• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.259-263
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• 1996

In diagnosing of mechanical machinery, it is often improtant to get information about the movement inside the machine casing. If the values of internal tities may be derived from the measurement using sensors installed on the external casing, it would be much better in many senses. This paper discusses extracting internal gear movements byfrequencydemodulation from gear meshing force signatures which can be recovered from the vibrations though inverse filter. There are several way in demodulating signals. In this paper, especially, Hibert Transform, Wigner-Ville distribution, and Teager energy operator are examined and compared. Effects of noise on the frequency demodulation methods and the behavior of bandpass filtered noisy signal are discussed using simulated time-varying frequency signals.

• Journal of the Institute of Convergence Signal Processing
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• v.18 no.2
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• pp.43-48
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• 2017

The underwater transients signals contain the characteristics of complexity, time varying, nonlinear, and short duration. So it is very hard to model for these signals with reference patterns. In this paper we separate the whole length of signals into some short duration of constant length with overlapping frame by frame. The 20th LPC(Linear Predictive Coding) coefficients are extracted from the original signals using Durbin algorithm and applied to neural network. The 65% of whole signals were learned and 35% of the signals were tested in the neural network with two hidden layers. The types of the whales for sound classification are Blue whale, Dulsae whale, Gray whale, Humpback whale, Minke whale, and Northern Right whale. Finally, we could obtain more than 83% of classification rate from the test signals.

• Journal of the Society of Naval Architects of Korea
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• v.31 no.2
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• pp.22-28
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• 1994

A data acquisition and processing system. using an IBM PC, an AD converter, and a printer, has been developed to monitor rapidly and significantly varying signals. The sister takes live signals and computes and displays the trend of the moving averages of the signals in real time. The system has been applied to monitor the shaft horsepower and revolution and the speed of ships for their speed trial. The reliable interpretation of the measured data using moving average can eliminate unnecessary arguments between the owner and yard on the performance of the newly built ships. Other applications of the system-inspection of engine hunting, providing data for ship maneuvering analysis, vibration data analysis, extending to the ship performance monitoring system-are also demonstrated and discussed.

• Journal of Korea Multimedia Society
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• v.23 no.3
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• pp.402-411
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• 2020

Power line noise in electrocardiogram signals makes it difficult to diagnose cardiovascular disease. ECG signals without power line noise are needed to increase the accuracy of diagnosis. In this paper, it is proposed DNN(Deep Neural Network) model to remove the power line noise in ECG. The proposed model is learned with noisy ECG, and clean ECG. Performance of the proposed model were performed in various environments(varying amplitude, frequency change, real-time amplitude change). The evaluation used signal-to-noise ratio and root mean square error (RMSE). The difference in evaluation metrics between the noisy ECG signals and the de-noising ECG signals can demonstrate effectiveness as the de-noising model. The proposed DNN model learning result was a decrease in RMSE 0.0224dB and a increase in signal-to-noise ratio 1.048dB. The results performed in various environments showed a decrease in RMSE 1.7672dB and a increase in signal-to-noise ratio 15.1879dB in amplitude changes, a decrease in RMSE 0.0823dB and a increase in signal-to-noise ratio 4.9287dB in frequency changes. Finally, in real-time amplitude changes, RMSE was decreased 0.3886dB and signal-to-noise ratio was increased 11.4536dB. Thus, it was shown that the proposed DNN model can de-noise power line noise in ECG.

• Journal of Biomedical Engineering Research
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• v.28 no.2
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• pp.271-279
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• 2007

We suggest a novel method for the evaluation of cochlear implant (CI) speech processing strategy based on neural spike train decoding. From formant trajectories of input speech and auditory nerve responses responding to the electrical pulse trains generated from a specific CI speech processing strategy, optimal linear decoding filter was obtained, and used to estimate formant trajectory of incoming speech. Performance of a specific strategy is evaluated by comparing true and estimated formant trajectories. We compared a newly-developed strategy rooted from a closer mimicking of auditory periphery using nonlinear time-varying filter, with a conventional linear-filter-based strategy. It was shown that the formant trajectories could be estimated more exactly in the case of the nonlinear time-varying strategy. The superiority was more prominent when background noise level is high, and the spectral characteristic of the background noise was close to that of speech signals. This confirms the superiority observed from other evaluation methods, such as acoustic simulation and spectral analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4835-4855
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• 2018

In this paper, we study the sparse signal recovery that uses information of both support and amplitude of the sparse signal. A convergent iterative algorithm for sparse signal recovery is developed using Majorization-Minimization-based Non-convex Optimization (MM-NcO). Furthermore, it is shown that, typically, the sparse signals that are recovered using the proposed iterative algorithm are not globally optimal and the performance of the iterative algorithm depends on the initial point. Therefore, a modified MM-NcO-based iterative algorithm is developed that uses prior information of both support and amplitude of the sparse signal to enhance recovery performance. Finally, the modified MM-NcO-based iterative algorithm is used to estimate the time-varying sparse wireless channels with temporal correlation. The numerical results show that the new algorithm performs better than related algorithms.

• Proceedings of the KSR Conference
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• 2009.05b
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• pp.409-417
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• 2009

This paper is focused on a relationship between the noise and vibration of a wheelset and the railway irregularity of a high speed railway using a time-varying frequency transform for a preliminary research of the railway condition monitoring by an in-service high-speed railway vehicle. Generally, the monitoring has been performed by a special railway inspection vehicle or industrial engineers for railway maintenance. However, they have been limited at night due to the in-service high-speed railway vehicles, and too slow to monitor all of the section. To solve this problem, the monitoring should be performed by an in-service high-speed railway vehicle. For the research, the noise and vibration of a wheelset are utilized, because they are closely related to the railway condition. They are measured by using some microphones and accelerometers, and stored in an on-board data acquisition system. The signals are post-processed by a time-varying frequency analysis and compared with the result of a railway geometry and profile measurement system. From the comparison, it is able to observe the relationship between the noise and vibration of a wheelset and the irregularity of a high-speed railway. Also, some distinct frequency components are observed, which are not observed in the railway geometry and profile.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.4
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• pp.171-181
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• 2013

Recently, ground penetrating radar(GPR) has been widely used in detecting metallic and nonmetallic buried landmines and a number of related researches have been reported. A novel preprocessing method is proposed in this paper to flag potential locations of buried mine-like objects from GPR array measurements. GPR operates by measuring the reflection of an electromagnetic pulse from discontinuities in subsurface dielectric properties. As the GPR pulse propagates in the geologic medium, it suffers nonlinear attenuation as the result of absorption and dispersion, besides spherical divergence. In the proposed algorithm, a logarithmic transformed regression model which successfully represents the time-varying signal amplitude of the GPR data is estimated at first. Then, background signals may be densely distributed near the regression model and candidate signals of targets may be far away from the regression model in the time-amplitude space. Based on the observation, GPR signals are decomposed into candidate signals of targets and background signals using residuals computed from the estimated value by regression and the measurement of GPR. Candidate signals which may contain target signals and noise signals need to be refined. Finally, targets are detected through the refinement of candidate signals based on geometric signatures of mine-like objects. Our algorithm is evaluated using real GPR data obtained from indoor controlled environment and the experimental results demonstrate remarkable performance of our mine-like object detection method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.456-463
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• 2021

In this paper, Ultra Wide-Band(UWB) radar sensor is employed to detect flying drones and avoid collision in dense clutter environments. UWB signal is preferred when high resolution range measurement is required for moving targets. However, the time varying motion of flying drones may increase clutter noises in return signals and deteriorates the target detection performance, which lead to the performance degradation of anti-collision radars. We adopt a dynamic clutter suppression algorithm to estimate the time-varying distances to the moving drones with enhanced accuracy. A modified Constant False Alarm Rate(CFAR) is developed using an adaptive filter algorithm to suppress clutter while the false detection performance is well maintained. For this purpose, a velocity dependent CFAR algorithm is implemented to eliminate the clutter noise against dynamic target motions. Experiments are performed against flying drones having arbitrary trajectories to verify the performance improvement.

• The Journal of the Acoustical Society of Korea
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• v.29 no.4
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• pp.229-236
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• 2010

Phase coherent modulation techniques in underwater acoustic channel can improve bandwidth efficiency and data reliability, but they are made difficult by time-varying intersymbol interference. This paper proposes an adaptive equalizer combined with phase estimator which compensates distortions caused by time-varying multipath and phase variation. The experiment in the East sea demonstrates phase coherent signals are distorted by time-varying multipath propagation and the proposed scheme equalizes them. Bit error rate of BPSK and QPSK are 0.0078 and 0.0376 at 300 meter horizontal distance and 0.0146 and 0.0293 at 1000 meter respectively.