• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.11
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• pp.637-644
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• 2014

This paper investigates the detection performance of fault location using STDR(sequence time domain reflectometry) and SSTDR(spread spectrum time domain reflectometry) with various length and types of sequences, and then, proposes an improved detection technique by eliminating the injected signal in SSTDR. The detection error rates are compared and analyzed in power line channel model with various fault locations, fault types, and spreading sequences such as m-sequence, binary Barker sequence, and 4-phase Frank sequence. It is shown that the proposed technique is able to improve the detection performance obviously when the reflected signal is weak or the fault location is extremely close.

• The Journal of the Korea Contents Association
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• v.17 no.12
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• pp.158-168
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• 2017

As the big data technologies advance, there is an increasing interest in systematic methodologies for data-based policy determination especially in the public health area. This study proposes a method to develop an issue detection model through the collaboration with domain experts in order to intelligently detect major socio-national issues on infectious diseases based on data. At first, the factors influencing the 'domestic inflow of foreign infectious diseases' are determined and variables representing the factors are set. Thereafter, by using system dynamics methods, the causal analysis is made to find causal map indicating main influential factors. In this process, an empirical modeling is conducted through collaboration between data analysts and experts in the infectious disease domain. The proposed issue detection approach based on domain knowledges will make it possible to make a decision on policies more efficiently if the detection system is capable of continuos monitoring of the related issues.

• Journal of Microbiology and Biotechnology
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• v.33 no.7
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• pp.964-972
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• 2023

Bacteriophage endolysins are peptidoglycan hydrolases composed of cell binding domain (CBD) and an enzymatically active domain. A phage endolysin CBD can be used for detecting bacteria owing to its high specificity and sensitivity toward the bacterial cell wall. We aimed to develop a method for detection of Enterococcus faecalis using an endolysin CBD. The gene encoding the CBD of ECP3 phage endolysin was cloned into the Escherichia coli expression vector pET21a. A recombinant protein with a C-terminal 6-His-tag (CBD) was expressed and purified using a His-trap column. CBD was adsorbed onto epoxy magnetic beads (eMBs). The bacterial species specificity and sensitivity of bacterial binding to CBD-eMB complexes were determined using the bacterial colony counting from the magnetic separations after the binding reaction between bacteria and CBD-eMB complexes. E. faecalis could bind to CBD-eMB complexes, but other bacteria (such as Enterococcus faecium, Staphylococcus aureus, Escherichia coli, Acinetobacter baumannii, Streptococcus mutans, and Porphyromonas gingivalis) could not. E. faecalis cells were fixed onto CBD-eMB complexes within 1 h, and >78% of viable E. faecalis cells were recovered. The E. faecalis recovery ratio was not affected by the other bacterial species. The detection limit of the CBD-eMB complex for E. faecalis was >17 CFU/ml. We developed a simple method for the specific detection of E. faecalis using bacteriophage endolysin CBD and MBs. This is the first study to determine that the C-terminal region of ECP3 phage endolysin is a highly specific binding site for E. faecalis among other bacterial species.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.11
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• pp.1082-1089
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• 2012

Recent malicious attempts in Cyber space are intended to emerge national threats such as Suxnet as well as to get financial benefits through a large pool of comprised botnets. The evolved botnets use the Domain Name System(DNS) to communicate with the C&C server and zombies. DNS is one of the core and most important components of the Internet and DNS traffic are continually increased by the popular wireless Internet service. On the other hand, domain names are popular for malicious use. This paper studies on DNS-based cyber threats domain detection by data classification based on supervised learning. Furthermore, the developed cyber threats domain detection system using DNS traffic analysis provides collection, analysis, and normal/abnormal domain classification of huge amounts of DNS data.

• Smart Structures and Systems
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• v.6 no.7
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• pp.811-833
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• 2010

Vibration control and health monitoring of building structures have been actively investigated in recent years but treated separately according to the primary objective pursued. This paper presents a general approach in the time domain for integrating vibration control and health monitoring of a building structure to accommodate various types of control devices and on-line damage detection. The concept of the time-domain approach for integrated vibration control and health monitoring is first introduced. A parameter identification scheme is then developed to identify structural stiffness parameters and update the structural analytical model. Based on the updated analytical model, vibration control of the building using semi-active friction dampers against earthquake excitation is carried out. By assuming that the building suffers certain damage after extreme event or long service and by using the previously identified original structural parameters, a damage detection scheme is finally proposed and used for damage detection. The feasibility of the proposed approach is demonstrated through detailed numerical examples and extensive parameter studies.

• Journal of Advanced Navigation Technology
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• v.7 no.1
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• pp.38-50
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• 2003

In this paper, a new high resolution reflectometry scheme, time-frequency domain reflectometry (TFDR), is proposed to detect and locate fault in wiring. Traditional reflectometry methods have been achieved in either the time domain or frequency domain only. However, time-frequency domain reflectometry utilizes time and frequency information of a transient signal to detect and locate the fault. The time-frequency domain reflectometry approach described in this paper is characterized by time-frequency reference signal design and post-processing of the reference and reflected signals to detect and locate the fault. Design of the reference signal in time-frequency domain reflectometry is based on the determination of the frequency bandwidth of the physical properties of cable under test. The detection and estimation of the fault on the time-frequency domain reflectometry relies on the time-frequency domain reflectometry is compared with commercial time domain reflectomtery (TDR) instrument. In these experiments provided in this paper, TFDR locates the fault with smaller error than TDR. Knowledge of time and frequency localized information for the reference and reflected signal gained via time-frequency analysis, allows one to detect the fault and estimate the location accurately.

• Geophysics and Geophysical Exploration
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• v.7 no.1
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• pp.45-50
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• 2004

This paper describes a method of detecting formation boundaries, and permeable fractures, from frequency-domain Stoneley wave logs. Field data sets were collected between the depths of 330 and 360 m in well EE-4 in the Higashi-Hachimantai geothermal field, using a monopole acoustic logging tool with a source central frequency of 15 kHz. Stoneley wave amplitude spectra were calculated by performing a fast Fourier transform on the waveforms, and the spectra were then collected into a frequency-depth distribution of Stoneley wave amplitudes. The frequency-domain Stoneley wave log shows four main characteristic peaks at frequencies 6.5, 8.8, 12, and 13.3 kHz. The magnitudes of the Stoneley wave at these four frequencies are affected by formation properties. The Stoneley wave at higher frequencies (12 and 13.3 kHz) has higher amplitudes in hard formations than in soft formations, while the wave at lower frequencies (6.5 and 8.8 kHz) has higher amplitudes in soft formations than in hard formations. The correlation of the frequency-domain Stoneley wave log with the logs of lithology, degree of welding, and P-wave velocity is excellent, with all of them showing similar discontinuities at the depths of formation boundaries. It is obvious from these facts that the frequency-domain Stoneley wave log provides useful clues for detecting formation boundaries. The frequency-domain Stoneley wave logs are also applicable to the detection of a single permeable fracture. The procedure uses the Stoneley wave spectral amplitude logs at the four frequencies, and weighting functions. The optimally weighted sum of the four Stoneley wave spectral amplitudes becomes almost constant at all depths, except at the depth of a permeable fracture. The assumptions that underlie this procedure are that the energy of the Stoneley wave is conserved in continuous media, but that attenuation of the Stoneley wave may occur at a permeable fracture. This attenuation may take place at anyone of the four characteristic Stoneley wave frequencies. We think our multispectral approach is the only reliable method for the detection of permeable fractures.

• Journal of Korea Multimedia Society
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• v.17 no.3
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• pp.287-293
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• 2014

Early fire detection is very important to avoid loss of lives and material damage. The conventional smoke detector sensors have difficulties in detecting smoke in large outdoor areas. The video-based smoke detection can overcome these drawbacks. This paper proposes a new smoke detection method in video sequences. It uses the ratio of variation rate of subband energy in the wavelet transform domain. In order to reduce the false alarm, candidate smoke blocks are detected by using motion, decrease of chromaticity and the average intensity of block in the YUV color space. Finally, it decides whether the candidate smoke blocks are smokes or not by using their temporal changes of subband energies in the wavelet transform domain. Experimental results show that the proposed method noticeably increases the accuracy of smoke detection and reduces false alarm compared with the conventional smoke detection methods using wavelets.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.689-690
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• 2012

• 한국어정보학회:학술대회논문집
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• 2017.10a
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• pp.56-59
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• 2017

CNN(Convolutional Neural Network)을 이용하여 발화 주제 다중 분류 task를 multi-labeling 방법과, cluster 방법을 이용하여 수행하고, 각 방법론에 MSE(Mean Square Error), softmax cross-entropy, sigmoid cross-entropy를 적용하여 성능을 평가하였다. Network는 음절 단위로 tokenize하고, 품사정보를 각 token의 추가한 sequence와, Naver DB를 통하여 얻은 named entity 정보를 입력으로 사용한다. 실험결과 cluster 방법으로 문제를 변형하고, sigmoid를 output layer의 activation function으로 사용하고 cross entropy cost function을 이용하여 network를 학습시켰을 때 F1 0.9873으로 가장 좋은 성능을 보였다.