• Journal of the Korean Society of Radiology
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• v.16 no.4
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• pp.395-403
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• 2022

Flat-panel detector (FPD) used in digital radiographic imaging systems was used to perform a quantitative power spectrum evaluation as a result of the thickness change of polymethyl methacrylate (PMMA), a tissue equivalent. As the PMMA thickness increases with the resolution-chart phantom image, the effect of the scattering line increases, indicating that the modulation characteristics decrease, and the image is bright. The results show that the noise of the image increases, and noise-power spectral images are obtained by Fourier transform to confirm by spatial frequency. Thus, it can be verified that the PMMA thickness and noise are proportional through the result of evaluating the change of resolution characteristics and representing the 2D noise-power spectrum as one-dimensional values by evaluating the change of scattering line with MTF as the PMMA thickness increases in the image.

• Journal of Advanced Navigation Technology
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• v.27 no.1
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• pp.63-70
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• 2023

In this work, a capacitive wireless power transfer (C-WPT) system is presented for wireless sensor system (WSS) applications in marine propulsion shafts. For a single Q factor on both sides of the coupling capacitor and reactive power removal from the circuit, a double-sided LCLC converter and transformers topology are designed to drive the rotary C-WPT system for WSS on the shaft. Parallel-connected parallel plate rotating capacitors with a capacitance of 170 pF are designed and implemented for the C-WPT system on a snow rotating shaft. In the experimental results, the C-WPT system achieved a transmission efficiency of 66.67% with 7.8 W output power at 3 mm distance and 1 MHz operating frequency. Therefore, it was proved that the fabricated C-WPT system can supply power to the WSS of the rotating shaft.

• Journal of Broadcast Engineering
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• v.28 no.1
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• pp.3-20
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• 2023

A computer-generated hologram (CGH) is a digitally calculated and recorded hologram in which the amplitude and phase information of an image is transmitted in free space. The CGH is in the form of a complex hologram, but it is converted into a phase-only hologram to display through a phase-only spatial light modulator (SLM). In this paper, in the process of including the amplitude information of an object in the phase information, when a technique that includes subsampling such as DPAC is used, we showed experimentally that the bandwidth of the phase-only hologram increases, and as a result, aliasing that was not present in the complex hologram can occur. In addition, it was experimentally shown that it is possible to generate a high-quality phase-only hologram by restricting the spatial frequency range even at a distance where the numerical reconstruction performance is degraded by aliasing.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.84-90
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• 2022

Machine tool state monitoring is a process that automatically detects the states of machine. In the manufacturing process, the efficiency of machining and the quality of the product are affected by the condition of the tool. Wear and broken tools can cause more serious problems in process performance and lower product quality. Therefore, it is necessary to develop a system to prevent tool wear and damage during the process so that the tool can be replaced in a timely manner. This paper proposes a method for diagnosing five tool states using a deep learning-based hierarchical convolutional neural network to change tools at the right time. The one-dimensional acoustic signal generated when the machine cuts the workpiece is converted into a frequency-based power spectral density two-dimensional image and use as an input for a convolutional neural network. The learning model diagnoses five tool states through three hierarchical steps. The proposed method showed high accuracy compared to the conventional method. In addition, it will be able to be utilized in a smart factory fault diagnosis system that can monitor various machine tools through real-time connecting.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.357-363
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• 2023

In underwater signal processing, separating individual signals from mixed signals has long been a challenge due to low signal quality. The common method using Short-time Fourier transform for spectrogram analysis has faced criticism for its complex parameter optimization and loss of phase data. We propose a Triple-path Recurrent Neural Network, based on the Dual-path Recurrent Neural Network's success in long time series signal processing, to handle three-dimensional tensors from multi-channel sensor input signals. By dividing input signals into short chunks and creating a 3D tensor, the method accounts for relationships within and between chunks and channels, enabling local and global feature learning. The proposed technique demonstrates improved Root Mean Square Error and Scale Invariant Signal to Noise Ratio compared to the existing method.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.845-850
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• 2023

In this paper, we propose a method for diagnosing ball bearing vibration using transfer learning. STFT, which can analyze vibration signals in time-frequency, was used as input to CNN to diagnose failures. In order to rapidly learn CNN-based deep artificial neural networks and improve diagnostic performance, we proposed a transfer learning-based deep learning learning technique. For transfer learning, the feature extractor and classifier were selectively learned using a VGG-based image classification model, the data set for learning was publicly available ball bearing vibration data provided by Case Western Reserve University, and performance was evaluated by comparing the proposed method with the existing CNN model. Experimental results not only prove that transfer learning is useful for condition diagnosis in ball bearing vibration data, but also allow other industries to use transfer learning to improve condition diagnosis.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.5
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• pp.315-321
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• 2023

Long-span bridges are flexible structures with low natural frequencies and damping ratios, making them susceptible to vibrational serviceability problems. However, the current design guideline of South Korea assumes a uniform threshold of wind speed or vibrational amplitude to assess the occurrence of harmful vibrations, potentially overlooking the complex vibrational patterns observed in long-span bridges. In this study, we propose a pointwise vortex-induced vibration (VIV) detection method using a deep-learning-based signalsegmentation model. Departing from conventional supervised methods of data acquisition and manual labeling, we synthesize training data by generating sinusoidal waves with an envelope to accurately represent VIV. A Fourier synchrosqueezed transform is leveraged to extract time-frequency features, which serve as input data for training a bidirectional long short-term memory model. The effectiveness of the model trained on synthetic VIV data is demonstrated through a comparison with its counterpart trained on manually labeled real datasets from an actual cable-supported bridge.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.510-515
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• 2022

In this paper, a microstrip patch sensor antenna (MPSA) for detecting the concentration of an ethanol-water solution in a microliter volume is proposed. A rectangular slot was added at the radiating edge of the patch to increase the sensitivity to the relative permittivity change. To improve a low input resistance caused by placing an ethanol-water solution, which is a polar liquid with high dielectric constant and high loss tangent, on the patch, a quarter-wave impedance transformer was added between the 50-ohm feedline and the patch, and the MPSA was fabricated on a 0.76 mm-thick RF-35 substrate. A cylindrical container was made of acryl, and 15 microliters of the ethanol-water solution was tested from 0% to 100% of ethanol concentration at 20% intervals. Experiment results show that the resonant frequency increased from 1.947 GHz to 2.509 GHz when the ethanol concentration of the ethanol-water solution was increased from 0% to 100%, demonstrating the performance as a concentration detecting sensor.

• Tribology and Lubricants
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• v.39 no.6
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• pp.221-227
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• 2023

A rotordynamic system consists of components that undergo rotational motion. These components include shafts, impellers, thrust collars, and components that support rotation, such as bearings and seals. The motion of this type of rotating system can be modeled as two-dimensional motion and, accordingly, the equation of motion for the rotordynamic system can be represented using complex coordinates. The directional frequency response function (dFRF) can be derived from this complex coordinate system and used as an effective analytical tool for rotating machinery. However, the dFRF is not widely used in the field because most previous studies and commercial software are based on real coordinate systems. The objective of the current study is to introduce the dFRF and show that it can be an effective tool in rotordynamic analysis. In this study, the normal frequency response function (nFRF) and dFRF are compared under rotordynamic analysis for isotropic and unisotropic rotors. Results show that in the nFRF, the magnitude of the response is the same for both positive and negative frequencies, and the response is similar under all modes. Consequently, the severity of the mode cannot be identified. However, in the dFRF, the forward and backward modes are clearly distinguishable in the frequency domain of the isotropic rotor, and the severity of the mode can be identified for the unisotropic rotor.

• The Journal of the Acoustical Society of Korea
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• v.27 no.7
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• pp.372-378
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• 2008

Voice activity detectors (VAD) are important in wireless communication and speech signal processing, In the conventional VAD methods, an expression for the likelihood ratio test (LRT) based on statistical models is derived in discrete Fourier transform (DFT) domain, Then, speech or noise is decided by comparing the value of the expression with a threshold, This paper presents a new statistical VAD method based on a signal subspace approach, The probabilistic principal component analysis (PPCA) is employed to obtain a signal subspace model that incorporates probabilistic model of noisy signal to the signal subspace method, The proposed approach provides a novel decision rule based on LRT in the signal subspace domain, Experimental results show that the proposed signal subspace model based VAD method outperforms those based on the widely used Gaussian distribution in DFT domain.