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Disentangling topographic contributions to near-field scanning microwave microscopy images
摘要： We develop empirical models to predict the contribution of topographic variations in a sample to near-field scanning probe microwave microscopy (NSMM) images. In particular, we focus on |S11| images of a thin Perovskite photovoltaic material and a GaN nanowire. The difference between the measured NSMM image and this prediction is our estimate of the contribution of material property variations to the measured image. Prediction model parameters are determined from either a reference sample that is nearly free of material property variations or directly from the sample of interest. The parameters of the prediction model are determined by robust linear regression so as to minimize the effect of material property variations on results. For the case where the parameters are determined from the reference sample, the prediction is adjusted to account for instrument drift effects. Our statistical approach is fully empirical and thus complementary to current approaches based on physical models that are often overly simplistic.
关键词： Near-field scanning probe microwave microscopy,Signal extraction,GaN nanowire,Statistical methods,Perovskite materials,Atomic force microscopy
更新于2025-09-23 15:23:52
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Using the bootstrap to assess uncertainties of VLBI results – I. The method and image-based errors
摘要： Very long baseline interferometric (VLBI) observations of quasar jets enable one to measure many theoretically expected effects. Estimating the signi?cance of observational ?ndings is complicated by the correlated noise in the image plane. A reliable and well justi?ed approach to estimating the uncertainties of VLBI results is needed as well as signi?cance testing criteria. We propose to use the bootstrap for both tasks. Using simulations we ?nd that bootstrap-based errors for the full intensity, rotation measure, and spectral index maps have coverage closer to the nominal values than conventionally obtained errors. The proposed method naturally takes into account heterogeneous interferometric arrays (such as Space VLBI) and can be easily extended to account for instrumental calibration factors.
关键词： radio continuum: galaxies,methods: statistical,methods: data analysis,techniques: interferometric,galaxies: jets
更新于2025-09-23 15:21:21
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Performance and degradation assessment of large-scale grid-connected solar photovoltaic power plant in tropical semi-arid environment of India
摘要： The performance and degradation of a 1 MWp utility-scale photovoltaic (PV) system located in the tropical semi-arid climate of India is investigated based on four years of monitored data. The reference yield, final yield, system efficiency, capacity factor, and performance ratio are 4.64 h/day 6.23 h/day, 11%, 19.33%, and 74.73%, respectively, according to the standard IEC 61724. The performance is compared to other large-scale PV systems in different climate conditions. The degradation of the PV plant is quantified by using various statistical methods. These methods include the linear least-squares regression (LLS), the classical seasonal decomposition (CSD), the Holt-Winters seasonal model (HW), and the seasonal and trend decomposition using loess (STL). The degradation rate is estimated at 0.27%/year, 0.32%/year, 0.50%/year, and 0.27%/year, respectively, after 50 months operating period. The degradation accuracy analysis classifies the LLS and HW as lower accuracy methods (0.22%) than CSD (0.11%) and STL (0.15%). A comparison of the degradation of mono-Si PV systems for various locations is performed using different statistical methods. This study contributes to the improvements in the knowledge of PV degradation in the Indian climate.
关键词： Semi-arid climates,Utility-scale PV system,Photovoltaic degradation,Statistical methods,Photovoltaic performance
更新于2025-09-23 15:19:57
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Spatial deconvolution of spectropolarimetric data: an application to quiet Sun magnetic elements
摘要： Context. One of the difficulties in extracting reliable information about the thermodynamical and magnetic properties of solar plasmas from spectropolarimetric observations is the presence of light dispersed inside the instruments, known as stray light. Aims. We aim to analyze quiet Sun observations after the spatial deconvolution of the data. We examine the validity of the deconvolution process with noisy data as we analyze the physical properties of quiet Sun magnetic elements. Methods. We used a regularization method that decouples the Stokes inversion from the deconvolution process, so that large maps can be quickly inverted without much additional computational burden. We applied the method on Hinode quiet Sun spectropolarimetric data. We examined the spatial and polarimetric properties of the deconvolved profiles, comparing them with the original data. After that, we inverted the Stokes profiles using the Stokes Inversion based on Response functions (SIR) code, which allow us to obtain the optical depth dependence of the atmospheric physical parameters. Results. The deconvolution process increases the contrast of continuum images and makes the magnetic structures sharper. The deconvolved Stokes I profiles reveal the presence of the Zeeman splitting while the Stokes V profiles significantly change their amplitude. The area and amplitude asymmetries of these profiles increase in absolute value after the deconvolution process. We inverted the original Stokes profiles from a magnetic element and found that the magnetic field intensity reproduces the overall behavior of theoretical magnetic flux tubes, that is, the magnetic field lines are vertical in the center of the structure and start to fan when we move far away from the center of the magnetic element. The magnetic field vector inferred from the deconvolved Stokes profiles also mimic a magnetic flux tube but in this case we found stronger field strengths and the gradients along the line-of-sight are larger for the magnetic field intensity and for its inclination. Moreover, the discontinuity between the magnetic and non magnetic environment in the flux tube gets sharper. Conclusions. The deconvolution process used in this paper reveals information that the smearing induced by the point spread function (PSF) of the telescope hides. Additionally, the deconvolution is done with a low computational load, making it appealing for its use on the analysis of large data sets.
关键词： Sun: photosphere,techniques: polarimetric,methods: statistical,methods: data analysis,Sun: magnetic fields,techniques: spectroscopic
更新于2025-09-19 17:15:36
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Time series forecasting of solar power generation for large-scale photovoltaic plants
摘要： Accurate solar power forecasting is essential for grid-connected photovoltaic (PV) systems especially in case of fluctuating environmental conditions. The prediction of PV power output is critical to secure grid operation, scheduling and grid energy management. One of the key elements in PV output prediction is time series analysis especially in locations where the historical solar radiation measurements or other weather parameters have not been recorded. In this work, several time series prediction methods including the statistical methods and those based on artificial intelligence are introduced and compared rigorously for PV power output prediction. Moreover, the effect of prediction time horizon variation for all the algorithms is investigated. Hourly solar power forecasting is carried out to verify the effectiveness of different models. The data utilized in the current work comprises 3640 hours of operation data taken from a 20 MW grid-connected PV station in China.
关键词： neural network,statistical methods,PV power forecasting,time series analysis,deep learning,grid-connected PV plant
更新于2025-09-12 10:27:22
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Hamiltonian Monte Carlo solution of tomographic inverse problems
摘要： We present the theory for and applications of Hamiltonian Monte Carlo (HMC) solutions of linear and nonlinear tomographic problems. HMC rests on the construction of an artificial Hamiltonian system where a model is treated as a high-dimensional particle moving along a trajectory in an extended model space. Using derivatives of the forward equations, HMC is able to make long-distance moves from the current towards a new independent model, thereby promoting model independence, while maintaining high acceptance rates. Following a brief introduction to HMC using common geophysical terminology, we study linear (tomographic) problems. Though these may not be the main target of Monte Carlo methods, they provide valuable insight into the geometry and the tuning of HMC, including the design of suitable mass matrices, and the length of Hamiltonian trajectories. This is complemented by a self-contained proof of the HMC algorithm in the Appendix. A series of tomographic/imaging examples is intended to illustrate (i) different variants of HMC, such as constrained and tempered sampling, (ii) the independence of samples produced by the HMC algorithm, and (iii) the effects of tuning on the number of samples required to achieve practically useful convergence. Most importantly, we demonstrate the combination of HMC with adjoint techniques. This allows us to solve a fully nonlinear, probabilistic traveltime tomography with several thousand unknowns on a standard laptop computer, without any need for supercomputing resources.
关键词： Seismic tomography,Probability distributions,Statistical methods,Inverse theory,Numerical solutions
更新于2025-09-09 09:28:46
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EXPRESS: An Optimizing Dynamic Spectrum Differential Extraction Method for Noninvasive Blood Component Analysis
摘要： The dynamic spectrum (DS) theory can greatly reduce the influence of individual differences and measurement environment by extracting the absorbance of pulsating blood at multiple wavelengths, and it is expected to achieve noninvasive detection of blood components. Extracting high-quality DS is the prerequisite for improving detection accuracy. This paper proposed an optimizing differential extraction method in view of the deficiency of existing extraction methods. In the proposed method, the sub-dynamic spectrum (sDS) is composed by sequentially extracting the absolute differences of two sample points corresponding to the height of the half peak on the two sides of the lowest point in each period of the logarithm PPG signal. The study was based on clinical trial data from 231 volunteers. Single-trial extraction method, original differential extraction method, and optimizing differential extraction method were used to extract DS from the volunteers' experimental data. Partial least squares regression (PLSR) and a radial basis function (RBF) neural network were used for modeling. According to the PLSR modeling effect, by extracting DS using the proposed method, the correlation coefficient of prediction set (Rp) has been improved by 17.33% and the root mean square error (RMSEP) has been reduced by 7.10% compared with the original differential extraction method. Compared with single-trial extraction method, the correlation coefficient of calibration set (Rc) has increased from 0.747659 to 0.8244, with an increase of 10.26%, while the correlation coefficient of prediction set (Rp) decreased slightly by 3.22%, much lower than the increase of correction set. The result of RBF neural network modeling also shows that the accuracy of the optimizing differential method is better than the other two methods both in calibration set and prediction set. In general, the optimizing differential extraction method improves the data utilization and credibility compared with the existing extraction methods, and the modeling effect is better than the other two methods.
关键词： DS,dynamic spectrum,Optimized differential extraction,noninvasive detection,statistical methods
更新于2025-09-04 15:30:14