It is challenging to cluster multi-view data in which the clusters have overlapping areas.Existing multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them int...It is challenging to cluster multi-view data in which the clusters have overlapping areas.Existing multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters,increasing clustering errors.Our solution,the multi-view dynamic kernelized evidential clustering method(MvDKE),addresses this by assigning these objects to meta-clusters,a union of several related singleton clusters,effectively capturing the local imprecision in overlapping areas.MvDKE offers two main advantages:firstly,it significantly reduces computational complexity through a dynamic framework for evidential clustering,and secondly,it adeptly handles non-spherical data using kernel techniques within its objective function.Experiments on various datasets confirm MvDKE's superior ability to accurately characterize the local imprecision in multi-view non-spherical data,achieving better efficiency and outperforming existing methods in overall performance.展开更多
Introduction: The Six Sigma methodology is an opportunity for a better understanding of the performance of analytical methods and for a better adaptation of the quality control management policy of the medical biology...Introduction: The Six Sigma methodology is an opportunity for a better understanding of the performance of analytical methods and for a better adaptation of the quality control management policy of the medical biology laboratory. Using the sigma metric, this study assessed the performance of the Biochemistry analytical system of a medical biology laboratory in Côte d'Ivoire. Methods: Six Sigma methodology was applied to 3 analytes (alanine aminotransferase, glucose and creatinine). Performance indicators such as measurement imprecision and bias were determined based on the results of internal and external quality controls. The sigma number was calculated using the total allowable error values proposed by Ricos et al. Results: For both control levels, ALT had a sigma number greater than 6 (7.6 for normal control and 7.9 for pathological control). However, low sigma numbers, less than or equal to 2 for creatinine (1.4 for normal control and 2 for pathological control) and less than 1 for glucose were found. Conclusion: This study revealed good analytical performance of ALT from the point of view of 6 sigma analysis. However, modifications to the overall quality control procedure for glucose and creatinine are needed to improve their analytical performance. The study should be extended to the entire laboratory’s analytes in order to modify the strategies of quality control procedures based on metric analysis for an overall improvement in analytical performance.展开更多
The distribution-free P-box process serves as an effective quantification model for timevarying uncertainties in dynamical systems when only imprecise probabilistic information is available.However,its application to ...The distribution-free P-box process serves as an effective quantification model for timevarying uncertainties in dynamical systems when only imprecise probabilistic information is available.However,its application to nonlinear systems remains limited due to excessive computation.This work develops an efficient method for propagating distribution-free P-box processes in nonlinear dynamics.First,using the Covariance Analysis Describing Equation Technique(CADET),the dynamic problems with P-box processes are transformed into interval Ordinary Differential Equations(ODEs).These equations provide the Mean-and-Covariance(MAC)bounds of the system responses in relation to the MAC bounds of P-box-process excitations.They also separate the previously coupled P-box analysis and nonlinear-dynamic simulations into two sequential steps,including the MAC bound analysis of excitations and the MAC bounds calculation of responses by solving the interval ODEs.Afterward,a Gaussian assumption of the CADET is extended to the P-box form,i.e.,the responses are approximate parametric Gaussian P-box processes.As a result,the probability bounds of the responses are approximated by using the solutions of the interval ODEs.Moreover,the Chebyshev method is introduced and modified to efficiently solve the interval ODEs.The proposed method is validated based on test cases,including a duffing oscillator,a vehicle ride,and an engineering black-box problem of launch vehicle trajectory.Compared to the reference solutions based on the Monte Carlo method,with relative errors of less than 3%,the proposed method requires less than 0.2% calculation time.The proposed method also possesses the ability to handle complex black-box problems.展开更多
Assessment of imprecise time-variant reliability in engineering is a critical task when accounting for both the variability of structural properties and loads over time and the presence of uncertainties involved in th...Assessment of imprecise time-variant reliability in engineering is a critical task when accounting for both the variability of structural properties and loads over time and the presence of uncertainties involved in the ambiguity of parameters simultaneously.To estimate the Imprecise Time-variant Failure Probability Function(ITFPF)and derive the imprecise reliability results as a byproduct,Adaptive Combination Augmented Line Sampling(ACALS)is proposed.It consists of three integrated features:Augmented Line Sampling(ALS),adaptive strategy,and the optimal combination.ALS is adopted as an efficient analysis tool to obtain the failure probability function w.r.t.imprecise parameters.Then,the adaptive strategy iteratively applies ALS while considering both imprecise parameters and time simultaneously.Finally,the optimal combination algorithm collects all result components in an optimal manner to minimize the Coefficient of Variance(C.o.V.)of the ITFPF estimate.Overall,the proposed ACALS method outperforms the original ALS method by efficiently estimating the ITFPF while guaranteeing a minimal C.o.V.Thus,the proposed approach can serve as an effective tool for imprecise time-variant reliability analysis in real engineering applications.Several examples are presented to demonstrate the superiority of the proposed approach in addressing the challenges of estimating the ITFPF.展开更多
A fuzzy observations-based radial basis function neural network （FORBFNN） is presented for modeling nonlinear systems in which the observations of response are imprecise but can be represented as fuzzy membership fu...A fuzzy observations-based radial basis function neural network （FORBFNN） is presented for modeling nonlinear systems in which the observations of response are imprecise but can be represented as fuzzy membership functions. In the FORBFNN model, the weight coefficients of nodes in the hidden layer are identified by using the fuzzy expectation-maximization （ EM ） algorithm, whereas the optimal number of these nodes as well as the centers and widths of radial basis functions are automatically constructed by using a data-driven method. Namely, the method starts with an initial node, and then a new node is added in a hidden layer according to some rules. This procedure is not terminated until the model meets the preset requirements. The method considers both the accuracy and complexity of the model. Numerical simulation results show that the modeling method is effective, and the established model has high prediction accuracy.展开更多
AIM To study sigma metrics and quality goal index ratio(QGI). METHODS The retrospective study was conducted at the Clinical Biochemistry Laboratory, PGIMS, Rohtak, which recently became a National Accreditation Board ...AIM To study sigma metrics and quality goal index ratio(QGI). METHODS The retrospective study was conducted at the Clinical Biochemistry Laboratory, PGIMS, Rohtak, which recently became a National Accreditation Board for Testing and Calibration of Laboratories accredited lab as per the International Organization for Standardization 15189:2012 and provides service to a > 1700-bed tertiary care hospital. Data of 16 analytes was extracted over a period of one year from January 2017 to December 2017 for calculation of precision, accuracy, sigma metrics, total error, and QGI. RESULTS The average coefficient of variation ranged from 2.12%(albumin) to 5.42%(creatinine) for level 2 internal quality control and 2%(albumin) to 3.62%(high density lipoprotein-cholesterol) for level 3 internal quality control. Average coefficient of variation of all the parameters was below 5%, reflecting very good precision. The sigma metrics for level 2 indicated that 11(68.5%) of the 16 parameters fall short of meeting Six Sigma quality performance. Of these, five failed to meet minimum sigma quality performance with metrics less than 3, and another six just met minimal acceptable performance with sigma metrics between 3 and 6. For level 3, the data collected indicated eight(50%) of the parameters did not achieve Six Sigma quality performance, out of which three had metrics less than 3, and five had metrics between 3 and 6. QGI ratio indicated that the main problem was inaccuracy in the case of total cholesterol, aspartate transaminase, and alanine transaminase(QGI > 1.2), imprecision in the case of urea(QGI < 0.8), and both imprecision and inaccuracy for glucose.CONCLUSION On the basis of sigma metrics and QGI, it may be concluded that the Clinical Biochemistry Laboratory, PGIMS, Rohtak was able to achieve satisfactory results with world class performance for many analytes one year preceding the accreditation by the National Accreditation Board for Testing and Calibration of Laboratories. Aspartate transaminase and alanine transaminase required strict external quality assurance scheme monitoring and modification in quality control procedure as their QGI ratio showed inaccuracy.展开更多
The decrease of wind velocity （wake losses） in downstream area of wind turbine is generally quantified using wake models. The overall estimated power of wind farm varies according to reliability of wake model used, ...The decrease of wind velocity （wake losses） in downstream area of wind turbine is generally quantified using wake models. The overall estimated power of wind farm varies according to reliability of wake model used, however it＇s unclear which model is most appropriate and able to give a high performance in predicting wind velocity deficit. In this subject, a qualification of three analytical wake models （Jensen, lshihara and Frandsen） based on three principal criteria is presented in this paper： （i） the parsimony which characterizes the inverse of model complexity, （ii） the accuracy of estimation in which wake model is compared with the experimental data and （iii） imprecision that is related to assumptions and uncertainty on the value of variables considered in each model. This qualitative analysis shows the inability of wake models to predict wind velocity deficit due to the big uncertainty of variables considered and it sensitivity to wind farm characteristic.展开更多
In this paper, Bayesian computational method is used to estimate inverse Rayleigh Scale parameter with fuzzy data. Based on imprecision data, the Bayes estimates cannot be obtained in explicit form. Therefore, we prov...In this paper, Bayesian computational method is used to estimate inverse Rayleigh Scale parameter with fuzzy data. Based on imprecision data, the Bayes estimates cannot be obtained in explicit form. Therefore, we provide Tierney and Kadane’s approximation to compute the Bayes estimates of the scale parameter under Square error and Precautionary loss function using Non-informative Jefferys Prior. Also, we provide compared numerically through Monte-Carlo simulation study to obtained estimates of the scale parameter in terms of mean squared error values.展开更多
This paper proposes a novel model named as “imprecise stochastic process model” to handle the dynamic uncertainty with insufficient sample information in real-world problems. In the imprecise stochastic process mode...This paper proposes a novel model named as “imprecise stochastic process model” to handle the dynamic uncertainty with insufficient sample information in real-world problems. In the imprecise stochastic process model, the imprecise probabilistic model rather than a precise probability distribution function is employed to characterize the uncertainty at each time point for a time-variant parameter, which provides an effective tool for problems with limited experimental samples. The linear correlation between variables at different time points for imprecise stochastic processes is described by defining the auto-correlation coefficient function and the crosscorrelation coefficient function. For the convenience of analysis, this paper gives the definition of the P-box-based imprecise stochastic process and categorizes it into two classes: parameterized and non-parameterized P-box-based imprecise stochastic processes. Besides, a time-variant reliability analysis approach is developed based on the P-box-based imprecise stochastic process model,through which the interval of dynamic reliability for a structure under uncertain dynamic excitations or time-variant factors can be obtained. Finally, the effectiveness of the proposed method is verified by investigating three numerical examples.展开更多
The goal of quality-of-service （QoS） multicast routing is to establish a multicast tree which satisfies certain constraints on bandwidth, delay and other metrics. The network state information maintained at every no...The goal of quality-of-service （QoS） multicast routing is to establish a multicast tree which satisfies certain constraints on bandwidth, delay and other metrics. The network state information maintained at every node is often im- precise in a dynamic environment because of non-negligible propagation delay of state messages, periodic updates due to overhead concern, and hierarchical state aggregation. The existing QoS multicast routing algorithms do not provide satisfactory performance with imprecise state information. We propose a distributed QoS multicast routing scheme based on traffic lights, called QMRI algorithm, which can probe multiple feasible tree branches, and select the optimal or near-optimal branch through the UR or TL mode for constructing a multicast tree with QoS guarantees if it exists. The scheme is designed to work with imprecise state information. The proposed algorithm considers not only the QoS requirements but also the cost optimality of the multicast tree. The correctness proof and the complexity analysis about the QMRI algorithm are also given. In addition, we develop NS2 so that it is able to simulate the imprecise network state information. Extensive simulations show that our algorithm achieves high call-admission ratio and low-cost multicast trees with modest message overhead.展开更多
This paper proposes a novel dynamic Petri net (PN) model based on Dempster-Shafer (D-S) evidence theory, and this improved evidential Petri net (EPN) model is used in knowledge inference and reliability analysis of co...This paper proposes a novel dynamic Petri net (PN) model based on Dempster-Shafer (D-S) evidence theory, and this improved evidential Petri net (EPN) model is used in knowledge inference and reliability analysis of complex mechanical systems. The EPN could take epistemic uncertainty such as interval information, subjective information into account by applying D-S evidence quantification theory. A dynamic representation model is also proposed based on the dynamic operation rules of the EPN model, and an improved artificial bee colony (ABC) algorithm is employed to proceed optimization calculation during the complex systems' learning process. The improved ABC algorithm and D-S evidence theory overcome the disadvantage of extremely subjective in traditional knowledge inference efficiently and thus could improve the accuracy of the EPN learning model. Through a simple numerical case and a satellite driving system analysis, this paper proves the superiority of the EPN and the dynamic knowledge representation method in reliability analysis of complex systems.展开更多
This work presents the first-order comprehensive adjoint sensitivity analysis methodology (1st-CASAM) for computing efficiently, exactly, and exhaustively, the first-order sensitivities of scalar-valued responses (res...This work presents the first-order comprehensive adjoint sensitivity analysis methodology (1st-CASAM) for computing efficiently, exactly, and exhaustively, the first-order sensitivities of scalar-valued responses (results of interest) of coupled nonlinear physical systems characterized by imprecisely known model parameters, boundaries and interfaces between the coupled systems. The 1st-CASAM highlights the conclusion that response sensitivities to the imprecisely known domain boundaries and interfaces can arise both from the definition of the system’s response as well as from the equations, interfaces and boundary conditions defining the model and its imprecisely known domain. By enabling, in premiere, the exact computations of sensitivities to interface and boundary parameters and conditions, the 1st-CASAM enables the quantification of the effects of manufacturing tolerances on the responses of physical and engineering systems. Ongoing research will generalize the methodology presented in this work, aiming at computing exactly and efficiently higher-order response sensitivities for coupled systems involving imprecisely known interfaces, parameters, and boundaries.展开更多
Two types of uncertainty co-exist in the theory of evidence： discord and non-specificity.From 90s, many mathematical expressions have arisen to quantify these two parts in an evidence.An important aspect of each meas...Two types of uncertainty co-exist in the theory of evidence： discord and non-specificity.From 90s, many mathematical expressions have arisen to quantify these two parts in an evidence.An important aspect of each measure presented is the verification of a coherent set of properties.About non-specificity, so far only one measure verifies an important set of those properties. Very recently, a new measure of non-specificity based on belief intervals has been presented as an alternative measure that quantifies a similar set of properties（Yang et al., 2016）. It is shown that the new measure really does not verify two of those important properties. Some errors have been found in their corresponding proofs in the original publication.展开更多
In traditional Bayesian software reliability models, it was assume that all probabilities are precise. In practical applications the parameters of the probability distributions are often under uncertainty due to stron...In traditional Bayesian software reliability models, it was assume that all probabilities are precise. In practical applications the parameters of the probability distributions are often under uncertainty due to strong dependence on subjective information of experts＇ judgments on sparse statistical data. In this paper, a quasi-Bayesian software reliability model using interval-valued probabilities to clearly quantify experts＇ prior beliefs on possible intervals of the parameters of the probability distributions is presented. The model integrates experts＇ judgments with statistical data to obtain more convincible assessments of software reliability with small samples. For some actual data sets, the presented model yields better predictions than the Jelinski-Moranda （JM） model using maximum likelihood （ML）.展开更多
Traditional structural reliability analysis methods adopt precise probabilities to quantify uncertainties and they are suitable for systems with sufficient statistical data.However,the problem of insufficient data is ...Traditional structural reliability analysis methods adopt precise probabilities to quantify uncertainties and they are suitable for systems with sufficient statistical data.However,the problem of insufficient data is often encountered in practical engineering.Thus,structural reliability analysis methods under insufficient data have caught more and more attentions in recent years and a lot of nonprobabilistic reliability analysis methods are put forward to deal with the problem of insufficient data.Non-probabilistic structural reliability analysis methods based on fuzzy set,Dempster-Shafer theory,interval analysis and other theories have got a lot of achievements both in theoretical and practical aspects and they have been successfully applied in structural reliability analysis of largescale complex systems with small samples and few statistical data.In addition to non-probabilistic structural reliability analysis methods,structural reliability analysis based on imprecise probability theory is a new method proposed in recent years.Study on structural reliability analysis using imprecise probability theory is still at the start stage,thus the generalization of imprecise structural reliability model is very important.In this paper,the imprecise probability was developed as an effective way to handle uncertainties,the detailed procedures of imprecise structural reliability analysis was introduced,and several specific imprecise structural reliability models which are most effective for engineering systems were given.At last,an engineering example of a cantilever beam was given to illustrate the effectiveness of the method emphasized here.By comparing with interval structural reliability analysis,the result obtained from imprecise structural reliability model is a little conservative than the one resulted from interval structural reliability analysis for imprecise structural reliability analysis model considers that the probability of each value is taken from an interval.展开更多
We present a new nonparametric predictive inference(NPI)method using a power-normal model for accelerated life testing(ALT).Combined with the accelerating link function and imprecise probability theory,the proposed me...We present a new nonparametric predictive inference(NPI)method using a power-normal model for accelerated life testing(ALT).Combined with the accelerating link function and imprecise probability theory,the proposed method is a feasible way to predict the life of the product using ALT failure data.To validate the method,we run a series of simulations and conduct accelerated life tests with real products.The NPI lower and upper survival functions show the robustness of our method for life prediction.This is a continuous research,and some progresses have been made by updating the link function between different stress levels.We also explain how to renew and apply our model.Moreover,discussions have been made about the performance.展开更多
Model predictive control (MPC) could not be deployed in real-time control systems for its computation time is not well defined. A real-time fault tolerant implementation algorithm based on imprecise computation is pro...Model predictive control (MPC) could not be deployed in real-time control systems for its computation time is not well defined. A real-time fault tolerant implementation algorithm based on imprecise computation is proposed for MPC, according to the solving process of quadratic programming (QP) problem. In this algorithm, system stability is guaranteed even when computation resource is not enough to finish optimization completely. By this kind of graceful degradation, the behavior of real-time control systems is still predictable and determinate. The algorithm is demonstrated by experiments on servomotor, and the simulation results show its effectiveness.展开更多
The calculation of the overall profit Malmquist productivity index(MPI)requires precise and accurate information on the input,output,input-output prices of each decision making unit(DMU).However,in many situations,som...The calculation of the overall profit Malmquist productivity index(MPI)requires precise and accurate information on the input,output,input-output prices of each decision making unit(DMU).However,in many situations,some inputs and/or outputs and input-output prices are imprecise.As such,we consider the overall profit MPI problem when the input,output,and input-output prices are imprecise and vary over intervals,showing that method(MCM 54:2827–2838,2011)has some shortfalls.To remedy these shortfalls,we propose another method for measuring the overall profit MPI when the inputs,outputs,and price vectors vary over intervals.That is,to calculate the overall profit efficiency intervals,cone-ratio data envelopment analysis models can be applied to the incorporated information as weight restrictions.Further,we provide a new approach to calculating the upper bound of the overall profit efficiency of each DMU.A numerical example is provided for illustrating the proposed method.展开更多
This paper presents a study of sustainable regional development using multi-criteria analysis. The aim of this paper is to provide an evaluation framework that can be used for the assessment of sustainable regional de...This paper presents a study of sustainable regional development using multi-criteria analysis. The aim of this paper is to provide an evaluation framework that can be used for the assessment of sustainable regional development using multi criteria linked to development scenarios set by stakeholders. This study was carried out in Jambi Province in Indonesia where balancing sustainable development is constrained by the fact that conservation areas make up the majority of the region. The study employs four alternative policy scenarios for regional sustainable development： （1） business as usual; （2） development based on regional competitiveness; （3） development based on local resources; and （4） regional development based on non-extractive scenario. These four scenarios were assessed using the FLAG Model and the Imprecise Decision Model. Results from analysis show that development policy scenarios based on utilization of local resources and non-extractive economic activities are the most sustainable way of regional development. The study shows the trade-off among policy scenarios must be faced by policy makers in the region either to pursue high economic growth at the cost of the environment or vice versa.展开更多
This work illustrates the application of the 1<sup>st</sup>-CASAM to a paradigm heat transport model which admits exact closed-form solutions. The closed-form expressions obtained in this work for the sens...This work illustrates the application of the 1<sup>st</sup>-CASAM to a paradigm heat transport model which admits exact closed-form solutions. The closed-form expressions obtained in this work for the sensitivities of the temperature distributions within the model to the model’s parameters, internal interfaces and external boundaries can be used to benchmark commercial and production software packages for simulating heat transport. The 1<sup>st</sup>-CASAM highlights the novel finding that response sensitivities to the imprecisely known domain boundaries and interfaces can arise both from the definition of the system’s response as well as from the equations, interfaces and boundary conditions that characterize the model and its imprecisely known domain. By enabling, in premiere, the exact computations of sensitivities to interface and boundary parameters and conditions, the 1<sup>st</sup>-CASAM enables the quantification of the effects of manufacturing tolerances on the responses of physical and engineering systems.展开更多
基金supported in part by the Youth Foundation of Shanxi Province(5113240053)the Fundamental Research Funds for the Central Universities(G2023KY05102)+2 种基金the Natural Science Foundation of China(61976120)the Natural Science Foundation of Jiangsu Province(BK20231337)the Natural Science Key Foundation of Jiangsu Education Department(21KJA510004)。
文摘It is challenging to cluster multi-view data in which the clusters have overlapping areas.Existing multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters,increasing clustering errors.Our solution,the multi-view dynamic kernelized evidential clustering method(MvDKE),addresses this by assigning these objects to meta-clusters,a union of several related singleton clusters,effectively capturing the local imprecision in overlapping areas.MvDKE offers two main advantages:firstly,it significantly reduces computational complexity through a dynamic framework for evidential clustering,and secondly,it adeptly handles non-spherical data using kernel techniques within its objective function.Experiments on various datasets confirm MvDKE's superior ability to accurately characterize the local imprecision in multi-view non-spherical data,achieving better efficiency and outperforming existing methods in overall performance.
文摘Introduction: The Six Sigma methodology is an opportunity for a better understanding of the performance of analytical methods and for a better adaptation of the quality control management policy of the medical biology laboratory. Using the sigma metric, this study assessed the performance of the Biochemistry analytical system of a medical biology laboratory in Côte d'Ivoire. Methods: Six Sigma methodology was applied to 3 analytes (alanine aminotransferase, glucose and creatinine). Performance indicators such as measurement imprecision and bias were determined based on the results of internal and external quality controls. The sigma number was calculated using the total allowable error values proposed by Ricos et al. Results: For both control levels, ALT had a sigma number greater than 6 (7.6 for normal control and 7.9 for pathological control). However, low sigma numbers, less than or equal to 2 for creatinine (1.4 for normal control and 2 for pathological control) and less than 1 for glucose were found. Conclusion: This study revealed good analytical performance of ALT from the point of view of 6 sigma analysis. However, modifications to the overall quality control procedure for glucose and creatinine are needed to improve their analytical performance. The study should be extended to the entire laboratory’s analytes in order to modify the strategies of quality control procedures based on metric analysis for an overall improvement in analytical performance.
基金supported by the major advanced research project of Civil Aerospace from State Administration of Science,Technology and Industry of China.
文摘The distribution-free P-box process serves as an effective quantification model for timevarying uncertainties in dynamical systems when only imprecise probabilistic information is available.However,its application to nonlinear systems remains limited due to excessive computation.This work develops an efficient method for propagating distribution-free P-box processes in nonlinear dynamics.First,using the Covariance Analysis Describing Equation Technique(CADET),the dynamic problems with P-box processes are transformed into interval Ordinary Differential Equations(ODEs).These equations provide the Mean-and-Covariance(MAC)bounds of the system responses in relation to the MAC bounds of P-box-process excitations.They also separate the previously coupled P-box analysis and nonlinear-dynamic simulations into two sequential steps,including the MAC bound analysis of excitations and the MAC bounds calculation of responses by solving the interval ODEs.Afterward,a Gaussian assumption of the CADET is extended to the P-box form,i.e.,the responses are approximate parametric Gaussian P-box processes.As a result,the probability bounds of the responses are approximated by using the solutions of the interval ODEs.Moreover,the Chebyshev method is introduced and modified to efficiently solve the interval ODEs.The proposed method is validated based on test cases,including a duffing oscillator,a vehicle ride,and an engineering black-box problem of launch vehicle trajectory.Compared to the reference solutions based on the Monte Carlo method,with relative errors of less than 3%,the proposed method requires less than 0.2% calculation time.The proposed method also possesses the ability to handle complex black-box problems.
基金The Aeronautical Science Foundation of China(Nos.20170968002,20230003068002)The National Major Science and Technology Projects of China(Nos.J2019-II-0022-0043,J2019-VII-0013-0153).
文摘Assessment of imprecise time-variant reliability in engineering is a critical task when accounting for both the variability of structural properties and loads over time and the presence of uncertainties involved in the ambiguity of parameters simultaneously.To estimate the Imprecise Time-variant Failure Probability Function(ITFPF)and derive the imprecise reliability results as a byproduct,Adaptive Combination Augmented Line Sampling(ACALS)is proposed.It consists of three integrated features:Augmented Line Sampling(ALS),adaptive strategy,and the optimal combination.ALS is adopted as an efficient analysis tool to obtain the failure probability function w.r.t.imprecise parameters.Then,the adaptive strategy iteratively applies ALS while considering both imprecise parameters and time simultaneously.Finally,the optimal combination algorithm collects all result components in an optimal manner to minimize the Coefficient of Variance(C.o.V.)of the ITFPF estimate.Overall,the proposed ACALS method outperforms the original ALS method by efficiently estimating the ITFPF while guaranteeing a minimal C.o.V.Thus,the proposed approach can serve as an effective tool for imprecise time-variant reliability analysis in real engineering applications.Several examples are presented to demonstrate the superiority of the proposed approach in addressing the challenges of estimating the ITFPF.
基金The National Natural Science Foundation of China(No.51106025,51106027,51036002)Specialized Research Fund for the Doctoral Program of Higher Education(No.20130092110061)the Youth Foundation of Nanjing Institute of Technology(No.QKJA201303)
文摘A fuzzy observations-based radial basis function neural network （FORBFNN） is presented for modeling nonlinear systems in which the observations of response are imprecise but can be represented as fuzzy membership functions. In the FORBFNN model, the weight coefficients of nodes in the hidden layer are identified by using the fuzzy expectation-maximization （ EM ） algorithm, whereas the optimal number of these nodes as well as the centers and widths of radial basis functions are automatically constructed by using a data-driven method. Namely, the method starts with an initial node, and then a new node is added in a hidden layer according to some rules. This procedure is not terminated until the model meets the preset requirements. The method considers both the accuracy and complexity of the model. Numerical simulation results show that the modeling method is effective, and the established model has high prediction accuracy.
文摘AIM To study sigma metrics and quality goal index ratio(QGI). METHODS The retrospective study was conducted at the Clinical Biochemistry Laboratory, PGIMS, Rohtak, which recently became a National Accreditation Board for Testing and Calibration of Laboratories accredited lab as per the International Organization for Standardization 15189:2012 and provides service to a > 1700-bed tertiary care hospital. Data of 16 analytes was extracted over a period of one year from January 2017 to December 2017 for calculation of precision, accuracy, sigma metrics, total error, and QGI. RESULTS The average coefficient of variation ranged from 2.12%(albumin) to 5.42%(creatinine) for level 2 internal quality control and 2%(albumin) to 3.62%(high density lipoprotein-cholesterol) for level 3 internal quality control. Average coefficient of variation of all the parameters was below 5%, reflecting very good precision. The sigma metrics for level 2 indicated that 11(68.5%) of the 16 parameters fall short of meeting Six Sigma quality performance. Of these, five failed to meet minimum sigma quality performance with metrics less than 3, and another six just met minimal acceptable performance with sigma metrics between 3 and 6. For level 3, the data collected indicated eight(50%) of the parameters did not achieve Six Sigma quality performance, out of which three had metrics less than 3, and five had metrics between 3 and 6. QGI ratio indicated that the main problem was inaccuracy in the case of total cholesterol, aspartate transaminase, and alanine transaminase(QGI > 1.2), imprecision in the case of urea(QGI < 0.8), and both imprecision and inaccuracy for glucose.CONCLUSION On the basis of sigma metrics and QGI, it may be concluded that the Clinical Biochemistry Laboratory, PGIMS, Rohtak was able to achieve satisfactory results with world class performance for many analytes one year preceding the accreditation by the National Accreditation Board for Testing and Calibration of Laboratories. Aspartate transaminase and alanine transaminase required strict external quality assurance scheme monitoring and modification in quality control procedure as their QGI ratio showed inaccuracy.
文摘The decrease of wind velocity （wake losses） in downstream area of wind turbine is generally quantified using wake models. The overall estimated power of wind farm varies according to reliability of wake model used, however it＇s unclear which model is most appropriate and able to give a high performance in predicting wind velocity deficit. In this subject, a qualification of three analytical wake models （Jensen, lshihara and Frandsen） based on three principal criteria is presented in this paper： （i） the parsimony which characterizes the inverse of model complexity, （ii） the accuracy of estimation in which wake model is compared with the experimental data and （iii） imprecision that is related to assumptions and uncertainty on the value of variables considered in each model. This qualitative analysis shows the inability of wake models to predict wind velocity deficit due to the big uncertainty of variables considered and it sensitivity to wind farm characteristic.
文摘In this paper, Bayesian computational method is used to estimate inverse Rayleigh Scale parameter with fuzzy data. Based on imprecision data, the Bayes estimates cannot be obtained in explicit form. Therefore, we provide Tierney and Kadane’s approximation to compute the Bayes estimates of the scale parameter under Square error and Precautionary loss function using Non-informative Jefferys Prior. Also, we provide compared numerically through Monte-Carlo simulation study to obtained estimates of the scale parameter in terms of mean squared error values.
基金supported by the Science Challenge Project,China(No.TZ2018007)the National Science Fund for Distinguished Young Scholars,China(No.51725502)+2 种基金the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(No.51621004)the Fundamental Research Foundation of China(No.JCKY2020110C105)the National Natural Science Foundation of China(No.52105253)。
文摘This paper proposes a novel model named as “imprecise stochastic process model” to handle the dynamic uncertainty with insufficient sample information in real-world problems. In the imprecise stochastic process model, the imprecise probabilistic model rather than a precise probability distribution function is employed to characterize the uncertainty at each time point for a time-variant parameter, which provides an effective tool for problems with limited experimental samples. The linear correlation between variables at different time points for imprecise stochastic processes is described by defining the auto-correlation coefficient function and the crosscorrelation coefficient function. For the convenience of analysis, this paper gives the definition of the P-box-based imprecise stochastic process and categorizes it into two classes: parameterized and non-parameterized P-box-based imprecise stochastic processes. Besides, a time-variant reliability analysis approach is developed based on the P-box-based imprecise stochastic process model,through which the interval of dynamic reliability for a structure under uncertain dynamic excitations or time-variant factors can be obtained. Finally, the effectiveness of the proposed method is verified by investigating three numerical examples.
文摘The goal of quality-of-service （QoS） multicast routing is to establish a multicast tree which satisfies certain constraints on bandwidth, delay and other metrics. The network state information maintained at every node is often im- precise in a dynamic environment because of non-negligible propagation delay of state messages, periodic updates due to overhead concern, and hierarchical state aggregation. The existing QoS multicast routing algorithms do not provide satisfactory performance with imprecise state information. We propose a distributed QoS multicast routing scheme based on traffic lights, called QMRI algorithm, which can probe multiple feasible tree branches, and select the optimal or near-optimal branch through the UR or TL mode for constructing a multicast tree with QoS guarantees if it exists. The scheme is designed to work with imprecise state information. The proposed algorithm considers not only the QoS requirements but also the cost optimality of the multicast tree. The correctness proof and the complexity analysis about the QMRI algorithm are also given. In addition, we develop NS2 so that it is able to simulate the imprecise network state information. Extensive simulations show that our algorithm achieves high call-admission ratio and low-cost multicast trees with modest message overhead.
基金supported by the National Basic Research Program of China(2013CB733002)
文摘This paper proposes a novel dynamic Petri net (PN) model based on Dempster-Shafer (D-S) evidence theory, and this improved evidential Petri net (EPN) model is used in knowledge inference and reliability analysis of complex mechanical systems. The EPN could take epistemic uncertainty such as interval information, subjective information into account by applying D-S evidence quantification theory. A dynamic representation model is also proposed based on the dynamic operation rules of the EPN model, and an improved artificial bee colony (ABC) algorithm is employed to proceed optimization calculation during the complex systems' learning process. The improved ABC algorithm and D-S evidence theory overcome the disadvantage of extremely subjective in traditional knowledge inference efficiently and thus could improve the accuracy of the EPN learning model. Through a simple numerical case and a satellite driving system analysis, this paper proves the superiority of the EPN and the dynamic knowledge representation method in reliability analysis of complex systems.
文摘This work presents the first-order comprehensive adjoint sensitivity analysis methodology (1st-CASAM) for computing efficiently, exactly, and exhaustively, the first-order sensitivities of scalar-valued responses (results of interest) of coupled nonlinear physical systems characterized by imprecisely known model parameters, boundaries and interfaces between the coupled systems. The 1st-CASAM highlights the conclusion that response sensitivities to the imprecisely known domain boundaries and interfaces can arise both from the definition of the system’s response as well as from the equations, interfaces and boundary conditions defining the model and its imprecisely known domain. By enabling, in premiere, the exact computations of sensitivities to interface and boundary parameters and conditions, the 1st-CASAM enables the quantification of the effects of manufacturing tolerances on the responses of physical and engineering systems. Ongoing research will generalize the methodology presented in this work, aiming at computing exactly and efficiently higher-order response sensitivities for coupled systems involving imprecisely known interfaces, parameters, and boundaries.
基金supported by the Spanish ‘‘Ministerio de Economíay Competitividad"by ‘‘Fondo Europeo de Desarrollo Regional"(FEDER)(No.TEC2015-69496-R)
文摘Two types of uncertainty co-exist in the theory of evidence： discord and non-specificity.From 90s, many mathematical expressions have arisen to quantify these two parts in an evidence.An important aspect of each measure presented is the verification of a coherent set of properties.About non-specificity, so far only one measure verifies an important set of those properties. Very recently, a new measure of non-specificity based on belief intervals has been presented as an alternative measure that quantifies a similar set of properties（Yang et al., 2016）. It is shown that the new measure really does not verify two of those important properties. Some errors have been found in their corresponding proofs in the original publication.
基金supported by the National High-Technology Research and Development Program of China (Grant Nos.2006AA01Z187,2007AA040605)
文摘In traditional Bayesian software reliability models, it was assume that all probabilities are precise. In practical applications the parameters of the probability distributions are often under uncertainty due to strong dependence on subjective information of experts＇ judgments on sparse statistical data. In this paper, a quasi-Bayesian software reliability model using interval-valued probabilities to clearly quantify experts＇ prior beliefs on possible intervals of the parameters of the probability distributions is presented. The model integrates experts＇ judgments with statistical data to obtain more convincible assessments of software reliability with small samples. For some actual data sets, the presented model yields better predictions than the Jelinski-Moranda （JM） model using maximum likelihood （ML）.
基金Joint Funds of the National Natual Foundation of China(NSAF)(No.U1330130)
文摘Traditional structural reliability analysis methods adopt precise probabilities to quantify uncertainties and they are suitable for systems with sufficient statistical data.However,the problem of insufficient data is often encountered in practical engineering.Thus,structural reliability analysis methods under insufficient data have caught more and more attentions in recent years and a lot of nonprobabilistic reliability analysis methods are put forward to deal with the problem of insufficient data.Non-probabilistic structural reliability analysis methods based on fuzzy set,Dempster-Shafer theory,interval analysis and other theories have got a lot of achievements both in theoretical and practical aspects and they have been successfully applied in structural reliability analysis of largescale complex systems with small samples and few statistical data.In addition to non-probabilistic structural reliability analysis methods,structural reliability analysis based on imprecise probability theory is a new method proposed in recent years.Study on structural reliability analysis using imprecise probability theory is still at the start stage,thus the generalization of imprecise structural reliability model is very important.In this paper,the imprecise probability was developed as an effective way to handle uncertainties,the detailed procedures of imprecise structural reliability analysis was introduced,and several specific imprecise structural reliability models which are most effective for engineering systems were given.At last,an engineering example of a cantilever beam was given to illustrate the effectiveness of the method emphasized here.By comparing with interval structural reliability analysis,the result obtained from imprecise structural reliability model is a little conservative than the one resulted from interval structural reliability analysis for imprecise structural reliability analysis model considers that the probability of each value is taken from an interval.
基金the National Natural Science Foundation of China(No.11272082)the China Scholarship Council State Scholarship Fund(No.201506070017)
文摘We present a new nonparametric predictive inference(NPI)method using a power-normal model for accelerated life testing(ALT).Combined with the accelerating link function and imprecise probability theory,the proposed method is a feasible way to predict the life of the product using ALT failure data.To validate the method,we run a series of simulations and conduct accelerated life tests with real products.The NPI lower and upper survival functions show the robustness of our method for life prediction.This is a continuous research,and some progresses have been made by updating the link function between different stress levels.We also explain how to renew and apply our model.Moreover,discussions have been made about the performance.
文摘Model predictive control (MPC) could not be deployed in real-time control systems for its computation time is not well defined. A real-time fault tolerant implementation algorithm based on imprecise computation is proposed for MPC, according to the solving process of quadratic programming (QP) problem. In this algorithm, system stability is guaranteed even when computation resource is not enough to finish optimization completely. By this kind of graceful degradation, the behavior of real-time control systems is still predictable and determinate. The algorithm is demonstrated by experiments on servomotor, and the simulation results show its effectiveness.
文摘The calculation of the overall profit Malmquist productivity index(MPI)requires precise and accurate information on the input,output,input-output prices of each decision making unit(DMU).However,in many situations,some inputs and/or outputs and input-output prices are imprecise.As such,we consider the overall profit MPI problem when the input,output,and input-output prices are imprecise and vary over intervals,showing that method(MCM 54:2827–2838,2011)has some shortfalls.To remedy these shortfalls,we propose another method for measuring the overall profit MPI when the inputs,outputs,and price vectors vary over intervals.That is,to calculate the overall profit efficiency intervals,cone-ratio data envelopment analysis models can be applied to the incorporated information as weight restrictions.Further,we provide a new approach to calculating the upper bound of the overall profit efficiency of each DMU.A numerical example is provided for illustrating the proposed method.
文摘This paper presents a study of sustainable regional development using multi-criteria analysis. The aim of this paper is to provide an evaluation framework that can be used for the assessment of sustainable regional development using multi criteria linked to development scenarios set by stakeholders. This study was carried out in Jambi Province in Indonesia where balancing sustainable development is constrained by the fact that conservation areas make up the majority of the region. The study employs four alternative policy scenarios for regional sustainable development： （1） business as usual; （2） development based on regional competitiveness; （3） development based on local resources; and （4） regional development based on non-extractive scenario. These four scenarios were assessed using the FLAG Model and the Imprecise Decision Model. Results from analysis show that development policy scenarios based on utilization of local resources and non-extractive economic activities are the most sustainable way of regional development. The study shows the trade-off among policy scenarios must be faced by policy makers in the region either to pursue high economic growth at the cost of the environment or vice versa.
文摘This work illustrates the application of the 1<sup>st</sup>-CASAM to a paradigm heat transport model which admits exact closed-form solutions. The closed-form expressions obtained in this work for the sensitivities of the temperature distributions within the model to the model’s parameters, internal interfaces and external boundaries can be used to benchmark commercial and production software packages for simulating heat transport. The 1<sup>st</sup>-CASAM highlights the novel finding that response sensitivities to the imprecisely known domain boundaries and interfaces can arise both from the definition of the system’s response as well as from the equations, interfaces and boundary conditions that characterize the model and its imprecisely known domain. By enabling, in premiere, the exact computations of sensitivities to interface and boundary parameters and conditions, the 1<sup>st</sup>-CASAM enables the quantification of the effects of manufacturing tolerances on the responses of physical and engineering systems.
