Monte Carlo Method for the Uncertainty Evaluation of Spatial Straightness Error Based on New Generation Geometrical Product Specification

Straightness error is an important parameter in measuring high-precision shafts. New generation geometrical product speeifieation（GPS） requires the measurement uncertainty characterizing the reliability of the results should be given together when the measurement result is given. Nowadays most researches on straightness focus on error calculation and only several research projects evaluate the measurement uncertainty based on ＂The Guide to the Expression of Uncertainty in Measurement（GUM）＂. In order to compute spatial straightness error（SSE） accurately and rapidly and overcome the limitations of GUM, a quasi particle swarm optimization（QPSO） is proposed to solve the minimum zone SSE and Monte Carlo Method（MCM） is developed to estimate the measurement uncertainty. The mathematical model of minimum zone SSE is formulated. In QPSO quasi-random sequences are applied to the generation of the initial position and velocity of particles and their velocities are modified by the constriction factor approach. The flow of measurement uncertainty evaluation based on MCM is proposed, where the heart is repeatedly sampling from the probability density function（PDF） for every input quantity and evaluating the model in each case. The minimum zone SSE of a shaft measured on a Coordinate Measuring Machine（CMM） is calculated by QPSO and the measurement uncertainty is evaluated by MCM on the basis of analyzing the uncertainty contributors. The results show that the uncertainty directly influences the product judgment result. Therefore it is scientific and reasonable to consider the influence of the uncertainty in judging whether the parts are accepted or rejected, especially for those located in the uncertainty zone. The proposed method is especially suitable when the PDF of the measurand cannot adequately be approximated by a Gaussian distribution or a scaled and shifted t-distribution and the measurement model is non-linear.

Uncertainty entropy-based exploratory evaluation method and its applications on missile effectiveness evaluation

Some attributes are uncertain for evaluation work because of incomplete or limited information and knowledge.It leads to uncertainty in evaluation results.To that end,an evaluation method,uncertainty entropy-based exploratory evaluation(UEEE),is proposed to guide the evaluation activities,which can iteratively and gradually reduce uncertainty in evaluation results.Uncertainty entropy(UE)is proposed to measure the extent of uncertainty.First,the belief degree distributions are assumed to characterize the uncertainty in attributes.Then the belief degree distribution of the evaluation result can be calculated by using uncertainty theory.The obtained result is then checked based on UE to see if it could meet the requirements of decision-making.If its uncertainty level is high,more information needs to be introduced to reduce uncertainty.An algorithm based on the UE is proposed to find which attribute can mostly affect the uncertainty in results.Thus,efforts can be invested in key attribute(s),and the evaluation results can be updated accordingly.This update should be repeated until the evaluation result meets the requirements.Finally,as a case study,the effectiveness of ballistic missiles with uncertain attributes is evaluated by UEEE.The evaluation results show that the target is believed to be destroyed.

Uncertainty Evaluation of Object Detection Algorithms for Autonomous Vehicles

The safety of the intended functionality(SOTIF)has become one of the hottest topics in the field of autonomous driving.However,no testing and evaluating system for SOTIF performance has been proposed yet.Therefore,this paper proposes a framework based on the advanced You Only Look Once(YOLO)algorithm and the mean Average Precision(mAP)method to evaluate the object detection performance of the camera under SOTIF-related scenarios.First,a dataset is established,which contains road images with extreme weather and adverse lighting conditions.Second,the Monte Carlo dropout(MCD)method is used to analyze the uncertainty of the algorithm and draw the uncertainty region of the predicted bounding box.Then,the confidence of the algorithm is calibrated based on uncertainty results so that the average confidence after calibration can better reflect the real accuracy.The uncertainty results and the calibrated confidence are proposed to be used for online risk identification.Finally,the confusion matrix is extended according to the several possible mistakes that the object detection algorithm may make,and then the mAP is calculated as an index for offline evaluation and comparison.This paper offers suggestions to apply the MCD method to complex object detection algorithms and to find the relationship between the uncertainty and the confidence of the algorithm.The experimental results verified by specific SOTIF scenarios proof the feasibility and effectiveness of the proposed uncertainty acquisition approach for object detection algorithm,which provides potential practical implementation chance to address perceptual related SOTIF risk for autonomous vehicles.

Pyramid Bayesian Method for Model Uncertainty Evaluation of Semantic Segmentation in Autonomous Driving

With the fast-developing deep learning models in the field of autonomous driving,the research on the uncertainty estima-tion of deep learning models has also prevailed.Herein,a pyramid Bayesian deep learning method is proposed for the model uncertainty evaluation of semantic segmentation.Semantic segmentation is one of the most important perception problems in understanding visual scene,which is critical for autonomous driving.This study to optimize Bayesian SegNet for uncertainty evaluation.This paper first simplifies the network structure of Bayesian SegNet by reducing the number of MC-Dropout layer and then introduces the pyramid pooling module to improve the performance of Bayesian SegNet.mIoU and mPAvPU are used as evaluation matrics to test the proposed method on the public Cityscapes dataset.The experimental results show that the proposed method improves the sampling effect of the Bayesian SegNet,shortens the sampling time,and improves the network performance.

Evaluation of Uncertainty of Earthquake Parameters for the Purpose of Seismic Zoning of Iran

An efficient procedure is used for explicit description and evaluation of uncertainty of earthquake parameters in the uniform catalog of earthquakes in Iran and neighboring regions.An inadequate number of local and regional seismographic stations,poor station distribution,and Inadequacy of velocity models have resulted in conspicuous uncertainty in different parameters of recorded events.In a comprehensive seismic hazard analysis such uncertainties should be considered.Uncertainty of magnitude and location of events are evaluated for three different time periods,namely,historical,early instrumental,and modern instrumental time periods,for which existing seismological information differ widely in quantity,quality,and type.It is concluded that an uncertainty of 0.2-0.3 units of magnitude and 10-15 km in epicenter determinations should be considered in the most favorable conditions.None of the hypocenters of earthquakes in Iran can be considered as reliable,unless supported by other information such as

The impact of geological uncertainty on primary production from a fluvial reservoir

Deposition of fluvial sandbodies is controlled mainly by characteristics of the system, such as the rate of avulsion and aggradation of the fluvial channels and their geometry. The impact and the interaction of these parameters have not received adequate attention. In this paper, the impact of geological uncertainty resulting from the interpretation of the fluvial geometry, maximum depth of channels, and their avulsion rates on primary production is studied for fluvial reservoirs. Several meandering reservoirs were generated using a process-mimicking package by varying several con- trolling factors. Simulation results indicate that geometrical parameters of the fluvial channels impact cumulative pro- duction during primary production more significantly than their avulsion rate. The most significant factor appears to be the maximum depth of fluvial channels. The overall net-to-gross ratio is closely correlated with the cumulative oil production of the field, but cumulative production values for individual wells do not appear to be correlated with the local net-to-gross ratio calculated in the vicinity of each well. Connectedness of the sandbodies to each well, defined based on the minimum time-of-flight from each block to the well, appears to be a more reliable indicator of well-scale production.

Group Performance Evaluation in Universities with Entropy Method

Due to the characteristics of variable quantity and great individual difference in group evaluation,an effective entropy-based method for group performance evaluation was proposed. First,a group evaluation indexing system was built upon the real scenarios of universities. Then, a continuous ordered weighted operator was introduced to eliminate uncertainty evaluation information. By using an entropy strategy,a systematic approach to group performance evaluation was established. Finally, an illustrative example was provided,showing the practicability of the proposed methods in real applications of the efficient performance appraisal theory of colleges and universities.

Multi-parameters uncertainty analysis of logistic support process based on GERT

The uncertainty analysis is an effective sensitivity analysis method for system model analysis and optimization. However,the existing single-factor uncertainty analysis methods are not well used in the logistic support systems with multiple decision-making factors. The multiple transfer parameters graphical evaluation and review technique（MTP-GERT） is used to model the logistic support process in consideration of two important factors, support activity time and support activity resources, which are two primary causes for the logistic support process uncertainty. On this basis,a global sensitivity analysis（GSA） method based on covariance is designed to analyze the logistic support process uncertainty. The aircraft support process is selected as a case application which illustrates the validity of the proposed method to analyze the support process uncertainty, and some feasible recommendations are proposed for aircraft support decision making on carrier.

The Study of Optimizing Reservoir Model Using Experimental Design in Stochastic Models

In response to the main problems in commonly used model selection methods,a method was proposed to apply the concept of experimental design to the optimization of uncertain reservoir models.Firstly,based on the actual situation of the oil field,the uncertain variables were determined that affect the geological reserves of the model and their possible range of variation,and experimental design was used to determine the modeling plan.Then,multiple geological models were established and reserves were calculated,and multiple regression was performed between uncertain variables and the corresponding geological reserves of the model.Finally,Monte Carlo simulation technology was applied to determine the parameters of the P10,P50,and P90 models for probabilistic reserves,and P10,P50,and P90 models were established.This method is not only more objective and time-saving in the application process,but also can determine the main geological variables that affect geological reserves,providing a new idea for evaluating the uncertainty of geological reserves.

Simultaneous Determination of Ten Endocrine Hormone Disrupters in Water Using SPE/LC-(ESI)MS-MS

The aim of this study was developed and validated an analytical method based on liquid chromatography and tandem mass spectrometry after solid phase extraction to monitorizing ten endocrine hormone disrupters in Lisbon drinking water system. Natural and synthetic hormones (17-β-estradiol, ethinylestradiol, estriol, estrone, progesterone, mestranol and diethylstilbestrol) and some industrial products (4-n-nonylphenol, 4-tert-octylphenol and bisphenol A) were studied. Mass spectrometer detection parameters were optimized, such as the best conditions for the precursor ion formation, namely cone voltage, when applying negative and positive electrospray ionization, and also collision energy for MRM1 and MRM2 transitions. The best conditions of the solid phase extraction (SPE) using Waters Oasis HLB (6 mL, 200 mg) and Isolute C18 (EC) (6 ml, 1000 mg) were also optimized. The method was validated through the application of several statistical tests and the uncertainty estimation of the analytical assay. This method showed a very good linear range for all the studied analytes with determination coefficients (r2) between 0.9962 and 0.9999 and coefficients of variation lower than 4%. There were no significant differences between recoveries obtained with the studied matrices, like groundwater, surface water and water for human consumption. In these matrices, the recovery values varied between 32 and 95%. The limits of method detection were between 0.28 and 22 ng/L. The validated method was applied for the analysis of water samples from the EPAL (Empresa Portuguesa das águas Livres, S.A.) water supply system including tap water, spring water, groundwater, and river water. Some target compounds (bisphenol A, progesterone, 4-tert-octylphenol, and 4-n-nonylphenol) were found in trace amounts in analysed waters.

Evaluation of measurement uncertainty of the high-speed variable-slit system based on the Monte Carlo method

This paper presents a dynamic and static error transfer model and uncertainty evaluation method for a high-speed variable-slit system based on a two- dimensional orthogonal double-layer air-floating guide rail structure. The motion accuracy of the scanning blade is affected by both the moving component it is attached to and the moving component of the following blade during high-speed motion. First, an error transfer model of the high-speed variable-slit system is established, and the influence coefficients are calculated for each source of error associated with the accuracy of the blade motion. Then, the maximum range of each error source is determined by simulation and experiment. Finally, the uncertainty of the blade displacement measurement is evaluated using the Monte Carlo method. The proposed model can evaluate the performance of the complex mechanical system and be used to guide the design.

Investigation of the influences of asphaltene deposition on oilfield development using reservoir simulation

This paper investigates the deposition of asphaltenes in the porous medium of the studied field in Russia and predicts production profiles based on uncertainty evaluation. This problem can be solved by dynamic modeling, during which production profiles are estimated in two scenarios: with and without the activation of the asphaltene option. Calculations are carried out for two development scenarios: field operation under natural depletion and water injection into the aquifer as a reservoir pressure maintenance system. A full-scale compositional reservoir simulation model of the Russian oilfield was created. Within a dynamic simulation, the asphaltene option was activated and the asphaltene behavior in oil and porous medium was tuned according to our own special laboratory experiments. The model was also matched to production historical data, and a pattern model was prepared using the full-scale simulation model. Technological and the asphaltene option parameters were used in sensitivity and an uncertainty evaluation. Furthermore, probable production profiles within a forecast period were estimated. The sensitivity analysis of the pattern model to input parameters of the asphaltene option allowed determining the following heavy-hitters on the objective function: the molar weight of dissolved asphaltenes as a function of pressure, the asphaltene dissociation rate, the asphaltene adsorption coefficient and the critical velocity of oil movement in the reservoir. Under the natural depletion scenario, our simulations show a significant decrease in reservoir pressure and the formation of drawdown cones leading to asphaltene deposition in the bottom-hole area of production wells, decreasing their productivity. Water injection generally allows us to significantly reduce the volume of asphaltene phase transitions and has a positive effect on cumulative oil production. Injecting water into aquifer can keep the formation pressure long above the pressure for asphaltene precipitation, preventing the asphaltene deposition resulted from interaction of oil and water, so this way has higher oil production.

Posterior probability calculation procedure for recognition rate comparison

This paper focuses on the recognition rate comparison for competing recognition algorithms, which is a common problem of many pattern recognition research areas. The paper firstly reviews some traditional recognition rate comparison procedures and discusses their limitations. A new method, the posterior probability calculation（PPC） procedure is then proposed based on Bayesian technique. The paper analyzes the basic principle, process steps and computational complexity of the PPC procedure. In the Bayesian view, the posterior probability represents the credible degree（equal to confidence level） of the comparison results. The posterior probability of correctly selecting or sorting the competing recognition algorithms is derived, and the minimum sample size requirement is also pre-estimated and given out by the form of tables. To further illustrate how to use our method, the PPC procedure is used to prove the rationality of the experiential choice in one application and then to calculate the confidence level with the fixed-size datasets in another application. These applications reveal the superiority of the PPC procedure, and the discussions about the stopping rule further explain the underlying statistical causes. Finally we conclude that the PPC procedure achieves all the expected functions and be superior to the traditional methods.