484results about How to "Reduce computational difficulty" patented technology

The invention relates to a method for controlling an index time-varying slide mode of flexible spacecraft characteristic shaft attitude maneuver, and belongs to the technical field of spacecraft control. The method comprises the steps that firstly, a system dynamically equivalent model, a dynamic model and a flexible vibration model are established under a spacecraft system, then, the vibration frequency and the damping ratio parameter of a closed loop system with the index time-varying slide mode control law are calculated, and a single-shaft multi-modality filtering input shaping device with a characteristic shaft as a rotary shaft is designed according to the designing method of the single-shaft input shaping device to restrain flexible vibration in three-shaft motion. Meanwhile, a state observer is designed to estimate flexible modal information in real time, and the method for controlling an output feedback index time-varying slide mode is formed. At last, saturability analysis is conducted on control torque so as to satisfy the physical saturation constraint of the control torque. By means of the method, the application range of existing input shaping is expanded, the input shaping technology is expanded from single-shaft maneuver to three-shaft maneuver, the self-robustness of filter input shaping is enhanced, and the purpose that the attitude maneuver path of the spacecraft is the shortest is achieved.

In a planning model, a decision variable optimization process (200) generates a planning function (122) describing the planning model, the planning function (122) depending upon a set of decision variables (125). The planning function (122) is separated into independent planning functions, SP_i, each of which depend upon different decision variables (125). Each of the independent planning functions, SP_i, is independently optimized to obtain decisions for the different decision variables (125), and an outcome is presented that indicates the decisions. The planning function (122) further includes an embedded constraint function that introduces an embedded constraint to weaken the coupling between decision variables (125) in the planning model, thereby reducing an N-dimensional optimization problem into a lower order optimization problem.

The present invention relates to an analysis and prediction method for dynamic performances of a rubber material structure, and particularly to a step-by-step analysis and prediction method for the dynamic performances of the rubber material structure. The step-by-step analysis and prediction method is used for performing finite element analysis and prediction of the high-frequency dynamic performances on the rubber material structure. The method comprises the following test and prediction steps: step one, fitting a super-elastic constitutive relation of the rubber material; step two, performing a viscoelasticity property test on the rubber material under different strain amplitudes; step three, fitting a viscosity constitutive relation of the rubber material; and step four, analyzing and predicating the dynamic performances of the rubber material structure. A calculation result is a final result, without the need of superposition of the calculation result, thereby reducing the computational difficulty and shortening the solution time; and an actual modeling and analysis process is more simple, clear, and easy to understand and operate, and has higher prediction accuracy, so as to meet the requirements of engineering analysis.

The invention provides a reconfiguration method for a compression sensing signal, which comprises the following steps: A) calculating an inner product (g1=phi Ty) of a signal y subjected to compression sensing treatment and each row of a sensing matrix phi, and taking the inner product as an inner product result of first iteration; B) judging if stopping iteration according to the inner product result gt of the iteration; if not, searching an index value lambda t corresponding to an element with the maximum absolute value from the inner product result gt of the iteration, adding the index value lambda t into an index set, calculating the inner product of the next iteration and starting the next iteration process, and returning to the step B, wherein t is the iteration index value and is the No. lambda t row of the matrix X=phi T phi; and if stopping, forming a matrix phi lambda by rows of the sensing matrix corresponding to different index values in the present index set, and reconfiguring the compression sensing signal according to the formed matrix phi lambda and the signal y, wherein the rows in the matrix phi lambda are arrayed according to the sequence of the index values from low to high, and lambda is the set composed of different index values in the present index set. According to the reconfiguration method provided by the invention, the calculating time and resource are saved.

The invention discloses a cable fault detection and analysis method. According to the method, an impedance frequency spectrum and a phase frequency spectrum of a cable are taken as basis, subsection characteristic impedance on an impedance frequency spectrum attenuation trend curve is calculated with a subsection interception method, an equation of equivalent impedance is established, R, L, G and C parameters of cable transmission impedance are calculated, a transmission impedance model based on R, L, G and C parameters is obtained, an error distance graph with a cable distance length serving as a variable is obtained through error calculation and comparison of the transmission impedance model and the tested impedance frequency spectrum and phase frequency spectrum, finally, local zero points on the error distance graph are analyzed, and fault detection and fault type identification are realized. Compared with the prior art, on the basis of a relative signal transmission rate calculation method with low calculation difficulty and high calculation accuracy, accurate fault positioning and multi-point fault positioning can be realized, and the fault type identification can be realized.

The invention discloses an electronic nose feature selection optimization method on the basis of multiple Fisher kernel discriminant analysis. The electronic nose feature selection optimization method comprises the following steps: firstly, acquiring a sample feature matrix; initializing parameters and establishing a fundamental kernel function according to the parameters; then calculating a composite kernel matrix on the basis of a fundamental kernel matrix, calculating a projection of the composite kernel matrix in a high-position feature space, then feeding the projection into a classifier to carry out mode identification to determine a kernel function with the highest identification rate; finally, on the basis of the kernel function, calculating a projection of a new sample matrix in the feature space, using the projection as an electronic nose signal and using the electronic nose signal as an input of the classifier to carry out mode identification. The electronic nose feature selection optimization method has the obvious effects of solving the problem of poor data discrimination after high-dimension projection is implemented by a single kernel function method, solving the problem of redundancies between sensors, optimizing a sensor array, reducing data dimensions and improving the identification rate of the electronic nose signal so as to provide beneficial guide for a doctor to select a suitable treatment method.

The invention discloses a system evaluation and detection method by a Bayesian model. The method includes: step A, establishing a logic relationship and an event relationship between a software system and a database therein after variable and relationship conversion; step B, establishing a fault tree model for a logic model according to the step A; step C, establishing a Bayesian network based on the fault tree model; step D, conducting system evaluation and detection based on the Bayesian network. The system evaluation and detection method by the Bayesian model has the advantages of low computational complexity, low computational difficulty, simplicity and practicality when used for system evaluation and detection by the Bayesian model.

The embodiment of the invention relates to the field of communication, and discloses a robot path planning method, a robot, electronic equipment and a storage medium. The method comprises the following steps: acquiring a target point of a robot in an artificial potential field grid map that is formed by superposing a basic grid map generated according to a local map and an obstacle cost map generated according to an obstacle; acquiring a plurality of alternative pose points of the robot after the preset duration, and determining a target pose point according to a path cost of each pose point to the target point, setting the target pose point to be the pose point with the minimum path cost; and generating a target path according to the state information of the target pose point. The path planning is carried out in the artificial potential field grid map, so that the situation that the path planning cannot be carried out because a grid map cannot be generated when a target point is on anobstacle is avoided, and the path planning efficiency and effect are improved; and path planning is carried out according to the pose point with the minimum path cost to ensure that the obtained pathis the optimal path.

The invention discloses a method for detaching products based on a connected piece level network diagram, aiming to solve the problems of effectively recycling waste and old materials to reduce the waste of the materials, and solving the problems of energy consumption, pollution and the like caused by the production of raw materials. The method comprises the following steps of: extracting/inputting attribute information of parts, extracting/inputting assembly information of the products, building and detaching models, generating detaching sequences and optimizing the detaching sequence. In the invention, the connected piece level network diagram is used as the detaching model, and an adjacent matrix is utilized to represent the adjacent relationship between nodes. The node of the model only represents the connected parts, not all parts, thereby effectively reducing the number of the nodes and edges in the diagram, reducing the dimensionality of the adjacent matrix, reducing the calculation difficulty and improving the processing speed. The detaching sequence of the product is generated based on a method of constraint removal, so that the obtained detaching sequence has more valuable meaning and maneuverability.

The invention discloses a liver cancer postoperative recurrence risk prediction method combining pathological images and clinical information, and belongs to the technical field of cancer postoperative recurrence risk prediction model construction. The method comprises the steps: taking patient clinical information and patient tumor area pathological image features extracted by applying an image processing technology as basic variables, further calculating the interaction between the basic variables as input data, performing the fitting of a survival random forest model, and precisely predicting the survival time of a patient. The result of the embodiment of the invention shows that the cross validation efficacy evaluation index C-index of the model provided by the invention is superior tothe result of prediction by only using pathological image features or clinical information, and the accuracy of liver cancer postoperative recurrence risk prediction is significantly improved; besides, the invention further provides a postoperative recurrence risk classification index, so patients can be divided into a high recurrence risk subgroup and a low recurrence risk subgroup, and a doctorcan be helped to make a targeted treatment scheme for the patient.

The invention discloses a target tracking method and system based on adaptive color characteristics and space-time contexts. The method comprises steps that after a first image of a video sequence is inputted, a rectangle frame is utilized to select and track a target; a target confidence graph of the first image is calculated; the tracking target confidence graph comprises a context prior model and a space context model; a space-time context model of a tth image is updated according to space context model learning; convolution operation for a (t+1) image is carried out according to the acquired space-time context model, a target confidence graph of the (t+1) image is calculated, and the maximial likelihood probability position of the target confidence graph is solved and is taken as the optimum target position; the optimum target position is taken as a new tracking target, steps are repeated according to the target confidence graph of the (t+1) image, and the optimum target position of a (t+2) image is determined; optimum target position determination of all the images is realized.

The invention discloses a complex behavior recognition method. The complex behavior recognition method comprises: acquiring three-dimensional bone joint point information of a target motion by using asensor; pre-processing the joint point information, and normalizing a coordinate system; extracting a motion trajectory of each joint point, projecting the motion trajectory to three two-dimensionalplanes; extracting a motion vector between each two frames and the length and the direction angle thereof, using the k-means algorithm to perform clustering to acquire motion primitives, and performing counting to acquire histograms; calculating the weight of each joint point is calculated to form a descriptor according to the time pyramid, the time information, and values of the clusters of allthe histograms; performing SVM classification to achieve motion recognition. The invention can perform feature extraction and effective represent on the motion bone joint point information, and can improve the accuracy of the motion recognition; all the motion information is completely retained, and the motion reconstruction can be performed; all motion classes are clustered, and motion features of the human are captured globally; low-level features are used, the computational difficulty is lowered, the motion recognition efficiency is improved, and the the real-time requirements of the systemis satisfied.

The invention belongs to the technical field of photoelectric detection, in particular to a distance measuring device and a method thereof. The method comprises the following steps of: assembling a large field by using a 2*3 array of multispectral cameras, obtaining background images and labeling characteristic reference points by using real-time image arrangement, and calculating an object distance by using an image convergence shooting algorithm. Because the invention adopts a large-field angle, observed objects are more easily found, and judging whether the observed objects are the same one is more accurate; and because the characteristic reference points are selected after image arrangement, the data volume of object calculation is smaller, and calculation is faster.

This invention discloses a method for studying Chinese new words and a device, which uses a process module to process the searched engine daily record input via an input module, deletes single Chinese word and inquiry words containing non-Chinese composition and puts the left in order according to the inquiry sequence to set a threshold value and delete the inquiry words with sequence lower than the value. The word-dividing module divides the rest inquiry words taking the present words with the number smaller or equal to 4, if the number is greater than 4, then each time takes 4 words and one from the beginning till the last word, then divides the words based on the four-words method. The filter module arrays the new words according to the appeared frequency, sets new threshold value and deletes the new words lower than the threshold value and outputs the left by an output module.

The invention provides a self-adaptive cruise system and a control method based on vehicle to vehicle communication, pertaining to the technical field of cars with the purpose of improving cruise accuracy. The system comprises a vehicle-mounted controller, a satellite positioning system and a wireless communication module. The satellite positioning system and the wireless communication module are connected with the vehicle-mounted controller. The vehicle-mounted controller is used for calculating the time that a car takes to overtake a front car based on information of the front car and the car. When the calculated time is less than the pre-set threshold value and the distance between two cars is larger than or equal to the safety distance threshold value, the car decelerates and determines to cruise at the constant speed based on the speed of the front car. The method comprises following steps of: A, establishing a communication network; B, determining whether a signal is emitted by a front car or not; C, cruising at the constant speed when no signal is emitted by the front car; D, calculating the time that the car takes to overtake the front one based on information of the front car. When the calculated time is less than the pre-set threshold value, the car decelerates and sends a early warning signal to the front car. The system and the method help raise cruise accuracy and self-adaptation.

The invention relates to a modeling calculation method for a noise sound field of a transformer substation. The method comprises the following steps: buildinga transformer substation model according to the actual scene and dividing a sound source of the transformer substation model into an outdoor sound source and an indoor sound source; measuring sound pressure level corresponding to the outdoor sound source of the transformer substation model on the actual scene and obtaining sound pressure level of the outdoor sound source; measuring sound pressure level corresponding to the indoor sound source of the transformer substation model on the actual scene, converting the sound pressure level into an equivalent outdoor sound source based on the built transformer substation model and obtaining sound pressure level of the equivalent outdoor sound source; converting the sound pressure level of the outdoor sound source and the sound pressure level of the equivalent outdoor sound source into the acoustic power level of the outdoor sound source and the acoustic power level of the equivalent outdoor sound source; and calculating to obtain noise value of the sound field of the transformer substation based on the acoustic power level of the outdoor sound source and the equivalent outdoor sound source. Compared with the prior art, the modeling calculation method for the noise sound field of the transformer substation has the following advantages of easy calculation, few calculated amount and convenient analyses.

The invention discloses an electronic nose parameter synchronous optimization algorithm based on an improved quantum particle swarm optimization algorithm. The method comprises performing wavelet transformation on obtained original electronic nose data; then performing weighting treatment of wavelet coefficients; through the improved quantum particle swarm optimization algorithm based on a novel local attractor computing manner, finding out a weighting coefficient corresponding to the highest electronic nose identifying rate, and classifier parameters to obtain a characteristic matrix of electronic nose signals; inputting the characteristic matrix into a classifier for mode identification. The electronic nose parameter synchronous optimization algorithm based on the improved quantum particle swarm optimization algorithm has the advantages of enhancing early-stage ergodicity and later-stage local optimizing capacity of particles, improving the capacity of quantum particle swarms in searching for global optimal values, and especially for wound infection detection, improving the identification rate of an electronic nose, thereby selecting appropriate treatment methods for doctors and providing beneficial guidance for promoting quick recovery of wounds.

The invention discloses a point cloud analysis-based multi-vegetation region power transmission corridor hidden danger point rapid extraction method. The method comprises the steps of 1, dividing point cloud data into a plurality of grids; Step 2, filtering the point cloud data in each grid; Step 3, extracting ground points from the point cloud data in each grid; 4, constructing a feature vector of the point cloud data to be classified; 5, constructing a training sample set and designing a classifier based on sparse representation; 6, optimizing the calculation method of the undetermined parameters of the classifier; 7, classifying the point cloud data in each grid; Step 8, searching hidden danger points in the grid according to a classification result; Step 9, combining hidden danger point data in each grid; The problems of complex calculation and long period caused by too many feature vector dimensions and classifier design defects in power transmission line corridor point cloud classification are solved.