1011 results about "Uncertainty factor" patented technology

A method and system for making financial or medical decisions. The method comprises training sets of models using classification training with sets of data derived from segregated data sources. Overall weighting of each model within the sets of models are determined for each of the sub-datasets. The sets of models, the overall weighting of each model and a number of examples provided from the data for each of the datasets are transmitted to a central server over a communication network, wherein the central server is configured to determine the relative weights of each of the sets of models in the overall ensemble model based on the number of examples, combine the sets of models, receive new application data, and predict at least one of outcome variables, an uncertainty factor for the variables, and drivers of the outcome variables based on the new application data.

A weather radar system includes processing electronics. The processing electronics sense weather and determine an uncertainty factor. A display can provide visual indicia of the uncertainty factor for weather in response to the processing electronics. The display can be a vertical profile display. The weather radar system can be an avionic weather radar system.

The invention discloses a multisource information fusion method in an evidence high-conflict environment, which comprises the following steps of: determining the mutual support degree of evidences according to evidence distances, further calculating the relative discount factor of each evidence by taking a characteristic vector corresponding to the maximum characteristic value of a evidence support degree matrix pattern as a weighting vector of the evidence, wherein the evidence with the maximum weighing coefficient is a key evidence; and discounting each evidence information, and fusing the information of multisource sensor data by using the D-S (Dempster / Shafer) rule so as to increase the accuracy and precision of a monitoring proposition judgment under an evidence high-conflict status.The method can effectively eliminate the uncertainties of multisource information, increase the accuracy of information evaluation and overcome the limitation that correct evaluation results can not be acquired by using the traditional D-S evidence theory under the evidence high-conflict status, and has the advantages of simple algorithm, high convergence rate and the like.

The invention discloses a risk assessment method based on a fuzzy matrix and an analytic hierarchy process. The risk assessment method based on the fuzzy matrix and the analytic hierarchy process comprises the steps that a plurality of evaluation indexes are set to generate an evaluation index domain; a plurality of evaluation scales are set to generate an evaluation scale domain, and each evaluation scale corresponds to a scale fuzzy subset; the membership of a dangerous source for each scale fuzzy subset is calculated, and a membership matrix is constructed; weight vectors of the evaluation indexes are calculated; a fuzzy comprehensive evaluation result vector of the dangerous source is synthesized; a risk assessment value is calculated; a risk value is calculated, and the risk condition of the dangerous source is judged according to a risk matrix and the risk value. Through the application of the fuzzy matrix and the analytic hierarchy process in risk assessment, the risk assessment method is used for analyzing various undetermined factors and indexes in the risk assessment process; first-scale indexes and second-scale indexes are set for evaluation from multiple aspects, so that analysis is more comprehensive and a danger value of possibility and a danger value of severity are formed; curving of a risky area chart guarantees stable risk conditions of the dangerous source.

The invention belongs to the technical field of airplane flight control systems, and particularly relates to a manned / unmanned selectable dual-mode flight control system. The manned / unmanned selectable dual-mode flight control system is composed of a fly-by-wire flight control system and an expended aircraft management system. During whole airspace flight, an unmanned control mode can be selected for conducting automatic flight, and an aviator plays the role of a highly-intelligent information identification system to monitor the flight state, the airspace control, external information and the like. An aircraft management subsystem can be used for processing uncertain factors according to predetermined programs or predetermined processing principles, the uncertain factors can influence task completion, online task / flight path planning and task redistribution or threat avoidance are conducted automatically, and under necessary conditions, the aviator can exit the unmanned control mode to perform manual handling. In the aviator control mode, the aircraft management subsystem plays the role of a senior assistant decision-making system to provide flight and task guide information so as to assist the aviator to complete tasks.

The invention discloses a virtual power plant optimization trading strategy model based on two-stage stochastic programming. The virtual power plant optimization trading strategy model based on two-stage stochastic programming comprises the following steps of S1, according to a power market rule, summarizing a VPP bidding process and the theoretical basis such as a multi-scenario technique; S2, establishing the objective function of the virtual power plant optimization trading strategy based on two-stage stochastic programming; S3, establishing the constraint condition of the virtual power plant optimization trading strategy based on two-stage stochastic programming. In consideration of the influence of clean energy output and market electricity price uncertainty on the participation of avirtual power plant in the current market and balanced market bidding strategies, the multi-scenario technique is used to simulate a uncertainty factor and construct the virtual power plant optimization trading strategy model based on two-stage stochastic programming to maximize the operation efficiency of the virtual power plant.

The invention discloses a method for automatically testing parameters of a millimeter wave power amplifier and a system. The system comprises the structure that a millimeter wave excitation signal power control unit of a piece to be tested is connected with a millimeter wave excitation signal power generation unit of the piece to be tested; a millimeter wave excitation signal power detection unit of the piece to be tested is connected with the millimeter wave excitation signal power control unit of the piece to be tested; the input end of the piece to be tested is connected with the millimeter wave excitation signal power detection unit of the piece to be tested; the output end of the piece to be tested is connected with the millimeter wave excitation signal power detection unit of the piece to be tested; a direct current signal supply unit of the piece to be tested is connected with the piece to be tested; and a test system control unit is respectively connected with the millimeter wave excitation signal power generation unit of the piece to be tested, the millimeter wave excitation signal power detection unit of the piece to be tested, the direct current signal supply unit of the piece to be tested and an output signal detection unit of the piece to be tested. According to the method and the system disclosed by the invention, the automatic test of multiple millimeter wave power parameters is realized; the difficulty in lacking of test equipment is overcome; and the defects of manual test efficiency, low accuracy, uncertain factors and the like are avoided.

The invention discloses a multiplexing-oriented embedded software testing method. According to the method, a model library is arranged in a testing system, a tested software model, a crosslinked environmental model, a testing exemplar model, a testing execution model and a testing result model are stored in the testing system, and testing is finished in the converting process of each model. The software testing method is an effective testing method specific to embedded system software; in a testing process, multiplex testing products can be produced, and multiplex products in the model library can be directly used simultaneously, so that the testing efficiency is increased, the generation of a plurality of man-made errors and undefined factors is avoided, and the controllability and the specificity of a simulated testing process are ensured. During testing of difference software, the testing model becomes a multiplex effective assembly in a testing process, so that repeated work in the testing process can be reduced greatly in particular for production line software, and the testing efficiency is increased to the maximum extent.

The invention discloses a highway tunnel traffic accident risk evaluation method based on fuzzy analytic hierarchy process. The method includes the following steps: formulating a plurality of evaluation indices to obtain an evaluation index domain, dividing a highway tunnel into three research sections, establishing respective index system for each research section; formulating a plurality of evaluation levels to obtain an evaluation level domain; separately computing the fuzzy degree of membership of respective evaluation index to respective evaluation level, constructing a degree of membership matrix; computing a weight vector of the evaluation indices; on the basis of the weight vector and the degree of membership matrix, synthesizing a comprehensive evaluation vector; computing a risk value, on the basis of the risk value, determining risk conditions. According to the invention, by using the fuzzy matrix and the application of the analytic hierarchy process to risk evaluation, the method can analyze respective uncertain factor and index that appear in the course of risk evaluation; by setting a first level index and a second level index, the method can consider from a multiple aspects and can deliver more comprehensive analysis by dividing the tunnel into three regions and make related talks, the method herein takes the minimum evaluation value as a final evaluation result. The evaluation is more much reasonable.

The invention discloses a dynamic reactive power compensation system and a control method thereof. The system comprises a static reactive power compensator (DSTATCOM) used for continuing a sub-system, and a thyristor switching capacitor (TSC) used for scattering the sub-system, and a controller, wherein, the static reactive power compensator consists of a three-phase voltage type inverter and an inductor, and is connected with a plurality of thyristor switching capacitors in parallel on an electric network; wherein, the controller comprises an artificial neural net controller which is in reactive power intelligent control grade and is accessed into the DSTATCOM and a capacitor circulated controller which is in decision processing grade and is accessed into the TSC for circulated switching control of the capacitors. The invention realizes high capacity reactive power quick and continuous compensation with low cost, high efficiency and energy conservation; the control method based on the artificial neural net provided by the invention can deal with various uncertain factors of a complicated power system during operation process so as to improve the anti-interference capability of the system, not only has better dynamic compensation characteristic, but also improves the robustness and control precision of the system.

The invention provides an active disturbance rejection control-based method for controlling synchronization of a photovoltaic power generation system. The photovoltaic power generation system is a non-linear system, influenced by power grid and environment and strongly disturbed by the outside and has nonlinear uncertain factors. According to the work characteristics of the system, the active disturbance rejection control technology is adopted to realize effective control of the system. The system dynamically observes the uncertain factors in the system model and external disturbance by utilizing an extended state observer of an active disturbance rejection controller (ADRC) to ensure that the system has good adaptability to disturbance, can adapt to changes of an object model and the surrounding environment in a large range without influencing the control quality of the controller, and represents well robustness.

The invention provides a KNN-based improved missing data filling algorithm, which comprises the steps of (1) improving a traditional multiple correlation coefficient inverse weighting method and calculating the importance of each attribute on a missing value-containing attribute by using an improved algorithm, deleting a few of attributes with relatively small correlation with a key attribute and carrying out streamlined operation on an attribute set to obtain a data sample set which only contains the streamlined attribute set; (2) comprehensively considering the advantages of the correlation between the attributes and the variability by using a mahalanobis distance, effectively predicting an uncertain factor-containing sample by combining a grey correlation analysis method and calculating K adjacent samples of a missing sample; and (3) giving entropy weight values to the attributes corresponding to the K samples according to the calculated K distance values and an entropy weight method and then calculating a final filling value by combining attribute values. According to the KNN-based improved missing data filling algorithm, the calculating complexity of the missing data algorithm can be reduced, the accuracy of the adjacent sample values is improved and the estimation accuracy of the data filing value is improved.

System(s) and method(s) to provide inventory optimization in a multi-echelon supply chain network are disclosed. An input data comprising one or more product supply parameters along with an uncertainty factor associated with the product supply parameters are received through a configurable user interface. The input data is used to create a multi-echelon supply chain network. Supplier nodes are selected based on optimizing parameters and are allocated with respect to demand nodes. A lead time demand and a safety stock parameter are calculated. An optimal inventory plan is generated for each supply chain member associated with the supply chain network along with the safety stock parameter by minimizing the uncertainty factor thereby providing the inventory optimization. The optimal inventory plan is displayed in one or more parameters over the configurable user interface.

The invention provides a converter steelmaking endpoint carbon content, a temperature control method and equipment. The method comprises the following steps that: a first detection point of a sublanceis taken as a demarcation point, and a converter steelmaking process is divided into a first stage and a second stage; in the first stage, according to characteristic parameters of an initial state of molten iron and smelting requirements of a target steel, an endpoint carbon content and a temperature are controlled based on an oxygen blowing prediction model and an endpoint carbon content prediction model; in this way, the shortcoming of insufficient precision due to artificial experience prediction is overcome, at the same time, influence of uncertain factors such as molten steel splash andlate decarburization reaction deviation is considered; if carbon content and the temperature of the first detection point of the sublance do not meet tapping requirements when the first stage is completed, the second stage is performed until the carbon content and the temperature meet the tapping requirements, and therefore precision of smelting control is improved; in addition, the endpoint carbon content prediction model has more tapping cycles for training samples, and parameters for training neural networks are complex, and obtained neural network error is small.

The invention provides a random reactive optimization method for a power distribution network including a wind power plant, which is used for eliminating adverse effects caused by various undetermined factors of the power distribution network and keeping the power distribution network run in a qualified voltage state. The method comprises the following specific steps: firstly, establishing a probability calculation model considering various random factors; secondly, establishing determining trend including a fan equivalent circuit, establishing a random trend model for the power distribution network including the wind power plant in combination with a semi invariant and Gram-Charlier series expansion method, and solving the voltage of each node and the probability distribution of reactive output of a generator; and lastly, establishing a reactive optimization model including the wind power plant by taking the voltage of each node and the probability distribution of the reactive output of the generator as the chance constraints of reactive optimization, taking the reactive output limit of the wind power plant as a constraint condition and taking a minimum active consumption expected value as a target function, and solving by using a genetic algorithm.

The invention relates to a turbine disk probability failure physical life predicting method based on Bayes information update, which comprises the following steps that 1, failure physical information of a turbine disk is determined in a reliability analyzing method based on physics of failure according to the structural characteristics of the aircraft engine turbine disk; 2, the failure physical information of the aircraft engine turbine disk obtained in step 1 and the existing failure information obtained by maintenance data statistics are analyzed in an FTA (failure tree analysis) / FMECA (failure mode, effects & criticality analysis) method to obtain the main failure mode and the failure position of the turbine disk, and uncertain factors and inaccurate data of the turbine disk facing whole life cycle are collected and cleared up; and 3, a failure physical life predicting model of the aircraft engine turbine disk is established according to the main failure mode and the failure position of the turbine disk obtained in step 2. The method disclosed by the invention has the beneficial effects that the design cycle of the aircraft engine turbine disk can be obviously shortened, the development cost is reduced, and the life reliability of the aircraft engine turbine disk can be obviously improved.

The invention relates to a reliability analysis method of an electric power system, specifically a wind power grid-connection system operational reliability evaluation method considering uncertainty factors, and solves the problems of incomplete consideration, long time consumption, slow convergence, resource occupation and incapability of realizing real-time prediction and evaluation of the existing method. A scheme comprises the processes as follows: I, uncertainty factor modeling: A, load comprehensive uncertainty; B, a conventional power generator comprehensive time varying operation model; C, comprehensive time varying operation modeling; D, wind power plant operational reliability modeling; and II, a Markov chain model: E, a Markov process and a Markov chain; F, a wind power Markov chain model; and G a wind power reliability evaluation index. The beneficial effects are achieved as follows: by performing quantitative analysis and comparison on the degree of influence of load fluctuation, element state and wind power output on the operational state and reliability level, the reliability level at the current moment can be evaluated, and the state probability at the future momentcan be quickly predicted, so that planning, overhaul, operation optimization and dispatching of the electric power system can be guided, thereby overcoming the shortcomings of the conventional evaluation method.

The invention discloses a distributed formation method of an unmanned aerial vehicle cluster based on reinforcement learning. The distributed formation method comprises the steps that step (1), a formation target state function and a simulation model of environmental uncertainty factors are obtained, and an unmanned aerial vehicle formation simulation model is established; step (2), under the interference of the environmental uncertainty factors, based on the unmanned aerial vehicle formation simulation model established in the step (1), a Q learning method is adopted to train the unmanned aerial vehicle cluster to update a flight strategy table; step (3), the value of the completion degree of the formation target state is calculated according to the obtained formation target state function, the obtained value of the completion degree of the formation target state is compared with a preset value of the formation target state, whether the formation target state is reached or not is judged according to the comparison results, if the formation target state is reached, a step (4) is performed, and if not, the step (2) is entered; and step (4), the updated flight strategy table is saved. According to the distributed formation method of the unmanned aerial vehicle cluster based on reinforcement learning, flight strategy parameters with adaptability are provided for the cluster, and the stability and robustness of the unmanned aerial vehicle cluster formation are guaranteed.

The invention discloses a specific primer, a liquid-phase chip and a method for SNP detection of CYP2C9 and VKORC1 genes. The liquid-phase chip comprises wild-type and mutable-type ASPE primer pairs and microspheres coated by a specific anti-tag sequence respectively, which are designed respectively aiming at each type of mutable loci, primers used for amplifying a CYP2C9 gene target sequence having CYP2C9*2, CYP2C9*3, CYP2C9*5 and CYP2C9*6SNP loci, and / or primers used for amplifying a VKORC1 gene target sequence having G1639A, G1173T and G3730A SNP loci. The liquid phase chip of the invention has a quite good signal-noise ratio, and the cross reaction does not happen between a designed probe and the anti-tag sequence basically; the ASPE primer designed by the invention has quite good specificity, and can accurately differentiate various types of mutable loci; and the detection method has the advantages that: a few steps are adopted, 7 types of SNP loci can be detected in one step, the operation is convenient, a lot of uncertain factors existing in a process of repeated operations can be avoided, and the detection accuracy is greatly improved.

The invention discloses a disturbance-observer-based multi-mobile-robot formation robust control method. A multi-mobile-robot formation control system model under the condition of considering uncertainty factor influences like the model parameter disturbance and external disturbance is established; on the basis of a dynamic formation error model, an auxiliary speed controller stabilizing a formation error is designed for a following robot; and the output of the auxiliary speed controller is used as a speed given signal of a dynamic model of the following robot and an adaptive sliding mode controller based on a disturbance observer is designed at the dynamic level. With the control method disclosed by the invention, the adverse influence of the sum disturbance in the multi-mobile-robot formation system can be eliminated effectively; the response speed of the formation system is increased; and the system stability and robustness are improved.

The invention discloses a CYP3A4 gene SNP detection specific primer, a liquid-phase chip and a method; and the liquid-phase chip comprises wild-type and mutant ASPE primers which are respectively designed for each type of mutation points, microspheres which are respectively coated with specific anti-tag sequences, and the primer which is used for amplifying a CYP3A4 gene target sequence with CYP3A4*1B, CYP3A4*2, CYP3A4*3, CYP3A4*4, CYP3A4*5, CYP3A4*6, CYP3A4*15, CYP3A4*17, CYP3A4*18 and / or CYP3A4*19SNP sites. The CYP3A4 gene SNP detection liquid-phase chip has very good signal-noise ratio, and a designed probe and the anti-tag sequences essentially have no cross reaction. The designed ASPE primers have very good specificity and can accurately distinguish all types of mutation points. The detection method has simple steps and ten types of SNP sites can be detected at one step, so that the operation is convenient, thereby preventing a plurality of uncertain factors in a plurality of operation processes, and greatly improving the detection accuracy rate.

The invention provides a method and device for realizing virtual network function deployment specification configuration. The method comprises the following steps: receiving an instantiation request of a virtual network function VNF, wherein the instantiation request carries a virtual network function identifier VNFD and a VNF deployment specification identifier; and carrying out instantiation processing of the VNF according to the VNFD corresponding to the VNFD identifier and a VNF deployment specification corresponding to the VNF deployment specification identifier, wherein the VNFD describes one or more VNF deployment specifications and one or more configuration description information for the selection of the VNF deployment specification, and the VNF deployment specification comprises one or more configuration description information. By adopting the method and device provided by the invention, the VNFD can provide a flexible mechanism, different formulation functions are provided for a VNF developer and a VNF deployer during the VNF deployment of multiple specifications, and artificial intervention and uncertain factors in the VNF deployment process are reduced.

The invention discloses an energy scheduling method for a microgrid, which is characterized by including adopting the Monte Carlo method to conduct stochastic simulation on uncertainty factors in operation of the microgrid to generate samples formed by output power of a power supply with renewable energy sources and the like; providing the confidence level of load satisfying rate of the microgrid, setting rotating reserve probability constraint condition of the microgrid and forming a target function of microgrid energy scheduling in minimum mode through planning operation cost of energy scheduling time periods of the microgrid; designing energy scheduling algorithm combining the stochastic simulation in the Monte Carlo method and genetic algorithm to conduct solution of the target function of microgrid energy scheduling to obtain the optimum energy scheduling scheme of the microgrid. The energy scheduling method achieves balance between operation economical performance and operation reliability of the microgrid and meets actual operation requirements of the microgrid by setting the confidence level of the load satisfying rate of the microgrid, introducing the rotating reserve probability constraint condition of the microgrid and processing various uncertain factors in the microgrid.

In order to provide an intelligent unmanned aerial vehicle remote control way and reducing unreliable and uncertain factors in the prior art, the invention provides a voice control method for an unmanned aerial vehicle. The method comprises: (1), a remote control unit receives a voice instruction; (2), the voice instruction is sent to a control unit of an unmanned aerial vehicle; (3), the control unit of the unmanned aerial vehicle is decomposed into a plurality of instructions of separate motions; (4), the control unit of the unmanned aerial vehicle carries out rehearsing on the plurality of instructions of separate motions; and (5), the unmanned aerial vehicle provides feedback information for the remote control unit according to the rehearsing result. According to the invention, the high-reliability transmission requirement of the flight parameter and working parameter of the unmanned aerial vehicle is eliminated; a feasible scheme can be estimated intelligently and can be executed automatically; and the remote control efficiency of the unmanned aerial vehicle is substantially improved. Besides, with the voice identification technology, safety of remote control of the unmanned aerial vehicle can be strengthened.

The invention relates to an active real-time scheduling method for a large power grid based on model predictive control, and belongs to the technical field of electric power system operation and control. Firstly, the load force of a future optimization period is predicted by a load prediction model, prior knowledge is provided for optimizing an MPC (multimedia personal computer), an active real-time scheduling model based on a model predictive control strategy is built, and the optimal force result sequence of a real-time scheduling unit is obtained by a rolling optimized real-time scheduling solving strategy. By introducing model predictive theory in the active real-time scheduling process, the problems of dynamic characteristic change of traditional active real-time scheduling and poor effects of a complicated system with uncertain factors can be effectively solved, and the influence of load prediction deviation and other deviation is eliminated in advance. By the method, the operational risk of the power grid is reduced, and operational economy of the power grid is improved.

The invention discloses an emergency material scheduling optimization method for multiple disaster places and multiple retrieval depots, and the method gives consideration to the impact from uncertainfactors in an emergency rescue process, such as traffic jam, weather and road conditions. The method comprises the steps: employing a section number for representing the uncertainty of transportationtime; building a multi-object model which meets requirements that emergency time does not exceed an emergency time limit, the truth degree is maximum and the transportation cost is minimum; constructing an emergency material scheduling optimization model through the multiple objects that the reliability is the highest and the cost is the lowest; introducing a solving algorithm of an ideal point proposing model based on a condition that the reliability and the cost do not have the uniformity; searching an ideal point between the reliability and the cost as an optimal scheme, and providing a solution for the emergent scheduling of emergency materials for multiple disaster places and multiple retrieval depots. The method is simple and convenient in solving and calculation, and the required basic data is easy to obtain. After the occurrence of disasters, the method can help a decision maker quickly determine a rescue scheme, saves the precious rescue time for the rescue activity, and is good in application prospect.

The invention discloses an active distribution network multi-stage planning method. How to comprehensively consider the uncertainty of source-load network storage in distribution network planning is aproblem that needs to be studied in-depth. The invention comprises the following steps: inputting power system parameters, historical operation data, regional development planning demand and predicted uncertain factor data; Calculating the shadow price of each planning decision quantity in each running scenario with multi-time scales; According to the shadow price and the occurrence probability of each scene, establishing the running constrained scene library including extreme scenes and classical scenes. According to the constrained scenario library, modifying the upper-level multi-stage planning optimization model. The invention screens out the planning scene database with pertinence and typicality to revise the optimization model of planning investment, which is used to simulate the feedback of the operation decision-maker to the planning decision-maker, thereby improving the observability of the massive operation scene result of the distribution network, hierarchically solving thedecision variables of different time scales, and reducing the solution difficulty.