• Korean Journal of Optics and Photonics
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• v.31 no.6
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• pp.268-273
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• 2020

We analyze the characteristics of the coherent beam combination of lasers by monitoring the speckle pattern of the beam reflected from a scattering medium. Three collimated laser sources with high coherence are focused on a scattering target using a lens, and we then examine the speckle pattern of the returned beam in the Fourier domain. We observe that the size of the speckle pattern changes, depending on the focused-beam size or degree of spatial overlap of the three beams. Furthermore, through Fourier-domain analysis of the speckle pattern we obtain the monitoring variable to qualify the efficiency of the coherent beam combination.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.685-692
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• 2024

In this paper, we propose a new CNN model to improve the recognition performance of command voice signals. This method obtains a spectrogram image after performing a short-time Fourier transform (STFT) of the input signal and improves command recognition performance through supervised learning using a CNN model. After Fourier transforming the input signal for each short-time section, a spectrogram image is obtained and multi-classification learning is performed using a CNN deep learning model. This effectively classifies commands by converting the time domain voice signal to the frequency domain to express the characteristics well and performing deep learning training using the spectrogram image for the conversion parameters. To verify the performance of the speech recognition system proposed in this study, a simulation program using Tensorflow and Keras libraries was created and a simulation experiment was performed. As a result of the experiment, it was confirmed that an accuracy of 92.5% could be obtained using the proposed deep learning algorithm.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.12 no.7
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• pp.1110-1114
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• 2001

The discrete wavelet concept in the k-domain is applied to efficiently represent Green function of integral equations. Application of discrete wavelet concept to Green function in the k-domain can be implemented equivalently by using spatial domain variable-sized windows. The proposed method consists of constant Q-filtering, changing the center of coordinates, and transforming spatially filtered Green functions into those in the k-domain. A mathematical expression of Green function based on the discrete wavelet concept is derived and its characteristics are discussed.

• The Journal of the Acoustical Society of Korea
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• v.35 no.1
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• pp.8-15
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• 2016

As a generalized form of the conventional Fourier transform, fractional Fourier transform (FrFT) can analyze a signal at intermediate domain between time and frequency domains with a transform order ${\alpha}$. Especially, FrFT has a number of advantages in the analysis of LFM (Linear Frequency Modulation) signals due to its robustness to noise. In this paper, we have proposed a new method to detect and estimate the distance of the target from the FrFT spectrum of the received echo signal. Experimental results have validated the proposed method, and shown that reliable target distance could be estimated in noise and reverberation environments.

• The Proceeding of the Korean Institute of Electromagnetic Engineering and Science
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• v.3 no.2
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• pp.46-55
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• 1992

In this paper, A spectral inversion scheme in cylinderical coordinates, appling the moment method procedure is suggested to reconstruct permittivity profiles of inhomogeneous dielectric objects. Angular spectral domain reconstruction is shown to be less sensitive to the ill-posedness due to the noise in the scattered field then the configuration reconstruction

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.7 no.3
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• pp.239-245
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• 1996

The time-domain response of a two-conductor-structure transmission line excited by an incident electromagnetic pulse is numerically analyzed using the Finite-Difference Time-Domain (FDTD) method. The external electromagnetic pulse is generated by ultilizing a TEM cell. The simulated time-domain response is compared with the time-domain response which is obtained by the Inverse Fast Fourier Transform(IFFT) of the frequency domain measurement data.

• Journal of Korean Tunnelling and Underground Space Association
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• v.5 no.1
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• pp.13-21
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• 2003

To investigate the safety and stability of a concrete lining, numerous studies have been conducted over the years and several methods have been developed. Most signal processing techniques of NDT have been based on Fourier analysis. However, the application of Fourier analysis to analyze recorded vibrational signal shows results in the frequency domain only, and it is not enough to analyze transient waves precisely. In this study, Wavelet theory was employed for the analysis of non-stationary wave induced by mechanical impact on tunnel concrete lining. The Wavelet transform of transient signals provides a method for mapping the frequency spectrum as a function of time. To verify the availability of Wavelet transform as a time-frequency analysis tool, model experiments have been conducted and the thickness of the concrete lining was estimated based on the proposed theory. From this study, it was found that the contour map by Wavelet transform provides more distinct results than the power spectrum by Fourier transform and it was also found that Wavelet transform was also an effective tool for the analysis of dispersive waves in tunnel concrete linings.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.3
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• pp.385-396
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• 1993

In this paper, a improved method of digital image analysis required in basic medical science for diagnosis of cells was proposed. The object image was the thyroid gland cell image, and the purpose was automatic discrimination of three classes cells(normal cell, follicular neoplastic cells, and papillary neoplastic cells) by difference of chromatin patterns. To segment the cell nucleus from background, the region segmentation algorithm by edge tracing was proposed. And feature parameter was obtained from discrete Fourier transformation of image. After construct a feature sample group of each cells, experiment of discrimination was executed with any verification cells. As a consequency of using features proposed in this paper, get a better recognition rate(70-90%) than previously reported papers, and this method give shape to get objectivity and fixed quantity in diagnosis of cells, The methods described in this paper be used immediately for discrimination of neoplastic cells.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.115-126
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• 2023

Due to the fluctuating random and periodical nature of renewable energy generation power quality disturbances occurred more frequently in power generation transformation transmission and distribution. Various power quality disturbances may lead to equipment damage or even power outages. Therefore it is essential to detect and classify different power quality disturbances in real time automatically. The traditional PQD identification method consists of three steps: feature extraction feature selection and classification. However, the handcrafted features are imprecise in the feature selection stage, resulting in low classification accuracy. This paper proposes a deep neural architecture based on Convolution Neural Network and Long Short Term Memory combining the time and frequency domain features to recognize 16 types of Power Quality signals. The frequency-domain data were obtained from the Fast Fourier Transform which could efficiently extract the frequency-domain features. The performance in synthetic data and real 6kV power system data indicate that our proposed method generalizes well compared with other deep learning methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.1
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• pp.214-223
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• 1996

Noise effects on image profiles reconstructed by the spectral inverse scattering technique is studied based on moment method with series-expanded basis function. It is found that the Fourier series expansion to the field distribution and the averaging of the reconstructed profile in each enlarged cell provides an effective tool for the reduction of noise effects.