Gaussian Distribution Model Research Articles

Probability density function analysis of water entry sound based on α stable distribution model

The detection of water entry sound can be used as an early warning indicator for underwater vehicle. The present work is dedicated to investigate the amplitude distribution characteristics of the water entry sound. Understanding the time series amplitude distribution of the water entry sound can facilitate water entry sound detection method based on the probability distribution function (PDF) change. To shed light into needed model of amplitude distribution, this paper focusses on α stable distribution model, which is appropriate for leptokurtic data. The PDFs of numerical simulation and experimental measurement data of water entry sound are modeled with α stable distribution and normal models, respectively. The results demonstrate that: (i) The water entry sound shows noteworthy non-Gaussian characteristics. (ii) The α stable distribution model has higher goodness of fit than the normal distribution model for water entry noise. (iii) The characteristic exponent α is of great significance for describing the PDF of water entry sound, which allows deriving effective guidelines for the detection of water entry sound.

Analysis of the Current Transport Characteristics (CTCs) in the Au/n-Si Schottky Diodes (SDs) with Al2O3 Interfacial Layer over Wide Temperature Range

Temperature dependent electrical-parameters and CTCs in Au/Al2O3/n-Si SDs have been analysed between 200 K and 400 K using current/voltage (IV) characteristics. While the value of quality/ideality factor (n) decreases, zero bias potential barrier height (BH, ΦB0) increases with increasing temperature. The value of Richardson constant (A*) and activation energy (Ea) were also derived from the conventional Richardson plot (RP) as 0.567 eV and 7.34 × 10−3 A.cm−2K−2, respectively. This low A* value shows that deviation from standard thermionic-emission (TE) model and to determine whether this situation may be described by Gaussian-distribution (GD) model or not, ΦBo vs n and ΦBo vs q/(2kT) curves were illustrated. The Φ ¯ B value was found as 1.19 eV from the linear ΦB0 vs n plot for ideal case (n = 1). The mean-value of BH ( Φ ¯ B 0 ) and standart deviation (σ s) values were found from the ΦBo vs q/(2kT) curve as 1.130 eV and 0.127 V, respectively. By using value of σ s, RP was modified and then Φ ¯ B 0 and A* values were found as 1.128 eV and 108.88 Acm−2K−2, respectively, and this value of A* very close to its theoretical value (=112 Acm−2K−2). The surface state density (Nss) were also obtained from the forward bias IV data for three-temperatures.

Relationship between meteorological factors and mortality in patients with coronavirus disease 2019: A cross-sectional study

BackgroundRecent studies on COVID-19 have demonstrated that poverty, comorbidities, race/ethnicity, population density, mobility, hygiene and use of masks are some of the important correlates of COVID-19 outcomes. In fact, weather conditions also play an important role in enhancing or eradicating health issues. Based on Chinese experience, the development of SARS and COVID-19 is partially associated with alterations in climate that align with the seasonal shifts of the “24 solar terms.” However, the applicability of this pattern to other countries, particularly the United States, which has the highest global incidence and mortality rates, remains subject to ongoing investigation. We need to find more evidence to in the U.S. states verify the relationship between meteorological factors and COVID-19 outcomes to provide epidemiological and environmental support for the COVID-19 pandemic prevention and resource preservation. ObjectiveTo evaluate the relationship between meteorological factors and Coronavirus Disease 2019 (COVID-19) mortality. MethodsWe conducted an ecological cross-sectional study to evaluate the relationship between meteorological factors (maximum temperature, minimum temperature, humidity, wind speed, precipitation, atmospheric pressure) and COVID-19 mortality. This retrospective observational study examines mortality rates among COVID-19 patients in the three US states, California, Texas, and New York, with the highest fatality numbers, between March 7, 2020 and March 7, 2021. The study draws upon data sourced from the publicly accessible Dryad database. The daily corresponding meteorological conditions were retrieved from the National Oceanic and Atmospheric Administration Global Meteorological website (https://www.ncei.noaa.gov/maps/hourly/). This study employed multivariate linear regression analysis to assess the correlation between six meteorological factors and COVID-19 mortality. Gaussian distribution models were utilized to generate smooth curves for examining the linear association between maximum or minimum temperature and mortality. Additionally, breakpoint analysis was conducted to evaluate the threshold effect of temperature. ResultsWe found that the death toll of patients with COVID-19 decreased with an increase in the highest and lowest ambient temperatures (p < 0.001). In our study, we observed a seasonal difference in mortality rates, with a higher number of deaths occurring during winter months, particularly in January and February. However, mortality rates decreased significantly in March. Notably, we found no statistically significant correlation between relative humidity, average precipitation, and average wind speed with COVID-19 mortality (all p > 0.05). Daily COVID-19 death was negatively correlated with the maximum temperature (β = −22, 95% CI, −26.2 to −17.79 –, p < 0.01), while the maximum temperature was below 30 °C. Similarly, the number of deaths was negatively correlated with the minimum temperature (β = −27.46, 95% CI, −31.48 to −23.45, p < 0.01), when the minimum temperature was below 8 °C. Our study found a significant association between temperature and COVID-19 mortality, with every 1 °C increase in maximum or minimum temperature resulting in a decrease of 22 and 27 deceased cases, respectively. The relationship between atmospheric pressure and COVID-19 mortality was not fully elucidated due to its complex interaction with maximum temperature. ConclusionsThis empirical study adds to the existing body of research on the impact of climate factors on COVID-19 prevention and resource allocation. Policymakers and health scientists may find these findings useful in conjunction with other social factors when making decisions related to COVID-19 prevention and resource allocation.

Analysis of space-based large light concentration reflective surfaces with errors

Space-based 100-m light condensing reflectors can undergo considerable structural deformations. The simple and accurate analysis of a concentrator system is important, especially when the system has large deformation errors. Further, slope error measurement of large reflectors is challenging. Gaussian distribution and other calculation models cannot accurately characterize the continuous changes of the actual model. Moreover, the analysis of fully fitted deformed surfaces is complicated. Therefore, this paper proposes a method for approximating surfaces by planers. The side lengths of the planes affect the speed and accuracy of modeling. Therefore, this paper analyzes the influence of the side length of the modeling plane and derives the allowable maximum side length of the plane. The model is also analyzed to ensure that its slope error does not change compared to the actual model during the modeling process. Spherical concentrators have great advantages in spatial concentrating owing to their rotational symmetry. Herein, the error model of a spherical low-ratio concentrator is calculated. Finally, an experiment is designed to verify the correctness of the calculations.

Multi-target reconstruction based on subspace decision optimization for bioluminescence tomography

Background and objectiveBioluminescence tomography (BLT) is a noninvasive optical imaging technique that provides qualitative and quantitative information on the spatial distribution of tumors in living animals. Researchers have proposed a list of algorithms and strategies for BLT reconstruction to improve its reconstruction quality. However, multi-target BLT reconstruction remains challenging in practical clinical applications due to the mutual interference of optical signals and difficulty in source separation. MethodsTo solve this problem, this study proposes the subspace decision optimization (SDO) approach based on the traditional iterative permissible region strategy. The SDO approach transforms a single permissible region into multiple subspaces by clustering analysis. These subspaces are shrunk based on subspace shrinking optimization to achieve spatial continuity of the permissible regions. In addition, these subspaces are merged to construct a new permissible region and then the next iteration of reconstruction is carried out to ensure the stability of the results. Finally, all the iterative results are optimized based on the normal distribution model and the distribution properties of the targets to ensure the sparsity of each target and the non-biasing of the overall results. ResultsExperimental results show that the SDO approach can automatically identify and separate different targets, ensuring the accuracy and quality of multi-target BLT reconstruction results. Meanwhile, SDO can combine various types of reconstruction algorithms and provide stable and high-quality reconstruction results independent of the algorithm parameters. ConclusionsThe SDO approach provides an integrated solution to the multi-target BLT reconstruction problem, realizing the whole process including target recognition, separation, reconstruction, and result enhancement, which can extend the application domain of BLT.

Investigation of the Static Performance of Hydrostatic Thrust Bearings Considering Non-Gaussian Surface Topography

The dynamic and static characteristics of hydrostatic thrust bearings are significantly affected by the bearing surface topography. Previous studies on hydrostatic thrust bearings have focused on Gaussian distribution models of bearing surface topography. However, based on actual measurements, the non-Gaussianity of the distribution characteristics of bearing surface topography is clear. To accurately characterize the non-Gaussian distribution of bearing surface topography, the traditional probability density function of Gaussian distribution was modified by introducing Edgeworth expansion. The non-Gaussian surface was then reflected by two parameters: kurtosis and skewness. This had an effect on the static characteristics of hydrostatic thrust bearings with both circumferential and radial surface topographies. The comparison between the Gaussian distribution results and those of the non-Gaussian model showed that errors between the two models could reach more than 10%. Therefore, it is important to take into account the non-Gaussianity of bearing surface when discussing static characteristics of hydrostatic thrust bearings considering the surface topography.

Model uncertainties of SPT, CPT, and VS-based simplified methods for soil liquefaction assessment

Simplified methods for assessing soil liquefaction potential based on the standard penetration test (SPT) are prevalent in practice and widely accepted by several seismic design codes. When encountering sites that have not been investigated using SPT, such as offshore sites or sites with a high level of gravel content, engineers can only substitute the methods based on piezocone penetration test data (CPT-qc methods) or shear wave velocity measurements (VS-based methods); however, these two approaches perform inconsistently with methods based on SPT data (SPT-N methods). As a result, this paper exploits the datasets consisting of SPT, CPTU, and in situ seismic test measurements from 13 alluvium sites in the Taipei Basin to compare the performance of prevalent SPT-N, CPT-qc, and VS-based methods. The discrepancies (uncertainties) of these methods are characterized as Gaussian distribution models, which is believed to be a feasible strategy for predicting equivalent results for SPT-N methods when SPT data are not available. Finally, the application of the proposed models to liquefaction potential index evaluation is demonstrated using a real case study.

Study on the driving mechanism of lagged effects based on different time scales in a karst drainage basin in South China

Compared to earthquakes and volcanoes, drought is one of the most damaging natural disasters and is mainly affected by rainfall losses, especially by the runoff regulation ability of the underlying watershed surface. Based on monthly rainfall runoff data recorded from 1980 to 2020, in this study, the distributed lag regression model is used to simulate the rainfall-runoff process in the karst distribution region of South China, and a time series of watershed lagged-flow volumes is calculated. The watershed lagged effect is analyzed by four distribution models, and the joint probability between the lagged intensity and frequency is simulated by the copula function family. The results show that (1) the watershed lagged effects simulated by the normal, log-normal, P-III and log-logistic distribution models in the karst drainage basin are particularly significant, with small mean square errors (MSEs) and significant time-scale characteristics. (2) Affected by spatiotemporal distribution differences in rainfall and the impacts of different basin media and structures, the lag response of runoff to rainfall differs significantly among different time scales. Especially at the 1-, 3- and 12-month scales, the coefficient of variation (Cv) of the watershed lagged intensity is greater than 1, while it is less than 1 at the 6- and 9-month scales. (3) The lagged frequencies simulated by the log-normal, P-III and log-logistic distribution models are relatively high (with medium, medium–high and high frequencies, respectively), while that simulated by the normal distribution is relatively low (medium–low and low frequencies). (4) There is a significant negative correlation (R < − 0.8, Sig. < 0.01) between the watershed lagged intensity and frequency. For the joint probability simulation, the fitting effect of the gumbel Copula is the best, followed by the Clayton and Frank-1 copulas, and while that of the Frank-2 copula is relatively weak. Consequently, the propagation mechanism from meteorological drought to agricultural or hydrological drought and the conversion mechanism between agricultural and hydrological drought are effectively revealed in this study, thereby providing a scientific basis for the rational utilization of water resources and drought resistance and disaster relief in karst areas.

Assessing Urban Sustainability and the Potential to Improve the Quality of Education and Gender Equality in Phnom Penh, Cambodia

This research assessed the urban sustainability of all 14 districts of the Cambodian capital Phnom Penh to identify weaknesses and improvement potentials to achieve the national development goals; the New Urban Agenda (NUA); and the Sustainable Development Goals (SDGs) 11 (sustainable cities and communities), 4 (quality education), and 5 (gender equality). The indicators’ selection was based on available data. The analysis of the indicators and their weights was based on the analytic hierarchy process (AHP). Indicator weights were used to improve assessment accuracy and identify each district’s unique characteristics and specific strengths and weaknesses. The normal distribution model was used to standardize the variables before comparison. Among the quality education indicators, the access to education and vocational training obtained the highest weight of 0.38, followed by education staff with 0.33 and facilities with 0.29. Among gender-equality-related indicators, the indicators related to professions obtained the highest weight with 0.34, followed by schools with 0.33 and decision-making with 0.32. The most sustainable district was Boeng Keng Kong, with a consolidated result of 22.81 for quality education and gender equality assessment based on indicator weights, followed by the districts Doun Penh with 20.51, Prampir Makara with 19.95, and Chamkarmon with 19.75. This research identified district-specific strengths and weaknesses, whereas the weak points unveil the improvement potential of specific districts.

Trends in regolith thickness in a headwater catchment, Sierra Nevada, California

AbstractRooting in deep regolith enables forests to withstand seasonal and annual precipitation shortfalls. Despite its ecological importance, spatial patterns in regolith thickness within forest ecosystems are scarcely documented. Regolith thickness was estimated at 66 sites throughout a 543‐ha watershed in the southern Sierra Nevada by hand auger to point of failure or a maximum depth of 7.5 m, describing a minimum thickness estimate. Regolith consists of 1–2 m of soil overlying thick and porous weathered granodiorite. Depth to auger failure ranged from 1.52 to an indeterminate depth beyond 7.5 m. A total of 27 points exceeded 7.5 m depth. Normal, lognormal, and gamma data distribution models were fitted to observations to extrapolate thickness across the watershed and estimate thicknesses beyond the measurement limitation. Predictions for the 95th percentile of regolith thickness varied substantially; 26.05 m for lognormal, 16.87 m for gamma, and 9.56 m for normal. Considering any best fit model, &gt;55% of the watershed area was deeper than 5 m. Depth classes were formed to evaluate the extent to which topography is associated with spatial trends in regolith thickness. Spatial patterns were related to two covariate proxies (distance from stream channel and topographic wetness) with the general landscape trend of shallow depth classes (&lt;3.3 m) in lowlands and deeper regolith classes (&gt;7.5 m) in uplands. The normalized difference vegetation index signatures over the late stages of a 5‐year drought were greener in the lowlands. In contrast, upland forests displayed widespread tree die‐off, suggesting deep water storage does not maintain forests over long‐term drought.

Characterization method of core pore structure based on truncated Gaussian and its application in shale cores

The core pore structure is mainly described by fractal theory. Conventional single fractal models cannot express the multi-peak distribution characteristics of coal rock, shale, carbonate rock, and other cores. A multifractal model has numerous dimensions and discontinuous functions, making the model parameter selection difficult. Based on the capillary bundle theory and the truncated Gaussian distribution model, a model for characterizing the multi-peak distribution structure of cores is developed in this study. The model's accuracy is verified using the tested NMR experimental data of shale bimodal distribution and the cited coal trimodal distribution. This study further analyzes the influence of tortuosity and tortuosity fractal dimensions on this model. The results show that tortuosity and tortuosity fractal dimensions are conducive to improving the accuracy of the multi-peak pore structure model. The structure model, which can characterize the multi-scale and multi-peak distribution characteristics of shale pore diameter, provides a new concept for describing the pore structure of porous media.

Hydrological drought forecasting under a changing environment in the Luanhe River basin

Abstract. Forecasting the occurrence of hydrological drought according to a forecasting system is an important disaster reduction strategy. In this paper, a new drought prediction model adapted to changing environments was constructed. Taking the Luanhe River basin in China as an example, first, nonstationarity analysis of hydrological sequences in the basin was carried out. Then, conditional distribution models with the human activity factor as an exogenous variable were constructed to forecast hydrological drought based on meteorological drought, and the results were compared with the traditional normal distribution model and conditional distribution model. Finally, a scoring mechanism was applied to evaluate the performance of the three drought forecasting models. The results showed that the runoff series of the Luanhe River basin from 1961 to 2010 were nonstationary; moreover, when human activities were not considered, the hydrological drought class tended to be the same as the meteorological drought class. The calculation results of the models involving HI as an exogenous variable were significantly different from the models that did not consider human activities. When the current drought class tended towards less severe or normal, the meteorological drought tended to turn into more severe hydrological drought with the increase in human index values. According to the scores of the three drought forecasting models, the conditional distribution models involving the human index can further improve the forecasting accuracy of drought in the Luanhe River basin.

Uncertainty analysis of wind speed of wind turbine

AbstractThe uncertainty of wind speed has a great impact on the reliability of wind turbines. However, previous studies usually used average wind speed or rated wind speed as inputs, ignoring the impact of time and space distribution on wind speed. Based on the hourly ground observation data of meteorological stations, four observation stations are taken as the research object of the uncertainty of wind speed spatial distribution. The four observation stations are divided into single day and one week, and seven data analysis indicators and normal distribution models are used day and night to analyze the uncertainty of wind speed time distribution. The results show that both spatial distribution and temporal distribution are important factors that cause the uncertainty of wind speed. Due to different geographical locations of four stations, their wind speed time domain diagrams are different, and the maximum wind speed, fluctuation frequency and fluctuation amplitude of wind speed at each observation station are different; The wind speed distribution of a single day is quite different from that of a week. The data of a single day and a week are analyzed by day and night, which is quite different from the overall data analysis. The necessity of wind speed uncertainty research of wind turbine considering the temporal and spatial distribution is verified, and it is concluded that normal distribution is not suitable for fitting the distribution model of four stations under study, which lays a foundation for determining the wind speed distribution model, analyzing the power characteristics of wind turbine, evaluating the generation capacity of wind turbine, and analyzing the reliability of wind turbine.

Probabilistic recovery resilience model of mooring failure accident based on correlated schedule-uncertainty analysis

Floating production system (FPS) may face various risks during their service life, among which mooring failure is one of the most disastrous events. In order to assess and quantify the resilience of FPS under mooring failure, it is essential to use reliable restoration fragility models. But for now, there are no available specific recovery models. The main barrier is uncertainty of nature and complexity of recovery actions. This paper aims to take uncertainty of recovery schedule into account and propose recovery models for mooring failure accident. Based on literature review and a detailed questionnaire that elicits knowledge from experts, the restoration schedule and uncertainty are defined and quantified. Through correlated schedule uncertainty analysis model (CSUAM), uncertainty influence on recovery model is measured. And after Monte Carlo Simulation (MCS), Gaussian mixture model (GMM) and normal distribution model are introduced to establish probabilistic recovery model of different damage level. Recovery models are then presented and validated for a certain FPS in South China Sea, including restoration task scheduling, seasonal influence, spare availability and two-mobilizations for different damage levels.

Rethinking the Operation Pattern for Anomaly Detection in Industrial Cyber–Physical Systems

Anomaly detection has been proven to be an efficient way to detect malicious behaviour and cyberattacks in industrial cyber–physical systems (ICPSs). However, most detection models are not entirely adapted to the real world as they require intensive computational resources and labelled data and lack interpretability. This study investigated the traffic behaviour of a real coal mine system and proposed improved features to describe its operation pattern. Based on these features, this work combined the basic deterministic finite automaton (DFA) and normal distribution (ND) models to build an unsupervised anomaly detection model, which uses a hierarchical structure to pursue interpretability. To demonstrate its capability, this model was evaluated on real traffic and seven simulated attack types and further compared with nine state-of-the-art works. The evaluation and comparison results show that the proposed method achieved a 99% F1-score and is efficient in detecting sophisticated attacks. Furthermore, it achieved an average 17% increase in precision and a 12% increase in F1-Score compared to previous works. These results confirm the advantages of the proposed method. The work further suggests that future works should investigate operation pattern features rather than pursuing complex algorithms.

Economic Valuation and Benefit Transfer of Restoring the Teesta Riverine Ecosystem

This study seeks to understand the socio-economic and ecological impacts of the hydroelectric power projects along the upper basin of the river Teesta in Sikkim. This study estimates the non-market benefits of restoring the Teesta riverine ecosystem and evaluates the transferability of welfare estimates. This study is a first of its kind undertaken in the Teesta basin which uses a unique dataset of 830 households obtained from the affected regions of the river basin. During the study, nine villages adjacent to the river Teesta, dams, and powerhouses were identified and surveyed. Double bounded dichotomous choice questions were used to elicit willingness to pay (WTP). Both the logistic and normal distribution models were fitted and the results were mostly similar. The median WTP was INR 373.00 and the variables that described the rating on dams, ownership of property, monthly expenditure of the household, informal employment status, and satisfaction about the state of the river Teesta were among the significant variables in the model. The benefit function value transfer estimated was INR 232.00 with the percentage transfer error (PTE) of 61.9%.

더블링 기법을 활용한 대응분석에 대한 민감도분석

Correspondence analysis is generally a data analysis technique that expresses a two-dimensional or higher contingency table in a low-dimensional space to see the combination of rows and columns. In general, a statistical analysis method applied to contingency table analysis uses a chi-square test, but the chi-square test has a limitation in that it cannot show the coupling pattern for rows and columns. As an alternative, correspondence analysis is used. In general, the row profiles of correspondence analysis sum to 1. For data evaluated on the Likert scale for various attributes of each entity, the sum of rows is different for each row, so general correspondence analysis cannot be performed. Correspondence analysis can be applied to such data by doubling the number of column categories by adding the positive and negative values of each attribute so that the sum is equal. This is called the doubling technique. As a result of exploratory multivariate data analysis that does not rely on normal distribution and statistical models for Likert data, a correspondence analysis study using a doubling technique was proposed (Han, 2019). By quantifying the examiners and subjects, and expressing the result using a graphic technique, it was easy to visually recognize and interpret geometrically clear meaning. However, for the method proposed by Han (2019), the stability evaluation for quantification analysis results has not yet been developed. Therefore, in order to develop a methodology for the stability evaluation of the materialistic method for Likert data analysis, which is often seen in real life such as public opinion surveys or consumer preference surveys, but lacks development of analysis methods, and to show its usefulness through case analysis do.

Stripping mechanism and Gaussian distribution model of a concave bionic comb for stripping prior to cutting

Stripping prior to cutting is a harvesting technique that only strips rice grains and obtains complete straws. This paper aims to solve the problems of high loss rate and short throwing distance for stripping prior to cutting. A concave bionic comb was developed based on the filiform papilla structure on the surface of a cattle tongue tip. The mechanism analysis and comparative research of the flat comb and the bionic comb were carried out. The results showed that when the arc radius was 5.0 mm, the magnification ratio of filiform papilla was 4.0, and the concave angle was 60°, the loss rate of falling grain was 4.3 %, and the loss rate of uncombed grain was 2.8 %. The diffusion angle of bionic comb was smaller than that of flat comb. The distribution characteristics of the thrown materials conformed to Gaussian distribution. Under the same working conditions, the falling grain loss rate and uncombed loss rate of the bionic comb were always lower than that of the flat comb. This study provides reference for the cross application of bionic technology and crop production field, promotes the harvesting way application of stripping prior to cutting in gramineous plants such as rice, wheat and sorghum, and provides a basis for harvesting the whole straws and expanding the way of comprehensive utilization of straws.

Analysis on the Effects of Different Receiver Structures and Porous Parameters on the Volumetric Effects and Heat Transfer Performance of Porous Volumetric Solar Receiver

The volumetric solar receiver is an important heat transfer component of the concentrated solar power (CSP) system. Moreover, in order to improve the absorption of concentrated solar radiation, the porous media are widely used in volumetric solar receiver. In recent years, many studies were concerned with the effects of porosity, pore number density and size, Reynolds number, and Darcy number on heat and flow performance in volumetric solar receiver. However, there are few studies on the effects of structure type and geometric parameters on the volumetric effects and the radiation characteristics of a porous volumetric solar receiver with nonuniform heat flux boundary condition. In this contribution, in order to analyze the effects of structures on the volumetric effects and heat transfer performance, we design six types of porous volumetric receiver structures, whereas the volume of different structures keeps consistent. Then, we apply the local non-thermal-equilibrium model and also consider the effect of structure shape on the concentrating solar radiation transport characteristic. Furthermore, we apply the Gaussian distribution model (GDM) to simulate the actual nonuniform heat flux boundary condition. The result shows that drum-type structure (RT-III) has the best heat transfer performance among the six structures; e.g., the outlet average temperature of fluid is up to 851 K when the pore size and porosity of porous media are 2 mm and 0.7, respectively, which is 41 K increased than the scaled cone-type structure. In addition, the pore diameter has less influence on the outlet temperature of fluid, but it has greater influence on the pressure drop. The contribution can provide a reference for this type of solar receiver design and reconstruction.

Performance Analysis of the Thermal Automatic Tracking Method Based on the Model of the UAV Dynamic Model in a Thermal and Cubature Kalman Filter

Fixed-wing, solar-powered unmanned aerial vehicles (SUAVs) can use thermals to expand the duration of flight. Nevertheless, due to the demand for calculating the thermal state parameters of the SUAV during flight, the existing methods still have some shortcomings in their practical applications, such as an inaccurate location estimation of the thermal and an insufficient seeking efficiency. In this paper, by integrating the Gaussian distribution model of thermal updraft of the pitching and roll moment of SUAVs, it is demonstrated that the approach introduced is superior to the traditional methods, disregarding the pitching moment. The simulation indicated that the accuracy and convergence speed of the thermal state estimation, performed while employing the cubature Kalman filter (CKF), are significantly improved after the SUAVs pitching moment is considered. The proposed method improves the automaticity and intelligence of SUAVs autonomous thermal search and enhances the cognitive and decision-making capabilities of SUAVs.