• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1064-1066
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• 2003

In this paper, ground penetrating radar (GPR), which has the capability to detect non metal and plastic mines, is proposed to detect and discriminate antipersonnel (AP) landmines. The time domain GPR - Impulse radar and frequency domain GPR - SFCW (Stepped Frequency Continuous Wave) radar is utilized for metal and non-metal landmine detection and its performance is investigated. Since signal processing is vital for target reorganization and clutter rejection, we implemented the MUSIC (Multiple Signal Classification) algorithm for the signal processing of SFCW radar data and SAR (Synthetic Aperture Radar) processing method for the signal processing of Impulse radar data.

• Journal of Korea Multimedia Society
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• v.19 no.3
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• pp.557-566
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• 2016

Heart sounds generated by the beating heart and blood flow reflect the turbulence created when the heart valves snap shut. Cardiac diagnosis is typically started by an auscultation using a stethoscope, from which a medical doctor, depending on his hearing capabilities and training, listens and interprets the acoustic signal. This method of diagnostic is uncertain, mostly due to the fact that human ear loses the acoustic frequency sensitivity through the years. Even though an auscultation has some weaknesses like uncertainty, it is considered as a primary tool due to its simplicity. In this paper, heart murmur detection algorithm is proposed using time and frequency characteristics of heart sound. The propose heart murmur detection method adapted conventional primary heart sound detection method in time domain and modified spectral flatness method in frequency domain for detecting heart murmurs. From experimental results, it is confirmed that the proposed algorithm detect the heart murmurs efficiently.

• Journal of IKEEE
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• v.20 no.4
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• pp.367-372
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• 2016

We Designed and Conduct a study on the basic intrusion detection research for outside intruder, which can determine the location and the weight of an intruder into infrastructure, by using Fiber-Optic ROTDR( Rayleigh Optical Time Domain Reflectometer) sensor, which are buried in the sand, were prepared to respond the intruder effects. The signal of ROTDR was analyzed to confirm the detection performance. The weight could be detected as 4 grades, such as 20kg, 40kg, 60kg, and 80kg. which used long distance fiber for intruder detection on wide area. This sensor was possible for application of real-time monitoring of infrastructures.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.527-544
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• 2018

This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the $3.7{\mu}m$ and $10.8{\mu}m$ channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation-maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.

• Korean Journal of Radiology
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• v.24 no.7
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• pp.698-714
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• 2023

In recent years, artificial intelligence, especially object detection-based deep learning in computer vision, has made significant advancements, driven by the development of computing power and the widespread use of graphic processor units. Object detection-based deep learning techniques have been applied in various fields, including the medical imaging domain, where remarkable achievements have been reported in disease detection. However, the application of deep learning does not always guarantee satisfactory performance, and researchers have been employing trial-and-error to identify the factors contributing to performance degradation and enhance their models. Moreover, due to the black-box problem, the intermediate processes of a deep learning network cannot be comprehended by humans; as a result, identifying problems in a deep learning model that exhibits poor performance can be challenging. This article highlights potential issues that may cause performance degradation at each deep learning step in the medical imaging domain and discusses factors that must be considered to improve the performance of deep learning models. Researchers who wish to begin deep learning research can reduce the required amount of trial-and-error by understanding the issues discussed in this study.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.977-978
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• 2013

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.6
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• pp.990-996
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• 2015

This paper proposes an enhanced scheme adding a sign detector at the front of the correlator in STDR (sequence time domain reflectometry) system. We have executed simulations to show the improvement of detection performance in two fault types and various fault locations. Consequently, it can be shown that the proposed scheme improves the detection performance of the location of far-fault without increasing the computational complexity.

• Journal of Korean Society of Water and Wastewater
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• v.20 no.6
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• pp.893-904
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• 2006

To improve sensitivity of biosensor as yeast two-hybrid detection system for estrogenic activity of suspected chemicals, we tested effects of several combinations of the bait and fish components in the two-hybrid system on Saccharomyces cerevisiae inducted a chromosome-integrated lacZ reporter gene that was under the control of CYC1 promoter and the upstream Gal4p-binding element $UAS_{GAL}$. The bait components that were fused with the Gal4p DNA binding domain are full-length human estrogen receptor ${\alpha}$ and its ligand-binding domain. The fish components that were fused with the Gal4p transcriptional activation domain were nuclear receptor-binding domains of co-activators SRC1 and TIF2. We found that the combination of the full-length human estrogen receptor ${\alpha}$ with the nuclear receptor-binding domain of co-activator SRC1 was most effective for the estrogen-dependent induction of reporter activity among the two-hybrid systems so far reported. The relative strength of transcriptional activation by representative natural and xenobiotic chemicals was well correlated with their estrogenic potency that had been reported with other assay systems.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.40-42
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• 2009

This paper presents an algorithm, which is based on Otsu's method, for accurate and robust endpoint detection for speech recognition under noisy environments. The features are extracted in time domain, and then an optimal threshold is selected by minimizing the discriminant criterion, so as to maximize the separability of the speech part and environment part. The simulation results show that the method play a good performance in detection accuracy.

• Journal of Communications and Networks
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• v.9 no.1
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• pp.75-83
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• 2007

Orthogonal frequency division multiplexing (OFDM) systems with multiple transmit antennas can exploit space-time block coding on each subchannel for reliable data transmission. Spacetime coded OFDM systems, however, are very sensitive to time variant channels because the channels need to be static over multiple OFDM symbol periods. In this paper, we propose to mitigate the channel variations in the frequency domain using a linear filter in the frequency domain that exploits the sparse structure of the system matrix in the frequency domain. Our approach has reduced complexity compared with alternative approaches based on time domain block-linear filters. Simulation results demonstrate that our proposed frequency domain block-linear filter reduces computational complexity by more than a factor of ten at the cost of small performance degradation, compared with a time domain block-linear filter.