96 results about "Theory method" patented technology

The invention discloses an image enhancement method on the basis of an improved multi-scale Retinex theory. The method comprises the following steps of: carrying out nonlinear adjustment on the details of dark areas and the brightness of highlighted areas by virtue of a global brightness adjustment function; enhancing an image by virtue of a canonical gain compensation multi-scale Retinex algorithm; and according to the mean brightness value of a selected area, calculating the parameters of an S curve, adaptively adjusting the S curve, and carrying out the procedures of nonlinear mapping and the like on the enhanced image, thus finishing the enhancement on the image. The method disclosed by the invention solves the problems that when the conventional multi-scale Retinex theory method is used, a halo phenomenon is caused, the overall brightness of an enhanced high dynamic range image is insufficient, and the contrast ratio of the image is low. According to the invention, the S curve is self-adaptively adjusted according to the brightness of the central area of the image, and then the nonlinear mapping is performed on the image, so that the gradation of the image is stretched, and the contrast ratio of the image is improved; and the robustness of the algorithm on a complex night vision image is improved.

Method for determining reservoir permeability from Stoneley wave attenuation extracted from conventional sonic logs by inversion of the full Biot wave equations for a porous medium. Frequency-dependent Stoneley-wave attenuation is extracted by analyzing array sonic measurements. Then, based on Biot's full theory applied to a borehole model and the standard logs (gamma ray, caliper, density, neutron, resistivity, sonic, etc.), a simulation model with the same parameters as the Stoneley-wave measurements is built. Next, a theoretical Stoneley-wave attenuation is computed for a given permeability. Finally, reservoir permeability is determined by comparing the modeled Stoneley-wave attenuation with the measured Stoneley-wave attenuation by an iterative inversion process.

Methods for determining cyber-attack targets include collecting and storing network event information from sensors to extract information regarding an attacker; forming an attack scenario tree that encodes network topology and vulnerability information including paths from known compromised nodes to a set of potential targets; calculating a likelihood for each of the paths using a processor; calculating a probability distribution for the set of potential targets to determine which potential targets are most likely pursued by the attacker; calculating a probability distribution over a set of nodes and node vulnerability types already accessed by the attacker; determining a network graph edge to remove which minimizes a defender's expected uncertainty over the potential targets; and removing the determined network graph edge.

The invention provides a robot failure diagnosis method achieved by multi-mode fusion inference. According to the method, results obtained from inference of various inference engines (such as inference engines based on rules, neural networks, the Bayesian network and evidence theories) are fused with the evidence theory method to obtain an inference result with higher credibility. The method mainly comprises the steps of determining an identification frame, converting the inference results of the various inference engines into elementary probability assignments, assigning a weight to each inference method, fusing the elementary probability assignments with the Dempster combination rule, and making decisions by means of gamble probability conversion. According to existing fusing inference methods, either a result obtained from last inference is used by next inference or different kinds of inference are adopted in different stages of a system, and therefore inference of the same information is only conducted with one inference method and the certainty can not be guaranteed. According to the robot failure diagnosis method achieved by multi-mode fusion inference, parallel inference is adopted, and the inference results obtained from the four inference methods are fused to improve the certainty of the inference result. The robot failure diagnosis method achieved by multi-mode fusion inference can be applied to failure diagnosis of multiple fields such as robots and can determine failures of a failure equipment system with a large number of uncertain factors.

The invention discloses a pulsar signal simulator mainly solving the problem that X-ray pulsar signals only can be obtained by a satellite carrying X-ray detector at present. The system mainly comprises a pulsar analog computer, a data receiving buffer board and an analog signal output board, wherein, the pulsar analog computer stimulates user input according to human-computer interaction interface, extracts information in a pulsar characteristic parameter library and a satellite ephemeris library, generates pulsar data received in the satellite operation process, and transmits the pulsar data to the data receiving buffer board via a data port; the data receiving buffer board puts received data into a cache and transmits the data to an analog output board if needed; the analog output board converts the received digital signal into an analog signal so as to stimulate and generate X-ray pulsar signals. The invention can stimulate the pulsar signal received in the satellite operation process, has flexible arrangement and wide application range, and can be used for proving the X-ray pulsar navigation theory method and researching engineering technology.

The invention discloses a cloud computing resource scheduling method based on game theory. The method is characterized by including the specific steps of step 1, receiving a user request, analyzing and judging whether available resources meet a demand, rejecting the request if the available resources do not meet the demand, and otherwise, entering the next step; step 2, issuing a receiving request confirmation message; step 3, establishing a cloud user resource scheduling game model with users as game participants or a cloud provider resource scheduling game model with available physical machines of a cloud data center as game participants, and forming an optimal resource allocation scheme; and step 4, completing a virtual unit creation process, and executing the task request of the user. According to the method, a theory method of the game theory is introduced, different optimization targets are customized for different cloud computing environments, the corresponding resource scheduling game models are established, the cloud user resource scheduling game model with the cloud users and cloud providers as the game participants is established, and problems of unfair resource allocation and an insufficient resource utilization ratio are solved by corresponding resource scheduling algorithms.

The invention discloses an automatic scale segmentation parameter selection method for object remote sensing image analysis. Optimum space scale segmentation parameters are determined through calculating an average local variance curve range method; optimum attribute scale segmentation parameter are determined through a local variance histogram estimation method; optimum merging threshold parameters are determined by employing anisotropy space correlation statistics to calculate horizontal and vertical ranges; scale effect evaluation and scale segmentation parameter optimization adjustment on a segmentation result are carried out by employing an index evaluation method. In combination with the geological spatial statistics and a mode identification theory method, the automatic scale segmentation parameter selection method realizes automatic selection of the optimum scale segmentation parameters before segmentation, can clearly explain the determined optimum scale segmentation parameters based on statistics theories, has higher theory credibility and improves information extraction and analysis efficiency and precision for object remote sensing images.

The invention discloses a greenhouse multi-model information fusion based technology for regulating and controlling the greenhouse environment. According to environmental information such as the temperature, the humidity, the illumination intensity and the carbon dioxide concentration inside a greenhouse, which is obtained by a greenhouse environment information collecting sensor, three kinds of greenhouse environment control models which are based on a set value, the illumination and the accumulated temperature are used respectively for extracting feature vectors, multi-information fusion of feature vectors of three kinds of regulating and controlling methods is conducted by using a Dempster / Shafer (D-S) evidence theory method, an environment optimization controlling and regulating decision strategy is established, and coordination of short-scale and long-scale control requirements of the greenhouse is achieved. An actuating mechanism is controlled by a greenhouse environment controller to act to achieve real-time control of greenhouse environmental parameters.

The present invention discloses a mud-rock flow early warning method based on the rainfall-probability, belonging to the mountainous region calamity field. According to the contingent probability theory of the generation of the mud-rock flow, a construction method of an early waning index system through combination of the probability of reflecting the nondeterminacy of the mud-rock flow and the rainfall formed by the mud-rock flow is realized through combination of the calculation determination of test and theory calculating of the critical rainfall of the generation of the mud-rock flow and the calculation of the rainfall condition probability. The theory method is reliable, the calculation is scientific and rapid, and the early warning index is clear to solve the critical value problem of the starting of the mud-rock flow induced by the rainfall and reflect the nondeterminacy in the formation and motion processes of the mud-rock flow so as to improve the forecast reliability, accuracy and scientificity. The mud-rock flow early warning method based on the rainfall-probability can directly provide technical support for the monitoring early warning and forecast of the rainfall-type mud-rock flow.

The invention discloses a health management decision-making method suitable for a complex process system. The method comprises the steps of 1, acquiring a system measuring point parameter; 2, pre-processing the system measuring point parameter; 3, performing real-time fault detection on the pre-processed system measuring point parameter through an adaptive threshold analysis method, and performing fault diagnosis on the fault detection result by using a knowledge reasoning method in combination with historical data, a fault mode and an influence analysis table; 4, evaluating the health degrees of single-parameter sensors by combining the fault diagnosis result and using a grey theory method, and fusing the health degrees of the single-parameter sensors by using a fuzzy set fusion theory to obtain a fault mode health parameter; 5, predicting a fault mode health parameter by using the obtained fault mode health parameter through a relevance vector machine method; and 6, fusing maintenance decisions of multiple decision theories by using a grey group decision-making theory to obtain a maintenance decision result. The method can be used for fault prediction for next operation of the system, and provides a maintenance advice for each fault mode.

In the invention, an AGV simulation chart model and a mathematical model of AGV quantity configuration are established, the mathematical model is analyzed by combining a queuing theory method, an inequality with a parameter is given that the optimal quantity n should meet, then the number of n AGVs are placed on the simulation chart model, the simulation model is operated to obtain statistical data, and the statistical data are substituted into the inequality with the parameter to verify whether the inequality is true so as to determine the optimal number of AGVs. The method combines both an analytical method and a simulation method, simplifies the operation and statistical process, with the obtained number of AGVs being close to the actual situation, and can provide the best amount of AGV information suitable for the situation at the scene before the procurement and delivery of the AGVs so as to achieve the purposes of scientific decision-making and cost reduction.

The embodiment of the invention provides a software performance evaluation method and a system thereof; the method comprises the following steps: step S1, an evaluated object and an evaluation factor set of the software performance are set; step S2, the evaluation index of the evaluation factor is set; step S3, the weight of the evaluation factor is calculated; step S4, the membership degree of the evaluated object to the evaluation factor is calculated; step S5, the evaluated result is obtained according to the weight and the membership degree. In the embodiment of the invention, by adopting a fuzzy mathematics theory method, the whole solution on the evaluation of the software performance is obtained, all evaluation factors of the software performance are considered comprehensively, the uncertainty and fuzziness of the software performance evaluation are reduced, so as to carry out quantitative, scientific, rational and comprehensive evaluation to the software performance when the software performance has multiple evaluation factors and indexes.

The invention discloses a multi-stress limit determination method for an intelligent ammeter. The method comprises the following steps: determining the sensitive stress of the intelligent ammeter through FMEA (Failure Mode and Effects Analysis); respectively setting the stepping reinforcement stress tests of both temperature and electric stress; testing the output characteristics of the intelligent ammeter; processing the test result; and determining the mechanism consistency condition. The method provided by the invention is specially for determining the mechanism consistency of the intelligent ammeter and makes up the blank that a matched method specially researched and developed for the intelligent ammeter is unavailable in the present market. The conventional reinforcement test data of the intelligent ammeter is less, and can not be analyzed more accurately by adopting a traditional data processing method. The grey theory method provided by the invention can be used under the small-sample poor-information condition, thereby compensating defects when the reinforcement testing data is processed by the traditional method.

The present invention provides systems and methods for risk-adjusted performance analysis for a specific healthcare test, market or opportunity by evaluating patient outcomes against a real-time benchmark portfolio of patient outcomes. The risk-adjusted performance measures are based on financial methods such as CAPM, single-index model and arbitrage pricing theory methods. In place of examining the financial returns for a portfolio of companies against a financial benchmark, the outcomes for a patient or a portfolio of patients is compared to a benchmark portfolio of patient outcomes. The risk-adjusted performance measures including the Sharpe's measure, Treynor's measure, Jensen's measure and similar analysis tools are then used to compare different healthcare groups. The method has utility in many areas of healthcare including management of healthcare facilities, providing insurance reimbursement to a healthcare facility (e.g., “pay-for-performance”), making investment decisions in the healthcare marketplace and developing dynamic prognostic dynamic medical tests.

The invention discloses a grid search support vector machine-based journey time prediction method and belongs to the intelligent transportation field. The method comprises the following steps that: 1, on the basis of ship automatic identification system data, and data preprocessing is performed according to the investigation of missing data, the division of upstream and downstream ships and the removal of redundant data; 2, a historical period-based inland ship journey time prediction model is constructed, and a training data set is obtained according to the model; 3, the optimal parameters of the prediction model are searched on the basis of a support vector machine grid search method; 4, the prediction of inland ship journey time is realized on the basis of the optimal parameters; and 5, an evaluation result is predicted. With the method of the invention adopted, the ship automatic identification system data are mined and analyzed through using a data mining theory method, and the therefore, the prediction of inland ship journey time can be realized; the improvement of the management level of navigation affair management departments can be facilitated; and the rapid development of inland shipping can be promoted.

The topology of the elements of a metamaterial can be engineered from its desired electromagnetic constitutive tensor using an inverse group theory method. Therefore, given a desired electromagnetic response and a generic metamaterial elemental design, group theory is applied to predict the various ways that the element can be arranged in three dimensions to produce the desired functionality. An optimizer can then be applied to an electromagnetic modeling tool to fine tune the values of the electromagnetic properties of the resulting metamaterial topology.

The invention discloses a method for analyzing and evaluating decision supporting capability evaluation of a complex system, comprising the following steps of: establishing a system capability requirement model, generating a decision network model, establishing a decision supporting capability evaluation model and evaluating the networking decision supporting capability. In the invention, the method breaks through the conventional evaluation method only based on an index system, and comprehensively utilizes theory methods, such as comentropy, architecture and the like, so that the analysis is more scientific and the pertinence is more obvious, thereby the method can be used for guiding the design of the system structure of military electronics information system and organization operation of the system in fighting.

The invention relates to a mobile edge calculation scheme in an Internet of Vehicles scene, and the method optimizes the calculation task allocation and transmission power control problems of user equipment in a vehicle on the premise of meeting the time delay requirement. The energy loss of the equipment under the weighted calculation task allocation rate is taken as an objective function, a datatransmission model of the user equipment and the edge calculation node are obtained by using a queuing theory method, and the optimization problem is solved through non-linear fractional optimizationand iteration of an alternating direction multiplier method. And in each round of loop, the outer layer loop solves a nonlinear fractal programming problem, the inner layer loop updates an initial value and a variable until an iteration result meets a set threshold value, the task amount distribution ratio of each user equipment is determined, and the minimum energy consumption is obtained. According to the technical scheme provided by the invention, the energy consumption of the user equipment can be effectively reduced, the delay requirement is met, and the calculation capability of the whole network is improved.

The invention provides an internal solitary wave acting force characteristic analysis method considering a background flow field, and the method comprises the following steps: step 1, carrying out thenumerical simulation of a South China sea circulation through employing a three-dimensional ocean numerical model; step 2, establishing an internal wave numerical value water tank; and step 3, determining the effect of the internal wave on the ocean structure through the internal wave numerical value water tank and the ocean structure network model which consider the background process. Accordingto the invention, the problem that the background flow field cannot be considered in the internal wave wave-generating method is solved. The internal wave ocean mode, the internal wave computationalfluid mechanics method and the internal wave acting force theory method are combined together for the first time, and a complete calculation system of the internal wave generating method and the forcemeasuring method considering the background flow field is formed. According to the method, the effect research of the internal waves on the structure is closer to the actual situation, and the methodcan play a role in guiding the safety load calculation and the structure design of the offshore structure.

The invention discloses an expert system and a method which are applied to the blast furnace-converter sector production scheduling process control of steel enterprises. The expert system comprises a knowledge base, a control rule set, an inference engine and an information processing part, the operation experience of workers is integrated to carry out reasoning discrimination and make necessary decision to realize reasonable scheduling on the basis of a queuing theory method and a system and simulation theory. A computer is controlled by a programmable logic controller suitable for process control to reasonably design a batch-arrival closed type repairable queuing network system which is composed of stations in a blast furnace-converter sector and has multiple service counters in parallel-series connection. The control system has simpleness and strong adaptability, can directly influence productivity of related equipment and running cost of the system, can satisfy actual production requirement, can save capital and can be widely applied to blast furnace-converter sector production scheduling system control of large-scale integrated iron and steel works.

Provided is a medium-term regional power load prediction method based on a data clustering theory. The method includes region partition, influence factor correlation analysis and load space distribution prediction. By utilizing a clustering theory method and adopting a region block as a basic unit, each region block within a district is repartitioned and combined according to the similarity to serve as a basic unit for later load prediction. By utilizing a neural network algorithm to predict the load density and the synchronous rate, the time, space and other multi-attribute multilayer factorsare taken into account, the influence factors of different layers on the load density and the synchronous rate are mined, the precision and efficiency of the load prediction method can be improved, and refined planning of a power grid is facilitated. Meanwhile, a BP neural network algorithm is suitable for load prediction and has the advantages that calculation is stable, the classification capability in an arbitrary complicated pattern is excellent, and the multi-dimensional function mapping capability is great.

Method for determining reservoir permeability from Stoneley wave attenuation extracted from conventional sonic logs by inversion of the full Biot wave equations for a porous medium. Frequency-dependent Stoneley-wave attenuation is extracted by analyzing array sonic measurements. Then, based on Biot's full theory applied to a borehole model and the standard logs (gamma ray, caliper, density, neutron, resistivity, sonic, etc.), a simulation model with the same parameters as the Stoneley-wave measurements is built. Next, a theoretical Stoneley-wave attenuation is computed for a given permeability. Finally, reservoir permeability is determined by comparing the modeled Stoneley-wave attenuation with the measured Stoneley-wave attenuation by an iterative inversion process.

The invention discloses a non-linear function solving method based on an adaptive variable-step chaos wolf pack searching optimization algorithm; the content comprises the following steps: initializing a wolf pack, and using a chaotic variable as a wolf pack initialization position; selecting a candidate wolf with the best position to be the leader wolf; allowing other artificial wolves to move to the leader wolf; allowing other wolves to execute siege behaviors by taking the leader wolf as center when the leader wolf finds a prey; updating the colony according to a principle in which the fittest survives; iterating said steps until the maximum iteration frequency is reached, and outputting the optimal solution. The method can improve a wolf pack search algorithm (LWPS) based on the leader strategy, and can solve the complex non-linear function solving problems; the non-linear function solving method can make up LWPS algorithmic insufficient, can provide good superiority in the searching optimization precision and convergence speed aspects, has better searching optimization capability, can more accurately and fast search the complex non-linear function optimal solution, thus enriching non-linear function solving theory methods.

The present invention discloses a decision optimization method of multi-land seed selection based on combination optimization. Aiming at a multi-land seed selection decision problem, a multi-land grain seed selection decision model based on a combination optimization theory method is constructed to achieve decision optimization of multi-land seed selection. The method comprises the steps of: constructing a training sample set; obtaining key factors which influence a yield; constructing a neural network model and performing training; constructing a training test set; obtaining a yield prediction value and a variance of a parcel through the trained neural network model; constructing a multi-land grain seed selection decision optimization model based on combination optimization; and employinga decomposition algorithm to solve an optimal seed selection proportion to obtain the optimal varieties of the seeds and a usage proportion. The decision optimization method of multi-land seed selection based on combination optimization can perform analysis and optimization of grain plantation and seed selection, and can guide concrete land agriculture plantation and agriculture plantation planning; and moreover, the decision optimization method provides technical support for macroscopic agriculture policy or regional sales and a stocking strategy so as to have great economic and social values.

The invention discloses an aero-engine time lag-containing non-determinacy fuzzy dynamic model modeling method comprising the following steps: 1, building an engine multi-dynamics component-level model; 2, building an engine state variable model; 3, building a nominal system model; 4, building an engine fuzzy dynamic model based on a fuzzy theory. The method employs rotordynamics, volume dynamicsand heat transfer theory methods to build an aero-engine nonlinear component-level model; the component-level model modeling method adds the model dynamic information, avoids massive iteration solvingprocess, and improves the model calculation speed and timeliness; the method employs a heredity algorithm to modify design points and non-design points of the aero-engine nonlinear component-level model, and employs the heredity algorithm to modify the aero-engine rotor component flow and an efficiency modification factor, so the design point parameter relative error is kept within 1%, the non-design point parameter relative error is kept within 2%, and the temperature change scope is within 10K.

The invention provides a propagation graph theory based channel modeling method in a non-line-of-sight type interval tunnel environment. The method mainly comprises the steps of dividing a tunnel geometric model into multiple scattering point sets; arranging and combining the scattering point sets to obtain propagation paths; obtaining a transfer function of each propagation path according to cascading calculation of sub transfer functions between adjacent two scattering point sets on each propagation path in sequence, and adjusting the transfer function of each propagation path based on transfer probability; calculating the sum of the transfer functions of the adjusted propagation paths to obtain a transfer function of the non-line-of-sight type tunnel system; and performing inverse Fourier transform on the transfer function to obtain time domain channel impulse response of the non-line-of-sight type tunnel system. According to the propagation graph theory based channel modeling scheme for the non-line-of-sight type interval tunnel system provided by the embodiment of the invention, the propagation graph theory method is improved; the concepts of transfer probability and division planes are proposed; and therefore, the channel modeling method can be well suitable for channel modeling for interval tunnel systems with non-line-of-sight type rectangular tunnels, U-shaped tunnels and the like.

The invention discloses an intensive seismic zone landslide prevention and treatment engineering health assessment and maintaining method which comprises the steps of establishing of an assessment system of a blocking wall, an anti-sliding pile health diagnostic indicator system and a pre-stressed anchor cable health diagnostic indicator system. The assessment system of the blocking wall is subjected to assessment with a fuzzy comprehensive method and a grey theory method. According to the anti-sliding pile health diagnostic indicator system, lists are used for carrying out field investigation, deforming measuring, foundation pile non-loss detecting and drill core sampling verifying, and finally a conclusion is obtained. According to the pre-stressed anchor cable health diagnostic indicator system, a fuzzy comprehensive assessment method is used, assessment of sensitive factors (a damage range is larger than or equal to 60%) is carried out first, and when the damage range of anchor cable engineering is limited, general factor assessment is carried out. The method has the advantages that through health assessment of intensive seismic zone (side) landslide prevention and treatment engineering, the health state of certain prevention and treatment engineering can be quickly and efficiently judged, a structure damage position, a damage type and the damage degree are obtained, and accordingly preventive measures are carried out in advance.

The invention discloses a software dynamic behavior modeling method and a software dynamic behavior modeling device based on control flow analysis. This method traces the function execution trajectoryof software dynamic execution process, establishes a software dynamic behavior sequence database described by function execution; analyzes the software dynamic behavior sequence, extracts the function call logic relation and the function call statistics to form function call control flow information; and abstract the software system into a multi-label dynamic software behavior network model according to the function call control flow information. From the angle of control flow analysis and statistics, combined with the theory method of complex network, the dynamic calling relation of the software function is analyzed in a more comprehensive and detailed way and the software behavior can be expressed and measured in a more comprehensive and scientific way.

The invention discloses a gas magnetic bearing electric spindle and a control system thereof. The gas magnetic bearing electric spindle structurally comprises a radial-axial auxiliary bearing, a radial displacement sensor, a radial displacement sensor bracket, a radial hybrid magnetic bearing, a limiting sleeve, a high-speed motor, a steel cylinder, an axial thrust gas bearing, a radial auxiliary bearing, a rotating shaft, a front end cover and a rear end cover. For the spindle with the novel structure, the invention provides a fuzzy control system of the gas magnetic bearing electric spindle based on a rough set theory method. The fuzzy control system comprises a controller, a power amplification module, a prototype body and a displacement detection module. Compared with a conventional fuzzy control system, the control system introduces a fuzzy control method of a rough set theory, simplifies original fuzzy control rules, and can effectively remove redundant information knowledge, so that the control system is more simple and easier to control, the dynamic performance of a spindle system is improved, and the performance of the control system can be effectively enhanced.