Statistical Scatter Research Articles

Thermal comfort evaluation using different glazing technologies in an energy model of rural housing in a cold climate

Building energy efficiency, consumption and comfort are aspects to be evaluated in the energy simulation of buildings (BES). However, their results deviate from reality leading to the need to improve their accuracy through calibration. In this regard, a rural house located in a cold climate at more than 3000 meters above sea level, is energetically modeled with its subsequent calibration and evaluation of thermal comfort. The study is developed in four stages, starting with the monitoring of climatological variables and followed by the house energy modeling. Subsequently, manual and iterative calibration is conducted by means of statistical comparison and scatter plots. In this way, a model with more than 80% accuracy between monitored and simulated data is achieved. Based on the previously calibrated model, the thermal comfort is evaluated using different glazing technologies and then, the number of hours for two levels of thermal sensation are evaluated (comfortable and not comfortable). For this purpose, statistical methods such as predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD) are used, which are included in the Design Builder software. This study identified that single bronze glazing technology increased thermal comfort hours by 14.2% with respect to the case study, allowing the development of a methodology that improves thermal comfort through passive heating in rural dwellings in cold climates.

Very high cycle fatigue properties of short glass fiber reinforced polyetheretherketone (PEEK)

The fatigue properties of 14 wt-% short glass fiber reinforced polyetheretherketone (PEEK–GF14) have been investigated in the high and very high cycle fatigue (VHCF) regime. Experiments were performed at a load ratio of –1 with servohydraulic and electrodynamic equipment at cycling frequency 10–20 Hz, and with ultrasonic equipment at 19 kHz. A new specimen geometry has been developed that allows ultrasonic tests up to high stress amplitudes. The same specimen shape was used in both testing series to exclude size effects, which enabled to focus on the influence of cycling frequency and testing technique. Ultrasonic fatigue testing with intermittent loading served to avoid heating of specimens. The S-N curves measured at 10–20 Hz and 19 kHz show a similar slope exponent (i.e., 10 % deviation). Mean S-N curve determined with ultrasonic equipment is shifted to slightly lower stress amplitudes, which may be attributed to statistical scatter. PEEK–GF14 does not show a fatigue limit and failures still occurred above 109 cycles. The VHCF strength of PEEK-GF14 is approximately two times higher compared with unreinforced PEEK. Fractographic investigations revealed fiber fracture and, less frequently, fiber pull-out.

Advancing Reservoir Water Level Predictions: Evaluating Conventional, Ensemble and Integrated Swarm Machine Learning Approaches

AbstractAccurate estimation of reservoir water level fluctuation (WLF) is crucial for effective dam operation and environmental management. In this study, seven machine learning (ML) models, including conventional, integrated swarm, and ensemble learning methods, were employed to estimate daily reservoir WLF. The models comprise multi-linear regression (MLR), shallow neural network (SNN), deep neural network (DNN), support vector regression (SVR) integrated with homonuclear molecules optimization (HMO) and particle swarm optimization (PSO) meta-heuristic algorithms, classification and regression tree (CART), and random forest (RF). These models were trained and evaluated using in situ data from three embankment dams in Algeria: the Kramis dam, the Bougous dam, and the Fontaine Gazelles dam. Performance evaluation was conducted using statistical indices, scatter plots, violin plots, and Taylor diagrams. The results revealed superior prediction accuracy for the Fontaine Gazelles dam compared to Kramis and Bougous dams. Particularly, the RF, DNN, and SVR-HMO models exhibited consistent and excellent predictive performance for WLF at the Fontaine Gazelles dam with RMSE values of 0.502 m, 0.536 m, and 0.57 m, respectively. The RF model demonstrates remarkable accuracy across all three case studies. This can be attributed to the ensemble structure of RF, as evidenced by the results. This study underscores the significance of considering factors such as seepage flow intensity in understanding WLF variability. Furthermore, the proposed ML models offer promising capabilities in WLF prediction, highlighting their potential utility in enhancing reservoir management practices and addressing the limitations of traditional regression models. Keys words. Embankment dam, Water level fluctuations, Seepage, Artificial neural network, meta-heuristic algorithm.

Multi-objective optimization for minimize cutting force and power consumption in corn stalk chopping processes

This study was on optimization of the cutting processes for corn stalks, which serve as significant sources of biomass and forage. The reduction of cutting force is crucial for enhancing energy efficiency. Cutting velocity rises, which can lead to significant power usage with minimal shear forces. Therefore, the objective is to minimize both the cutting force and power consumption. To address this multi-objective optimization problem, a modified Taguchi method was employed. Chauvenet's criterion was used to assess the validity of statistical scatter in repeated test data. The test samples had an 81 % moisture content. Through analysis of variance (ANOVA), the optimal chopping process parameters were identified as velocity of 4.4 m/s, approach angle of 30°, and feeding angle of 50°. The refined empirical relationships indicated that the cutting force ranges from 180.55 N to 393.37 N, while the cutting power ranges from 16.81 W to 44.05 W. In a single test, the output responses for the optimal parameter set were 26.32 W for power consumption and 242.60 N for cutting force, both are within the estimated range. The considerable scatter observed in repeated test data is likely due to variations in corn stalk thickness. These findings are valuable for the operation of chopping machines, ensuring minimal cutting force and power consumption when handling corn stalks residues.

Abstract PO5-07-08: A Serum Biomarker for the Early Detection of Breast cancer

Abstract Background: After decades of use, clinicians and medical societies are still debating on the clinical utility of mammography, the standard of care for breast cancer (BC) screening, because of its limitations such as subjective visual interpretation, high rate of false-positive and false-negative (in dense breast) and concerns over radiation exposure. There are 55 million women, and about 45% of them considered dense breast, in need of an accurate, easy to use test for the screening of BC regardless of breast tissue complexities to differentiate between BC and benign findings such as calcification and fibrosis. To this date there is no blood protein biomarker (BM) in clinical use for the early detection of BC. We have developed a novel BC specific protein biomarker -based blood test to detect patients with early-stage BC and established herein the diagnostic performance of our BM in discriminating BC patients from healthy individuals and benign breast cases. Methods: A total of 433 serum samples (318 primary BC, 32 benign breast and 83 normal breast) were obtained from the Cooperative Human Tissue Network and from the IRCCS San Raffaele Pisana Research Center (blind samples). 247 BC samples were provided as blind samples and were used to validate the screening utility. Serum samples were tested with our BC specific BM by sandwich ELISA. Receiver operating characteristic (ROC) curves were calculated to evaluate the diagnostic performance, by determining sensitivity (SE), specificity (SP), area under the curve (AUC), and confidence interval (CI). Statistical analysis, ROC curves and scatter plots were performed with GraphPad Prism 7.0. Results: BM elevation in early cancer cases (stage I-II, Ductal Carcinoma) was found to be statistically significant (p< 0.0001) as compared to healthy and benign cases. Our test discriminated early BC patients from non-cancer cases with 87.3% SE at 85.2% SP and was further validated in a blind set including 247 BC (Ductal & Lobular Carcinoma, stage I-III), 30 benign and 83 normal breast cases (88%SE, 86%SP). The sensitivity for early-stage (I-II) and late stage (III) BC was 86.8% and 94.3%, respectively. While lobular carcinoma is particularly not well detected by mammography with a sensitivity as low as 57% and much lower for women with dense breast, it was detected with high sensitivity (90%) in our test. 80% of fibrocystic changes (FCC), the most common form of benign breast disease in women over 50 years of age were negative with our test. Although it is a benign condition, FCC may be misdiagnosed as a malignant lesion by physical examination and imaging. Conclusion: Our results support the utility of our blood-based test for the early detection of breast cancer which presents improved features over mammography such as higher lobular cancer sensitivity, lower detection rate of common form of benign condition often misdiagnosed by imaging, absence of radiation and expected high compliance. Further testing is needed to confirm accuracy of our test in the dense breast population. Citation Format: Christine Chavany, Jeffrey Dea, Monica Guevara, Rafael Hernandez-Gonzalez, Patrizia Ferroni, Fiorella Guadagni, Rosaura Valle, Moncef Jendoubi. A Serum Biomarker for the Early Detection of Breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-07-08.

Assessment of the stand structure of protective forest monitoring based on statistical models in Irano-Turanian phytogeographical regions of Iran.

One of the most important data that forest management planners need for effective decisions is the data related to the forest structure. The aim of the study is to investigate and analyse the structure of protective forests in Irano-Turanian phytogeographical regions, Iran. Since there are many shrubs in this phytogeographical region, it is very difficult to measure the stand diameter at any height (breast or root height). For this reason, it is necessary to analyse the parameters of height and crown cover to investigate and analyse forest structure. For that purpose, two study plots were selected, and basic data were analysed by using statistical distributions, scatter plots and R2 coefficients. With EasyFit software and Anderson‒Darling test, it was found that the Weibull (3P) and Pearson 6 (4P) distributions for the crown cover factor and the Gen-Pareto and Pert distributions for the height factor have the best goodness-of-fit for the distribution of the different crown cover classes and heights in the studied forest. Moreover, the results confirm that there is a very weak R2 coefficient between crown cover and root collar diameter, with R2 = 0.513 and 0.369 in plots 1 and 2, respectively. Therefore, the combination of crown cover and height parameters is more suitable for use when analysing stand structure in such forests, although the values of R2 are still low (0.673 and 0.524 in plots 1 and 2, respectively). The results of this study show that in protective forests with many shrubs, it is better to focus on the height and crown cover of ​trees and of shrubs rather than on parameters related to stand/tree diameter when analysing stand structure.

What We Can and Cannot Learn from a Single Shear Test of a Very Large RC Beam

In the existing database on shear load capacity, tests of very large beams are scarce. Valuable additions to the database have recently been made in 2021 at the University of California, Berkeley (UCB), and in 2015 at the University of Toronto. These two tests were the largest ever among the standard three-point-bend type tests conducted so far. They verified the effects of beam size and of steel stirrups on the ultimate load, Vu, provided that the same concrete and steel are used. The present analysis, which deals in detail only with the UCB test, shows that the subsequent public blind competitions to predict the Vu measured in both tests were meritless and potentially misleading. The reason is that, similar to design codes, the only information provided to the competitors (besides the E modulus) was the required concrete compression strength, fc′, whereas the mean compressive and tensile strengths, fracture energy, initial creep data, and so on, were not provided. The fault of a competition of this kind is evidenced by (1) finite-element fracture simulations, (2) analysis of the huge statistical scatter of a database of 784 tests and a previous database in which fc′ was also the only concrete property used, like in the design code, and (3) estimation of the statistical error due to anchoring code provisions to the classical shear strength approximation 2fc′ (psi), which was set at about 65% below the mean of the data cloud in the database. The winning prediction of the UCB competition had an error of only 2.7% of the measured failure load, even though the probability of success is here shown to have been between 0.14% and 8.46%, with 0.90% being the best estimate. Hence, competitions of this type are, in essence, a lottery. Furthermore, the fact that the winning predictions in both competitions happened to be obtained by cross-section strain analysis based on beam mechanics, and no fracture mechanics, is potentially misleading. This, of course, does not detract from the value of the UCB and Toronto experiments as important and unique additions to the database and as verifications of the load capacity for the particular concrete used.

Information preservation optimization method based on the advection upstream splitting method in supersonic rarefied flow simulation

Here, we propose an information preservation (IP) optimization approach for supersonic flow simulations. Based on the conversion of variables integrated in a half-space and analogical analysis, we obtained a relationship between the integrated variables, used in the governing equations of the preserved information, and the macroscopic variables used in Navier–Stokes equations. Computationally, the flux for the correlation terms is reconstructed using the Advection Upstream Splitting Method (AUSM) scheme, giving the IP method windward characteristics. Additionally, the pressure boundary treatment is modified to improve the stability and accuracy of the IP method in the simulation of rarefied supersonic flows driven by high pressures. The particle number threshold method is proposed to solve the “carbuncle point” problem, which is caused by insufficient local collisions. The IP optimization method combines the advantages of the AUSM scheme and low statistical scatter method. Thus, it can be used to investigate the real-time variations in a rarefied supersonic flow through simulations of the supersonic microscale jet flow and hypersonic macroscale nozzle flow. The results indicate that the real-time statistical accuracy of the IP optimization method is significantly higher than that of the direct-simulation Monte Carlo method, and the correctness and accuracy of this method are satisfactory as well. Therefore, the proposed method can unveil the mechanisms underlying the variations and unsteadiness in rarefied supersonic flows on different scales.

A comparison of artificial intelligence approaches in predicting discharge coefficient of streamlined weirs

Abstract In the present research, three different data-driven models (DDMs) are developed to predict the discharge coefficient of streamlined weirs (Cdstw). Some machine-learning methods (MLMs) and intelligent optimization models (IOMs) such as Random Forest (RF), Adaptive Neuro-Fuzzy Inference System (ANFIS), and gene expression program (GEP) methods are employed for the prediction of Cdstw. To identify input variables for the prediction of Cdstw by these DMMs, among potential parameters on Cdstw, the most effective ones including geometric features of streamlined weirs, relative eccentricity (λ), downstream slope angle (β), and water head over the crest of the weir (h1) are determined by applying Buckingham π-theorem and cosine amplitude analyses. In this modeling, by changing architectures and fundamental parameters of the aforesaid approaches, many scenarios are defined to obtain ideal estimation results. According to statistical metrics and scatter plot, the GEP model is determined as a superior method to estimate Cdstw with high performance and accuracy. It yields an R2 of 0.97, a Total Grade (TG) of 20, RMSE of 0.032, and MAE of 0.024. Besides, the generated mathematical equation for Cdstw in the best scenario by GEP is likened to the corresponding measured ones and the differences are within 0–10%.

Elastic–Plastic Properties of Mesoscale Electrodeposited LIGA Nickel Alloy Films: Analysis of Measurement Uncertainties

Abstract It is well documented that the microstructure and properties of electrodeposited films, such as lithographie, galvanoformung, abformung (LIGA) Ni and its alloys, are highly sensitive to processing conditions hence the literature shows large discrepancies in mechanical properties, even for similar alloys. Given this expected material variability as well as the experimental challenges with small-scale mechanical testing, measurement uncertainties are needed for property values to be applied appropriately, and yet are uncommon in micro- and mesoscale tensile testing studies. In a separate paper, we reported the elastic–plastic properties of 200 μm-thick freestanding films of LIGA-fabricated nanocrystalline Ni-10%Fe and microcrystalline Ni-10%Co, with specimen gauge widths ranging from 75 μm to 700 μm, and tensile tested at strain rates 0.001 s−1 and 1 s−1. The loads were applied by commercial miniature and benchtop load frames, and strain was measured by digital image correlation. In this paper, we examine the measurement uncertainties in the ultimate tensile strength, apparent Young's modulus, 0.2% offset yield strength, and strain hardening parameters, and compare them to the standard deviations. For several of these properties, the standard deviation cannot be interpreted as the statistical scatter because the measurement uncertainty was larger. Microplasticity affects the measurement of the Young's modulus, thus we recommended measuring the modulus after specimens have been cyclically loaded. These measurement uncertainty issues might be relevant to similar works on small-scale tensile testing and might help the reader to interpret the discrepancies in literature values of mechanical properties for LIGA and electrodeposited films.

Cosmic-ray propagation under consideration of a spatially resolved source distribution

Cosmic rays (CRs) are an integral component of the interstellar medium, producing broadband emission while interacting with other Galactic matter components like the interstellar gas or magnetic fields. In addition to observations, numerical simulations of CR propagation through the Galaxy help to increase the level of understanding of Galactic CR transport and diffuse γ-ray emission as seen by different experiments. Up to now, the standard approach at modelling source distributions used as input for such transport simulations often rely on radial symmetry and analytical functions rather than individual, observation-based sources. We aim at a redefinition of existing CR source distributions by combining sources observed with the H.E.S.S. experiment and simulated random sources, which follow the matter density in the Milky Way. As a result, H.E.S.S.-inspired Galactic CR source distributions are inferred.We use the PICARD code to perform 3D-simulations of nuclei and electrons in CR propagation using our hybrid source distribution models. Furthermore, also gamma-ray maps and spectra, simulated with the redefined source models, are evaluated in different regions in the Galaxy and compared with each other to determine the statistical scatter of the underlying distributions. We find global consistency between our models and in comparison to previous simulations, with only some localised fluctuations, e.g. in the spiral arms.This implementation of a three-dimensional source model based on observations and simulations enables a new quality of propagation modelling. It offers possibilities for more realistic CR transport scenarios beyond radial symmetry and delivers meaningful results in both the arm and interarm regions of the Galaxy. This gives a more realistic picture of the Galactic γ-ray sky by including structures from the source model and not just the gas distributions.

WISE-PS1-STRM: neural network source classification and photometric redshifts for WISE×PS1

ABSTRACT We cross-match between the WISE All-Sky and PS1 3π DR2 source catalogues. The resulting catalogue has 354 590 570 objects, significantly fewer than the parent PS1 catalogue, but its combination of optical and infrared colours facilitate both better source classification and photometric redshift estimation. We perform a neural network-based classification of the objects into galaxies, quasars, and stars, then run neural network-based photometric redshift estimation for the galaxies. The star sample purity and quasar sample completeness measures improve substantially, and the resulting photo-z’s are significantly more accurate in terms of statistical scatter and bias than those calculated from PS1 properties alone. The catalogue will be a basis for future large-scale structure studies, and will be made available as a high-level science product via the Mikulski Archive for Space Telescopes.

Reconstructing Daily Discharge in a Megadelta Using Machine Learning Techniques

AbstractIn this study, six machine learning (ML) models, namely, random forest (RF), Gaussian process regression (GPR), support vector regression (SVR), decision tree (DT), least squares support vector machine (LSSVM), and multivariate adaptive regression spline (MARS) models, were employed to reconstruct the missing daily‐averaged discharge in a mega‐delta from 1980 to 2015 using upstream‐downstream multi‐station data. The performance and accuracy of each ML model were assessed and compared with the stage‐discharge rating curves (RCs) using four statistical indicators, Taylor diagrams, violin plots, scatter plots, time‐series plots, and heatmaps. Model input selection was performed using mutual information and correlation coefficient methods after three data pre‐processing steps: normalization, Fourier series fitting, and first‐order differencing. The results showed that the ML models are superior to their RC counterparts, and MARS and RF are the most reliable algorithms, although MARS achieves marginally better performance than RF. Compared to RC, MARS and RF reduced the root mean square error (RMSE) by 135% and 141% and the mean absolute error by 194% and 179%, respectively, using year‐round data. However, the performance of MARS and RF developed for the climbing (wet season) and recession (dry season) limbs separately worsened slightly compared to that developed using the year‐round data. Specifically, the RMSE of MARS and RF in the falling limb was 856 and 1,040 m3/s, respectively, while that obtained using the year‐round data was 768 and 789 m3/s, respectively. In this study, the DT model is not recommended, while the GPR and SVR models provide acceptable results.

GNSS Ocean Bistatic Statistical Scattering in the Time-Varying Regime: Simulation and Model Validation

This work develops around the problem of modeling statistics and correlation properties of the GNSS L-band signal scattered by the moving ocean surface, presented in the previous paper [1]. The modeling work is here completed by deriving an effective method for simulation of the received signal as well as by making a validation study based on simulated and real data. The simulation of the stochastic process embedding the design statistical properties, the temporal correlation model and the system and surface parameters is intricate, in general, and provides a useful tool for researchers interested in modeling and simulation of GNSS reflected signals from a time evolving sea surface. The GNSS received waveforms after 1 ms coherent integration are simulated and the correlation properties of the specular and near-specular points are compared to the theoretical model and to results from real data. Real raw data were collected during an aircraft mission in the Gulf of Finland and in a spaceborne scenario with CYGNSS satellites. Comparisons show good agreement among simulations, model and real data, and demonstrate that the estimated correlation time of the GNSS-R signal, fits real data with higher accuracy with respect to the case in which sea correlation is not accounted, especially when the receiving platform velocity is moderate.

Pulse engineering of a global field for robust and universal quantum computation

Global control strategies for arrays of qubits are a promising pathway to scalable quantum computing. A continuous-wave global field provides decoupling of the qubits from background noise. However, this approach is limited by variability in the parameters of individual qubits in the array. Here we show that by modulating a global field simultaneously applied to the entire array, we are able to encode qubits that are less sensitive to the statistical scatter in qubit resonance frequency and microwave amplitude fluctuations, which are problems expected in a large scale system. We name this approach the SMART (Sinusoidally Modulated, Always Rotating and Tailored) qubit protocol. We show that there exist optimal modulation conditions for qubits in a global field that robustly provide improved coherence times. We discuss in further detail the example of spins in silicon quantum dots, in which universal one- and two-qubit control is achieved electrically by controlling the spin-orbit coupling of individual qubits and the exchange coupling between spins in neighbouring dots. This work provides a high-fidelity qubit operation scheme in a global field, significantly improving the prospects for scalability of spin-based quantum computer architectures.

A probabilistic approach to evaluate dynamic and static strength of quasi-brittle materials through high-rate testing

Results of mechanical tests, especially in dynamics, obtained under similar input conditions always have a statistical scatter. Currently, there is no generally accepted engineering approach to the correct assessment of critical stresses for a material under dynamic loads. For example, there is no clear understanding of how many specimens need to be tested to estimate a specific ultimate stress for a specific impact rate. The present study enhances the applicability of the randomized algorithm of Sign-Perturbed Sums to solve the mentioned problem of the dynamic strength evaluation under typical restrictions on experimental results such as few numbers of data points with unknown random noises. The combination of the incubation time criteria and this new probabilistic approach permits to estimate accurately material strength parameters: the critical failure stress and the incubation time with a given level of confidence. Comparative analysis of developing approach and different modifications of the scaling law obtained by Kimberly and Ramesh showed that both models are well agreed with empirically observed data. Experimental data of dynamic test on split Hopkinson pressure bars and a drop tower setup for different quasi-brittle materials are taken to demonstrate advantages of the new method. It is shown that incubation time of failure and critical stress values of certain materials can be estimated with a high level of confidence. These results can be used to optimize experimental studies and develop new standards for dynamic testing of materials.

Discussion of the statistical representativeness of the results in: Lomov, Breite, Melnikov, Mesquita, Swolfs and Abaimov [Int. J. Solids Struct 225 (2021) 111061

The variability of the statistical physics descriptors of failure in an impregnated fibre bundle model is investigated. Using the same bundle and fibre parameters as in (Lomov et al., 2021), 50 random realisations were built for VF50 and for VF60 models. The same descriptors of the bundle damage, clusters, and avalanches were calculated, and their statistical scatter characteristics were analysed. The conclusions of (Lomov et al., 2021) are confirmed to be statistically representative for an ensemble of random fibre placement realisations.

Effect of statistical scatter in the elastic properties on the predictability of first ply failure of a polymer composite pressure vessel

Statistical variations in the elastic properties of composites are unavoidable. This work is concerned with the assessment of the probabilistic first ply failure of pressure vessel in response to the variations of elastic properties of T700 carbon/epoxy composite among the published literature. Initially, deterministic failure pressure is estimated using Finite Element Analysis software ANSYS. The uncertainties with respect to elastic properties are quantified by using Statistical software MINITAB. Then, the probability design system platform in ANSYS software has been utilized to perform probabilistic failure analysis by Monte Carlo simulations. The statistical results in terms of the mean and standard deviation of first ply failure pressure are computed. The probabilistic first ply failure pressure range determined is within 3 limits with 99.997% confidence level. It is helpful to the designers to assess the factor of safety in the case of anticipated variations in properties.

Statistical Scattering Component-Based Subspace Alignment for Unsupervised Cross-Domain PolSAR Image Classification

Increasing amounts of polarimetric synthetic aperture radar (PolSAR) images from different sensors covering different scenes are available, but limited labeled samples and trained models can hardly work well in these cross-domain data interpretations. Fortunately, domain adaptation (DA) can transfer knowledge in existing images to new yet related images. DA shows attractive potential for PolSAR classification, and it is still challenging due to more complex domain shifts caused by different sensors, imaging conditions, and distributions. Inspired by the widely applicable polarimetric scattering mechanisms and DA ability of subspace alignment (SA), this article is devoted to constructing a robust unsupervised cross-domain PolSAR classification framework, by exploring scattering and statistical characteristics mapping between the source and target domains. First, classical scattering components of both source and target data were extracted, and Wishart clustering was adopted to derive the statistical information of scattering components at patch level. Second, the intrinsic polarimetric scattering components were estimated and extracted, which were called statistical scattering components (SSCs). Third, by applying SA, the source SSC was aligned with target SSC, and domain shift was further reduced. Finally, the target PolSAR image was classified based on labeled samples from source domain, and unsupervised cross-domain classification was achieved by SSC-based SA (SSC-SA). The unsupervised cross-domain experiments are conducted on 49 units among 11 data sets, including Radarsat-2, Gaofen-3, AIRSAR, and Pi-SAR images. With randomly selected labeled samples (about 2%–10%) from source domain, the accuracies of the proposed cross-domain classifications range between 80.20% and 95.64%. Also, the proposed SSC feature pattern is proved extensible for other polarimetric basis and decompositions.

Assessment of the Probability of Fatigue Fracture of Structural Components with Accounting for the Statistical Scatter of Mechanical Properties of the Material and the Residual Defectness

Assessment of the Probability of Fatigue Fracture of Structural Components with Accounting for the Statistical Scatter of Mechanical Properties of the Material and the Residual Defectness