Many performance indices for parallel mechanism are put forward in the phase of dimensional synthesis,except for identifiability index,which determines the difficulty of kinematical calibration.If the dimensional para...Many performance indices for parallel mechanism are put forward in the phase of dimensional synthesis,except for identifiability index,which determines the difficulty of kinematical calibration.If the dimensional parameters are inappropriately selected,the existing methods for optimizing identifiability will not effectively work.Thus,with the aim of studying identifiability optimization in dimensional synthesis for 3-PRS mechanism,kinematics with structural errors is analyzed to provide theoretical bases for kinematical model.Then through a comparison of two 3-PRS mechanisms with different dimensional parameters,identifiability performance is proved to be necessary and feasible for optimization in the phase of dimensional design.Finally,an index δ is proposed to scale the identifiability performance.With the index,identifiability analysis and dimensional synthesis simulation in the whole workspace is completed.The index is verified to be correct and feasible,and based on the index,a procedure of dimensional synthesis,as well as an example set of non-dimensional parameters of 3-PRS mechanism,is proposed.The proposed identifiability index and design method can effectively introduce identifiability optimization into dimensional synthesis,and will obviously benefit later kinematical calibration.展开更多
In this paper an original method based on the link between a piecewise identifiability analysis and a piecewise numerical estimation is presented for estimating parameters of a phenomenological diesel engine combustio...In this paper an original method based on the link between a piecewise identifiability analysis and a piecewise numerical estimation is presented for estimating parameters of a phenomenological diesel engine combustion model. This model is used for design, validation and pre-tuning of engine control laws. A cascade algebro-differential elimination method is used for studying identifiability. This investigation is done by using input-output-parameter relationship. Then these relations are transformed by using iterated integration. They are combined with an original numerical derivative estimation based on distribution theory which gives explicit point-wise derivative?estimation formulas for each given order. Then new approximate relations, linking block of parameters and outputs (without derivative) are obtained. These relations are linear relatively to the blocks of parameters and yield a first estimation of parameters which is used as initial guess for a local optimization method (least square method and a local search genetic algorithm).展开更多
Parameter identification involves the estimation of undisclosed parameters within a system based on observed data and mathematical models.In this investigation,we employ DAISY to meticulously examine the structural id...Parameter identification involves the estimation of undisclosed parameters within a system based on observed data and mathematical models.In this investigation,we employ DAISY to meticulously examine the structural identifiability of parameters of a within-host SARS-CoV-2 epidemic model,taking into account an array of observable datasets.Furthermore,Monte Carlo simulations are performed to offer a comprehensive practical analysis of model parameters.Lastly,sensitivity analysis is employed to ascertain that decreasing the replication rate of the SARS-CoV-2 virus and curbing the infectious period are the most efficacious measures in alleviating the dissemination of COVID-19 amongst hosts.展开更多
Mathematical models provide a quantitative framework with which scientists can assess hypotheses on the potential underlying mechanisms that explain patterns in observed data at different spatial and temporal scales,g...Mathematical models provide a quantitative framework with which scientists can assess hypotheses on the potential underlying mechanisms that explain patterns in observed data at different spatial and temporal scales,generate estimates of key kinetic parameters,assess the impact of interventions,optimize the impact of control strategies,and generate forecasts.We review and illustrate a simple data assimilation framework for calibrating mathematical models based on ordinary differential equation models using time series data describing the temporal progression of case counts relating,for instance,to population growth or infectious disease transmission dynamics.In contrast to Bayesian estimation approaches that always raise the question of how to set priors for the parameters,this frequentist approach relies on modeling the error structure in the data.We discuss issues related to parameter identifiability,uncertainty quantification and propagation as well as model performance and forecasts along examples based on phenomenological and mechanistic models parameterized using simulated and real datasets.展开更多
The identification of load model parameters from practical measurement data has become an essential method to build load models for power system simulation,analysis and control.In practical situations,the accuracy of ...The identification of load model parameters from practical measurement data has become an essential method to build load models for power system simulation,analysis and control.In practical situations,the accuracy of the load model parameters identification results is impacted by data quality and measurement accuracy,which leads to the problem of identifiability.In this paper,an identifiability analysis methodology of load model parameters,by estimating the confidential intervals(CIs)of the parameters,is proposed.The load model structure and the combined optimization and regression method to identify the parameters are first introduced.Then,the definition and analysis method of identifiability are discussed.The CIs of the parameters are estimated through the profile likelihood method,based on which a practical identifiability index(PII)is defined to quantitatively evaluate identifiability.Finally,the effectiveness of the proposed analysis approach is validated by the case study results in a practical provincial power grid.The results show that the impact of various disturbance magnitudes,measurement errors and data length can all be reflected by the proposed PII.Furthermore,the proposed PII can provide guidance in data length selection in practical load model identification situations.展开更多
Introduction:Conceptual hydrological models are useful tools to support catchment water management.However,the identifiability of parameters and structural uncertainties in conceptual rainfall-runoff modeling prove to...Introduction:Conceptual hydrological models are useful tools to support catchment water management.However,the identifiability of parameters and structural uncertainties in conceptual rainfall-runoff modeling prove to be a difficult task.Here,we aim to evaluate the performance of a conceptual semi-distributed rainfall-runoff model,HBV-light,with emphasis on parameter identifiability,uncertainty,and model structural validity.Results:The results of a regional sensitivity analysis(RSA)show that most of the model parameters are highly sensitive when runoff signatures or combinations of different objective functions are used.Results based on the generalized likelihood uncertainty estimation(GLUE)method further show that most of the model parameters are well constrained,showing higher parameter identifiability and lower model uncertainty when runoff signatures or combined objective functions are used.Finally,the dynamic identifiability analysis(DYNIA)shows different types of parameter behavior and reveals that model parameters have a higher identifiability in periods where they play a crucial role in representing the predicted runoff.Conclusions:The HBV-light model is generally able to simulate the runoff in the Pailugou catchment with an acceptable accuracy.Model parameter sensitivity is largely dependent upon the objective function used for the model evaluation in the sensitivity analysis.More frequent runoff observations would substantially increase the knowledge on the rainfall-runoff transformation in the catchment and,specifically,improve the distinction of fast surface-near runoff and interflow components in their contribution to the total catchment runoff.Our results highlight the importance of identifying the periods when intensive monitoring is critical for deriving parameter values of reduced uncertainty.展开更多
In this paper, the inverse linear quadratic(LQ) problem over finite time-horizon is studied.Given the output observations of a dynamic process, the goal is to recover the corresponding LQ cost function. Firstly, by co...In this paper, the inverse linear quadratic(LQ) problem over finite time-horizon is studied.Given the output observations of a dynamic process, the goal is to recover the corresponding LQ cost function. Firstly, by considering the inverse problem as an identification problem, its model structure is shown to be strictly globally identifiable under the assumption of system invertibility. Next, in the noiseless case a necessary and sufficient condition is proposed for the solvability of a positive semidefinite weighting matrix and its unique solution is obtained with two proposed algorithms under the condition of persistent excitation. Furthermore, a residual optimization problem is also formulated to solve a best-fit approximate cost function from sub-optimal observations. Finally, numerical simulations are used to demonstrate the effectiveness of the proposed methods.展开更多
This paper gives a definition of identifiability for multidimensional linear input-output systems and presents a necessary and sufficient condition for its satisfaction.For a class of identifiable systems it is also s...This paper gives a definition of identifiability for multidimensional linear input-output systems and presents a necessary and sufficient condition for its satisfaction.For a class of identifiable systems it is also shown that the unknown coeffcients of the system can consistently be estimated by a recursive algorithm.展开更多
We discuss the discovery of causal mechanisms and identifiability of intermediate variables on a causal path. Different from variable selection, we try to distinguish intermediate variables on the causal path from oth...We discuss the discovery of causal mechanisms and identifiability of intermediate variables on a causal path. Different from variable selection, we try to distinguish intermediate variables on the causal path from other variables. It is also different from ordinary model selection approaches which do not concern the causal relationships and do not contain unobserved variables. We propose an approach for selecting a causal mechanism depicted by a directed acyclic graph （DAG） with an unobserved variable. We consider several causal networks, and discuss their identifiability by observed data. We show that causal mechanisms of linear structural equation models are not identifiable. Furthermore, we present that causal mechanisms of nonlinear models are identifiable, and we demonstrate the identifiability of causal mechanisms of quadratic equation models. Sensitivity analysis is conducted for the identifiability.展开更多
This article introduces a three-parameter Lehman-type t distribution having 2 degrees of freedom,that is capable of fitting positive and negative skewed data sets.It is shown that the density and hazard functions of t...This article introduces a three-parameter Lehman-type t distribution having 2 degrees of freedom,that is capable of fitting positive and negative skewed data sets.It is shown that the density and hazard functions of the proposed distribution are uni-model.Ordinary moments,entropy measure,ordering,identifiability and order statistics are investigated.Since the quantile function is explicitly defined,quantile-based statistics are also discussed for the proposed distribution.These properties include measures of skewness and kurtosis,L-moments,quantile density and hazard functions,mean residual life function and Parzen's score function.Mechanisms of maximum likelihood,bias correction and matching of percentiles are employed for estimating the unknown parameters of the distribution.Simulation experiments are conducted to compare the performance of these three estimation methods.A real-life data set consisting of strength of glass fibres is fitted to show the adequacy of the proposed distribution over some extensions of the normal and t distributions.Parametric regression model is developed along with its parameter estimation using the maximum likelihood approach.Simulation study for the regression model is also presented that endorsed the asymptotic properties of the estimators.展开更多
Array configuration of multiple-input multiple-output （MIMO） radar with non-uniform linear array （NLA） is proposed. Unlike a standard phased-array radar where NLA is used to generate thinner beam patterns, in MIMO...Array configuration of multiple-input multiple-output （MIMO） radar with non-uniform linear array （NLA） is proposed. Unlike a standard phased-array radar where NLA is used to generate thinner beam patterns, in MIMO radar the property of NLA is exploited to get more distinct virtual array elements so as to improve pa- rameter identifiability, which means the maximum number of targets that can be uniquely identified by the radar. A class of NLA called minimum redundancy linear array （MRLA） is employed and a new method to construct large MRLAs is descrihed. The numerical results verify that compared to uniform linear array （ULA） MIMO radars, NLA MIMO radars can retain the same parameter identifiability with fewer physical antennas and achieve larger aperture length and lower Cramer-Rao bound with the same number of the physical antennas.展开更多
In order to identify the tilt direction of the self-mixing signals under weak feedback regime interfered by noise,a deep learning method is proposed.The one-dimensional U-Net(1D U-Net)neural network can identify the d...In order to identify the tilt direction of the self-mixing signals under weak feedback regime interfered by noise,a deep learning method is proposed.The one-dimensional U-Net(1D U-Net)neural network can identify the direction of the self-mixing fringes accurately and quickly.In the process of measurement,the measurement signal can be normalized and then the neural network can be used to discriminate the direction.Simulation and experimental results show that the proposed method is suitable for self-mixing interference signals with noise in the whole weak feedback regime,and can maintain a high discrimination accuracy for signals interfered by 5 dB large noise.Combined with fringe counting method,accurate and rapid displacement reconstruction can be realized.展开更多
Patients with cheiro-oral syndrome(COS) often present with minor perioral and upper extremity sensory disturbances,which can be easily overlooked in busy emergency departments(EDs).^([1]) COS,a rare spectrum of stroke...Patients with cheiro-oral syndrome(COS) often present with minor perioral and upper extremity sensory disturbances,which can be easily overlooked in busy emergency departments(EDs).^([1]) COS,a rare spectrum of stroke syndromes,necessitates expeditious and aggressive modification of risk factors.展开更多
TarGuess-I is a leading model utilizing Personally Identifiable Information for online targeted password guessing.Due to its remarkable guessing performance,the model has drawn considerable attention in password secur...TarGuess-I is a leading model utilizing Personally Identifiable Information for online targeted password guessing.Due to its remarkable guessing performance,the model has drawn considerable attention in password security research.However,through an analysis of the vulnerable behavior of users when constructing passwords by combining popular passwords with their Personally Identifiable Information,we identified that the model fails to consider popular passwords and frequent substrings,and it uses overly broad personal information categories,with extensive duplicate statistics.To address these issues,we propose an improved password guessing model,TGI-FPR,which incorporates three semantic methods:(1)identification of popular passwords by generating top 300 lists from similar websites,(2)use of frequent substrings as new grammatical labels to capture finer-grained password structures,and(3)further subdivision of the six major categories of personal information.To evaluate the performance of the proposed model,we conducted experiments on six large-scale real-world password leak datasets and compared its accuracy within the first 100 guesses to that of TarGuess-I.The results indicate a 2.65%improvement in guessing accuracy.展开更多
The analysis of the ejaculate,better known as spermiogram,represents the first and main step to identify whether a series of sperm quality parameters are within the norm and therefore are consistent with normal sperm ...The analysis of the ejaculate,better known as spermiogram,represents the first and main step to identify whether a series of sperm quality parameters are within the norm and therefore are consistent with normal sperm fertilizing capacity.Among these,sperm concentration and motility are the first parameters to be evaluated through an estimation carried out by expert examiners.展开更多
Metal-organic frameworks(MOFs)have garnered widespread attention due to their designability and diversity[1].Customization has always been a pursuit of chemists and materials scientists[2].Topology provides a means of...Metal-organic frameworks(MOFs)have garnered widespread attention due to their designability and diversity[1].Customization has always been a pursuit of chemists and materials scientists[2].Topology provides a means of abstracting the complex structures of MOFs by identifying and classifying the fundamental building units and connection patterns,simplifying the understanding of MOF structures[3].展开更多
Smart grid substation operations often take place in hazardous environments and pose significant threats to the safety of power personnel.Relying solely on manual supervision can lead to inadequate oversight.In respon...Smart grid substation operations often take place in hazardous environments and pose significant threats to the safety of power personnel.Relying solely on manual supervision can lead to inadequate oversight.In response to the demand for technology to identify improper operations in substation work scenarios,this paper proposes a substation safety action recognition technology to avoid the misoperation and enhance the safety management.In general,this paper utilizes a dual-branch transformer network to extract spatial and temporal information from the video dataset of operational behaviors in complex substation environments.Firstly,in order to capture the spatial-temporal correlation of people's behaviors in smart grid substation,we devise a sparse attention module and a segmented linear attention module that are embedded into spatial branch transformer and temporal branch transformer respectively.To avoid the redundancy of spatial and temporal information,we fuse the temporal and spatial features using a tensor decomposition fusion module by a decoupled manner.Experimental results indicate that our proposed method accurately detects improper operational behaviors in substation work scenarios,outperforming other existing methods in terms of detection and recognition accuracy.展开更多
Clarifying the system structure of various influencing factors is a crucial prerequisite for identifying the key action point to address the“Energy Trilemma”in China’s natural gas industry.Based on the three-dimens...Clarifying the system structure of various influencing factors is a crucial prerequisite for identifying the key action point to address the“Energy Trilemma”in China’s natural gas industry.Based on the three-dimensional system of“safety and stability-economic feasibility-low-carbon and environmental protection,”an influencing factor system for the“Energy Trilemma”in the natural gas industry is constructed.展开更多
Electrical insulation faults produce partial discharges(PD),which can be analysed to identify specific types of defects.PD clustering is a widely used method to identify PD sources,although its success depends largely...Electrical insulation faults produce partial discharges(PD),which can be analysed to identify specific types of defects.PD clustering is a widely used method to identify PD sources,although its success depends largely on the feature maps used.In this paper,three widely used feature maps,or separation maps,are compared:chromatic,energy wavelet with principal component analysis(EW-PCA),and time-frequency(TF).To compare and evaluate,five scenarios with multi-PD environments with noise were developed.The clustering ability of the maps was evaluated using two performance indicators:intercluster distance and intracluster distance.The results indicate that the EW-PCA map performed the best in all scenarios,correctly identifying the largest number of data points and producing the clearest and most distinct clusters.The TF map created distinct clusters in several scenarios,but not all.The chromatic map created distinct clusters in all scenarios but was not as well defined as the other two separation maps.Given the results,it is important in fieldwork to use a wide range of PD clustering,accompanied by performance metrics that support a less biased decision tailored to the test object.展开更多
基金supported by National Natural Science Foundation of China (Grant No. 50775125)National Hi-tech Research and Development Program of China (863 Program,Grant No. 2007AA042003,No. 2007AA041901)
文摘Many performance indices for parallel mechanism are put forward in the phase of dimensional synthesis,except for identifiability index,which determines the difficulty of kinematical calibration.If the dimensional parameters are inappropriately selected,the existing methods for optimizing identifiability will not effectively work.Thus,with the aim of studying identifiability optimization in dimensional synthesis for 3-PRS mechanism,kinematics with structural errors is analyzed to provide theoretical bases for kinematical model.Then through a comparison of two 3-PRS mechanisms with different dimensional parameters,identifiability performance is proved to be necessary and feasible for optimization in the phase of dimensional design.Finally,an index δ is proposed to scale the identifiability performance.With the index,identifiability analysis and dimensional synthesis simulation in the whole workspace is completed.The index is verified to be correct and feasible,and based on the index,a procedure of dimensional synthesis,as well as an example set of non-dimensional parameters of 3-PRS mechanism,is proposed.The proposed identifiability index and design method can effectively introduce identifiability optimization into dimensional synthesis,and will obviously benefit later kinematical calibration.
文摘In this paper an original method based on the link between a piecewise identifiability analysis and a piecewise numerical estimation is presented for estimating parameters of a phenomenological diesel engine combustion model. This model is used for design, validation and pre-tuning of engine control laws. A cascade algebro-differential elimination method is used for studying identifiability. This investigation is done by using input-output-parameter relationship. Then these relations are transformed by using iterated integration. They are combined with an original numerical derivative estimation based on distribution theory which gives explicit point-wise derivative?estimation formulas for each given order. Then new approximate relations, linking block of parameters and outputs (without derivative) are obtained. These relations are linear relatively to the blocks of parameters and yield a first estimation of parameters which is used as initial guess for a local optimization method (least square method and a local search genetic algorithm).
基金This work is partially supported by Humanities and Social Foundation of Ministry of Education of China(22YJAZH129)the National Natural Science Foundation of China(No.12271143,No.61573016)+1 种基金the Shanxi Province Science Foundation(No.20210302123454)Shanxi Scholarship Council of China(2023–024).
文摘Parameter identification involves the estimation of undisclosed parameters within a system based on observed data and mathematical models.In this investigation,we employ DAISY to meticulously examine the structural identifiability of parameters of a within-host SARS-CoV-2 epidemic model,taking into account an array of observable datasets.Furthermore,Monte Carlo simulations are performed to offer a comprehensive practical analysis of model parameters.Lastly,sensitivity analysis is employed to ascertain that decreasing the replication rate of the SARS-CoV-2 virus and curbing the infectious period are the most efficacious measures in alleviating the dissemination of COVID-19 amongst hosts.
基金Authors acknowledge financial support from the NSF grant 1610429 and the NSF grant 1414374 as part of the joint NSFNIH-USDA Ecology and Evolution of Infectious Diseases programUK BiotechnologyBiological Sciences Research Council grant BB/M008894/1 and the Division of International Epidemiology and Population Studies,National Institutes of Health.
文摘Mathematical models provide a quantitative framework with which scientists can assess hypotheses on the potential underlying mechanisms that explain patterns in observed data at different spatial and temporal scales,generate estimates of key kinetic parameters,assess the impact of interventions,optimize the impact of control strategies,and generate forecasts.We review and illustrate a simple data assimilation framework for calibrating mathematical models based on ordinary differential equation models using time series data describing the temporal progression of case counts relating,for instance,to population growth or infectious disease transmission dynamics.In contrast to Bayesian estimation approaches that always raise the question of how to set priors for the parameters,this frequentist approach relies on modeling the error structure in the data.We discuss issues related to parameter identifiability,uncertainty quantification and propagation as well as model performance and forecasts along examples based on phenomenological and mechanistic models parameterized using simulated and real datasets.
基金supported by National Natural Science Foundation of China under Grant No.52107066 and 5210071352.
文摘The identification of load model parameters from practical measurement data has become an essential method to build load models for power system simulation,analysis and control.In practical situations,the accuracy of the load model parameters identification results is impacted by data quality and measurement accuracy,which leads to the problem of identifiability.In this paper,an identifiability analysis methodology of load model parameters,by estimating the confidential intervals(CIs)of the parameters,is proposed.The load model structure and the combined optimization and regression method to identify the parameters are first introduced.Then,the definition and analysis method of identifiability are discussed.The CIs of the parameters are estimated through the profile likelihood method,based on which a practical identifiability index(PII)is defined to quantitatively evaluate identifiability.Finally,the effectiveness of the proposed analysis approach is validated by the case study results in a practical provincial power grid.The results show that the impact of various disturbance magnitudes,measurement errors and data length can all be reflected by the proposed PII.Furthermore,the proposed PII can provide guidance in data length selection in practical load model identification situations.
基金This research was jointly funded by Robert Bosch Foundation and Beijing Municipal Commission of Education(Key Laboratory for Silviculture and Conservation).
文摘Introduction:Conceptual hydrological models are useful tools to support catchment water management.However,the identifiability of parameters and structural uncertainties in conceptual rainfall-runoff modeling prove to be a difficult task.Here,we aim to evaluate the performance of a conceptual semi-distributed rainfall-runoff model,HBV-light,with emphasis on parameter identifiability,uncertainty,and model structural validity.Results:The results of a regional sensitivity analysis(RSA)show that most of the model parameters are highly sensitive when runoff signatures or combinations of different objective functions are used.Results based on the generalized likelihood uncertainty estimation(GLUE)method further show that most of the model parameters are well constrained,showing higher parameter identifiability and lower model uncertainty when runoff signatures or combined objective functions are used.Finally,the dynamic identifiability analysis(DYNIA)shows different types of parameter behavior and reveals that model parameters have a higher identifiability in periods where they play a crucial role in representing the predicted runoff.Conclusions:The HBV-light model is generally able to simulate the runoff in the Pailugou catchment with an acceptable accuracy.Model parameter sensitivity is largely dependent upon the objective function used for the model evaluation in the sensitivity analysis.More frequent runoff observations would substantially increase the knowledge on the rainfall-runoff transformation in the catchment and,specifically,improve the distinction of fast surface-near runoff and interflow components in their contribution to the total catchment runoff.Our results highlight the importance of identifying the periods when intensive monitoring is critical for deriving parameter values of reduced uncertainty.
文摘In this paper, the inverse linear quadratic(LQ) problem over finite time-horizon is studied.Given the output observations of a dynamic process, the goal is to recover the corresponding LQ cost function. Firstly, by considering the inverse problem as an identification problem, its model structure is shown to be strictly globally identifiable under the assumption of system invertibility. Next, in the noiseless case a necessary and sufficient condition is proposed for the solvability of a positive semidefinite weighting matrix and its unique solution is obtained with two proposed algorithms under the condition of persistent excitation. Furthermore, a residual optimization problem is also formulated to solve a best-fit approximate cost function from sub-optimal observations. Finally, numerical simulations are used to demonstrate the effectiveness of the proposed methods.
基金This project is supported by the National Natural Science Foundation of Chinathe TWAS RG MP898-117
文摘This paper gives a definition of identifiability for multidimensional linear input-output systems and presents a necessary and sufficient condition for its satisfaction.For a class of identifiable systems it is also shown that the unknown coeffcients of the system can consistently be estimated by a recursive algorithm.
文摘We discuss the discovery of causal mechanisms and identifiability of intermediate variables on a causal path. Different from variable selection, we try to distinguish intermediate variables on the causal path from other variables. It is also different from ordinary model selection approaches which do not concern the causal relationships and do not contain unobserved variables. We propose an approach for selecting a causal mechanism depicted by a directed acyclic graph （DAG） with an unobserved variable. We consider several causal networks, and discuss their identifiability by observed data. We show that causal mechanisms of linear structural equation models are not identifiable. Furthermore, we present that causal mechanisms of nonlinear models are identifiable, and we demonstrate the identifiability of causal mechanisms of quadratic equation models. Sensitivity analysis is conducted for the identifiability.
基金the financial support from Science and Engineering Research Board,Department of Science&Technology,Government of India,under the scheme Early Career Research Award(file no.ECR/2017/002416)Dr.Sharma also acknowledges Banaras Hindu University,Varanasi,India,for providing financial support as seed grant under the Institute of Eminence Scheme(Scheme no.Dev.6031).
文摘This article introduces a three-parameter Lehman-type t distribution having 2 degrees of freedom,that is capable of fitting positive and negative skewed data sets.It is shown that the density and hazard functions of the proposed distribution are uni-model.Ordinary moments,entropy measure,ordering,identifiability and order statistics are investigated.Since the quantile function is explicitly defined,quantile-based statistics are also discussed for the proposed distribution.These properties include measures of skewness and kurtosis,L-moments,quantile density and hazard functions,mean residual life function and Parzen's score function.Mechanisms of maximum likelihood,bias correction and matching of percentiles are employed for estimating the unknown parameters of the distribution.Simulation experiments are conducted to compare the performance of these three estimation methods.A real-life data set consisting of strength of glass fibres is fitted to show the adequacy of the proposed distribution over some extensions of the normal and t distributions.Parametric regression model is developed along with its parameter estimation using the maximum likelihood approach.Simulation study for the regression model is also presented that endorsed the asymptotic properties of the estimators.
基金Supported by the Aeronautic Science Foundation of China(2008ZC52026)the Innovation Foundation of Nanjing University of Aeronautics and Astronautics~~
文摘Array configuration of multiple-input multiple-output （MIMO） radar with non-uniform linear array （NLA） is proposed. Unlike a standard phased-array radar where NLA is used to generate thinner beam patterns, in MIMO radar the property of NLA is exploited to get more distinct virtual array elements so as to improve pa- rameter identifiability, which means the maximum number of targets that can be uniquely identified by the radar. A class of NLA called minimum redundancy linear array （MRLA） is employed and a new method to construct large MRLAs is descrihed. The numerical results verify that compared to uniform linear array （ULA） MIMO radars, NLA MIMO radars can retain the same parameter identifiability with fewer physical antennas and achieve larger aperture length and lower Cramer-Rao bound with the same number of the physical antennas.
文摘In order to identify the tilt direction of the self-mixing signals under weak feedback regime interfered by noise,a deep learning method is proposed.The one-dimensional U-Net(1D U-Net)neural network can identify the direction of the self-mixing fringes accurately and quickly.In the process of measurement,the measurement signal can be normalized and then the neural network can be used to discriminate the direction.Simulation and experimental results show that the proposed method is suitable for self-mixing interference signals with noise in the whole weak feedback regime,and can maintain a high discrimination accuracy for signals interfered by 5 dB large noise.Combined with fringe counting method,accurate and rapid displacement reconstruction can be realized.
文摘Patients with cheiro-oral syndrome(COS) often present with minor perioral and upper extremity sensory disturbances,which can be easily overlooked in busy emergency departments(EDs).^([1]) COS,a rare spectrum of stroke syndromes,necessitates expeditious and aggressive modification of risk factors.
基金supported by the Joint Funds of National Natural Science Foundation of China(Grant No.U23A20304)the Fund of Laboratory for Advanced Computing and Intelligence Engineering(No.2023-LYJJ-01-033)+1 种基金the Special Funds of Jiangsu Province Science and Technology Plan(Key R&D ProgramIndustryOutlook and Core Technologies)(No.BE2023005-4)the Science Project of Hainan University(KYQD(ZR)-21075).
文摘TarGuess-I is a leading model utilizing Personally Identifiable Information for online targeted password guessing.Due to its remarkable guessing performance,the model has drawn considerable attention in password security research.However,through an analysis of the vulnerable behavior of users when constructing passwords by combining popular passwords with their Personally Identifiable Information,we identified that the model fails to consider popular passwords and frequent substrings,and it uses overly broad personal information categories,with extensive duplicate statistics.To address these issues,we propose an improved password guessing model,TGI-FPR,which incorporates three semantic methods:(1)identification of popular passwords by generating top 300 lists from similar websites,(2)use of frequent substrings as new grammatical labels to capture finer-grained password structures,and(3)further subdivision of the six major categories of personal information.To evaluate the performance of the proposed model,we conducted experiments on six large-scale real-world password leak datasets and compared its accuracy within the first 100 guesses to that of TarGuess-I.The results indicate a 2.65%improvement in guessing accuracy.
文摘The analysis of the ejaculate,better known as spermiogram,represents the first and main step to identify whether a series of sperm quality parameters are within the norm and therefore are consistent with normal sperm fertilizing capacity.Among these,sperm concentration and motility are the first parameters to be evaluated through an estimation carried out by expert examiners.
基金supported by the National Natural Science Foundation of China(22101039,22471027,22311530679)the Science Fund for Creative Research Groups of the National Natural Science Foundation of China(22021005)the Fundamental Research Funds for the Central Universities(DUT24LK004).
文摘Metal-organic frameworks(MOFs)have garnered widespread attention due to their designability and diversity[1].Customization has always been a pursuit of chemists and materials scientists[2].Topology provides a means of abstracting the complex structures of MOFs by identifying and classifying the fundamental building units and connection patterns,simplifying the understanding of MOF structures[3].
文摘Smart grid substation operations often take place in hazardous environments and pose significant threats to the safety of power personnel.Relying solely on manual supervision can lead to inadequate oversight.In response to the demand for technology to identify improper operations in substation work scenarios,this paper proposes a substation safety action recognition technology to avoid the misoperation and enhance the safety management.In general,this paper utilizes a dual-branch transformer network to extract spatial and temporal information from the video dataset of operational behaviors in complex substation environments.Firstly,in order to capture the spatial-temporal correlation of people's behaviors in smart grid substation,we devise a sparse attention module and a segmented linear attention module that are embedded into spatial branch transformer and temporal branch transformer respectively.To avoid the redundancy of spatial and temporal information,we fuse the temporal and spatial features using a tensor decomposition fusion module by a decoupled manner.Experimental results indicate that our proposed method accurately detects improper operational behaviors in substation work scenarios,outperforming other existing methods in terms of detection and recognition accuracy.
基金Western Project of the National Social Science Fund of China (22XGL019)Major Project of the National Social Science Fund of China (22&ZD105)+1 种基金Special Academic Research Grant at the Key Research Base of Philosophy and Social Sciences in Sichuan Province (SC24E091)Chengdu Philosophy and Social Science Planning Project 2024 (2024BS072)。
文摘Clarifying the system structure of various influencing factors is a crucial prerequisite for identifying the key action point to address the“Energy Trilemma”in China’s natural gas industry.Based on the three-dimensional system of“safety and stability-economic feasibility-low-carbon and environmental protection,”an influencing factor system for the“Energy Trilemma”in the natural gas industry is constructed.
基金support of the Secretaría Nacional de Ciencia,Tecnología e Innovación(SENACYT)under Grant IDDSE19-007the Agencia Nacional de Investigación y Desarrollo(ANID)under Grants Fondecyt 1230135 and Fondef TA24I10002the Sistema Nacional de Investigación(SNI)of Panama under Grant 16-2021.
文摘Electrical insulation faults produce partial discharges(PD),which can be analysed to identify specific types of defects.PD clustering is a widely used method to identify PD sources,although its success depends largely on the feature maps used.In this paper,three widely used feature maps,or separation maps,are compared:chromatic,energy wavelet with principal component analysis(EW-PCA),and time-frequency(TF).To compare and evaluate,five scenarios with multi-PD environments with noise were developed.The clustering ability of the maps was evaluated using two performance indicators:intercluster distance and intracluster distance.The results indicate that the EW-PCA map performed the best in all scenarios,correctly identifying the largest number of data points and producing the clearest and most distinct clusters.The TF map created distinct clusters in several scenarios,but not all.The chromatic map created distinct clusters in all scenarios but was not as well defined as the other two separation maps.Given the results,it is important in fieldwork to use a wide range of PD clustering,accompanied by performance metrics that support a less biased decision tailored to the test object.
