Decentralized robust stabilization problem of discrete-time fuzzy large-scale systems with parametric uncertainties is considered. This uncertain fuzzy large-scale system consists of N interconnected T-S fuzzy subsyst...Decentralized robust stabilization problem of discrete-time fuzzy large-scale systems with parametric uncertainties is considered. This uncertain fuzzy large-scale system consists of N interconnected T-S fuzzy subsystems, and the parametric uncertainties are unknown but norm-bounded. Based on Lyapunov stability theory and decentralized control theory of large-scale system, the design schema of decentralized parallel distributed compensation （DPDC） fuzzy controllers to ensure the asymptotic stability of the whole fuzzy large-scale system is proposed. The existence conditions for these controllers take the forms of LMIs. Finally a numerical simulation example is given to show the utility of the method proposed.展开更多
Geochemical survey data are essential across Earth Science disciplines but are often affected by noise,which can obscure important geological signals and compromise subsequent prediction and interpretation.Quantifying...Geochemical survey data are essential across Earth Science disciplines but are often affected by noise,which can obscure important geological signals and compromise subsequent prediction and interpretation.Quantifying prediction uncertainty is hence crucial for robust geoscientific decision-making.This study proposes a novel deep learning framework,the Spatially Constrained Variational Autoencoder(SC-VAE),for denoising geochemical survey data with integrated uncertainty quantification.The SC-VAE incorporates spatial regularization,which enforces spatial coherence by modeling inter-sample relationships directly within the latent space.The performance of the SC-VAE was systematically evaluated against a standard Variational Autoencoder(VAE)using geochemical data from the gold polymetallic district in the northwestern part of Sichuan Province,China.Both models were optimized using Bayesian optimization,with objective functions specifically designed to maintain essential geostatistical characteristics.Evaluation metrics include variogram analysis,quantitative measures of spatial interpolation accuracy,visual assessment of denoised maps,and statistical analysis of data distributions,as well as decomposition of uncertainties.Results show that the SC-VAE achieves superior noise suppression and better preservation of spatial structure compared to the standard VAE,as demonstrated by a significant reduction in the variogram nugget effect and an increased partial sill.The SC-VAE produces denoised maps with clearer anomaly delineation and more regularized data distributions,effectively mitigating outliers and reducing kurtosis.Additionally,it delivers improved interpolation accuracy and spatially explicit uncertainty estimates,facilitating more reliable and interpretable assessments of prediction confidence.The SC-VAE framework thus provides a robust,geostatistically informed solution for enhancing the quality and interpretability of geochemical data,with broad applicability in mineral exploration,environmental geochemistry,and other Earth Science domains.展开更多
Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for rese...Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.展开更多
Processing large-scale 3-D gravity data is an important topic in geophysics field. Many existing inversion methods lack the competence of processing massive data and practical application capacity. This study proposes...Processing large-scale 3-D gravity data is an important topic in geophysics field. Many existing inversion methods lack the competence of processing massive data and practical application capacity. This study proposes the application of GPU parallel processing technology to the focusing inversion method, aiming at improving the inversion accuracy while speeding up calculation and reducing the memory consumption, thus obtaining the fast and reliable inversion results for large complex model. In this paper, equivalent storage of geometric trellis is used to calculate the sensitivity matrix, and the inversion is based on GPU parallel computing technology. The parallel computing program that is optimized by reducing data transfer, access restrictions and instruction restrictions as well as latency hiding greatly reduces the memory usage, speeds up the calculation, and makes the fast inversion of large models possible. By comparing and analyzing the computing speed of traditional single thread CPU method and CUDA-based GPU parallel technology, the excellent acceleration performance of GPU parallel computing is verified, which provides ideas for practical application of some theoretical inversion methods restricted by computing speed and computer memory. The model test verifies that the focusing inversion method can overcome the problem of severe skin effect and ambiguity of geological body boundary. Moreover, the increase of the model cells and inversion data can more clearly depict the boundary position of the abnormal body and delineate its specific shape.展开更多
Social media data created a paradigm shift in assessing situational awareness during a natural disaster or emergencies such as wildfire, hurricane, tropical storm etc. Twitter as an emerging data source is an effectiv...Social media data created a paradigm shift in assessing situational awareness during a natural disaster or emergencies such as wildfire, hurricane, tropical storm etc. Twitter as an emerging data source is an effective and innovative digital platform to observe trend from social media users’ perspective who are direct or indirect witnesses of the calamitous event. This paper aims to collect and analyze twitter data related to the recent wildfire in California to perform a trend analysis by classifying firsthand and credible information from Twitter users. This work investigates tweets on the recent wildfire in California and classifies them based on witnesses into two types: 1) direct witnesses and 2) indirect witnesses. The collected and analyzed information can be useful for law enforcement agencies and humanitarian organizations for communication and verification of the situational awareness during wildfire hazards. Trend analysis is an aggregated approach that includes sentimental analysis and topic modeling performed through domain-expert manual annotation and machine learning. Trend analysis ultimately builds a fine-grained analysis to assess evacuation routes and provide valuable information to the firsthand emergency responders<span style="font-family:Verdana;">.</span>展开更多
In the face of a growing number of large-scale data sets, affinity propagation clustering algorithm to calculate the process required to build the similarity matrix, will bring huge storage and computation. Therefore,...In the face of a growing number of large-scale data sets, affinity propagation clustering algorithm to calculate the process required to build the similarity matrix, will bring huge storage and computation. Therefore, this paper proposes an improved affinity propagation clustering algorithm. First, add the subtraction clustering, using the density value of the data points to obtain the point of initial clusters. Then, calculate the similarity distance between the initial cluster points, and reference the idea of semi-supervised clustering, adding pairs restriction information, structure sparse similarity matrix. Finally, the cluster representative points conduct AP clustering until a suitable cluster division.Experimental results show that the algorithm allows the calculation is greatly reduced, the similarity matrix storage capacity is also reduced, and better than the original algorithm on the clustering effect and processing speed.展开更多
Monitoring data are often used to identify stormwater runoff characteristics and in stormwater runoff modelling without consideration of their inherent uncertainties. Integrated with discrete sample analysis and error...Monitoring data are often used to identify stormwater runoff characteristics and in stormwater runoff modelling without consideration of their inherent uncertainties. Integrated with discrete sample analysis and error propagation analysis, this study attempted to quantify the uncertainties of discrete chemical oxygen demand （COD）, total suspended solids （TSS） concentration, stormwater flowrate, stormwater event volumes, COD event mean concentration （EMC）, and COD event loads in terms of flow measurement, sample collection, storage and laboratory analysis. The results showed that the uncertainties due to sample collection, storage and laboratory analysis of COD from stormwater runoff are 13.99%, 19.48% and 12.28%. Meanwhile, flow measurement uncertainty was 12.82%, and the sample collection uncertainty of TSS from stormwater runoff was 31.63%. Based on the law of propagation of uncertainties, the uncertainties regarding event flow volume, COD EMC and COD event loads were quantified as 7.03%, 10.26% and 18.47%.展开更多
Simulation and interpretation of marine controlled-source electromagnetic(CSEM) data often approximate the transmitter source as an ideal horizontal electric dipole(HED) and assume that the receivers are located on a ...Simulation and interpretation of marine controlled-source electromagnetic(CSEM) data often approximate the transmitter source as an ideal horizontal electric dipole(HED) and assume that the receivers are located on a flat seabed.Actually,however,the transmitter dipole source will be rotated,tilted and deviated from the survey profile due to ocean currents.And free-fall receivers may be also rotated to some arbitrary horizontal orientation and located on sloping seafloor.In this paper,we investigate the effects of uncertainties in the transmitter tilt,transmitter rotation and transmitter deviation from the survey profile as well as in the receiver's location and orientation on marine CSEM data.The model study shows that the uncertainties of all position and orientation parameters of both the transmitter and receivers can propagate into observed data uncertainties,but to a different extent.In interpreting marine data,field data uncertainties caused by the position and orientation uncertainties of both the transmitter and receivers need to be taken into account.展开更多
We introduce a framework for statistical inference of the closure coefficients using machine learning methods.The objective of this framework is to quantify the epistemic uncertainty associated with the closure model ...We introduce a framework for statistical inference of the closure coefficients using machine learning methods.The objective of this framework is to quantify the epistemic uncertainty associated with the closure model by using experimental data via Bayesian statistics.The framework is tailored towards cases for which a limited amount of experimental data is available.It consists of two components.First,by treating all latent variables(non-observed variables)in the model as stochastic variables,all sources of uncertainty of the probabilistic closure model are quantified by a fully Bayesian approach.The probabilistic model is defined to consist of the closure coefficients as parameters and other parameters incorporating noise.Then,the uncertainty associated with the closure coefficients is extracted from the overall uncertainty by considering the noise being zero.The overall uncertainty is rigorously evaluated by using Markov-Chain Monte Carlo sampling assisted by surrogate models.We apply the framework to the Spalart-Allmars one-equation turbulence model.Two test cases are considered,including an industrially relevant full aircraft model at transonic flow conditions,the Airbus XRF1.Eventually,we demonstrate that epistemic uncertainties in the closure coefficients result into uncertainties in flow quantities of interest which are prominent around,and downstream,of the shock occurring over the XRF1 wing.This data-driven approach could help to enhance the predictive capabilities of CFD in terms of reliable turbulence modeling at extremes of the flight envelope if measured data is available,which is important in the context of robust design and towards virtual aircraft certification.The plentiful amount of information about the uncertainties could also assist when it comes to estimating the influence of the measured data on the inferred model coefficients.Finally,the developed framework is flexible and can be applied to different test cases and to various turbulence models.展开更多
The mathematic theory for uncertainty model of line segment are summed up to achieve a general conception, and the line error hand model of εσ is a basic uncertainty model that can depict the line accuracy and quali...The mathematic theory for uncertainty model of line segment are summed up to achieve a general conception, and the line error hand model of εσ is a basic uncertainty model that can depict the line accuracy and quality efficiently while the model of εm and error entropy can be regarded as the supplement of it. The error band model will reflect and describe the influence of line uncertainty on polygon uncertainty. Therefore, the statistical characteristic of the line error is studied deeply by analyzing the probability that the line error falls into a certain range. Moreover, the theory accordance is achieved in the selecting the error buffer for line feature and the error indicator. The relationship of the accuracy of area for a polygon with the error loop for a polygon boundary is deduced and computed.展开更多
Various kinds of data are used in new product design and more accurate datamake the design results more reliable. Even though part of product data can be available directlyfrom the existing similar products, there sti...Various kinds of data are used in new product design and more accurate datamake the design results more reliable. Even though part of product data can be available directlyfrom the existing similar products, there still leaves a great deal of data unavailable. This makesdata prediction a valuable work. A method that can predict data of product under development basedon the existing similar products is proposed. Fuzzy theory is used to deal with the uncertainties indata prediction process. The proposed method can be used in life cycle design, life cycleassessment (LCA) etc. Case study on current refrigerator is used as a demonstration example.展开更多
The hydrological uncertainty about NASH model parameters is investigated and addressed in the paper through “ideal data” concept by using the Generalized Likelihood Uncertainty Estimation (GLUE) methodology in an ap...The hydrological uncertainty about NASH model parameters is investigated and addressed in the paper through “ideal data” concept by using the Generalized Likelihood Uncertainty Estimation (GLUE) methodology in an application to the small Yanduhe research catchment in Yangtze River, China. And a suitable likelihood measure is assured here to reduce the uncertainty coming from the parameters relationship. “Ideal data” is assumed to be no error for the input-output data and model structure. The relationship between parameters k and n of NASH model is clearly quantitatively demonstrated based on the real data and it shows the existence of uncertainty factors different from the parameter one. Ideal data research results show that the accuracy of data and model structure are the two important preconditions for parameter estimation. And with suitable likelihood measure, the parameter uncertainty could be decreased or even disappeared. Moreover it is shown how distributions of predicted discharge errors are non-Gaussian and vary in shape with time and discharge under the single existence of parameter uncertainty or under the existence of all uncertainties.展开更多
Complex engineered systems are often difficult to analyze and design due to the tangled interdependencies among their subsystems and components. Conventional design methods often need exact modeling or accurate struct...Complex engineered systems are often difficult to analyze and design due to the tangled interdependencies among their subsystems and components. Conventional design methods often need exact modeling or accurate structure decomposition, which limits their practical application. The rapid expansion of data makes utilizing data to guide and improve system design indispensable in practical engineering. In this paper, a data driven uncertainty evaluation approach is proposed to support the design of complex engineered systems. The core of the approach is a data-mining based uncertainty evaluation method that predicts the uncertainty level of a specific system design by means of analyzing association relations along different system attributes and synthesizing the information entropy of the covered attribute areas, and a quantitative measure of system uncertainty can be obtained accordingly. Monte Carlo simulation is introduced to get the uncertainty extrema, and the possible data distributions under different situations is discussed in detail The uncertainty values can be normalized using the simulation results and the values can be used to evaluate different system designs. A prototype system is established, and two case studies have been carded out. The case of an inverted pendulum system validates the effectiveness of the proposed method, and the case of an oil sump design shows the practicability when two or more design plans need to be compared. This research can be used to evaluate the uncertainty of complex engineered systems completely relying on data, and is ideally suited for plan selection and performance analysis in system design.展开更多
A new type controller, BP neural-networks-based sliding mode controller is developed for a class of large-scale nonlinear systems with unknown bounds of high-order interconnections in this paper. It is shown that dece...A new type controller, BP neural-networks-based sliding mode controller is developed for a class of large-scale nonlinear systems with unknown bounds of high-order interconnections in this paper. It is shown that decentralized BP neural networks are used to adaptively learn the uncertainty bounds of interconnected subsystems in the Lyapunov sense, and the outputs of the decentralized BP neural networks are then used as the parameters of the sliding mode controller to compensate for the effects of subsystems uncertainties. Using this scheme, not only strong robustness with respect to uncertainty dynamics and nonlinearities can be obtained, but also the output tracking error between the actual output of each subsystem and the corresponding desired reference output can asymptotically converge to zero. A simulation example is presented to support the validity of the proposed BP neural-networks-based sliding mode controller.展开更多
Reliability analysis and reliability-based optimization design require accurate measurement of failure probability under input uncertainties.A unified probabilistic reliability measure approach is proposed to calculat...Reliability analysis and reliability-based optimization design require accurate measurement of failure probability under input uncertainties.A unified probabilistic reliability measure approach is proposed to calculate the probability of failure and sensitivity indices considering a mixture of uncertainties under insufficient input data.The input uncertainty variables are classified into statistical variables,sparse variables,and interval variables.The conservativeness level of the failure probability is calculated through uncertainty propagation analysis of distribution parameters of sparse variables and auxiliary parameters of interval variables.The design sensitivity of the conservativeness level of the failure probability at design points is derived using a semi-analysis and sampling-based method.The proposed unified reliability measure method is extended to consider p-box variables,multi-domain variables,and evidence theory variables.Numerical and engineering examples demonstrate the effectiveness of the proposed method,which can obtain an accurate confidence level of reliability index and sensitivity indices with lower function evaluation number.展开更多
Major interactions are known to trigger star formation in galaxies and alter their color.We study the major interactions in filaments and sheets using SDSS data to understand the influence of large-scale environments ...Major interactions are known to trigger star formation in galaxies and alter their color.We study the major interactions in filaments and sheets using SDSS data to understand the influence of large-scale environments on galaxy interactions.We identify the galaxies in filaments and sheets using the local dimension and also find the major pairs residing in these environments.The star formation rate(SFR) and color of the interacting galaxies as a function of pair separation are separately analyzed in filaments and sheets.The analysis is repeated for three volume limited samples covering different magnitude ranges.The major pairs residing in the filaments show a significantly higher SFR and bluer color than those residing in the sheets up to the projected pair separation of~50 kpc.We observe a complete reversal of this behavior for both the SFR and color of the galaxy pairs having a projected separation larger than 50 kpc.Some earlier studies report that the galaxy pairs align with the filament axis.Such alignment inside filaments indicates anisotropic accretion that may cause these differences.We do not observe these trends in the brighter galaxy samples.The pairs in filaments and sheets from the brighter galaxy samples trace relatively denser regions in these environments.The absence of these trends in the brighter samples may be explained by the dominant effect of the local density over the effects of the large-scale environment.展开更多
Aviation data analysis can help airlines to understand passenger needs,so as to provide passengers with more sophisticated and better services.How to explore the implicit message and analyze contained features from la...Aviation data analysis can help airlines to understand passenger needs,so as to provide passengers with more sophisticated and better services.How to explore the implicit message and analyze contained features from large amounts of data has become an important issue in the civil aviation passenger data analysis process.The uncertainty analysis and visualization methods of data record and property measurement are offered in this paper,based on the visual analysis and uncertainty measure theory combined with parallel coordinates,radar chart,histogram,pixel chart and good interaction.At the same time,the data source expression clearly shows the uncertainty and hidden information as an information base for passengers’service展开更多
This study focuses on the risks associated with the on-balance sheet recognition of data resources.At the legal level,disputes over ownership often arise due to unclear data property rights,while privacy protection,cy...This study focuses on the risks associated with the on-balance sheet recognition of data resources.At the legal level,disputes over ownership often arise due to unclear data property rights,while privacy protection,cybersecurity,and cross-border data flows create additional compliance challenges.In terms of recognition,the subjectivity of traditional valuation methods,the lack of active markets,and the rapid depreciation of data value caused by technological iteration hinder reliable measurement.With respect to disclosure,organizations face a dilemma between transparency and confidentiality.Collectively,these issues exacerbate audit risks.It is therefore imperative to establish an appropriate legal,accounting,and auditing framework to mitigate such risks and remove barriers to the proper recognition of data assets on balance sheets.展开更多
The increasing prevalence of multi-view data has made multi-view clustering a crucial technique for discovering latent structures from heterogeneous representations.However,traditional fuzzy clustering algorithms show...The increasing prevalence of multi-view data has made multi-view clustering a crucial technique for discovering latent structures from heterogeneous representations.However,traditional fuzzy clustering algorithms show limitations with the inherent uncertainty and imprecision of such data,as they rely on a single-dimensional membership value.To overcome these limitations,we propose an auto-weighted multi-view neutrosophic fuzzy clustering(AW-MVNFC)algorithm.Our method leverages the neutrosophic framework,an extension of fuzzy sets,to explicitly model imprecision and ambiguity through three membership degrees.The core novelty of AWMVNFC lies in a hierarchical weighting strategy that adaptively learns the contributions of both individual data views and the importance of each feature within a view.Through a unified objective function,AW-MVNFC jointly optimizes the neutrosophic membership assignments,cluster centers,and the distributions of view and feature weights.Comprehensive experiments conducted on synthetic and real-world datasets demonstrate that our algorithm achieves more accurate and stable clustering than existing methods,demonstrating its effectiveness in handling the complexities of multi-view data.展开更多
基金This project was supported by NSFC Project (60474047), (60334010) and GuangDong Province Natural Science Foundationof China(31406)and China Postdoctoral Science Foundation (20060390725).
文摘Decentralized robust stabilization problem of discrete-time fuzzy large-scale systems with parametric uncertainties is considered. This uncertain fuzzy large-scale system consists of N interconnected T-S fuzzy subsystems, and the parametric uncertainties are unknown but norm-bounded. Based on Lyapunov stability theory and decentralized control theory of large-scale system, the design schema of decentralized parallel distributed compensation （DPDC） fuzzy controllers to ensure the asymptotic stability of the whole fuzzy large-scale system is proposed. The existence conditions for these controllers take the forms of LMIs. Finally a numerical simulation example is given to show the utility of the method proposed.
基金supported by the National Natural Science Foundation of China(Nos.42530801,42425208)the Natural Science Foundation of Hubei Province(China)(No.2023AFA001)+1 种基金the MOST Special Fund from State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences(No.MSFGPMR2025-401)the China Scholarship Council(No.202306410181)。
文摘Geochemical survey data are essential across Earth Science disciplines but are often affected by noise,which can obscure important geological signals and compromise subsequent prediction and interpretation.Quantifying prediction uncertainty is hence crucial for robust geoscientific decision-making.This study proposes a novel deep learning framework,the Spatially Constrained Variational Autoencoder(SC-VAE),for denoising geochemical survey data with integrated uncertainty quantification.The SC-VAE incorporates spatial regularization,which enforces spatial coherence by modeling inter-sample relationships directly within the latent space.The performance of the SC-VAE was systematically evaluated against a standard Variational Autoencoder(VAE)using geochemical data from the gold polymetallic district in the northwestern part of Sichuan Province,China.Both models were optimized using Bayesian optimization,with objective functions specifically designed to maintain essential geostatistical characteristics.Evaluation metrics include variogram analysis,quantitative measures of spatial interpolation accuracy,visual assessment of denoised maps,and statistical analysis of data distributions,as well as decomposition of uncertainties.Results show that the SC-VAE achieves superior noise suppression and better preservation of spatial structure compared to the standard VAE,as demonstrated by a significant reduction in the variogram nugget effect and an increased partial sill.The SC-VAE produces denoised maps with clearer anomaly delineation and more regularized data distributions,effectively mitigating outliers and reducing kurtosis.Additionally,it delivers improved interpolation accuracy and spatially explicit uncertainty estimates,facilitating more reliable and interpretable assessments of prediction confidence.The SC-VAE framework thus provides a robust,geostatistically informed solution for enhancing the quality and interpretability of geochemical data,with broad applicability in mineral exploration,environmental geochemistry,and other Earth Science domains.
文摘Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.
基金Supported by Project of National Natural Science Foundation(No.41874134)
文摘Processing large-scale 3-D gravity data is an important topic in geophysics field. Many existing inversion methods lack the competence of processing massive data and practical application capacity. This study proposes the application of GPU parallel processing technology to the focusing inversion method, aiming at improving the inversion accuracy while speeding up calculation and reducing the memory consumption, thus obtaining the fast and reliable inversion results for large complex model. In this paper, equivalent storage of geometric trellis is used to calculate the sensitivity matrix, and the inversion is based on GPU parallel computing technology. The parallel computing program that is optimized by reducing data transfer, access restrictions and instruction restrictions as well as latency hiding greatly reduces the memory usage, speeds up the calculation, and makes the fast inversion of large models possible. By comparing and analyzing the computing speed of traditional single thread CPU method and CUDA-based GPU parallel technology, the excellent acceleration performance of GPU parallel computing is verified, which provides ideas for practical application of some theoretical inversion methods restricted by computing speed and computer memory. The model test verifies that the focusing inversion method can overcome the problem of severe skin effect and ambiguity of geological body boundary. Moreover, the increase of the model cells and inversion data can more clearly depict the boundary position of the abnormal body and delineate its specific shape.
文摘Social media data created a paradigm shift in assessing situational awareness during a natural disaster or emergencies such as wildfire, hurricane, tropical storm etc. Twitter as an emerging data source is an effective and innovative digital platform to observe trend from social media users’ perspective who are direct or indirect witnesses of the calamitous event. This paper aims to collect and analyze twitter data related to the recent wildfire in California to perform a trend analysis by classifying firsthand and credible information from Twitter users. This work investigates tweets on the recent wildfire in California and classifies them based on witnesses into two types: 1) direct witnesses and 2) indirect witnesses. The collected and analyzed information can be useful for law enforcement agencies and humanitarian organizations for communication and verification of the situational awareness during wildfire hazards. Trend analysis is an aggregated approach that includes sentimental analysis and topic modeling performed through domain-expert manual annotation and machine learning. Trend analysis ultimately builds a fine-grained analysis to assess evacuation routes and provide valuable information to the firsthand emergency responders<span style="font-family:Verdana;">.</span>
基金This research has been partially supported by the national natural science foundation of China (51175169) and the national science and technology support program (2012BAF02B01).
文摘In the face of a growing number of large-scale data sets, affinity propagation clustering algorithm to calculate the process required to build the similarity matrix, will bring huge storage and computation. Therefore, this paper proposes an improved affinity propagation clustering algorithm. First, add the subtraction clustering, using the density value of the data points to obtain the point of initial clusters. Then, calculate the similarity distance between the initial cluster points, and reference the idea of semi-supervised clustering, adding pairs restriction information, structure sparse similarity matrix. Finally, the cluster representative points conduct AP clustering until a suitable cluster division.Experimental results show that the algorithm allows the calculation is greatly reduced, the similarity matrix storage capacity is also reduced, and better than the original algorithm on the clustering effect and processing speed.
基金supported by the National Natural Science Foundation of China(No.50778098)the Youth Project of Fujian Provincial Department of Science&Technology(No.2007F3093)
文摘Monitoring data are often used to identify stormwater runoff characteristics and in stormwater runoff modelling without consideration of their inherent uncertainties. Integrated with discrete sample analysis and error propagation analysis, this study attempted to quantify the uncertainties of discrete chemical oxygen demand （COD）, total suspended solids （TSS） concentration, stormwater flowrate, stormwater event volumes, COD event mean concentration （EMC）, and COD event loads in terms of flow measurement, sample collection, storage and laboratory analysis. The results showed that the uncertainties due to sample collection, storage and laboratory analysis of COD from stormwater runoff are 13.99%, 19.48% and 12.28%. Meanwhile, flow measurement uncertainty was 12.82%, and the sample collection uncertainty of TSS from stormwater runoff was 31.63%. Based on the law of propagation of uncertainties, the uncertainties regarding event flow volume, COD EMC and COD event loads were quantified as 7.03%, 10.26% and 18.47%.
基金funded by the National Natural Science Foundation of China (41130420)the State High-Tech Development Plan of China (2012AA09A20101)
文摘Simulation and interpretation of marine controlled-source electromagnetic(CSEM) data often approximate the transmitter source as an ideal horizontal electric dipole(HED) and assume that the receivers are located on a flat seabed.Actually,however,the transmitter dipole source will be rotated,tilted and deviated from the survey profile due to ocean currents.And free-fall receivers may be also rotated to some arbitrary horizontal orientation and located on sloping seafloor.In this paper,we investigate the effects of uncertainties in the transmitter tilt,transmitter rotation and transmitter deviation from the survey profile as well as in the receiver's location and orientation on marine CSEM data.The model study shows that the uncertainties of all position and orientation parameters of both the transmitter and receivers can propagate into observed data uncertainties,but to a different extent.In interpreting marine data,field data uncertainties caused by the position and orientation uncertainties of both the transmitter and receivers need to be taken into account.
文摘We introduce a framework for statistical inference of the closure coefficients using machine learning methods.The objective of this framework is to quantify the epistemic uncertainty associated with the closure model by using experimental data via Bayesian statistics.The framework is tailored towards cases for which a limited amount of experimental data is available.It consists of two components.First,by treating all latent variables(non-observed variables)in the model as stochastic variables,all sources of uncertainty of the probabilistic closure model are quantified by a fully Bayesian approach.The probabilistic model is defined to consist of the closure coefficients as parameters and other parameters incorporating noise.Then,the uncertainty associated with the closure coefficients is extracted from the overall uncertainty by considering the noise being zero.The overall uncertainty is rigorously evaluated by using Markov-Chain Monte Carlo sampling assisted by surrogate models.We apply the framework to the Spalart-Allmars one-equation turbulence model.Two test cases are considered,including an industrially relevant full aircraft model at transonic flow conditions,the Airbus XRF1.Eventually,we demonstrate that epistemic uncertainties in the closure coefficients result into uncertainties in flow quantities of interest which are prominent around,and downstream,of the shock occurring over the XRF1 wing.This data-driven approach could help to enhance the predictive capabilities of CFD in terms of reliable turbulence modeling at extremes of the flight envelope if measured data is available,which is important in the context of robust design and towards virtual aircraft certification.The plentiful amount of information about the uncertainties could also assist when it comes to estimating the influence of the measured data on the inferred model coefficients.Finally,the developed framework is flexible and can be applied to different test cases and to various turbulence models.
基金Project supported by the National Natural Science Foundation of China (No.40301043) .
文摘The mathematic theory for uncertainty model of line segment are summed up to achieve a general conception, and the line error hand model of εσ is a basic uncertainty model that can depict the line accuracy and quality efficiently while the model of εm and error entropy can be regarded as the supplement of it. The error band model will reflect and describe the influence of line uncertainty on polygon uncertainty. Therefore, the statistical characteristic of the line error is studied deeply by analyzing the probability that the line error falls into a certain range. Moreover, the theory accordance is achieved in the selecting the error buffer for line feature and the error indicator. The relationship of the accuracy of area for a polygon with the error loop for a polygon boundary is deduced and computed.
基金This project is supported by Ministry of Education, Culture, Sports, Science and Technology (MONBUSHO), Japan.
文摘Various kinds of data are used in new product design and more accurate datamake the design results more reliable. Even though part of product data can be available directlyfrom the existing similar products, there still leaves a great deal of data unavailable. This makesdata prediction a valuable work. A method that can predict data of product under development basedon the existing similar products is proposed. Fuzzy theory is used to deal with the uncertainties indata prediction process. The proposed method can be used in life cycle design, life cycleassessment (LCA) etc. Case study on current refrigerator is used as a demonstration example.
文摘The hydrological uncertainty about NASH model parameters is investigated and addressed in the paper through “ideal data” concept by using the Generalized Likelihood Uncertainty Estimation (GLUE) methodology in an application to the small Yanduhe research catchment in Yangtze River, China. And a suitable likelihood measure is assured here to reduce the uncertainty coming from the parameters relationship. “Ideal data” is assumed to be no error for the input-output data and model structure. The relationship between parameters k and n of NASH model is clearly quantitatively demonstrated based on the real data and it shows the existence of uncertainty factors different from the parameter one. Ideal data research results show that the accuracy of data and model structure are the two important preconditions for parameter estimation. And with suitable likelihood measure, the parameter uncertainty could be decreased or even disappeared. Moreover it is shown how distributions of predicted discharge errors are non-Gaussian and vary in shape with time and discharge under the single existence of parameter uncertainty or under the existence of all uncertainties.
基金Supported by National Hi-tech Research and Development Program of China(863 Program,Grant No.2015AA042101)
文摘Complex engineered systems are often difficult to analyze and design due to the tangled interdependencies among their subsystems and components. Conventional design methods often need exact modeling or accurate structure decomposition, which limits their practical application. The rapid expansion of data makes utilizing data to guide and improve system design indispensable in practical engineering. In this paper, a data driven uncertainty evaluation approach is proposed to support the design of complex engineered systems. The core of the approach is a data-mining based uncertainty evaluation method that predicts the uncertainty level of a specific system design by means of analyzing association relations along different system attributes and synthesizing the information entropy of the covered attribute areas, and a quantitative measure of system uncertainty can be obtained accordingly. Monte Carlo simulation is introduced to get the uncertainty extrema, and the possible data distributions under different situations is discussed in detail The uncertainty values can be normalized using the simulation results and the values can be used to evaluate different system designs. A prototype system is established, and two case studies have been carded out. The case of an inverted pendulum system validates the effectiveness of the proposed method, and the case of an oil sump design shows the practicability when two or more design plans need to be compared. This research can be used to evaluate the uncertainty of complex engineered systems completely relying on data, and is ideally suited for plan selection and performance analysis in system design.
基金The National Natural Science Foundations of China(50505029)
文摘A new type controller, BP neural-networks-based sliding mode controller is developed for a class of large-scale nonlinear systems with unknown bounds of high-order interconnections in this paper. It is shown that decentralized BP neural networks are used to adaptively learn the uncertainty bounds of interconnected subsystems in the Lyapunov sense, and the outputs of the decentralized BP neural networks are then used as the parameters of the sliding mode controller to compensate for the effects of subsystems uncertainties. Using this scheme, not only strong robustness with respect to uncertainty dynamics and nonlinearities can be obtained, but also the output tracking error between the actual output of each subsystem and the corresponding desired reference output can asymptotically converge to zero. A simulation example is presented to support the validity of the proposed BP neural-networks-based sliding mode controller.
基金supported by the Key Research and Development Program of Zhejiang Province(No.2021C01008)the National Natural Science Foundation of China(No.52105279)the Ningbo Natural Science Foundation of China(No.2021J163)。
文摘Reliability analysis and reliability-based optimization design require accurate measurement of failure probability under input uncertainties.A unified probabilistic reliability measure approach is proposed to calculate the probability of failure and sensitivity indices considering a mixture of uncertainties under insufficient input data.The input uncertainty variables are classified into statistical variables,sparse variables,and interval variables.The conservativeness level of the failure probability is calculated through uncertainty propagation analysis of distribution parameters of sparse variables and auxiliary parameters of interval variables.The design sensitivity of the conservativeness level of the failure probability at design points is derived using a semi-analysis and sampling-based method.The proposed unified reliability measure method is extended to consider p-box variables,multi-domain variables,and evidence theory variables.Numerical and engineering examples demonstrate the effectiveness of the proposed method,which can obtain an accurate confidence level of reliability index and sensitivity indices with lower function evaluation number.
基金financial support from the SERB,DST,Government of India through the project CRG/2019/001110IUCAA,Pune for providing support through an associateship program+1 种基金IISER Tirupati for support through a postdoctoral fellowshipFunding for the SDSS and SDSS-Ⅱhas been provided by the Alfred P.Sloan Foundation,the U.S.Department of Energy,the National Aeronautics and Space Administration,the Japanese Monbukagakusho,the Max Planck Society,and the Higher Education Funding Council for England。
文摘Major interactions are known to trigger star formation in galaxies and alter their color.We study the major interactions in filaments and sheets using SDSS data to understand the influence of large-scale environments on galaxy interactions.We identify the galaxies in filaments and sheets using the local dimension and also find the major pairs residing in these environments.The star formation rate(SFR) and color of the interacting galaxies as a function of pair separation are separately analyzed in filaments and sheets.The analysis is repeated for three volume limited samples covering different magnitude ranges.The major pairs residing in the filaments show a significantly higher SFR and bluer color than those residing in the sheets up to the projected pair separation of~50 kpc.We observe a complete reversal of this behavior for both the SFR and color of the galaxy pairs having a projected separation larger than 50 kpc.Some earlier studies report that the galaxy pairs align with the filament axis.Such alignment inside filaments indicates anisotropic accretion that may cause these differences.We do not observe these trends in the brighter galaxy samples.The pairs in filaments and sheets from the brighter galaxy samples trace relatively denser regions in these environments.The absence of these trends in the brighter samples may be explained by the dominant effect of the local density over the effects of the large-scale environment.
文摘Aviation data analysis can help airlines to understand passenger needs,so as to provide passengers with more sophisticated and better services.How to explore the implicit message and analyze contained features from large amounts of data has become an important issue in the civil aviation passenger data analysis process.The uncertainty analysis and visualization methods of data record and property measurement are offered in this paper,based on the visual analysis and uncertainty measure theory combined with parallel coordinates,radar chart,histogram,pixel chart and good interaction.At the same time,the data source expression clearly shows the uncertainty and hidden information as an information base for passengers’service
文摘This study focuses on the risks associated with the on-balance sheet recognition of data resources.At the legal level,disputes over ownership often arise due to unclear data property rights,while privacy protection,cybersecurity,and cross-border data flows create additional compliance challenges.In terms of recognition,the subjectivity of traditional valuation methods,the lack of active markets,and the rapid depreciation of data value caused by technological iteration hinder reliable measurement.With respect to disclosure,organizations face a dilemma between transparency and confidentiality.Collectively,these issues exacerbate audit risks.It is therefore imperative to establish an appropriate legal,accounting,and auditing framework to mitigate such risks and remove barriers to the proper recognition of data assets on balance sheets.
文摘The increasing prevalence of multi-view data has made multi-view clustering a crucial technique for discovering latent structures from heterogeneous representations.However,traditional fuzzy clustering algorithms show limitations with the inherent uncertainty and imprecision of such data,as they rely on a single-dimensional membership value.To overcome these limitations,we propose an auto-weighted multi-view neutrosophic fuzzy clustering(AW-MVNFC)algorithm.Our method leverages the neutrosophic framework,an extension of fuzzy sets,to explicitly model imprecision and ambiguity through three membership degrees.The core novelty of AWMVNFC lies in a hierarchical weighting strategy that adaptively learns the contributions of both individual data views and the importance of each feature within a view.Through a unified objective function,AW-MVNFC jointly optimizes the neutrosophic membership assignments,cluster centers,and the distributions of view and feature weights.Comprehensive experiments conducted on synthetic and real-world datasets demonstrate that our algorithm achieves more accurate and stable clustering than existing methods,demonstrating its effectiveness in handling the complexities of multi-view data.
