154 results about "Uncertainty model" patented technology

Collision detection and estimation from a monocular visual sensor is an important enabling technology for safe navigation of small or micro air vehicles in near earth flight. In this paper, we introduce a new approach called expansion segmentation, which simultaneously detects “collision danger regions” of significant positive divergence in inertial aided video, and estimates maximum likelihood time to collision (TTC) in a correspondenceless framework within the danger regions. This approach was motivated from a literature review which showed that existing approaches make strong assumptions about scene structure or camera motion, or pose collision detection without determining obstacle boundaries, both of which limit the operational envelope of a deployable system. Expansion segmentation is based on a new formulation of 6-DOF inertial aided TTC estimation, and a new derivation of a first order TTC uncertainty model due to subpixel quantization error and epipolar geometry uncertainty. Proof of concept results are shown in a custom designed urban flight simulator and on operational flight data from a small air vehicle.

The invention discloses a method for analyzing wing structure aero-elasticity stability based on aerodynamic force uncertain order reduction. The method is based on unsteady aerodynamic force model order reduction method based on CFD technology; uncertainty of numerical calculation and aerodynamic parameters during aerodynamic force identification process is taken into comprehensive consideration and is uniformly quantified as an uncertain bounded interval noise sequence in an identification model; uncertain order reduction of the aerodynamic force model is realized by means of interval set membership identification algorithm; unsteady aerodynamic uncertain order reduction model based on CFD technology is established; mathematical model of uncertain aero-elasticity system of state space form is established coupled with structural motion equation; an effective method for predicting system robustness stability boundary from the prospective of section state matrix characteristic values. The aero-elasticity system uncertainty modeling thinking and stability boundary prediction technology provided by the invention gives consideration to calculation efficiency, analysis precise and system robustness.

The invention includes following parts: building calculation model of hydraulics based on geo information of city pipeline network, description of uncertainty, building uncertainty model of hydraulics, and model modification. Through data structure, system identification, data mining, optimal control of robustness, and predictive control technique of robustness, the invention builds uncertain hydraulics model for urban sewerage and drainage system. Comparing prior art, the invention overcomes disadvantages of empirical estimating throughput of water and water level, and possesses advantages of forecasting generation of city flood effectively, scheduling operation of pumping station in real time so as to save energy.

The invention discloses a distributed optimal operation method considering the uncertainty for an active distribution network, which comprises the steps of firstly, building an uncertainty model of photovoltaic output by using a robust optimization method in consideration of the uncertainty of the photovoltaic output; secondly, building an optimal scheduling model of the active distribution network, wherein the model takes the minimum operation cost as an objective function and comprehensively considers power flow constraints, safe operation constraints and output constraints of adjustable andcontrollable resources; then building an optimal scheduling model of multiple microgrids connected to the distribution network, wherein the model takes the minimum operation cost of the microgrids asan objective function and adds the problem of renewable energy consumption serving as a penalty function to the objective function; and modifying the distribution network model and the microgrid model based on a Lagrange function in consideration of a coupling relationship between the distribution network and the microgrids in tie-line power so as to build a distributed optimal scheduling model of the active distribution network.

The invention provides an intelligent reflector robust beam forming method and system, and the method comprises the steps: building an uncertainty model of a channel based on the estimation error of an uncertainty channel; constructing a power budget constraint of the intelligent reflecting surface and a signal-to-noise ratio constraint of the receiver according to the uncertainty model, and constructing a beam forming optimization model of an uncertainty channel; and solving the beamforming optimization model according to an iterative algorithm, and jointly optimizing the active beamforming of the base station and the reflection coefficient of the intelligent reflecting surface to minimize the transmitting power of the base station. Based on the channel estimation error, the signal-to-noise ratio requirement of the most uncertain state of the receiver and the power budget constraint under the worst condition of the intelligent reflecting surface are considered, a robust beamforming optimization model is established, the optimization model is solved by using an iterative algorithm, active beamforming and IRS reflection coefficients are jointly optimized, and the optimal beamformingis realized. The transmission power of a multiple-input single-output (MISO) system is minimized.

The invention discloses a service fault diagnosis system and a method based on an active probe. The invention uses the active probe to effectively monitor the service performances in the network, so as to ensure the performances of using service of all the parts which are accessed in the network; when the invention discovers the abnormality by monitoring, the invention can rapidly and accurately position the source of fault according to the monitored symptoms. The invention has small cost of the monitoring and good fault diagnosis performance, at the same time, the invention selects the probe based on the uncertainty model, which can ensure the detection rate of each fault by smaller probe cost and obtain the good diagnosis performance.

The present invention proposes and discloses a method for dynamically simulating a land future use scenario. The method integrates a capability that a cellular automaton (CA) simulates a complex systematic space-time evolution process and an advantage that a CLUE-S model simulates multiple types of land use type competitions; and the method overcomes certain inherent defects of a traditional CA or a pure CLUE-S model through advantage integration; in addition, intelligent computation of a distributional probability is realized by adopting an artificial neural network (ANN) algorithm; synchronous simulation of multiple land use changes is realized by introducing a competition mechanism of a roulette, so that the new method is more suitable for multiple types of land use data; and moreover, compared with a traditional model, the method has the practical advantages of higher precision, suitability for multiple scales, low data demand, few parameters, simple and convenient operation, high speed and the like. According to the method for dynamically simulating the land future use scenario, the intelligent algorithm (ANN) and the uncertainty model (the roulette) are effectively combined; and the method is applied to the prediction of the land future use scenario.

The invention provides an engineering structure optimization design method used in an uncertain environment, and relates to the technical field of engineering structure reliability optimization. According to the method, an uncertain engineering structure optimization design problem is defined first, an engineering structure and uncertain information in the use environment are described as random variables, interval variables or combinations of the random variables and the interval variables according to existing samples, corresponding optimization models are established according to different types of uncertain variables, reliability indexes in the optimization models are calculated on the basis of the point collocation random/interval propagation analysis method, and finally different optimization solvers are selected according to actual problems for outer layer optimization. Reliability indexes of an inner layer failure function are obtained through efficient uncertain propagation analysis, and formation of the nested optimization problem is avoided; hybrid uncertainty models including random parameters and interval parameters in the same problem are considered, and the method has a practical engineering significance in interval parameter optimization problem under random process stimulation.

Flight management systems and control methods for meeting a required time of arrival (RTA) with reduced fuel burn. The method can account for probabilistic wind forecast uncertainty in RTA calculations by reformulating the speed and thrust profile problem as a multi-stage stochastic program, using a wind forecast uncertainty model to generate scenario sets for the fuel optimization problem. The method can iteratively calculate a fuel-efficient advised air speed for achieving an RTA over a flight path with an arbitrary number of recourse points.

Methods for creating wind forecast uncertainty models applicable to a variety of routes through a given airspace, and for use with the flight management systems and control methods. An example wind forecast uncertainty model can be position-specific, data-driven and based on a Markov chain representing error values between historical wind speed data and forecasted wind speed data long a planned flight route or between an origin-destination pair.

The invention discloses an optimization design method of a controller of a multi degree of freedom manipulator system. The optimization design method combines artificial neural network technology withevolutionary multi-objective optimization algorithm, the optimal parameters of multivariable PID controller are determined by the trajectory tracking control system of multi degree of freedom manipulator with nonlinear and uncertain mode; the controller is a multivariable PID controller, and can handle the uncertainty of the mechanical arm system model and the strong coupling phenomenon between joints, and obtain higher tracking accuracy and good dynamic performance; and in addition, the optimization design method optimizes the adopted evolutionary multi-objective optimization algorithm to comprehensively consider the tracking accuracy and dynamic performance of the system as well as the stability of the control, determine the most appropriate controller parameters, and the optimization design method has strong universality, and can also overcome the disadvantages brought by the common experimental repeated trial and error method.

The invention provides an uncertain fault diagnosis method for a nuclear power plant based on a multilayer flow model. When the measured value of the variable representing each functional component detected by the sensor exceeds the normal range, the alarm is displayed as a fault, and the location of the abnormal component is marked; according to the flow direction in the MFM model, the causal relationship that may lead to the fault is intercepted; the logical loop is cut off , form a micro-fault tree; transform the micro-fault tree into a micro-GO-FLOW method; process and merge the micro-GO-FLOW models, and combine them into a GO-FLOW model with the intersection of each abnormal function detected by the sensor as the final signal; Input the GO-FLOW model into the software for calculation, and finally get the probability of the intersection of each abnormal function caused by each basic fault. The invention is suitable for solving complex system problems, is easy to verify, has accurate results, is fast and meets the needs of real-time diagnosis.

The invention provides a fracture-cavity carbonate reservoir uncertainty modeling method and a device thereof. The method comprises steps: a modeling method of cause control is adopted, at least one uncertain geological parameter is selected to carry out uncertainty modeling on first discrete distribution models for different types of reservoirs, and second discrete distribution models are acquired; the second discrete distribution models are integrated to form a third discrete distribution model, wherein the third discrete distribution model contains multiple fracture-cavity reservoir three-dimensional discrete distribution models; and the fracture-cavity reservoir three-dimensional discrete distribution models in the third discrete distribution model are screened to acquire a fourth discrete distribution model with at least one fracture-cavity reservoir three-dimensional discrete distribution model kept. The scheme takes into account the differences of reservoir scales, the influences of uncertainty and the constraints of geological laws and causes on the modeling process, and also avoids the problems of inconsistent understanding of production dynamics and connectivity and inaccurate reserves when only one model is built in the past.

The invention discloses an electric power system dispatching method based on wind electricity output uncertainty aggregation, which comprises the following steps of: acquiring wind electricity output data of each wind electricity plant of an electric system, and determining a confidence interval of the wind electricity output; calculating the time predicated value of the wind electricity output uncertainty, and determining the time constraint set of wind electricity output according to the time predicated value; calculating the space predicated value of the wind electricity output uncertainty, and determining the space constraint set of the wind electric output according to the space predicated value; calculating the intersection of the confidence interval, the time constraint set and the space constraint set, thereby obtaining the uncertainty set of the wind electricity output; and acquiring the output power and the starting and stopping time of each machine set in the electric system according to an electric system robust dispatching method and the uncertainty set of the wind electric output. According to the invention, the difficulty of wind electricity output uncertainty modeling in the electric system robust dispatching problem containing large-scale wind electricity is solved, the safety and reliability of the power grid starting and stopping as well as dispatching plan are ensured, and the utilization ratio of wind electricity output of an electricity plant is improved effectively.

The invention relates to an energy-storage scheduling method and device for an intelligent power grid, and the method comprises the steps: obtaining a power generator uncertainty model, a load uncertainty model and an electric car charging uncertainty model in the intelligent power grid, wherein the intelligent power grid comprises a wind power generator, an energy storage apparatus, and an electric car charging station; carrying out the stochastic load flow calculation of the intelligent power grid through employing a two-point estimation method based on a fourth-order Gram-Charlier series expansion equation, carrying out the random sampling of a stochastic load flow calculation result, and obtaining an expected load flow distribution; determining a constraint condition according to the expected load flow distribution, solving a pre-built target function through employing a particle swarm optimization algorithm based on a segmented inertia decreasing weight, and obtaining an optimal energy storage scheme meeting the constraint condition; and carrying out the scheduling of the energy storage device according to the optimal energy storage scheme. The method can effectively inhibit the uncertainty of the intelligent power grid, and enables the intelligent power grid to operate safely and stably.

The invention relates to a static voltage stability risk evaluation method of a power grid and particularly relates to a static voltage stability risk evaluation method of the power grid based on load uncertainty modeling, aiming at solving the problem of an existing static voltage stability risk evaluation method that an evaluation result is inaccurate. The static voltage stability risk evaluation method of the power grid based on the load uncertainty modeling is realized by the following steps: (1) establishing a load uncertainty model; (2) establishing a trend operation model; and (3) establishing a risk evaluation index, and recognizing a high-operation-risk region in the power grid by applying the risk evaluation index. The static voltage stability risk evaluation method of the power grid based on the load uncertainty modeling is applicable to static voltage stability risk evaluation of the power grid.

The invention discloses an adaptive fault-tolerant attitude control method in an initial state stage of a satellite. The method comprises steps: in an initial state control stage after successful satellite and rocket separation, a satellite rotational inertia uncertainty model during a solar panel unfolding process is built; a satellite attitude dynamic model is built; a preset performance function is used to build an attitude nonlinear mapping model; and based on the built satellite rotational inertia uncertainty model during the solar panel unfolding process, the satellite attitude dynamic model and the attitude nonlinear mapping model, a backstepping method is used to design an adaptive fault-tolerant controller. Through the method, the rotational inertia uncertainty of the satellite inthe initial state control stage and the attitude stability problem in the case of actuator failure and under effects of an external disturbance torque can be solved, the fault-tolerant ability and the robustness of the attitude control system are ensured, and the attitude convergence speed, overshoot and convergence errors are ensured to meet the preset requirements.

A data processing system determines for all tasks in a project, material requirements and constraints, resource requirements and constraints, and a task uncertainty model. A task sequence and a critical chain are determined and an estimated project schedule including a project buffer is constructed. At the beginning of a project execution time interval the data processing system determines a criticality value for every task using the task uncertainty model, current criticality factor information, and task status information for tasks that are in-work. Project tasks are then prioritized based at least in part on task criticality. Tasks are assigned and executed and, at the end of the time interval, task status information is passed back to the data processing system for use in redetermining task criticality and reprioritizing the tasks for the next time interval.

The invention discloses a green data center energy-saving task scheduling strategy based on robust optimization. The green data center energy-saving task scheduling strategy is mainly used for solvingthe problems of high energy consumption, high electricity charge and high pollution of a data center. The green data center energy-saving task scheduling strategy deploys solar cell panels for the data center, and the data center can be powered by solar energy and a traditional power grid in a hybrid manner. In order to solve the characteristics of randomness, discontinuity and instability of solar power generation, the green data center energy-saving task scheduling strategy designs a novel and flexible uncertainty model, defines an uncertain set to limit fluctuations of solar power generation amount by introducing reference distribution, considers the electricity price difference and time-varying characteristic of geographic distributed computing nodes, designs the reasonable task scheduling strategy, and allocates requests submitted to the data center by users to computing nodes and time periods with high solar output and low electricity price for processing, so as to cost the lowest electricity charge and achieve the purposes of saving energy and protecting the environment.

The invention discloses an underactuation crane operating whole process active-disturbance-rejection control method of single parameter adjustment. The underactuation crane operation whole process active-disturbance-rejection control method is characterized in that according to starting and target locations of a trolley, safety constraint conditions such as the speed, acceleration, and operating environment of the trolley are considered, an ideal operating track, with efficiency priority, of the trolley is generated, and after compared with the actual location of the trolley, an error feedbackcontrol law of trolley movement is constructed; an expanding state observer is designed according to a crane load oscillation state equation, an error feedback control law suppressing load oscillation is constructed, and then the trolley operating track and a load oscillation active-disturbance-rejection controller which are independent of system model parameters and can effectively suppress disturbance are formed; and Hurwitz stable matrix eigenvalues are used for generating relation with control system gains, thus fussy control system parameters are adjusted and converted to single parameter adjustment easy to implement, a crane can be operated according to the set ideal track in the whole process under the uncertainty model parameters and external disturbance, and swing angles are madeas small as possible.

The invention provides a method for evaluating the reliability of a power system accessing new energy and relates to the technical field of power grids, and the reliability of the power system that accesses the new energy can be reasonably evaluated. The method includes a step of establishing uncertainty models for each piece of conventional equipment, a load, and new energy in a power system, a step of sampling the states of each piece of conventional equipment, the load and the new energy, a step of establishing a load-shedding optimization model of the power system and carrying out fault state judgment with a minimum load-shedding quantity as an optimization target to obtain a minimum load-shedding quantity of the power system in a current sampling state, a step of counting a load-shedding probability and a load-shedding expected value of the power system in the current sampling state, and a step of calculating a convergence index of a target function of the load-shedding optimization model and judging whether the convergence index satisfies a set requirement or not, ending sampling if so, evaluating the reliability of the power system with the load shedding probability and theload shedding expected value as reliability evaluation indexes, and sampling again if not.

The invention relates to a two-stage optimization scheduling method supporting source network load storage multivariate ubiquitous coordination. The method comprises the following steps: 1, a day-ahead stage: predicting next-day data according to historical data and a load uncertainty model considering demand side management; 2, taking low-carbon economy as a target, considering a deep peak regulation working condition and a normal operation working condition of the thermal power generating unit, carrying out random sampling by utilizing a Monte Carlo method, and solving a day-ahead low-carboneconomy scheduling model by utilizing a hybrid bat algorithm to obtain a low-carbon economy scheduling model; 3, in the intra-day stage, according to the ultra-short-term prediction values of the wind power plant and the photovoltaic power station and the intra-day system load considering the day-ahead price demand response, based on an intra-day thermal power generating unit correction model andan intra-day low-carbon economic dispatching model, determining the unit start-stop combination and the price and price type demand response quantity of each time period; and solving and adjusting the day-ahead scheduling plan by using a hybrid bat algorithm. The low-carbon economic dispatching of the power system is realized, the local optimum in a high-dimensional condition is effectively avoided, and the global optimal solution is quickly obtained.

The invention discloses an optimization design method of a controller of a multi-degree-of-freedom mechanical arm system. The method is characterized in that an artificial neural network technology and an evolutionary multi-objective optimization algorithm are combined, and an optimal parameter of the multi-variable PID controller is determined according to the multi-degree-of-freedom mechanical arm trajectory tracking control system with nonlinear and uncertainty models. The controller is the multi-variable PID controller, and relatively high tracking precision and good dynamic performance can be obtained when handling the model uncertainty of the mechanical arm system and strong coupling among joints; and in addition, the adopted evolutionary multi-objective optimization algorithm used for optimization can comprehensively consider the tracking precision and dynamic performance of the system and the stability of control, determined the most appropriate controller parameters , has relatively strong universality, and can overcome the defects caused by repeated cut-and-trial of common experiments.

The invention discloses a method for assessing measurement uncertainty of water content in cigarettes. An equation is established according to the relation between temperatures and humidity of different environments and the quality of cut tobacco before curing, and the variation quantity of cut tobacco before curing is calculated; an equation is established according to the relation between oven temperatures and the quality of cured cut tobacco, and the variation quantity of the cured cut tobacco is calculated; the measured water content and the standard deviation of the cured cut tobacco are taken as independent variables and substituted into an uncertainty model, and an uncertainty calculation value of the water content in a laboratory is obtained. The sensitive coefficient is calculated by use of partial derivatives and the correlation quantity is calculated by the aid of covariance. By use of the model, temperatures and humidity of different environments, water content of different samples and water content uncertainties under different oven temperature fluctuations can be calculated in different laboratories; the method has the characteristics of being low in cost, high in efficiency, simple and convenient to implement, high in practicability and the like, and uncertainty results of water content of cigarettes can be rapidly and accurately obtained. The method is applicable to assessment of other water content uncertainties similar to those of the measurement model and can overcome existing uncertainty assessment defects.

The invention discloses a demand response modeling and reliability evaluation method for a load aggregator, and the method comprises the steps: firstly building a response uncertainty model of a userinterruptible load based on a fuzzy mathematic theory and consumer psychology; secondly, determining interruptible load parameters of resident users and demand response task parameters of a load aggregator; further, establishing a demand response decision model of the load aggregator based on a fuzzy opportunity constraint programming theory; and finally, defining a demand response reliability index of the load aggregator, and respectively solving corresponding evaluation results. According to the method, the load aggregator can be helped to make a reliable demand response decision under the condition of considering the demand side resource response uncertainty, and the demand response reliability level of the load aggregator is effectively quantified and evaluated, so that reference can be provided for research and development of domestic load aggregators.