• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.649-655
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• 2022

In this paper, a determinant-based two-channel noise reduction method which utilizes speech presence probability (SPP) is proposed. The proposed method improves noise reduction performance from the conventional determinant-based two-channel noise reduction method in [7] by applying SPP to the Wiener filter gain. Consequently, the proposed method adaptively controls the amount of noise reduction depending on the SPP. For performance evaluation, the segmental signal-to-noise ratio (SNR), the perceptual evaluation of speech quality, the short time objective intelligibility, and the log spectral distance were measured in the simulated noisy environments considered various types of noise, reverberation, SNR, and the direction and number of noise sources. The experimental results presented that determinant-based methods outperform phase difference-based methods in most cases. In particular, the proposed method achieved the best noise reduction performance maintaining minimum speech distortion.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.694-702
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• 2022

This paper shows a small and low-powered wireless communication system based on the ATSC broadcasting system using the ISM frequency band that can be used as a PBS(Personal Broadcasting System). It is designed to demonstrate a channel-adaptive CR(Cognitive Radio) system to provide a better service quality in the unlicensed band where co-channel interference exists. And it achieved very reliable communications by a closed-loop active phased array antenna. This ATSC-based personal broadcasting platform can be modified easily with given flexibility by using GNU Radio as an open-source signal processing platform based on USRP and implementing additional functions in FPGA. In addition, the chosen communication frequency resource can be managed and controlled by the return channel that transmits the channel status and communication parameters between transmission and reception in real-time.

• Current Optics and Photonics
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• v.6 no.2
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• pp.171-182
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• 2022

A single-photon laser and mid-wave infrared (MWIR) common aperture optical system was designed and developed to detect and range a long-distance civil aviation aircraft. The secondary mirror of the Ritchey-Chretien (R-C) optical system was chosen as a dichroic lens to realize the design of a common aperture system for the laser and MWIR. Point spread function (PSF) ellipticity was introduced to evaluate the coupling efficiency of the laser receiving system. A small aperture stop and narrow filter were set in the secondary image plane and an afocal light path of the laser system, respectively, and the stray light suppression ability of the small aperture stop was verified by modeling and simulation. With high-precision manufacturing technology by single point diamond turning (SPDT) and a high-efficiency dichroic coating, the laser/MWIR common aperture optical system with a 𝜑300 mm aluminum alloy mirror obtained images of buildings at a distance of 5 km with great quality. A civil aviation aircraft detection experiment was conducted. The results show that the common aperture system could detect and track long-distance civil aviation aircraft effectively, and the coverage was more than 450 km (signal-to-noise ratio = 6.3). It satisfied the application requirements for earlier warning and ranging of long-range targets in the area of aviation, aerospace and ground detection systems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.29-33
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• 2022

In this paper, we propose the deep learning based pre interference cancellation scheme algorithm for power line communication (PLC) systems in smart grid. The proposed scheme estimates the channel noise information by applying a deep learning model at the transmitter. Then, the estimated channel noise is updated in database. In the modulator, the channel noise which reduces the power line communication performance is effectively removed through interference cancellation technique. As an impulsive noise model, Middleton Class A interference model was employed. The performance is evaluated in terms of bit error rate (BER). From the simulation results, it is confirmed that the proposed scheme has better BER performance compared to the theoretical model based on additive white Gaussian noise. As a result, the proposed interference cancellation with deep learning improves the signal quality of PLC systems by effectively removing the channel noise. The results of the paper can be applied to PLC for smart grid and general communication systems.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• Korean Journal of Cognitive Science
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• v.34 no.1
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• pp.1-20
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• 2023

In this study, we examined the effects of spatial attention in Hangul word recognition depending on visual factors. The visual complexity of words (Experiment 1) and contrast (Experiment 2) were manipulated to examine whether the effect of spatial attention differs depending on visual quality. Participants responded to words with and without codas in experiment 1 and words in high-contrast and low-contrast conditions in experiment 2. The effects of spatial attention were investigated by calculating the difference in performance between the condition where spatial cues were given at the target location (valid trial) and the condition where the spatial cues were not given at the target location (invalid trial) as the cuing effects. As a result, the cuing effects were similar depending on the complexity of the words. It indicates that the effects of spatial attention were not different across the visual complexity conditions. The cuing effects were greater in the low-contrast condition than in the high-contrast condition. The greater effect of spatial attention when the contrast is low was explained as a mechanism of signal enhancement.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1713-1719
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• 2022

As the noise canceler with a small processing delay increases the sampling frequency, a better-quality output can be obtained. For a single buffer, processing delay occurs because it is impossible to write new data while the processor is processing the data. When synthesizing with anti-noise and output signal, this processing delay creates additional buffering overhead to match the phase. In this paper, we propose an accelerator structure that minimizes processing delay and increases processing speed by alternately performing read and write operations using the Symmetric Even-Odd-buffer. In addition, we compare the structural differences between the two methods of noise cancellation (Fast Fourier Transform noise cancellation and adaptive Least Mean Square algorithm). As a result, using an Symmetric Even-Odd-buffer the processing delay was reduced by 29.2% compared to a single buffer. The proposed Symmetric Even-Odd-buffer structure has the advantage that it can be applied to various canceling algorithms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.584-588
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• 2021

The impedance of the speaker is likely to be recognized as a fixed value. However, speaker impedance continues to vary with frequency variation, especially larger in resonant frequency region. The sound pressure level of loudspeakers is determined by the current flowing throughout the coil that consists loudspeakers. If loudspeakers are driven by voltage, sound pressure level of the loudspeaker is distorted by the variation of loudspeaker impedance. Current-drive of loudspeakers can solve this problem, but distortion of sound pressure level occurs in low frequencies due to resonance. The distortion can degrade the sound quality of the sound system. So to solve this problem, In this paper, we propose a resonance compensation circuit using DSP. we simulates audio systems using an equivalent model of loudspeakers to verify distortion of sound pressure level due to impedance variation and propose a circuit to compensate it. The proposed circuit is configured using a state variable filter and it can adjust the center frequency and output, so it will be used various sound systems.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.78.1-78.1
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• 2019

There are a huge number of faint objects that have not been observed due to the lack of large and deep surveys. In this study, we demonstrate that a deep learning approach can produce a better quality deep image from a single pass imaging so that could be an alternative of conventional image stacking technique or the expensive large and deep surveys. Using data from the Sloan Digital Sky Survey (SDSS) stripe 82 which provide repeatedly scanned imaging data, a training data set is constructed: g-, r-, and i-band images of single pass data as an input and r-band co-added image as a target. Out of 151 SDSS fields that have been repeatedly scanned 34 times, 120 fields were used for training and 31 fields for validation. The size of a frame selected for the training is 1k by 1k pixel scale. To avoid possible problems caused by the small number of training sets, frames are randomly selected within that field each iteration of training. Every 5000 iterations of training, the performance were evaluated with RMSE, peak signal-to-noise ratio which is given on logarithmic scale, structural symmetry index (SSIM) and difference in SSIM. We continued the training until a GAN model with the best performance is found. We apply the best GAN-model to NGC0941 located in SDSS stripe 82. By comparing the radial surface brightness and photometry error of images, we found the possibility that this technique could generate a deep image with statistics close to the stacked image from a single-pass image.

• Journal of Yeungnam Medical Science
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• v.40 no.2
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• pp.136-145
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• 2023

Hepatic encephalopathy (HE) is a severe neuropsychiatric abnormality in patients with either acute or chronic liver failure. Typical brain magnetic resonance imaging findings of HE are bilateral basal ganglia high signal intensities due to manganese deposition in chronic liver disease and hyperintensity in T2, fluid-attenuated inversion recovery, or diffusion-weighted imaging (DWI) with hemispheric white matter changes including the corticospinal tract. Low values on apparent diffusion coefficient mapping of the affected area on DWI, indicating cytotoxic edema, can be observed in acute HE. However, neuropsychological impairment in HE ranges from mild deficits in psychomotor abilities affecting quality of life to stupor or coma with higher grades of hepatic dysfunction. In particular, the long-lasting compensatory mechanisms for the altered metabolism in chronic liver disease make HE imaging results variable. Therefore, the clinical relevance of imaging findings is uncertain and differentiating HE from other metabolic diseases can be difficult. The recent introduction of concepts such as "acute-on-chronic liver failure (ACLF)," a new clinical entity, has led to a change in the clinical view of HE. Accordingly, there is a need to establish a corresponding concept in the field of neuroimaging diagnosis. Herein, we review HE from a historical and etiological perspective to increase understanding of brain imaging and help establish an imaging approach for advanced new concepts such as ACLF. The purpose of this manuscript is to provide an understanding of HE by reviewing neuroimaging findings based on pathological and clinical concepts of HE, thereby assisting in neuroimaging interpretation.