder-Mead to acquire the optimized parameters. Finally, the duration time of the disturbance is determined by comparing the inner products, and the amplitude is achieved according to the matching expression of the harmonic or inter-harmonic. Simulations and actual signal tests are performed to illustrate the effectiveness and feasibility of the proposed method.

• 한국음향학회지
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• 제24권5호
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• pp.262-270
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• 2005

본 논문에서는 음성 신호를 시간축으로 분할하는 새로운 기법으로, 분할 시 왜곡과 엔트로피가 함께 고려된 기법이 제안되었다 시간축 분할에 필요한 보간 함수와 타겟 특징 벡터는 동적 프로그래밍 기법을 이용하여 왜곡과 엔트로피가 동시에 최소화되도록 얻어진다. 보간 함수는 학습 데이터를 이용하여 구성되도록 하였으며, 분할과 추정의 반복적인 수행에 의해 왜곡과 엔트로피가 지역적으로 최소화 되는 지점에서 설계되도록 하였다. 모의 실험에서 제안된 시간축 분할 기법은 현존 음성 부호화 기법에 널리 사용되고 있는 분할 벡터 양자화 기법과 비교하여, 왜곡-비트율 특성 관점에서 보다 우수한 성능을 나타내었으며, 주관적인 청취 테스트 결과, 음질적인 면에서도 기존의 벡터 양자화 기법에 비해 우수한 방법임을 알 수 있었다.

• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2178-2186
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• 2018

Online identification of load parameters is the premise of establishing a stable and highly-efficient ICPT (Inductive Coupled Power Transfer) system. However, compared with pure resistive load, precise identification of composite load, such as resistor-inductance load and resistance-capacitance load, is more difficult. This paper proposes a method for detecting the composite load parameters of ICPT system utilizing harmonics. In this system, the fundamental and harmonic wave channel are connected to the high frequency inverter jointly. The load parameter values can be obtained by setting the load equation based on the induced voltage of secondary-side network, the fundamental wave current, as well as the third harmonic current effective value received by the secondary-side current via Fourier decomposition. This method can achieve precise identification of all kinds of load types without interfering the normal energy transmission and it can not only increase the output power, but also obtain higher efficiency compared with the fundamental wave channel alone. The experimental results with the full-bridge LCCL-S type voltage-fed ICPT system have shown that this method is accurate and reliable.

• 한국세라믹학회지
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• 제39권10호
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• pp.988-993
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• 2002

Lanthanum Stannate Pyrochlore($La_2Sn_2O_7$) 촉매를 이용하여 $NO_x$ 제거를 위한 전기화학 촉매 셀을 제조하였다. 촉매전극은 수열합성법을 통해 합성한 $La_2Sn_2O_7$ 분말과 안정화 지르코니아(YSZ) 분말을 혼합하여 촉매층 페이스트를 제조한 후 이를 YSZ 디스크 고체전해질 위에 스크린프린팅하여 후막을 도포하였다. 위와 같이 제조한 전기화학 셀의 $NO_x$ 분해 실험은 galvanostat을 이용하여 셀에 일정한 전류를 인가하고 700${\circ}C$에서 NO 0.1%와 산소 2%의 반응가스에 대한 분해 정도를 gas chromatography와 NOx analyzer를 이용하여 측정을 하였다. 촉매 전극의 두께와 소성 온도에 따른 촉매전극의 미세구조가 $NO_x$ 분해에 미치는 영향과 전류량(0.05∼0.6A)에 따른 $NO_x$ 분해율을 측정하였다.

• 한국분말재료학회지
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• 제9권6호
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• pp.422-432
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• 2002

Mechanically driven decomposition of intermetallics during mechanical milling(MM 1 was investigated. This process for Fe-Ce and Fe-Sn system was studied using conventional XRD, DSC, magnetization and alternative current susceptibility measurements. Mechanical alloying and milling form products of the following composition (in sequence of increasing Gecontent): $\alpha$(${\alpha}_1$) bcc solid solution, $\alpha$+$\beta$-phase ($Fe_{2-x}Ge$), $\beta$-phase, $\beta$+FeGe(B20), FeGE(B20), FeGe(B20)+$FeGe_2$,$FeGe_2$,$FeGe_2$+Ge, Ge. Incongruently melting intermetallics $Fe_6Ge_5$ and $Fe_2Ge_3$ decompose under milling. $Fe_6Ge_5$ produces mixture of $\hat{a}$-phase and FeGe(B20), $Fe_2Ge_3$ produces mixture of FeGe(B20) and $FeGe_2$ phases. These facts are in good agreement with the model that implies local melting as a mechanism of new phase for-mation during medchanical alloying. Stability of FeGe(B20) phase, which is also incongruently melting compound, is explained as a result of highest density of this phase in Fe-Ge system. Under mechanical milling (MM) in planetary ball mill, FeSn intermetallic decomposes with formation $Fe_5Sn_3$ and $FeSn_2$ phases, which have the biggest density among the phases of Fe-Sn system. If decomposition degree of FeSn is relatively small(<60%), milled powder shows superparamagnetic behavior at room temperature. For this case, magnetization curves can be fitted by superposition of two Langevin functions. particle sizes for ferromagnetic $Fe_5Sn_3$ phase determined from fitting parameters are in good agreement with crystalline sizes determined from XRD data and remiain approximately chageless during MM. The decomposition of FeSn is attributed to the effects of local temperature and local pressure produced by ball collisions.

• 한국산업정보학회논문지
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• 제23권4호
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• pp.81-92
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• 2018

Bitcoin is a cryptographic digital currency and has been given a significant amount of attention in literature since it was first introduced by Satoshi Nakamoto in 2009. It has become an outstanding digital currency with a current market capitalization of approximately $60 billion. By 2019, it is expected to have over 5 million users. Nowadays, investing in Bitcoin is popular, and along with the advantages and disadvantages of Bitcoin, learning how to forecast is important for investors in their decision-making so that they are able to anticipate problems and earn a profit. However, most investors are reluctant to invest in bitcoin because it often fluctuates and is unpredictable, which may cost a lot of money. In this paper, we focus on solving the Bitcoin forecasting prediction problem based on deep learning structures and neural decomposition. First, we propose a deep learning-based framework for the bitcoin forecasting problem with deep feed forward neural network. Forecasting is a time-dependent data type; thus, to extract the information from the data requires decomposition as the feature extraction technique. Based on the results of the experiment, the use of neural decomposition and deep neural networks allows for accurate predictions of around 89%.