• Journal of the Korea society of information convergence
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• v.6 no.1
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• pp.1-6
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• 2013

The electrocardiogram(ECG) is widely used for the diagnosis of heart disease recent. In order to correct diagnosis, wavelet and thresholding is studied. In this study, we study hard inverse thresholding that is apply the existing hard thresholding. It apply to hard inverse thresholding on Pan-Tomkins algorism, that was simplified. The results of mit-bih No. 103 ECG signal is detected R peaks was detected unaffected by signal distortion and noise.

• Proceedings of the KIEE Conference
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• 2004.11a
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• pp.255-257
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• 2004

This paper was studied electromagnetic field distribution and damping of PD(Partial Discharge) signal in GIS(Gas Insulated Switchgear). Cut-off frequency of electromagnetic wave propagation modes were computed, electromagnetic field distribution of propagation modes in GIS by FEM(Finite Element Method) were simulated and simulated damping characteristic of electromagnetic waves in GIS by EMTP(Electromagnetic Transient Program) when generated PD pulse. Frequency band of $TE_{mn}/TM_{mn}$ modes were determinated by simulation results of electromagnetic field distribution and were discussed optimal position of UHF sensor from this results. Equivalent circuit was used to simulate signal damping of PD pulse in GIS by EMTP and compared with measured results in laboratory of KERI.

• Journal of the Korea Society of Computer and Information
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• v.18 no.1
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• pp.93-101
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• 2013

The accurate detection of R-wave is important for other feature extraction in ECG, and R-wave has a lot of medical information about heart. Numerous R-wave detection algorithms have been studied on the ECG signal shape analysis, but it was difficult to find accurate R-wave when the shape of R-wave is similar to the shape of P-wave. This paper presents an R-wave detection method based on the refractory period that is the period of depolarization and repolarization of the cell membrane after excitation. And we also use the shape of kurtosis in the refractory period. The proposed method is validated using the ECG records of the MIT-BIH arrhythmia database. Experimental results show that the proposed method significantly outperforms other method in case of 105 and 108 record that have R-wave similar to P-wave, as well as other records.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.200-207
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• 2014

Arrhythmia electrocardiogram signal contains a specific unusual heartbeat with abnormal morphology. Because unusual heartbeat is useful for diagnosis and classification of various diseases, such as arrhythmia, detection of unusual heartbeat from the arrhythmic ECG signal is very important. Amplitude and kurtosis at R-peak point and RR interval are characteristics of ECG signal on R-wave. In this paper, we provide a method for detecting unusual heartbeat based on these. Through the value of the attribute deviates more from the average value if unusual heartbeat is more certainly, the proposed method detects unusual heartbeat in order using the mean and standard deviation. From 15 ECG signals of MIT-BIH arrhythmia database which has R-wave distortion, we compare the result of conventional method which uses the fixed threshold value and the result of proposed method. Throughout the experiment, the sensitivity is significantly increased to 97% from 50% using the proposed method.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.62-69
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• 2022

An electrocardiogram (ECG) is one of the most important biosignals, and in particular, continuous ECG monitoring is very important in patients with arrhythmia. There are many different types of arrhythmia (sinus node, sinus tachycardia, atrial premature beat (APB), and ventricular fibrillation) depending on the cause, and continuous ECG monitoring during daily life is very important for early diagnosis of arrhythmias and setting treatment directions. The ECG signal of arrhythmia patients is very unstable, and it is difficult to detect the R-peak point, which is a key feature for automatic arrhythmias detection. In this study, we develped a continuous measuring Holter ECG monitoring device and software for analysis and confirmed the utility of R-peak of the ECG signal with MIT-BIH arrhythmia database. In future studies, it needs the validation of algorithms and clinical data for morphological classification and prediction of arrhythmias due to various etiologies.

• The Plant Pathology Journal
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• v.30 no.3
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• pp.304-309
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• 2014

The rpf genes and $colS_{XOO1207}/colR_{XOO1208}$ were known to require for virulence of Xanthomonas oryzae pv. oryzae (Xoo). In Xoo KACC10331 genome, two more colS/colR genes, $colS_{XOO3534}$ (raxH)/$colR_{XOO3535}$ (raxR) and $colS_{XOO3762}/colR_{XOO3763}$ were annotated. The $colS_{XOO3534}/colR_{XOO3535}$ were known to control AvrXa21 activity and functions of $colS_{XOO3762}/colR_{XOO3763}$ were unknown in Xoo. To characterize the relationship between rpf and colS/colR genes, expression of colS/colR genes in Rpf mutants of Xoo were analyzed with quantitative reverse transcription PCR (qRT-PCR). Expressions of all three colS/colR genes increased in the rpfF mutant in which DSF synthesis is defective. Expression of $colS_{XOO1207}/col-R_{XOO1208}$, $colS_{XOO3534}/colR_{XOO3535}$ and $colS_{XOO3762}/colR_{XOO3763}$ increased 2, 2-7, 3-13 folds respectively. Expression of $colS_{XOO3534}$ and $colS_{XOO3762}$ also increased 2-4 folds in the rpfG mutant in which the signal from DSF is no longer transferred to down-stream. Expression of the other colS/colR genes was not significantly changed in the rpfG mutant compared to the wild type. Since RpfF and RpfG are responsible for DSF synthesis and signal transfer from DSF to down-stream to regulate virulence gene expression, these results suggest that the DSF and DSF-mediated signal regulate negatively three colS/colR genes in Xoo.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.163-172
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• 2022

Typical anti-jamming technologies based on array antennas, Space Time Adaptive Process (STAP) & Space Frequency Adaptive Process (SFAP), are very effective algorithms to perform nulling and beamforming. However, it does not perform equally well for all types of jamming signals. If the anti-jamming algorithm is not optimized for each signal type, anti-jamming performance deteriorates and the operation stability of the system become worse by unnecessary computation. Therefore, jamming classification technique is required to obtain optimal anti-jamming performance. Machine learning, which has recently been in the spotlight, can be considered to classify jamming signal. In general, performing supervised learning for classification requires a huge amount of data and new learning for unfamiliar signal. In the case of jamming signal classification, it is difficult to obtain large amount of data because outdoor jamming signal reception environment is difficult to configure and the signal type of attacker is unknown. Therefore, this paper proposes few-shot jamming signal classification technique using meta-learning and transfer-learning to train the model using a small amount of data. A training dataset is constructed by anti-jamming algorithm input data within the GNSS receiver when jamming signals are applied. For meta-learning, Model-Agnostic Meta-Learning (MAML) algorithm with a general Convolution Neural Networks (CNN) model is used, and the same CNN model is used for transfer-learning. They are trained through episodic training using training datasets on developed our Python-based simulator. The results show both algorithms can be trained with less data and immediately respond to new signal types. Also, the performances of two algorithms are compared to determine which algorithm is more suitable for classifying jamming signals.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.55 no.1
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• pp.26-30
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• 2006

For the purpose of developing capacitive humidity sensor having interdigital electrodes, interdigital electrode was modeled and simulated to obtain capacitance and sensitivity as a function of geometric parameters like the structural gap and thickness. For the development of ASIC, switched capacitor signal processing circuits for capacitive humidity sensor were designed and simulated by Cadence using $0.25{\mu}m$ CMOS process parameters. The signal processing circuits are composed of amplifier for voltage gain control, and clock generator for sensor driving and switch control. The characteristics of the fabricated sensors are; 1) sensitivity is 9fF/%R.H., 2) temperature coefficient of offset(TCO) is $0.4%R.H./^{\circ}C$, 3) nonlinearity is 1.2%FS, 4) hysteresis is 1.5%FS in humidity range of $3%R.H.{\sim}98%R.H.$. The response time is 50 seconds in adsorption and 70 seconds in desorption. Fabricated process used in this capacitive humidity sensor having interdigital electrode are just as similar as conventional IC process technology. Therefore this can be easily mass produced with low cost, simple circuit and utilized in many applications for both industrial and environmental measurement and control system, such as monitoring system of environment, automobile, displayer, IC process room, and laboratory etc.

• Journal of Microbiology and Biotechnology
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• v.17 no.11
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• pp.1765-1771
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• 2007

A polymerase chain reaction (PCR)-based method was developed to detect the DNA of Ralstonia solanacearum, the causal agent of bacterial wilt in various crop plants. One pair of primers (RALSF and RALSR), designed using cytochrome c1 signal peptide sequences specific to R. solanacearum, produced a PCR product of 932 bp from 13 isolates of R. solanacearum from several countries. The primer specificity was then tested using DNA from 21 isolates of Ralstonia, Pseudomonas, Burkholderia, Xanthomonas, and Fusarium oxysporum f. sp. dianthi. The specificity of the cytochrome c1 signal peptide sequences in R. solanacearum was further confirmed by a DNA-dot blot analysis. Moreover, the primer pair was able to detect the pathogen in artificially inoculated soil and tomato plants. Therefore, the present results indicate that the primer pair can be effectively used for the detection of R. solanacearum in soil and host plants.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.2
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• pp.170-178
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• 2019

Classification of radar signals in the field of electronic warfare is a problem of discriminating threat types by analyzing enemy threat radar signals such as aircraft, radar, and missile received through electronic warfare equipment. Recent radar systems have adopted a variety of modulation schemes that are different from those used in conventional systems, and are often difficult to analyze using existing algorithms. Also, it is necessary to design a robust algorithm for the signal received in the real environment due to the environmental influence and the measurement error due to the characteristics of the hardware. In this paper, we propose a radar signal classification method which are not affected by radar signal modulation methods and noise generation by using deep learning techniques.