uncertainty

摘要： An uncertainty analysis method is proposed for the assessment of the residual strength of a casing subjected to wear and non-uniform load in a deep well.The influence of casing residual stress,out-of-roundness and non-uniform load is considered.The distribution of multi-source parameters related to the residual anti extrusion strength and residual anti internal pressure strength of the casing after wear are determined using the probability theory.Considering the technical casing of X101 well in Xinjiang Oilfield as an example,it is shown that the randomness of casing wear depth,formation elastic modulus and formation Poisson’s ratio are the main factors that affect the uncertainty of residual strength.The wider the confidence interval is,the greater the uncertainty range is.Compared with the calculations resulting from the proposed uncertainty analysis method,the residual strength obtained by means of traditional single value calculation method is either larger or smaller,which leads to the conclusion that the residual strength should be considered in terms of a range of probabilities rather than a single value.

摘要： A moving target tracking control problem for marching tank based on adaptive robust feedback control scheme is addressed.A series of preparations is needed for tank gun before shooting a target,the purpose of this paper is to design a control system to fulfill two requirements in this process:the turretbarrel system of tank needs to be adjusted from off-target position to command position and point to the moving target stably when there are strong uncertainties(modeling error,uncertain disturbance with unknown boundaries and road excitation) in the system.Considering the characteristic of coupled interaction,the first thing we do in this paper is to build a coupled analysis model of turret-barrel system with uncertainty term in state-space form.Second,an adaptive robust feedback control scheme is proposed by adding adaptive law to overcome the uncertainty.Third,multi-body dynamics software is used to establish the mechanical mechanism of the tank,and DC-motor module is established in SIMULINK environment,thus the target information and tracking error of the control system is collected and transferred,the gear-ball screw is derived directly by the output torque of the DC-motor module.Finally,the control system and the 3D model are combined together by means of Recur Dyn/SIMULINK co-simulation,the turret-barrel system of tank can approximately track the moving target in a certain range.With the adaptive robust feedback control,the target action is completely followed when the target location is constantly changing.

摘要： Pharmaceutical continuous manufacturing,especially under the context of COVID-19 pandemic,is regarded as an emerging technology that can guarantee the adequate quality assurance and mitigate process risk while guaranteeing the desirable economic performance.Flexibility analysis is one approach to quantitively assess the capability of chemical process to guarantee feasible operation in face of variations on uncertain parameters.The aim of this paper is to provide the perspectives on the flexibility analysis for continuous pharmaceutical manufacturing processes.State-of-the-art and progress in the flexibility analysis for chemical processes including concept evolution,mathematical model formulations,solution strategies,and applications are systematically overviewed.Recent achievements on the flexibility/feasibility analysis of the downstream dosage form manufacturing process are also touched upon.Further challenges and developments in the field of flexibility analysis for novel continuous manufacturing processes of active pharmaceutical ingredients along with the integrated continuous manufacturing processes are identified.

摘要： Earned duration management(EDM)is a methodology for project schedule management(PSM)that can be considered an alternative to earned value management(EVM).EDM provides an estimation of devia-tions in schedule and a final project duration estimation.There is a key difference between EDM and EVM:In EDM,the value of activities is expressed as work periods;whereas in EVM,value is expressed in terms of cost.In this paper,we present how EDM can be applied to monitor and control stochastic pro-jects.To explain the methodology,we use a real case study with a project that presents a high level of uncertainty and activities with random durations.We analyze the usability of this approach according to the activities network topology and compare the EVM and earned schedule methodology(ESM)for PSM.

摘要： Sequential Gaussian Simulation(SGSIM)as a stochastic method has been developed to avoid the smoothing effect produced in deterministic methods by generating various stochastic realizations.One of the main issues of this technique is,however,an intensive computation related to the inverse operation in solving the Kriging system,which significantly limits its application when several realizations need to be produced for uncertainty quantification.In this paper,a physics-informed machine learning(PIML)model is proposed to improve the computational efficiency of the SGSIM.To this end,only a small amount of data produced by SGSIM are used as the training dataset based on which the model can discover the spatial correlations between available data and unsampled points.To achieve this,the governing equations of the SGSIM algorithm are incorporated into our proposed network.The quality of realizations produced by the PIML model is compared for both 2D and 3D cases,visually and quantitatively.Furthermore,computational performance is evaluated on different grid sizes.Our results demonstrate that the proposed PIML model can reduce the computational time of SGSIM by several orders of magnitude while similar results can be produced in a matter of seconds.

摘要： Data-driven paradigms are well-known and salient demands of future wireless communication. Empowered by big data and machine learning techniques,next-generation data-driven communication systems will be intelligent with unique characteristics of expressiveness, scalability, interpretability, and uncertainty awareness, which can confidently involve diversified latent demands and personalized services in the foreseeable future. In this paper, we review a promising family of nonparametric Bayesian machine learning models,i.e., Gaussian processes(GPs), and their applications in wireless communication. Since GP models demonstrate outstanding expressive and interpretable learning ability with uncertainty, they are particularly suitable for wireless communication. Moreover, they provide a natural framework for collaborating data and empirical models(DEM). Specifically, we first envision three-level motivations of data-driven wireless communication using GP models. Then, we present the background of the GPs in terms of covariance structure and model inference. The expressiveness of the GP model using various interpretable kernels, including stationary, non-stationary, deep and multi-task kernels,is showcased. Furthermore, we review the distributed GP models with promising scalability, which is suitable for applications in wireless networks with a large number of distributed edge devices. Finally, we list representative solutions and promising techniques that adopt GP models in various wireless communication applications.

摘要： [Objectives]To determine the content of organic pollutants by CALUX bioassay.[Methods]According to JJF 1059.1-2012 Technical Specification for Evaluation and Expression of Uncertainty in Measurement,the determination results of organic pollutants were analyzed,various sources of uncertainty that may be introduced in the detection process were analyzed,and the mathematical model of uncertainty was established.Type A and B evaluation methods were used to calculate the components of uncertainty and extended uncertainty.[Results]In the range of 95%confidence interval,the determination result of organic pollutants was 6.59 pg/g fat,and the extended uncertainty was 0.0764 pg/g fat,so the determination results of organic pollutants could be expressed as(6.59±0.0764)pg/g fat(k=2).[Conclusions]This study can provide a basis for more accurate determination of organic pollutants.

摘要： Dear Editor,This letter is concerned with self-supervised monocular depth estimation.To estimate uncertainty simultaneously,we propose a simple yet effective strategy to learn the uncertainty for self-supervised monocular depth estimation with the discrete strategy that explicitly associates the prediction and the uncertainty to train the networks.Furthermore,we propose the uncertainty-guided feature fusion module to fully utilize the uncertainty information.Codes will be available at https://github.com/zhyever/Monocular-Depth-Estimation-Toolbox.Self-supervised monocular depth estimation methods turn into promising alternative trade-offs in both the training cost and the inference performance.However,compound losses that couple the depth and the pose lead to a dilemma of uncertainty calculation that is crucial for critical safety systems.To solve this issue,we propose a simple yet effective strategy to learn the uncertainty for self-supervised monocular depth estimation using the discrete bins that explicitly associate the prediction and the uncertainty to train the networks.This strategy is more pluggable without any additional changes to self-supervised training losses and improves model performance.Secondly,to further exert the uncertainty information,we propose the uncertainty-guided feature fusion module to refine the depth estimation.

摘要： This study examines the asymmetric effects of the structural oil price shocks and COVID-19 pandemic on four uncertainty indexes.The author used the SVAR approach for the period 31-Dec-2019 to 28-Jun-2020.The results indicate that the effects are asymmetric of oil price shocks.The author also finds that COVID-19 shocks lead to positive responses to the economic policy uncertainty index.In addition,oil prices(their shocks)have a negative impact on the four indicators of uncertainty.Consequently,governments should actively take effective measures to prevent crude oil prices from shocking and maintain stable economic policies.

摘要： Unsupervised clustering and clustering validity are used as essential instruments of data analytics.Despite clustering being realized under uncertainty,validity indices do not deliver any quantitative evaluation of the uncertainties in the suggested partitionings.Also,validity measures may be biased towards the underlying clustering method.Moreover,neglecting a confidence requirement may result in over-partitioning.In the absence of an error estimate or a confidence parameter,probable clustering errors are forwarded to the later stages of the system.Whereas,having an uncertainty margin of the projected labeling can be very fruitful for many applications such as machine learning.Herein,the validity issue was approached through estimation of the uncertainty and a novel low complexity index proposed for fuzzy clustering.It involves only uni-dimensional membership weights,regardless of the data dimension,stipulates no specific distribution,and is independent of the underlying similarity measure.Inclusive tests and comparisons returned that it can reliably estimate the optimum number of partitions under different data distributions,besides behaving more robust to over partitioning.Also,in the comparative correlation analysis between true clustering error rates and some known internal validity indices,the suggested index exhibited the highest strong correlations.This relationship has been also proven stable through additional statistical acceptance tests.Thus the provided relative uncertainty measure can be used as a probable error estimate in the clustering as well.Besides,it is the only method known that can exclusively identify data points in dubiety and is adjustable according to the required confidence level.