• The Journal of the Acoustical Society of Korea
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• v.35 no.2
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• pp.110-117
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• 2016

Passive sonar systems detect and classify the target by analyzing the radiated noises from vessels. If multiple noise sources exist within the sonar detection range, it gets difficult to classify each noise source because mixture of noise sources are observed. To overcome this problem, a beamforming technique is used to separate noise sources spatially though it has various limitations. In this paper, we propose a new method that uses a FDICA (Frequency Domain Independent Component Analysis) to separate noise sources from the mixture. For experiments, each noise source signal was synthesized by considering the features such as machinery tonal components and propeller tonal components. And the results of before and after separation were compared by using LOFAR (Low Frequency Analysis and Recording), DEMON (Detection Envelope Modulation On Noise) analysis.

• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.130-139
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• 2024

Artificial intelligence models are being used to detect facility anomalies using physics data such as vibration, current, and temperature for predictive maintenance in the manufacturing industry. Since the types of facility anomalies, such as facility defects and failures, anomaly detection methods using autoencoder-based unsupervised learning models have been mainly applied. Normal or abnormal facility conditions can be effectively classified using the reconstruction error of the autoencoder, but there is a limit to identifying facility anomalies specifically. When facility anomalies such as unbalance, misalignment, and looseness occur, the facility vibration frequency shows a pattern different from the normal state in a specific frequency range. This paper presents an N-segmentation anomaly detection method that performs anomaly detection by dividing the entire vibration frequency range into N regions. Experiments on nine kinds of anomaly data with different frequencies and amplitudes using vibration data from a compressor showed better performance when N-segmentation was applied. The proposed method helps materialize them after detecting facility anomalies.

• ETRI Journal
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• v.35 no.5
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• pp.739-747
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• 2013

This paper investigates the multifarious nature of the long-term evolution (LTE) scheme and that of the modified LTE scheme for symbol timing synchronization (STS). This investigation allows us to propose a new replica correlation-based STS scheme to overcome the inherent weaknesses of the other two schemes. The proposed STS signal combines a gold sequence and a half sine wave in the time domain, whereas conventional STS signals specify either binary sequences or complex sequences in the time domain or in the frequency domain. In the proposed scheme, a sufficient correlation property is realized by the gold sequence, and robustness against the frequency offset (FO) is achieved through the sine wave. Compared to the existing LTE-related schemes, the proposed scheme can better achieve immunity to FO and reduction in detector complexity, as well as a low peak-to-average power ratio and a low detection error rate. Performance evaluations through analysis and simulation are provided in the paper to demonstrate these attributes.

• Proceedings of the KOSOMBE Conference
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• v.1992 no.05
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• pp.133-138
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• 1992

Evoked otoacoustic emission (e-OAE) signals are measured from adult and analyzed by computer. Stimulation and detection are repeated and averaged 1000 times for noise cancellation. e-OAE signals are analyzed on frequency domain and time domain. The frequency domain analysis reveals that frequency of stimulus and emission has lineal relationship in 50 dB input sound amplitude. This result altos the cross correlation method to be applied for latency calculation. As the stimulus frequency grows higher, the latency tine is shorter and the gain or emission signal becomes greater. We introduced two mathmatical functions to identify these latecy and gain. These results can be utilized for cochlear modeling.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.10
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• pp.783-790
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• 2018

The frequency-hopping technique is one of the spread-spectrum techniques. Frequency hopping is a communication system in which the carrier frequency channel is hopped within the wideband. Therefore, a frequency-hopping system has such advantages as antijamming and low probability of intercept. This system is often used in military communications. Because frequency-hopping signal detection is difficult, it is an important research issue. A novel detection technique is proposed that can improve detection probability. When the received signal is transformed to a frequency domain sample by fast Fourier transform, spectral leakage lowers the detection probability. This problem can be solved by using the Hamming window, and the detection probability can be increased. However, in a frequency-hopping environment, the windowing technique lowers the detection probability. The proposed method solves this weakness. The simulation results show that the proposed detection technique improves the detection probability by as much as 13 %.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2001.09a
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• pp.36-43
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• 2001

In Korea, we are absolutely short of earthquake data in good quality from moderate and large earthquakes, which are needed fur the study of strong ground motion characteristics. This means that the best use of the available data is needed far the time being. In this respect, several methods are suggested in this paper, which can be applied in the process of data selection and analysis. First, it is shown that the calibration status of seismic stations can be easily checked by comparing the spectra from accelerometer and velocity sensor both of which are located at the same location. Secondly, it is recommended that S/N ratio in the frequency domain should be checked before discarding the data by only look of the data in time domain. Thirdly, the saturated earthquake data caused by ground motion level exceeding the detection limit of a seismograph are considered to see if such data can be used for spectrum analysis by performing numerical simulation. The result reveals that the saturated data can still be used within the dominant frequency range according to the levels of saturation. Finally, a technique to minimize the window effect that distorts the low frequency spectrum is suggested. This technique involves detrending in displacement domain once the displacement data are obtained by integration of low frequency components of the original data in time domain. Especially, the low frequency component can be separated by using discrete wavelet transform among many alternatives. All of these methods mentioned above may increase the available earthquake data and frequency range.

• Proceedings of the Korea Contents Association Conference
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• 2005.11a
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• pp.181-185
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• 2005

In this paper, the DWT(Discrete Wavelet Transform) based watermarking method for authentication and detection of image modification was proposed. The proposed algorithm inserts watermark into high frequency domain after 1-level wavelet transform by exchanging wavelet coefficients and embeds the characteristic values of high frequency domain of original image into the LSB part of watermarked image. Therefore, By extracting LSB values and watermark in the high frequency domain from the watermarked image, we can authenticate the image and detect modified positions.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.2
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• pp.102-108
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• 2007

The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. This study introduces a technique to detect and identify faults in induction motors. Stator currents were measured and stored by time domain. The time domain is not suitable for representing current signals, so wavelet transform is used to convert the signal; onto frequency domain. The raw signals can not show the significant feature, therefore difference values are applied. The difference values were transformed by wavelet transform and the features are extracted from the transformed signals. The dynamic time warping method was used to identify the four fault types. This study describes the results of detecting fault using wavelet analysis.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.8
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• pp.1179-1185
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• 2012

This paper presents a new approach for the detection of seismic events using accumulated changes on time-frequency domain and variable threshold. To detect seismic P-wave arrivals with rapidness and accuracy, it is that the changes on the time and the frequency domains are simultaneously used. Their changes are parameters appropriated to reflect characteristics of earthquakes over moderate magnitude(${\geq}$ magnitude 4.0) and microearthquakes. In addition, adaptively controlled threshold values can prevent false P-wave detections due to low SNR. We tested our method on real earthquakes those have various magnitudes. The proposed algorithm gives a good detection performance and it is also comparable to STA/LTA algorithm in computational complexity. Computer simulation results shows that the proposed algorithm is superior to the conventional popular algorithm (STA/LTA) in the seismic P-wave detection.

• Journal of Biomedical Engineering Research
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• v.34 no.4
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• pp.182-188
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• 2013

This study aims to develop a practical algorithm which can detect freezing of gait(FOG) in patients with Parkinson's disease(PD). Eighteen PD patients($68.8{\pm}11.1yrs.$) participated in this study, and three($68.7{\pm}4.0yrs.$) of them showed FOG. We suggested two time-domain algorithms(with 1-axis or 3-axes acceleration signals) and compared them with the frequency-domain algorithm in the literature. We measured the acceleration of left foot with a 3-axis accelerometer inserted at the insole of a shoe. In the time-domain method, the root-mean-square(RMS) acceleration was calculated in a moving window of 4s and FOG was defined as the periods during which RMS accelerations located within FOG range. The parameters in each algorithm were optimized for each subject using the simulated annealing method. The sensitivity and specificity were same, i.e., $89{\pm}8%$ for the time-domain method with 1-axis acceleration and were $91{\pm}7%$ and $90{\pm}8%$ for the time-domain method with 3-axes acceleration, respectively. Both performances were better in the time-domain methods than in the frequency-domain method although the results were statistically insignificant. The amount of calculation in the time-domain method was much smaller than in the frequency-domain method. Therefore it is expected that the suggested time domain algorithm would be advantageous in the systematic implementation of FOG detection.