scholarly journals On the Use of Weighted Least-Squares Approaches for Differential Interferometric SAR Analyses: The Weighted Adaptive Variable-lEngth (WAVE) Technique

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1103
Author(s):  
Francesco Falabella ◽  
Carmine Serio ◽  
Giovanni Zeni ◽  
Antonio Pepe
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Variable Length ◽  
Mathematical Framework ◽  
Ground Displacement ◽  
Phase Measurements ◽  
Temporal Sampling ◽  
Wave Technique ◽  
Interferometric Sar

This paper concentrates on the study of the Weighted Least-squares (WLS) approaches for the generation of ground displacement time-series through Differential Interferometric SAR (DInSAR) methods. Usually, within the DInSAR framework, the Weighted Least-squares (WLS) techniques have principally been applied for improving the performance of the phase unwrapping operations as well as for better conveying the inversion of sequences of unwrapped interferograms to generate ground displacement maps. In both cases, the identification of low-coherent areas, where the standard deviation of the phase is high, is requested. In this paper, a WLS method that extends the usability of the Multi-Temporal InSAR (MT-InSAR) Small Baseline Subset (SBAS) algorithm in regions with medium-to-low coherence is presented. In particular, the proposed method relies on the adaptive selection and exploitation, pixel-by-pixel, of the medium-to-high coherent interferograms, only, so as to discard the noisy phase measurements. The selected interferometric phase values are then inverted by solving a WLS optimization problem. Noteworthy, the adopted, pixel-dependent selection of the “good” interferograms to be inverted may lead the available SAR data to be grouped into several disjointed subsets, which are then connected, exploiting the Weighted Singular Value Decomposition (WSVD) method. However, in some critical noisy regions, it may also happen that discarding of the incoherent interferograms may lead to rejecting some SAR acquisitions from the generated ground displacement time-series, at the cost of the reduced temporal sampling of the data measurements. Thus, variable-length ground displacement time-series are generated. The mathematical framework of the developed technique, which is named Weighted Adaptive Variable-lEngth (WAVE), is detailed in the manuscript. The presented experiments have been carried out by applying the WAVE technique to a SAR dataset acquired by the COSMO-SkyMed (CSK) sensors over the Basilicata region, Southern Italy. A cross-comparison analysis between the conventional and the WAVE method has also been provided.

Weighted-Least Squares Optimization Method for Control and Shape Design of an Adaptive Blade Twist Distribution to Increase Wind Capture

2018 ◽  
Author(s):  
Fuzhao Mou ◽  
Hamid Khakpour Nejadkhaki ◽  
Aaron Estes ◽  
John Hall
Keyword(s):  
Wind Speed ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Twist Angle ◽  
Torque Control ◽  
Shape Design ◽  
Mathematical Framework ◽  
Relative Stiffness ◽  
Partial Load ◽  
And Control

This paper presents a novel wind turbine blade with an actively adaptable twist angle. A weighted-least square technique is proposed to design and control the blade in its application. Controlling the twist distribution provides new capabilities that may not be achievable with blade pitch or rotor torque control. An adaptive twist angle can reduce fatigue loads and improve the efficiency of wind energy conversion. Our previous work established the theoretical blade twist distribution that maximizes wind capture during partial load operation. The twist distribution changes continuously as a function of wind speed. In practice, it is a challenge to design and control the blade to adapt to this range of transformation. Accordingly, a blade concept and engineering design method are proposed to achieve this task. The blade is constructed from additively manufactured sections that are assumed to have tunable stiffness. The sections are mounted on a centralized spar that provides stiffness. The sections are actuated at each end and have two zones of stiffness. A mathematical framework prescribes (1) length of each blade section and (2) the relative stiffness between a pair of compliant shells. Establishing the section length effectively sets the points of actuation, while the relative stiffness establishes a nonlinear twist. These design selections determine the twist distribution. The method employing weighted-least squares is employed to optimize these selections. The approach biases the shape design and control towards the theoretical twist distribution at a range of designated wind speed. This enables a customized solution that maximizes the wind capture based on the wind conditions at a given installation site.

scholarly journals An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1687
Author(s):  
Fang Yuan ◽  
Jiang Guo ◽  
Zhihuai Xiao ◽  
Bing Zeng ◽  
Wenqiang Zhu ◽  
...  
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Phase Space ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Transformer Oil ◽  
Gas Content ◽  
Support Vector ◽  
Forecasting Model ◽  
Dissolved Gas

Transformer state forecasting and fault forecasting are important for the stable operation of power equipment and the normal operation of power systems. Forecasting of the dissolved gas content in oil is widely conducted for transformer faults, but its accuracy is affected by data scale and data characteristics. Based on phase space reconstruction (PSR) and weighted least squares support vector machine (WLSSVM), a forecasting model of time series of dissolved gas content in transformer oil is proposed in this paper. The phase spaces of time series of the dissolved gas content sequence are reconstructed by chaos theory, and the delay time and dimension are obtained by the C-C method. The WLSSVM model is used to forecast time series of dissolved gas content, the chemical reaction optimization (CRO) algorithm is used to optimize training parameters, the bootstrap method is used to build forecasting intervals. Finally, the accuracy and generalization ability of the forecasting model are verified by the analysis of actual case and the comparison of different models.

scholarly journals Improved weighted least-squares phase unwrapping method for interferometric SAR processing

2019 ◽  
Vol 2019 (20) ◽  
pp. 6471-6474 ◽  
Author(s):  
Yu Hui ◽  
Wang Wenying ◽  
Zhuang Long ◽  
Lei Wanming ◽  
Nie Xin ◽  
...  
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Phase Unwrapping ◽  
Interferometric Sar ◽  
Sar Processing

A Weighted Least Squares Approach for the Design of Adaptive Aerodynamic Structures Subjected to an Out-Of-Plane Transformation

2018 ◽  
Author(s):  
Fuzhao Mou ◽  
Hamid Khakpour Nejadkhaki ◽  
Aaron Estes ◽  
John Hall
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Turbine Blades ◽  
Operating Conditions ◽  
Torsional Stiffness ◽  
Power Coefficient ◽  
Mathematical Framework ◽  
Complex Needs ◽  
Least Squares Approach ◽  
The Ideal

An optimal design framework for adaptive wind turbine blades is presented. A mathematical framework establishes the topology of actuators and material compliance. These parameters are selected to adapt the blade twist distribution into a range of prescribed blade configurations. Our previous work established the ideal twist distribution configurations. The distributions improve the aerodynamic efficiency for a range of wind speeds in which the system operates at partial production. Within this range the nonlinear blade twist distribution changes in relation to the speed. The possibility of producing adaptively compliant structures is becoming increasingly possible with innovative materials and additive manufacturing (AM) processes. Our overarching goal is to create a comprehensive design infrastructure that integrates manufacturing and materials innovation with the complex needs of adaptive structures. This work proposes a method through which the ideal twist distribution can be actualized in structural implementation. The implementation involves a modular blade composed of flexible sections whose twist is modulated by actuators along the blade. Each flexible blade section is composed of two contiguous segments, each with a different torsional stiffness defined by a stiffness ratio. The stiffness variation within each section allows the blade to assume a nonlinear twist distribution when actuated. Errors relative to an ideal twist distribution are minimized by optimizing the stiffness ratios and twist actuator locations. The optimization is completed using a weighted least squares approach that allows the blade designer to bias blade performance toward different operating conditions. A quadratic weighting scheme that penalizes twist errors toward the blade tip is found to result in a higher power coefficient than other weighting schemes.

scholarly journals Comparison of neural networks and regression time series when predicting the export development from the USA to PRC

2019 ◽  
Author(s):  
Jakub Horák ◽  
Petr Šuleř ◽  
Jaromír Vrbka
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Practice Research ◽  
The Usa ◽  
Artificial Neural

Purpose – artificial neural networks are compared with mixed conclusions in terms of forecasting performance. The most researches indicate that deep-learning models are better than traditional statistical or mathematical models. The purpose of the article is to compare the accuracy of equalizing time series by means of regression analysis and neural networks on the example of the USA export to China. The aim is to show the possible uses and advantages of neural networks in practice. Research methodology – the period for which the data (USA export to the PRC) are available is the monthly balance starting from January 1985 to August 2018. First of all, linear regression as the relatively simple mathematical method is carried out. Subsequently, neural networks as the computational models used in artificial intelligence are used for regression. Findings – in terms of linear regression, the most suitable one appeared to be the curve obtained by means of the least squares methods by negative-exponential smoothing, and the curve obtained by means of the distance-weighted least squares method. In terms of neural networks, all retained structures appeared to be applicable in practice. Artificial neural networks have better representational power than traditional models. Research limitations – the simplification (quite a significant one) appears both in the cases of linear regression and regression by means of neural networks. We work only with two variables – input variable (time) and output variable (USA export to the PRC). Practical implications – in practice, the results – especially the method of artificial neural networks – can be used in the measurement and prediction of the development of exports, but especially in the short term. It can be stated that due to great simplification of the reality it isnʼt possible to predict extraordinary situations and their effect on the USA export to the PRC. Originality/Value – the article focuses on the comparison of two statistical methods, in particular, artificial intelligence is not used in such applications. However, in many economic industries, it has proven better results. It is found that artificial neural networks are able to effectively learn dependencies in and between the time series in the form of export development data.

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