315 results about "Uncertainty analysis" patented technology

A unified approach, a fusion technique, a space-time constraint, a methodology, and system architecture are provided. The unified approach is to fuse the outputs of monocular and stereo video trackers, RFID and localization systems and biometric identification systems. The fusion technique is provided that is based on the transformation of the sensory information from heterogeneous sources into a common coordinate system with rigorous uncertainties analysis to account for various sensor noises and ambiguities. The space-time constraint is used to fuse different sensor using the location and velocity information. Advantages include the ability to continuously track multiple humans with their identities in a large area. The methodology is general so that other sensors can be incorporated into the system. The system architecture is provided for the underlying real-time processing of the sensors.

The invention discloses a hydrological model comprehensive uncertainty analysis method based on a Copula function. The method comprises the following steps: (1) collecting data information of a water basin; (2) establishing a hydrological model to simulate a water basin outlet section flow process; (3) determining a marginal probability distribution function of actually measured flow and simulation flow; (4) using the Copula function to construct an united probability distribution function of the actually measured flow and the simulation flow; (5) solving a condition probability distribution function of the actually measured flow while the simulation flow is given; (6) acquiring median of the actually measured flow and an uncertainty interval. Through the adoption of the method disclosed by the invention, the uncertainties of the model parameter and the model structure can be more comprehensively considered at the same time so as to obtain the comprehensive uncertainty of the hydrological model. The method disclosed by the invention is independent from a certainty hydrological model, and can be synergistically integrated with the certainty hydrological model with any complexity without adding any assumption on the model, and a universal theoretical frame is provided for the analysis of the comprehensive uncertainty of the hydrological model.

A method and apparatus for determining standardized environmental attributes is disclosed, including a method and apparatus for generating and quantifying standardized carbon emission reduction credits. General data and site-specific data, if available, are input into a suitable model to determine the approximate change in the environmental attribute as a result of a human-caused change. An uncertainty analysis is conducted on the results to quantify and normalize the environmental attribute. Standardized environmental attributes may be compiled for trade and other standardized environmental attributes are placed in reserve.

The invention discloses a multidimensional space information quality detecting and analyzing model and a modeling method thereof. The method comprises the following steps of: carrying out the processing of logic inconsistency analysis, data incompletion analysis, data uncertainty analysis, error detection, error analysis, error correction, and the like in the three-dimensional geological modeling process by a multisource data analyzing module, an error detecting module, an error analyzing and correcting module, a quality assessing module, a quality standard module, and the like; assessing whether the precision and the reliability of the established three-dimensional geological model meet the practical and applicable requirement or not by the quality assessing module; and further perfecting the quality standard module on the basis of the processing. The method can improve the precision and the reliability of the three-dimensional geological modeling.

The invention discloses a petrochemical port area accident risk area quantitative assessment method comprising the following steps: (1) putting forward a petrochemical port area accident integrated risk area quantitative assessment mode; (2) building an improved accident probability calculation model based on an accident domino effect; (3) building an improved consequence intensity calculation model based on uncertainty analysis; and (4) getting the formation mechanism and the overall distribution characteristics of accident risk in a petrochemical port area according to the corrected accident integrated risk area quantitative assessment mode, the improved accident probability calculation model and the improved consequence intensity calculation model, and putting forward petrochemical port area accident risk control measures. By employing the method of the invention, the accuracy of accident probability calculation and consequence simulation is improved, and the guiding significance of risk assessment to risk prevention and control is enhanced.

A computer-based method for generating standardized emission reduction credits includes the steps of receiving site-specific data with respect to a geographic location regarding at least one variable impacting reduction of nitrous oxide in the atmosphere, retrieving data general to a geographic region encompassing the location regarding at least one variable impacting the nitrous oxide, processing the site-specific and the general data through a model running on a computer, to determine an approximate change in impact on the nitrous oxide at the location over a specified time period, conducting an uncertainty analysis on the approximate change at the location over the specified time period, via the computer, from the uncertainty analysis, identifying a quantity of emission reduction credits meeting an established standard of certainty as the standardized emission reduction credits; and reporting from the computer the identified quantity of the standardized emission reduction credits.

A method of analyzing well log data from thinly bedded reservoirs to obtain estimates of hydrocarbon pore volume. In the method, a model of the reservoir is established for an interval that is to be analyzed. Within the analysis interval, a set of bed types is identified consisting of sandstone beds and shale beds. For each bed type, parameter values are assigned and calculations are made of the theoretical log response of each bed type. The estimated log responses are compared to measured log responses to determine consistency. Based upon the model, an uncertainty analysis is made using a Monte Carlo technique for inversion of the model. The result is an estimate of the hydrocarbon pore volume of the thinly bedded reservoir interval with distribution statistics representing the expected value and the uncertainty in the estimate.

The invention discloses a non-probabilistic reliability optimization design method for a structural system. The non-probabilistic reliability optimization design method for the structural system comprises the following steps that (1) based on a safety factor design method, a design scheme of the structural system with a given safety factor is optimized; (2) based on quantitative results of uncertain sources in the structural system, dispersity of response of the structural system under multiple failure modes is obtained by an interval uncertainty analysis; (3) a non-probabilistic reliability index is introduced to calculate the reliability of the structural system under the multiple failure modes; (4) a non-probabilistic reliability optimization design model is established under the constraint of a non-probabilistic reliability or a given reliability of a safety factor scheme; (5) based on a conventional optimization algorithm to solve an optimization problem, the design scheme of the structural system with optimal reliability allocation is obtained. According to the non-probabilistic reliability optimization design method for the structural system, on the basis of ensuring the non-probabilistic reliability of the safety factor design scheme, the quality of the structural system is further reduced and the performance of the structural system is improved.

The invention discloses a comprehensive analysis method for spatial and temporal distribution of environment variables, and the method comprises the steps: calculating a test variation function valueat each spatial and temporal lag distance, and carrying out the fitting of a theoretical variation function model; combining the spatial and temporal sampling point data, carrying out the spatial andtemporal Kriging interpolation, and estimating the geographic attribute value of an unmeasured spatial and temporal position; building a quantitative relation between environment variable values and aregional position, forming a trend of the spatial and temporal distribution of environment variable values, and obtaining a prediction result based on the spatial and temporal Kriging; proposing various types of spatial and temporal uncertainty estimation methods; and finally estimating the spatial and temporal distribution of environment variables visually. The method gives full consideration tothe spatial and temporal structural property and continuity of the environment variables, achieves the full simulation and analysis of the spatial and temporal distribution characteristics of the environment variables from many aspects: modeling, prediction, trend analysis, uncertainty analysis and the space and time, and provides a basis for the spatial and temporal decision making and auxiliaryanalysis for regional environment estimation and related departments.

The invention relates to an uncertainty analysis method for predicting the performance of a periodic material. The method first utilizes a theory to derive and design a mesoscopic microstructure cell loading boundary condition, and proves that the equivalent performance obtained by finite element simulation calculation can represent the equivalent performance of the whole material under the condition. During the process of obtaining an equivalent elasticity modulus of the material by analyzing a typical micro unit cell scale configuration of the material, the invention takes full consideration of the uncertainty of cell dimension and base material performance and analyzes propagation of the uncertainty in the macroscopic and mesoscopic equivalent processes to obtain the actual fluctuation range of the performance of the material and to ensure security of the results.

The present invention provides a smart distribution network self-restoration optimization method considering a power quality and an uncertainty constraint thereof. With regard to each island, a load shedding optimization model is established for the goal of high important grade load optimization restoration and maximum total restoration quantity, a bi-level optimization model formed by a network reconstitution optimization model for the goal of the least number of switch motions and minimum transmission losses, and an optimal load shedding scheme is taken as the input of the network reconstitution optimization. Especially, in the network reconstitution optimization model, an uncertainty analysis of a negative sequence and a harmonic wave is performed through an entrance point method of estimation, so that a power quality comprehensive constraint set containing frequency constraint, the negative sequence, the harmonic wave and the uncertainty constraint thereof is established according to the possible variation range of the negative sequence and the harmonic wave obtained by the uncertainty analysis, therefore the power quality of the recovery scheme may be improved and the restoration failure may be avoided. Through the establishment of a power quality comprehensive constraint set, the goal of safe and reliable power supply restoration may be achieved.

The invention discloses a method for analyzing the uncertainty of the passenger flow of an urban mass transit terminal. The method for analyzing the uncertainty of the passenger flow of the urban mass transit terminal comprises the steps that firstly, sufficient historical passenger flow data are acquired through an AFC system; secondly, the original data are processed, so that a passenger flow sequence with delta T as the time interval is obtained; thirdly, the passenger flow sequence is fitted through an SARIMA model based on the obvious characteristic that the cycle of the passenger flow of the urban mass transit terminal is a week, and the SARIMA model obtained after fitting is taken as a mean equation of a GARCH model; fourthly, a conditional variance equation is established for a residual error sequence of the mean equation, a predicted confidence interval of the GARCH model is obtained through calculation, and the reliability of model prediction is evaluated according to the predicted confidence interval. The method for analyzing the uncertainty of the passenger flow of the urban mass transit terminal can be used for prediction of the short-time passenger flow of the urban mass transit, the credibility, namely the reliability, of the prediction of the short-time passenger flow of the urban mass transit can be improved, and the decision-making support is provided for operation and management of the urban mass transit.

The invention relates to a multi-group section perturbation method for uncertainty analysis of reactor physics calculation. The multi-group section perturbation method comprises the following steps: (1), on the basis of ENDF/B, making a multi-group section database and a point-by-point section database at different temperatures by using a nuclear database section processing program NJOY, and making the point-by-point section database in a resonant reaction channel at a resonant energy section into an ultra-fine-group database according to the equal rib width; (2), perturbing the point-by-point section database in the energy section corresponding to a certain energy group in a certain reaction channel so as to obtain a perturbed point-by-point section database; (3), strictly transferring perturbation of the point-by-point section database into the multi-group section database by using a linear and nonlinear perturbation transferring method; and (4), reconstructing by adopting a reaction channel section self-consistency principle to obtain a perturbed multi-group section database. According to the invention, the section of a basic reaction channel can be finely sampled; approximate treatment in the multi-group section perturbation process can also be reduced; and thus, a precise multi-group section sample can be obtained.

The invention discloses a passive type load simulator and a superfluous torque suppression method. The superfluous torque suppression method includes modeling an electric steering engine with the high speed reduction ratio indirect driving scheme and analyzing the interference torque of the electric steering engine; modeling a loading motor and an intermediate link and constructing a passive type load simulator mathematic model; analyzing the uncertainty of the constructed model and setting a composite control structure; and at the end, drawing a superfluous torque suppression curve to complete the suppression of the superfluous torque of the passive type load simulator. The method can effectively suppress the superfluous torque generated by the passive type load simulator, thereby improving the tracking and control accuracy.

The invention discloses a sudden water pollution accident early warning method based on Monte Carlo and an analytic hierarchy process. The method comprises the following steps: S1, pollutant simulation based on a Monte Carlo method, i.e., based on an uncertainty analysis, an uncertainty water quality model is constructed, and according to water quality, hydrology and meteorological data, time and space change rules of diffusion of pollutants are obtained through simulation and calculation by use of the Monte Carlo method; S2, calculation of a pollution accident occurrence probability, i.e., through defining occurrence conditions of downstream pollution accidents, through combination with a pollutant diffusion result, a probability density function between a pollutant peak concentration and a pollutant standard-exceeding duration is obtained, and the occurrence probability of the downstream pollutant accidents is further obtained; S3, calculation of pollution accident influences, i.e., by referring to the diffusion state of the pollutants, through combination with the analytic hierarchy process, sudden pollution accidents are evaluated, and health, economic, social and water supply system influences possibly caused by the pollution accidents are obtained; and S4, based on risk degree determination of a risk matrix, risks of early warning points are obtained comprehensively by use of a risk matrix method.

The remote monitoring bridge evaluating method adopts bridge structure reliability calculating and analyzing method, and includes the following steps: pre-treatment of bridge monitoring information, distribution and detection of bridge monitoring information, analysis of load effect statistic parameters, collection and accumulation of bridge structure construction data, uncertainty analysis of bridge structure cross section, material and calculation mode, analysis and calculation of bridge resistance statistic parameters, determination of bridge structure reliability calculation mode, reliability calculation of bridge structure control cross section, failure mode analysis of different kinds of bridge, calculation and analysis of bridge structure system reliability, determination of target reliability index and bridge safety evaluation and display.

The invention discloses a mountain torrent disaster comprehensive risk dynamic evaluation method based on multi-dimensional set information. The mountain torrent disaster comprehensive risk dynamic evaluation method comprises the following steps: step 1, mountain torrent risk evaluation based on corrected disaster rainfall; step 2, mountain torrent risk assessment based on water level-flow; step 3, performing mountain torrent risk assessment based on the flooding range, the flow velocity and the water depth; 4, determining a mountain torrent risk level; step 5, dynamically evaluating the mountain torrent risk; and step 6, uncertainty analysis of a mountain torrent risk assessment result. The mountain torrent disaster comprehensive risk dynamic evaluation method based on multi-dimensional set information is provided. The mountain torrent risk assessment method is suitable for mountain torrent risk refined assessment in areas without data, assessment indexes are more scientific and reliable, dynamic assessment of mountain torrent risks is carried out based on real-time monitoring data or simulation forecast data of models, and a more effective decision-making basis is provided for mountain torrent disaster defense.

The invention provides a multi-objective optimization sampling based hydrologic model uncertainty analysis method. The method specifically comprises the steps of 1) constructing a likelihood objective function by adopting a plurality of judgment standards; 2) clearly determining a value range and a prior distribution form of a hydrologic model parameter, and performing sampling by adopting an improved non-dominated sorting genetic algorithm epsilon-NSGAII; 3) analyzing the uncertainty of a hydrologic model; and 4) estimating a prediction uncertainty range of the hydrologic model. The method has the beneficial effects that multiple criteria are used as the judgment standards of a sampling effect, so that the sampling effect can be comprehensively reflected; the sampling is carried out by adopting the improved non-dominated sorting genetic algorithm epsilon-NSGAII, an elitist strategy is used, and non-dominated samples with low congestion degree are reserved only, so that the sampling efficiency, the parameter uncertainty and a flood prediction result can be effectively improved; and in flood control scheduling and risk control processes, the method can provide important reference information for decision makers.

The invention discloses a fault diagnosis system and a fault diagnosis method based on sequence and consequence analysis of an event tree. The system comprises a data input module, an event tree resolution module, a fault tree preprocessing module, a fault tree analysis module and a diagram display module, wherein the data input module is used for acquiring reliability data and structure data of a given system, and storing the data in modes of a fault tree model and an event tree model; the event tree resolution module is used for processing the given event tree model and the fault tree model, processing a successful branch of the event tree with a respective substitution method, and constructing a traditional sequence fault tree model and a consequence fault tree model; the fault tree preprocessing module is used for preprocessing the given fault tree model; the fault tree analysis module is used for performing fault mode analysis, probability computation, importance computation, sensitivity computation and uncertainty analysis on the given fault tree model; and the diagram display module is used for displaying a fault analysis result of the given system in a diagram form. The system and the method can accurately construct sequence and consequence fault trees, and perform various approximate computation and accurate computation on the successful branch of each function event of the event tree, so that the reliability is improved.

The invention discloses a data set tagging method. The method comprises the steps of selecting untagged data from original data according to a preset rule to obtain a candidate data set; performing uncertainty analysis on the candidate data set, and performing to-be-tagged data screening according to an analysis result, thereby obtaining a to-be-tagged data set; and performing tagging processing on the to-be-tagged data set according to received tagging information, thereby obtaining a tagged data set. The data set is subjected to data screening according to the uncertainty to obtain an uncertain data set suitable for model processing; the data set is tagged, so that the model training and testing efficiency can be improved, a better effect can be achieved by less data, and the overall efficiency of supervised learning is improved. The invention furthermore discloses a data set tagging apparatus, a server and a computer readable storage medium, which have the abovementioned beneficialeffects.

The invention discloses a runoff evolution uncertainty attribution method on the basis of large-region hydrological simulation. Uncertainty temporal-spatial runoff evolution attribution due to model parameter uncertainty evolvement and attribution path diversity is taken into consideration by the aid of the runoff evolution uncertainty attribution method. The runoff evolution uncertainty attribution method includes steps of setting up datasets; describing historical runoff evolution laws; building large-region hydrological models; analyzing the sensitivity of model parameters; analyzing the calibration and the uncertainty of the model parameters; analyzing runoff evolution uncertainty attribution. The runoff evolution uncertainty attribution method has the advantages that all-region runoffchange attribution results can be obtained by the aid of limited runoff monitoring materials, and the uncertainty due to model parameter uncertainty progression and the attribution path diversity canbe taken into sufficient consideration.