• ETRI Journal
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• 제36권4호
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• pp.545-553
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• 2014

This paper proposes an algorithm to sense orthogonal frequency-division multiplexing (OFDM) signals in cognitive radio (CR) systems. The basic idea behind this study is when a primary user is occupying a wireless channel, the covariance matrix is non-diagonal because of the time domain cross-correlation of the cyclic prefix (CP). In light of this property, a new decision metric that measures the power of the data found on two minor diagonals in the covariance matrix related to the CP is introduced. The impact of synchronization errors on the signal detection is analyzed. Besides this, a likelihood-ratio test is proposed according to the Neyman-Pearson criterion after deriving probability distribution functions of the decision metric under hypotheses of signal presence and absence. A threshold, subject to the requirement of probability of false alarm, is derived; also the probabilities of detection and false alarm are computed accordingly. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed algorithm.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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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.

• 한국멀티미디어학회논문지
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• 제19권3호
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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.

• 전기전자학회논문지
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• 제20권4호
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• pp.367-372
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• 2016

외부 지역에서 침투하는 외부침입자에 대한 침입탐지를 위한 기초적인 연구 수행을 위하여 광섬유 ROTDR (Rayleigh Optical Time Domain Reflectometer) 센서를 설계 및 기초 연구를 수행하였다. 외부침입자를 탐지하기 위한 센서는 침입 탐지판을 제작하여 모래 속에 매설하여 실내에서 모형을 설치하여 침입탐지 실험을 수행하였다. ROTDR센서의 신호 분석은 검출정도에 따른 신호의 특성을 분석하였다. 광섬유 ROTDR 센서는 크게 20kg, 40kg, 60kg, 그리고 80kg 등의 무게별로 4등급으로 구분하여 넓은 영역에 걸쳐 외부 침입자를 감시하기 위한 장거리용을 사용하였다. 결과 본 논문에서의 광섬유 센서는 사회중요 기반시설의 외부침입자 감시용 실시간 모니터링의 응용에 가능함을 확인 하였다.

• Asia-Pacific Journal of Atmospheric Sciences
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• 제54권4호
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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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• 제24권7호
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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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2013년도 춘계학술대회 논문집
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• pp.977-978
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• 2013

• 한국통신학회논문지
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• 제40권6호
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• pp.990-996
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• 2015

이 논문에서는 STDR(sequence time domain reflectometry) 기법의 상관기 앞단에 부호기를 넣음으로써 계산량을 증가시키지 않고도 원거리 고장의 위치 탐지 성능을 향상시킬 수 있는 방법을 제안한다. STDR 기법은 PN 수열을 인가하여 시간 영역 상관(time domain correlation)으로 고장 위치를 탐지하는 기법이므로, 상관기 앞단에 부호기를 넣음으로써 고장 위치에서 반사된 신호를 STDR 기법의 인가 신호에 가깝게 복원시켜 상관 값을 증가시킬 수 있다. 제안한 기법을 사용하여 원거리 고장에서 고장위치 탐지 성능을 크게 개선시킬 수 있음을 모의실험으로 확인한다.

• 상하수도학회지
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• 제20권6호
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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.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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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.