• 대한원격탐사학회지
• /
• 제26권6호
• /
• pp.681-691
• /
• 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.

• Current Optics and Photonics
• /
• 제1권3호
• /
• pp.233-238
• /
• 2017

In this paper we propose a freeform secondary lens for an LED fishing/working lamp (LFWL). This innovative LED lamp is used to replace the traditional HID fishing lamp, to satisfy the lighting demands of fishing and the on-board activities on fishing boats. To realize the freeform lens's geometry, Gaussian decomposition is involved in our optics-design process for approaching the targeted light intensity distribution curve (LIDC) of the LFWL lens. The simulated results show that the illumination on the deck, on the sea's surface, and underwater shows only small differences between LED fishing/working lamps and HID fishing lamps. Meanwhile, a lighting efficiency of 91% with just one third of the power consumption can be achieved, when the proposed LED fishing/working lamps are used instead of HID fishing lamps.

• 대한원격탐사학회지
• /
• 제37권6_1호
• /
• pp.1709-1718
• /
• 2021

항공수심라이다[airborne bathymetric LiDAR (Light Detection And Ranging)] 시스템의 웨이브폼(waveform) 데이터는 기존의 이산반환(discrete-return) 데이터에 비해 정확도, 해상도, 신뢰도가 향상된 데이터를 제공하며, 사용자의 데이터 처리 통제력을 강화하며 반환신호에 대한 추가적인 정보 추출을 가능하게 한다. 웨이브폼 분해(waveform decomposition)는 수면과 해저 반사, 수중산란 및 각종 노이즈가 혼재되어 수신된 웨이브폼으로부터 각각의 에코(echo)를 분리하는 기술로, 지형 포인트 추출을 위해 선행되어야 하는 처리 과정이자 측량 성과를 좌우하는 주요 기술이다. 본 연구에서는 항공수심라이다 원시 데이터로부터의 포인트 추출 성능을 향상시키 위해 새로운 웨이브폼 분해 기술을 개발하였다. 기존의 웨이브폼 분해 기술들이 웨이브폼 피크(peak)들을 가우시안(Gaussian) 분해의 초기값으로 사용하여 분해된 에코의 개수와 분해 성능이 피크 탐지 결과에 좌우되는데 반해, 제안한 기술은 최초 피크들 외에 잠재적인 피크 후보들을 추정하여 추가함으로써, 분해 모델의 근사 정확도를 향상시켰다. 국내 개발된 항공수심라이다 장비인 씨호크(Seahawk)로부터 동해안에서 취득된 웨이브폼 데이터를 이용한 실험 결과, 가우시안 분해 방법 대비 제안한 방법의 모델 근사 적합도(RMSE 기준)가 약 37% 향상된 결과를 얻었다.

• 천문학회보
• /
• 제46권2호
• /
• pp.68.3-69
• /
• 2021

We present a novel profile stacking technique based on optimal profile decomposition of a 3D spectral line data cube, and its performance test using the HI data cubes of sample galaxies from HI galaxy surveys, THINGS and LITTLE THINGS. Compared to the previous approach which aligns all the spectra of a cube using their central velocities derived from either moment analysis, single Gaussian or hermite h3 polynomial fitting, the new method makes a profile decomposition of the profiles from which an optimal number of single Gaussian components is derived for each profile. The so-called superprofile which is derived by co-adding all the aligned profiles from which the other Gaussian models are subtracted is found to have weaker wings compared to the ones constructed in a typical manner. This could be due to the reduced number of asymmetric profiles in the new method. A practical test made on the HI data cubes of the THINGS and LITTLE THINGS galaxies shows that our new method can extract more mass of kinematically cold HI components in the galaxies than the previous results. Additionally, we fit a double Gaussian model to the superprofiles whose S/N is boosted, and quantify not only their profile shapes but derive the ratio of the Gaussian model parameters, such as the intensity ratio and velocity dispersion ratio of the narrower and broader Gaussian components. We discuss how the superprofile properties of the sample galaxies are correlated with their other physical properties, including star formation rate, stellar mass, metallicity, and gas mass.

• 대한원격탐사학회지
• /
• 제25권6호
• /
• pp.547-557
• /
• 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.

• Wind and Structures
• /
• 제38권1호
• /
• pp.75-91
• /
• 2024

Reliable wind signal reconstruction can be beneficial to the operational safety of long-span bridges. Non-Gaussian characteristics of wind signals make the reconstruction process challenging. In this paper, non-Gaussian wind signals are converted into a combined prediction of two kinds of features, actual wind speeds and wind angles of attack. First, two decomposition techniques, empirical mode decomposition (EMD) and variational mode decomposition (VMD), are introduced to decompose wind signals into intrinsic mode functions (IMFs) to reduce the randomness of wind signals. Their principles and applicability are also discussed. Then, four artificial intelligence (AI) algorithms are utilized for wind signal reconstruction by combining the particle swarm optimization (PSO) algorithm with back propagation neural network (BPNN), support vector regression (SVR), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), respectively. Measured wind signals from a bridge site in a deep-cutting gorge are taken as experimental subjects. The results showed that the reconstruction error of high-frequency components of EMD is too large. On the contrary, VMD fully extracts the multiscale rules of the signal, reduces the component complexity. The combination of VMD-PSO-Bi-LSTM is demonstrated to be the most effective among all hybrid models.

• Journal of Communications and Networks
• /
• 제16권1호
• /
• pp.92-97
• /
• 2014

A novel method for extracting frequency slippage signal from radar full pulse sequence is presented. For the radar full pulse sequence received by radar interception receiver, radio frequency (RF) and time of arrival (TOA) of all pulses constitute a two-dimensional information sequence. In a complex and intensive electromagnetic environment, the TOA of pulses is distributed unevenly, randomly, and in a nonstationary manner, preventing existing methods from directly analyzing such time series and effectively extracting certain signal features. This work applies Gaussian noise insertion and structure function to the TOA-RF information sequence respectively such that the equalization of time intervals and correlation processing are accomplished. The components with different frequencies in structure function series are separated using empirical mode decomposition. Additionally, a chaos detection model based on the Duffing equation is introduced to determine the useful component and extract the changing features of RF. Experimental results indicate that the proposed methodology can successfully extract the slippage signal effectively in the case that multiple radar pulse sequences overlap.

• Structural Engineering and Mechanics
• /
• 제77권3호
• /
• pp.355-368
• /
• 2021

The time-varying mean (TVM) component of non-stationary wind speeds is commonly extracted utilizing empirical mode decomposition (EMD) in practice, whereas the accuracy of the extracted TVM is difficult to be quantified. To deal with this problem, this paper proposes an approach to identify and extract the optimal TVM from several TVM results obtained by the EMD. It is suggested that the optimal TVM of a 10-min time history of wind speeds should meet both the following conditions: (1) the probability density function (PDF) of fluctuating wind component agrees well with the modified Gaussian function (MGF). At this stage, a coefficient p is newly defined as an evaluation index to quantify the correlation between PDF and MGF. The smaller the p is, the better the derived TVM is; (2) the number of local maxima of obtained optimal TVM within a 10-min time interval is less than 6. The proposed approach is validated by a numerical example, and it is also adopted to extract the optimal TVM from the field measurement records of wind speeds collected during a sandstorm event.

• 제어로봇시스템학회논문지
• /
• 제4권5호
• /
• pp.630-635
• /
• 1998

The objective of this paper is to propose a new efficient technique for the estimation of time-delay parameters using wavelet theory and third-order cumulants, yielding good performance even in the case of low SNR. In particular, band-limited non-Gaussian signals with non-zero skewness and spatially correlated Gaussian noises are considered here. The approach is based on the fact that the effects of spatially correlated Gaussian noises on time-delay estimation can be reduced by using the projection sequences (based on the redundant wavelet decomposition) of given measurements in the higher-order cumulant domain. Finally, the performance of the proposed approach is demonstrated using simulations.

• 천문학회지
• /
• 제52권2호
• /
• pp.49-55
• /
• 2019

The New Vacuum Solar Telescope (NVST) is the largest solar telescope in China. When using CCDs for imaging, equal-thickness fringes caused by thin-film interference can occur. Such fringes reduce the quality of NVST data but cannot be removed using standard flat fielding. In this paper, a correction method based on multi-scale decomposition and adaptive partitioning is proposed. The original image is decomposed into several sub-scales by multi-scale decomposition. The region containing fringes is found and divided by an adaptive partitioning method. The interference fringes are then filtered by a frequency-domain Gaussian filter on every partitioned image. Our analysis shows that this method can effectively remove the interference fringes from a solar image while preserving useful information.