• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.681-691
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• 2010

LiDAR waveform decomposition plays an important role in LiDAR data processing since the resulting decomposed components are assumed to represent reflection surfaces within waveform footprints and the decomposition results ultimately affect the interpretation of LiDAR waveform data. Decomposing the waveform into a mixture of Gaussians involves two related problems; 1) determining the number of Gaussian components in the waveform, and 2) estimating the parameters of each Gaussian component of the mixture. Previous studies estimated the number of components in the mixture before the parameter optimization step, and it tended to suggest a larger number of components than is required due to the inherent noise embedded in the waveform data. In order to tackle these issues, a new LiDAR waveform decomposition algorithm based on the sequential approach has been proposed in this study and applied to the ICESat waveform data. Experimental results indicated that the proposed algorithm utilized a smaller number of components to decompose waveforms, while resulting IMP value is higher than the GLA14 products.

• Korean Journal of Remote Sensing
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• v.25 no.6
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• pp.547-557
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• 2009

LIDAR (LIght Detection And Ranging) is an active remote sensing technology which provides 3D coordinates of the Earth's surface by performing range measurements from the sensor. Early small footprint LIDAR systems recorded multiple discrete returns from the back-scattered energy. Recent advances in LIDAR hardware now make it possible to record full digital waveforms of the returned energy. LIDAR waveform decomposition involves separating the return waveform into a mixture of components which are then used to characterize the original data. The most common statistical mixture model used for this process is the Gaussian mixture. Waveform decomposition plays an important role in LIDAR waveform processing, since the resulting components are expected to represent reflection surfaces within waveform footprints. Hence the decomposition results ultimately affect the interpretation of LIDAR waveform data. Computational requirements in the waveform decomposition process result from two factors; (1) estimation of the number of components in a mixture and the resulting parameter estimates, which are inter-related and cannot be solved separately, and (2) parameter optimization does not have a closed form solution, and thus needs to be solved iteratively. The current state-of-the-art airborne LIDAR system acquires more than 50,000 waveforms per second, so decomposing the enormous number of waveforms is challenging using traditional single processor architecture. To tackle this issue, four parallel LIDAR waveform decomposition algorithms with different work load balancing schemes - (1) no weighting, (2) a decomposition results-based linear weighting, (3) a decomposition results-based squared weighting, and (4) a decomposition time-based linear weighting - were developed and tested with varying number of processors (8-256). The results were compared in terms of efficiency. Overall, the decomposition time-based linear weighting work load balancing approach yielded the best performance among four approaches.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1709-1718
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• 2021

The waveform data of the Airborne Bathymetric LiDAR (ABL; LiDAR: Light Detection And Ranging) system provides data with improved accuracy, resolution, and reliability compared to the discrete-return data, and increases the user's control over data processing. Furthermore, we are able to extract additional information about the return signal. Waveform decomposition is a technique that separates each echo from the received waveform with a mixture of water surface and seabed reflections, waterbody backscattering, and various noises. In this study, a new waveform decomposition technique based on a Gaussian model was developed to improve the point extraction performance from the ABL waveform data. In the existing waveform decomposition techniques, the number of decomposed echoes and decomposition performance depend on the peak detection results because they use waveform peaks as initial values. However, in the study, we improved the approximation accuracy of the decomposition model by adding the estimated potential peak candidates to the initial peaks. As a result of an experiment using waveform data obtained from the East Coast from the Seahawk system, the precision of the decomposition model was improved by about 37% based on evaluating RMSE compared to the Gaussian decomposition method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.3
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• pp.193-201
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• 2022

SOQPSK-TG is a modulated signal for aircraft telemetry with excellent frequency efficiency and power efficiency. In this paper, the phase waveform of the partial response SOQPSK-TG modulation is linearly approximated and modeled as a full response double duobinary SOQPSK (SOQPSK-DD) signal. And using the XTCQM method and the Laurent decomposition method, the SOQPSK-DD signal was approximated as OQPSK having linear pulse waveforms, and the results of the two methods were proved to be the same. In addition, it was confirmed that the Laurent decomposition waveform of the SOQPSK-DD signal approximates the Laurent decomposition waveform of the original SOQPSK-TG signal. And it was shown that the decision feedback IQ-detector, which applied the Laurent decomposition waveform of SOQPSK-DD to the detection filter, exhibits almost the same performance even with a simpler waveform than before.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.475-476
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• 2007

Electroglottography (EGG) is a technique used to register laryngeal behavior indirectly by a measuring the change in electrical impedance across the throat during speaking. However, EGG waveform is affected by laryngeal muscles which fluctuate the vocal cords, and which result in baseline wander. It is required to reduce baseline wander in EGG waveform, because EGG waveform is used for input signal of nonlinear speech synthesizer in next chapter. In vocal cords, the abduction-adduction of glottis is mainly controlled by the posterior cricoarytenoid (abductor) and interarytenoid (adductor) muscles respectively. Empirical Mode Decomposition method was adopted in cancellation of EGG waveform baseline wandering, and showd better performance than that of high pass filter with 500 order.

• Journal of Biomedical Engineering Research
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• v.15 no.3
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• pp.281-288
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• 1994

In this paper, a new decomposition method of the interference EMG signal using MAMDF filtering and digital signal processor. The efficient software and hardware signal processing techniques are employed. The MAMDF filter is employed in order to estimate the presence and likely location of the respective templates which may include in the observed mixture, and high-resolution waveform alignment is employed in order to provide the optimal combination set and time delays of the selected templates. The TMS320C25 digital signal processor chip is employed in order to execute the intensive calculation part of the software. The method is verified through a simulation with real templates which are obtain ed from needle EMG. As a result, the proposed method provides an overall speed improvement of 32-40 times.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.6
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• pp.399-405
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• 2015

In this work we use the mode decomposition technique employing chirplet transform, which is able to separate the individual modes from dispersive and multimodal waveform measured with the magnetostrictive sensor. The mode decomposition technique is also used to estimate the time-frequency centers and individual energies of the reflection, which would be used to locate and characterize axial defects. The arrival times of the separated modes are calculated and the axial defect lengths can be evaluated by using the estimated arrival time. Results from an experiment on a carbon steel pipe are presented and it is shown that the accurate and quantitative defect characterization could become enabled using the proposed technique.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2011.06a
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• pp.187-188
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• 2011

Independent component analysis (ICA) is a method separating the mixture of signals into statistically and mutually independent ones. It has been applied to not only the Cocktail-party problem but also EEG analysis using the EEG waveform, digital signal processing, image processing and cognitive technique field actively. This study aims to propose a procedure to estimate the modal responses and mode shapes of a floating structure by using the ICA method from measured responses of the floating structure.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1807-1813
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• 2018

In this paper, the fault line selection and location problems of single line-to-ground (SLG) fault in distribution network are addressed. Firstly, the adaptive filtering property for empirical mode decomposition is formulated. Then in view of the different characteristics showed by the intrinsic mode functions(IMF) under different fault inception angles obtained by empirical mode decomposition, the sign of peak value about the low-frequency IMF and the capacitance transient energy is chosen as the fault line selection criteria according to the different proportion occupied by the low-frequency components. Finally, the fault location is determined based upon the comparison result with adjacent fault passage indicators' (FPI) waveform on the strength of the interaction between the distribution terminal unit(DTU) and the FPI. Moreover, the logic nodes regarding to fault line selection and location are newly expanded according to IEC61850, which also provides reference to acquaint the DTU or FPI's function and monitoring. The simulation results validate the effectiveness of the proposed fault line selection and location methods.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.8
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• pp.636-642
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• 2014

The sensor configuration of the magnetostrictive guided wave system can be described as a single continuous transducing element which makes it difficult to separate the individual modes from the reflected signal. In this work we develop the mode decomposition technique employing chirplet transform, which is able to separate the individual modes from dispersive and multimodal waveform measured with the magnetostrictive sensor, and to estimate the time-frequency centers and individual energies of the reflection, which would be used to locate and characterize defects. The reflection coefficients are calculated using the modal energies of the separated mode. Results from experimental results on a carbon steel pipe are presented, which show that the accurate and quantitative defect characterization could become enabled using the proposed technique.