scholarly journals Estimation and Modeling of Fluctuating Wind Amplitude and Phase Spectrum Using APES Algorithm Based on Field Monitored Data

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Dan-hui Dan ◽  
Xiang-jie Wang ◽  
Xing-fei Yan ◽  
Wei Cheng
Keyword(s):  
Wind Velocity ◽  
Wind Field ◽  
Spectral Estimation ◽  
Amplitude Spectrum ◽  
Estimation Algorithm ◽  
Phase Spectrum ◽  
Velocity Data ◽  
Field Estimation ◽  
Local Wind Field ◽  
Fluctuating Wind

The fluctuating wind power spectrum (FWPS) in given specifications could only represent the second-order probabilistic characteristic, which indicates that it is not capable of fully expressing the stochastic wind field. Estimation and modeling of the fluctuating wind amplitude spectrum (FWAS) as well as the fluctuating wind phase spectrum (FWPhS) by using measured wind velocity data can make up for the deficiencies mentioned above. A high-resolution nonparametric spectral estimation algorithm—amplitude and phase estimation (APES)—is used to estimate the FWAS and the FWPhS, using the field measured wind velocity data of a certain cable-stayed bridge in Shanghai, China. An empirical expression (eFWAS) is introduced by dimensional analysis to model the random FWAS, and its specific Davenport form is proposed according to field measured data. The parameters of the Davenport eFWAS model are estimated by using the above measured FWAS, and three specific applications of this model are put forward when different known conditions are met. Compared with the measured FWAS, the stochastic Davenport eFWAS model proposed in this paper can accurately describe the statistical properties of the local wind field and improve the modeling accuracy of the FWAS, which is important in antiwind structural design and safety assessment.

scholarly journals Real-time estimation of airflow vector based on lidar observations for preview control

2020 ◽  
Vol 13 (12) ◽  
pp. 6543-6558
Author(s):  
Ryota Kikuchi ◽  
Takashi Misaka ◽  
Shigeru Obayashi ◽  
Hamaki Inokuchi
Keyword(s):  
Wind Velocity ◽  
Wind Field ◽  
Weather Prediction ◽  
Computational Cost ◽  
Time Estimation ◽  
Estimation Algorithm ◽  
Wind Data ◽  
Successful Implementation ◽  
Preview Control ◽  
Estimation Performance

Abstract. As part of control techniques, gust-alleviation systems using airborne Doppler lidar technology are expected to enhance aviation safety by significantly reducing the risk of turbulence-related accidents. Accurate measurement and estimation of the vertical wind velocity are very important in the successful implementation of such systems. An estimation algorithm for the airflow vector based on data from airborne lidars is proposed and investigated for preview control to prevent turbulence-induced aircraft accidents in flight. An existing technique – simple vector conversion – assumes that the wind field between the lidars is homogeneous, but this assumption fails when turbulence occurs due to a large wind-velocity fluctuation. The proposed algorithm stores the line-of-sight (LOS) wind data at every moment and uses recent and past LOS wind data to estimate the airflow vector and to extrapolate the wind field between the airborne twin lidars without the assumption of homogeneity. Two numerical experiments – using the ideal vortex model and numerical weather prediction, respectively – were conducted to evaluate the estimation performance of the proposed method. The proposed method has much better performance than simple vector conversion in both experiments, and it can estimate accurate two-dimensional wind-field distributions, unlike simple vector conversion. The estimation performance and the computational cost of the proposed method can satisfy the performance demand for preview control.

Determination of domain for diagnostic wind field estimation in Korea

2000 ◽  
Vol 34 (4) ◽  
pp. 595-601 ◽  
Author(s):  
Jin Young Kim ◽  
Young Sung Ghim ◽  
Yong Pyo Kim ◽  
Donald Dabdub
Keyword(s):  
Wind Field ◽  
Field Estimation

Influence of the synoptic circulation on the local wind field in a coastal area of the Tyrrhenian Sea

2008 ◽  
Vol 1 ◽  
pp. 012049 ◽  
Author(s):  
G Mastrantonio ◽  
I Petenko ◽  
A Viola ◽  
S Argentini ◽  
L Coniglio ◽  
...  
Keyword(s):  
Coastal Area ◽  
Wind Field ◽  
Tyrrhenian Sea ◽  
Local Wind ◽  
Synoptic Circulation ◽  
Local Wind Field

Wind velocity estimation algorithm based on Gaussian fitting in coherent lidar

2018 ◽  
Vol 47 (12) ◽  
pp. 1230006
Author(s):  
王平春 Wang Pingchun ◽  
陈廷娣 Chen Tingdi ◽  
周安然 Zhou Anran ◽  
韩 飞 Han Fei ◽  
王元祖 Wang Yuanzu ◽  
...  
Keyword(s):  
Wind Velocity ◽  
Estimation Algorithm ◽  
Velocity Estimation ◽  
Gaussian Fitting ◽  
Fitting In ◽  
Coherent Lidar

scholarly journals Grey Spectrum Analysis of Air Quality Index and Housing Price in Handan

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Kai Zhang ◽  
Yan Chen ◽  
Lifeng Wu
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
Amplitude Spectrum ◽  
Housing Price ◽  
Lag Time ◽  
Phase Spectrum ◽  
Air Quality Index ◽  
The Relationship ◽  
Grey Phase ◽  
Main Change

To analyze the relationship between air quality index (AQI) and housing price, six relationship indexes between air quality index and housing price were calculated using grey spectrum theory, specifically grey association spectrum, grey cospectrum, grey amplitude spectrum, grey phase spectrum, grey lag time length, and grey condense spectrum. Three main change periods were extracted. There was a negative correction between the air quality and the housing price in Handan. The results provide a basis for the government’s measures to prevent haze.

scholarly journals Image Saliency Prediction in Transformed Domain: A Deep Complex Neural Network Method

2019 ◽  
Vol 33 ◽  
pp. 8521-8528 ◽  
Author(s):  
Lai Jiang ◽  
Zhe Wang ◽  
Mai Xu ◽  
Zulin Wang
Keyword(s):  
Neural Network ◽  
Amplitude Spectrum ◽  
Ground Truth ◽  
Phase Spectrum ◽  
Saliency Maps ◽  
Image Saliency ◽  
Saliency Prediction ◽  
Network Method ◽  
Salient Regions ◽  
Robust To Noise

The transformed domain fearures of images show effectiveness in distinguishing salient and non-salient regions. In this paper, we propose a novel deep complex neural network, named SalDCNN, to predict image saliency by learning features in both pixel and transformed domains. Before proposing Sal-DCNN, we analyze the saliency cues encoded in discrete Fourier transform (DFT) domain. Consequently, we have the following findings: 1) the phase spectrum encodes most saliency cues; 2) a certain pattern of the amplitude spectrum is important for saliency prediction; 3) the transformed domain spectrum is robust to noise and down-sampling for saliency prediction. According to these findings, we develop the structure of SalDCNN, including two main stages: the complex dense encoder and three-stream multi-domain decoder. Given the new SalDCNN structure, the saliency maps can be predicted under the supervision of ground-truth fixation maps in both pixel and transformed domains. Finally, the experimental results show that our Sal-DCNN method outperforms other 8 state-of-theart methods for image saliency prediction on 3 databases.

A Near‐Real‐Time Version of the Cross‐Calibrated Multiplatform (CCMP) Ocean Surface Wind Velocity Data Set

2019 ◽  
Vol 124 (10) ◽  
pp. 6997-7010
Author(s):  
Carl A. Mears ◽  
Joel Scott ◽  
Frank J. Wentz ◽  
Lucrezia Ricciardulli ◽  
S. Mark Leidner ◽  
...  
Keyword(s):  
Real Time ◽  
Wind Velocity ◽  
Surface Wind ◽  
Ocean Surface ◽  
Velocity Data ◽  
Data Set ◽  
Ocean Surface Wind ◽  
The Cross
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