123 results about "Matlab programming" patented technology

Digital circuit is synthesized from algorithm described in the MATLAB programming language. A MATLAB program is compiled into RTL-VHDL, which is synthesizable using system-specific tools to develop ASIC or FPGA configuration. Intermediate transformations and optimizations are performed to obtain highly optimized description in RTL-VHDL or RTL Verilog of given MATLAB program. Optimizations include levelization, scalarization, pipelining, type-shape analysis, memory optimizations, precision analysis and scheduling.

The invention discloses a system for pre-testing and diagnosing the electro magnetic interference of electronic equipment, which is characterized by comprising a data collecting module, an interference source diagnosing and locating module, an environmental noise eliminating module and a visual time-frequency analyzer, wherein the data collecting module, the interference source diagnosing and locating module and the environmental noise eliminating module are respectively connected with the visual time-frequency analyzer, and the visual time-frequency analyzer achieves the drive of the data collecting module, the call of the interference source diagnosing and locating module and the environmental noise eliminating module and the monitoring and displaying of time-domain waveforms and frequency spectrums. The invention also discloses a method for pre-testing and diagnosing the electro magnetic interference of electronic equipment, which utilizes a LabView visual instrument platform and Matlab programming environmental developing software to pre-test and diagnose signals. The invention can pre-test and diagnose the electro magnetic interference of electronic equipment at common test fields, and has high automation and carrying degree and simple usage and reduces cost.

The utility model provides a life predicting model based on test equipment for room-temperature and high-pressure compression stress relaxation of rubber. The life predicting model comprises the following steps of: firstly, drawing stress relaxation change data acquired by the device into a diagram to obtain the relation between the stress relaxation condition and the ageing time and the relation between the speed constant and the ageing temperature; secondly, obtaining all coefficients in the equation by utilizing an approximation method, a linear regression method and MATLAB programming software, and judging the linear regression effect according to the related method; and finally, determining a rubber life predicting value after giving critical values of temperature and stress relaxation. The life predicting model has the advantages that the device for testing the compression stress relaxation of high-temperature rubber can be utilized for monitoring the change of the compression stress, so as to establish the life predicting model, and the accuracy, the repeatability and the reproducibility of a life predicting result are guaranteed.

The invention provides a wind power plant energy storage volume configuration method capable of improving the dispatching economy of an electric power system. The method is characterized by comprising the contents of the definition of prediction errors and the error rate, the relationship among the system wind power standby volume, the energy storage system volume and the wind-power power prediction errors, wind power standby volume determination, energy storage volume determination, confidence degree definition and the like. An energy storage system is used for compensating the time period power shortage with greater wind-power power prediction errors for power and energy time transfer features and fast response capability, a target function with the minimum sum of the system standby volume cost and the configured energy storage volume cost is built, matlab programming is utilized for solving the energy storage system volume in the target function, the goal of greatly reducing the wind-power standby volume of the system at less energy storage volume configuration can be realized, and the operation economy of the system can be improved.

Provided is a dense small commodity rapid detection recognition method based on target detection. The method includes: processing an acquired commodity picture through a python or matlab programming language; establishing a convolutional neural network model including a convolution layer, a pooling layer and a total connection layer, wherein the model comprises convolution and RoIpooling; performing multi-task combined training on classification of the convolutional neural network model and a region proposal network model and bounding box regression through a training sample, performing learning update on a convolution kernel parameter in the convolutional neural network model through reverse propagation, and determining a hyper-parameter of the convolutional neural network model through averification set until a loss function reaches a target set value; and testing the recognition precision of the trained convolutional neural network model through the test set. According to the method, high-efficiency and accurate statistics of the number and the distribution of the commodities can be realized, the work efficiency of commodity suppliers and shopping mall management personnel canbe greatly improved, the manpower cost is reduced, and the commercial value is high.

The invention discloses a space dimension chain tolerance analytical method based on graphic representation, which can achieve the purpose of computing generality and improving computing efficiency. The steps of the method is that constitution of a closed ring of a dimension chain is determined according to an assembly body or a part design drawing; constitution of the dimension chain is determined according to correlations between parts of the assembly body or structures of the parts of the assembly body; dimension parameters of each dimension are sequentially picked up according to dimension types, and dimension chain information is set; parameter sets of the closed ring is computed based on the set dimension chain information and using an arithmetic of a matrix laboratory (MATLAB) programme; and a maximum value (Max) and a minimum value (Min) of the dimension parameters are found out from the parameter sets of the closed ring, and a changing range [Min, Max] of the closed ring is obtained and output.

The invention relates to the technical field of mechanical arm control, and discloses a zero-gravity control method free from moment sensing. The zero-gravity control method comprises the following steps of step 1, obtaining absolute position parameter pulse values of each knuckle of a mechanical arm through an absolute value encoder, and converting the motor pulse capacity to pulse capacity of anorigin of a cartesian coordinate system; step 2, according to the definition of instantaneous velocity and instantaneous acceleration, calculating loop iteration to calculate the instantaneous velocity value and the instantaneous acceleration value; and step 3, performing programming with a software Matlab to determine the algebraic relation expression between the gravity moment of each knuckle arm and the position parameter of each knuckle of the mechanical arm. Through the adoption of the zero-gravity control method disclosed by the invention, for zero force dragging, a moment sensor is notadopted, so that the cost is low and the operability is high, the zero-gravity control method is easy for application and popularization of industrial automation; and besides, the method is mainly realized with the assistance of recognition of the parameters and a software algorithm, so that time and labor are saved, the programming showing and teaching efficiency is high, and later stage maintaining and upgrading are convenient and swift; and the programming efficiency and quality are improved through dragging slowing and teaching planning points, and a foundation is laid for the yield of products.

The invention puts forward a quick design optimization method of power assembly suspension decoupling. MATLAB (matrix laboratory) programming and a computing platform are adopted to realize the computation of the modal frequency and the decoupling rate of a power assembly six-degree-of-freedom rigid body; on the premise that a plurality of targets are fully considered, a modal frequency target model, a modal decoupling target model and a suspension rigidity manufacture target model are established; and finally, a target function is defined and optimized, and meanwhile, the installation position, the rigidity and the installation angle of a suspension system can be subjected to optimal design. The quick design optimization method does not depend on traditional commercial software, analyzes the power assembly suspension decoupling and greatly improves analysis efficiency. In addition, the quick design optimization method fully considers the installation position, the rigidity and the installation angle in a suspension optimization design process, enriches an optimal design variable, and provides rich solving resources for the power assembly design of a finished automobile concept design stage. Meanwhile, frequency avoidance and suspension manufacture constraints in a design process are considered, and the mismatching of a later period power assembly under the working situation of the finished automobile is avoided.

The invention provides a PSO neural network-based engineering ceramic electrospark machining effect prediction method. The method comprises the steps of building a three-layer BP neural network prediction model of an insulated engineering ceramic line electrode discharge grinding process effect by utilizing a BP neural network; in combination with an adaptive variation particle swarm algorithm, optimizing connection weight and threshold of the BP neural network; building a particle swarm neural network-based engineering ceramic line electrode discharge grinding process effect prediction modelby utilizing Matlab programming; and verifying the reliability of the engineering ceramic line electrode discharge grinding process effect prediction model built by optimizing the BP neural network bythe adaptive position variation particle swarm algorithm. The method remarkably reduces an iterative frequency, has relatively high prediction precision, reliability and validity, has a certain practical value, and can be used for optimizing engineering ceramic line electrode discharge grinding process electric parameters to further improve surface quality of workpieces.

The invention relates to the field of complex machinery product assembling programming and discloses a complex machinery product assembling sequence programming method based on a firework algorithm. The method comprises the steps of: carrying out product modeling based on assemblies so as to establish a basis for assembling sequence programming based on the firework algorithm; aimed at characteristics of the assembling sequence programming problem and according to basic principles of the firework algorithm, re-defining relative operation of the firework algorithm, and constructing the firework algorithm facing the assembling sequence programming; using assembling direction change times, assembling direction stability and assembling tool transformation times as three evaluation indexes, and utilizing an optimization searching mechanism of the firework algorithm to carry out optimization on an assembling sequence; and realizing the complex machinery product assembling sequence programming method based on the firework algorithm by means of MATLAB programming, and obtaining an optimized assembling sequence. According to the invention, the manufacturing cost of a complex machinery product is lowered, and the exploitation period of the product is shortened.

The invention discloses a method for extracting sensitive parameters of T2 spectrum based on nuclear magnetic resonance logging, and the method includes the following steps: S1, performing rock sampleanalysis and testing; S2, respectively performing physical property tests, nuclear magnetic resonance experiments and high pressure mercury injection; and S3, based on a MATLAB programming platform and by use of peak picking and quantity judgment, peak value picking and sequence sorting, waveform judgment and invalid peak screening, first and last peak judgment and sequence cycle calculation andother information statistics, extracting thirteen sensitive parameters from morphological features of the T2 spectrum. The advantages of the method are as follows: the information of the nuclear magnetic resonance T2 spectrum deeply dug and extracted, and the method is helpful for quantitative evaluation of formation pore structure by the nuclear magnetic resonance logging, and deepens he application of the nuclear magnetic resonance logging in pore structure evaluation of tight sandstone reservoirs.

The invention relates to a remote sensing classifying method for sea ice types. Polar region sea ice are sorted by adopting satellite radar height gauge Cryosat-2 data, so that large-scale sea ice classification based on novel means is implemented. The method includes steps of acquiring longitude and latitude coordinates of measurement points and corresponding radar echo waveforms from the heightgauge Cryosat-2 data and extracting related features of echo waveforms; downloading AARI sea ice type data and extracting sea ice type information at the corresponding measurement points; performing geographic matching on the above extracted data information and converting the data information to point data with longitude and latitude coordinates through MATLAB programming; taking the acquired waveform features of the points and the corresponding sea ice types as training data and training by utilizing a random forest classifier, performing sea ice classification on to-be-classified data; performing treatment including projection conversion, grid transformation and the like in ArcGIS on data subjected to classification and acquiring a sea ice type data set having a spatial resolution of 25km.

The invention provides a force deformation error calculation method for a pin roller linear guide rail pair under vertical loads, and belongs to the precision design field of linear guide rail pairs. The method aims at obtaining deformation of the linear guide rail pair under the load action and improving calculation efficiency. A force deformation error of the pin roller linear guide rail pair is calculated in consideration of the action of the outer loads. Three-dimensional CAD software is used for parametric modeling, the stress condition of the pin roller linear guide rail pair is analyzed, and a mechanical analysis model of the guide rail pair is established under the action of the outer loads. A linear guide rail pair contact model is then researched, and an improved Palmgren formula is applied to analyzing the relation between elastic deformation and the corresponding load when a single pin roller makes contact with a groove. Loads borne by rolling bodies are solved according to the outer loads, elastic deformation quantities of the rolling bodies are worked out, and the displacement quantity of a sliding block is deduced and serves as the force deformation error of the whole linear guide rail pair. Finally, Matlab programming parametric modeling is applied, and the conclusion provides a theoretical basis for movement precision and service life calculation of rolling linear guide rails.

The present invention provides a Monte Carlo method based tensegrity structure form-finding method, and belongs to the field of tensegrity structure statics analysis. Aiming at the problems of lack of corresponding programs, a relatively low calculation speed, and lack of definitions for basic concepts and a processing method for particular cases, the steps adopted by the method of the present invention are as follows: step 1. setting an initial condition; step 2. randomly generating an initial configuration; step 3. randomly generating a new configuration and determining whether to accept the new configuration; step 4. checking whether a system is balanced; and step 5. obtaining a form-finding result. According to the Monte Carlo method based tensegrity structure form-finding method provided by the present invention, the particular case and technical problem in form-finding calculation of a tensegrity structure are solved, so that a speed of form-finding calculation is quickened; and a form-finding calculation process of the tensegrity structure by a Monte Carlo method is implemented by using MATLAB programming, so that a self-balanced configuration can be obtained rapidly and accurately, and the effects are significant.

The invention discloses a method for setting a valve flow characteristic curve of a steam turbine and belongs to the technical field of steam turbines. An overlap function is calculated by using datain single valve operation of a unit to obtain a new valve flow curve, and the reliability of a new characteristic curve is verified by using the data obtained in sequence valve operation of the unit.According to the method, valve flow characteristic parameters of the steam turbine can be completely and specifically set, so that switching between single valve and sequence valves is smooth, steam flow change is gentle during switching, load fluctuation is small, segment temperature and vibration do not greatly change and safe and stable operation of the unit is ensured; the response rate of AGCis optimized and the primary frequency modulation effect is improved; the overlap of regulating valves is reduced to improve the economy of the unit; and computation is simplified by using MATLAB programming.

The invention discloses a global difference method based non-uniform temperature field thermal modal sensitivity analysis method. The method includes: using an intermediate function to represent a relation between a material parameter and a temperature, for affecting a structural dynamic characteristic, defining an intermediate function coefficient as an intermediate variable, and using the intermediate variable as an initial parameter of sensitivity calculation; establishing a structural finite element analysis model, and calculating a thermal modal parameter; acquiring a new material parameter value through a perturbation method, performing structural thermal modal analysis, solving the thermal modal frequency, calculating an approximate derivative of the thermal modal frequency for theinitial parameter according to a difference format of the global difference method, and acquiring the sensitivity. The material parameter associated with the temperature field is fitted into a linearfunction curve, and then sensitivity analysis of the material parameter filed in thermal modal under the non-uniform temperature field is converted into sensitivity analysis of the intermediate function coefficient, and an objective of reducing sensitivity parameters to be analyzed can be achieved. Parameter iteration difference calculation can be achieved on the basis of MATLAB programming, and the calculation efficiency is improved.

The invention relates to a low-cycle fatigue data analysis method based on matlab programming. The method aims to solve the problems that an experiment value needs to be manually input and input errors are likely to generate so as to influence an experiment analysis result and generate low data processing efficiency when an existing method is used and also solve the problems that an obtained image can not be subjected to secondary editing, the variable value of a processing process is not visual and a fitting curve numerical value can not be directly exported. The process of the method comprises the following steps that: generating total data matrix Data; calculating the upper limit and lower limit numerical values of Dfe, Ef and K corresponding to 95% of confidence interval; storing a result into result, and writing into a format file of which the filename is result; setting a font and a word size in the image; drawing three curves and scattered points on a fatigue life-strain image; and calling a plot function to draw a plastic strain-stable cyclic stress curve graph. The method is used for the field of low-cycle fatigue data analysis.

The invention discloses a copula theory based energy storage configuration selection method for an electric vehicle photovoltaic charging station. The method includes steps of selecting photovoltaic units and an electric vehicle charging load capacity as random variables, normalizing measured data and constructing edge distribution of the variables; based on the Copula theory, selecting Gumbel-Copula and Clayton-Copula for constructing a mixed Copula function for describing the relevance of non-symmetric rear molding features among the variables; sampling an annual net load capacity of the photovoltaic charging station on the basis of a joint capacity probability density function; establishing an energy storage optimization configuration model taking the minimization of annual operation cost of the photovoltaic electric vehicle charging state as a target function under the constrains of fluctuation rate, confidence coefficient and the like; performing Matlab programming for optimization computation on the optimal energy storage capacity. Through overall consideration of operation performance and economy, analysis of correctness and practicability of the integrated system energy storage configuration model considering relevance is performed and the optical configuration scheme can be selected, so that the optical energy utilization rate and the system economic benefit can be improved.

The embodiment of the invention discloses a method for simulating the spatial and temporal migration trend distribution of organic pollutants and relates to the field of organic pollutant migration distribution simulation. The method comprises the following steps that: S1, a database is established; S2, grid division is performed on a research area; S3, a four-level escape model is established, specifically, an escape calculation formula, a Z value calculation formula, a D value calculation formula and an advection calculation formula are integrated, and the formulas are programmed through a Matlab programming platform; and S4, four-stage multi-medium escape model simulation is performed by adopting the four-stage escape model and on the basis of advection rules. According to the method, spatial heterogeneity is solved by gridding the research area; a set of independent parameter files is configured for each grid and is corresponding to years in which simulation is performed, and therefore, the method more conforms to an actual situation; the advection rules are added; and the migration of the pollutants among grids is achieved through the advection rules, so that a simulation result is closer to an actual situation. Compared with an existing method, the method is closer to the actual situation.

The invention discloses a videoLan client (VLC)-based video image analysis platform and a VLC-based video image analysis method. The platform comprises a libvlc kernel, an algorithm processing buffer frame module, a video streaming output module and a client interface. Client/server architecture is adopted by the method. A client has a function of uploading video images and algorithms and can be used for watching video image processing results on line, and a server is used for offering a video image processing function, supporting the Matlab programming algorithm to process video images on line and transmitting processed video files to the client by means of VLC streaming media. According to the technology, the video files with various formats can be processed, and processed results can be watched in real time while Matlab programming is supported.