58results about How to "Narrow down the range of values" patented technology

The invention discloses a g2o-based SLAM rear end optimization algorithm method, pose node information of a robot is used as input data, a weight factor is added for each edge by focusing closed loop restriction with a rear end optimization algorithm, a value of the weight factor is obtained according to a mathematic relation between the derived weight factor and an information matrix, and after the read end optimization algorithm, the pose node information of the robot is corrected; a built-in optimization strategy of g2o is further optimized based on a g2o platform by a least square method, and a post node path more according with an actual path condition is constructed. The g2o-based SLAM rear end optimization algorithm method adopts a DCSI algorithm, and is used for solving the problem of robutness rear end optimization, complexity is reduced, operation time is reduced and the rate of convergence rate is improved. The g2o-based SLAM rear end optimization algorithm method has important meaning in correction and optimization of a topological map in an unknown environment.

The invention discloses a malicious code family clustering method and system, and the method comprises the steps: carrying out the dimension reduction visualization of an original malicious code execution sequence through employing a T-SNE algorithm, which specifically comprises the steps: carrying out the modeling of the neighbor distribution of each data point through employing the T-SNE algorithm, wherein neighbors refer to set of data points which are close to each other; constructing a model, and mapping data points to corresponding probability distribution through nonlinear function transformation; training the constructed model, and calculating the gradient of a loss function by calculating the conditional probability of a low-dimensional space; adopting a K-means algorithm to cluster malicious code families, which specifically comprises the steps that the classification number K and a clustering center are determined, cluster division is performed on all the objects by calculating the distances between the objects and the clustering center, and a new clustering center is recalculated, and whether conditions are met or not is judged. The system comprises a dimension reduction visualization module and a clustering module. According to the method, the problem of how to determine k in the K-means algorithm is solved, and the accuracy of malicious code family clustering is improved.

The invention discloses a synthetic steering vector based beam forming meter-wave radar height measurement method, and mainly aims to solve the problems of large signal processing operation and computational complexity of the prior art during height measurement. The method includes extracting a target signal from a radar echo and determining a target elevation angle searching range; building a radar wave reflection model and computing ground arc length between a reflection point and a radar center and ground arc length between the reflection point and a target by a searched elevation angle; computing equivalent heights of the target and the radar center and then computing a reflection angle and wave-path difference between a reflection wave and a direct wave; computing a synthetic steering vector; and after data beam forming of an echo, selecting an angle corresponding to a maximum normalization power value as a target elevation angle and computing the target height. Beam forming and the synthetic steering vector are combined, the target reflection model is simplified, elevation angle estimation precision of a meter-wave radar is guaranteed, computation speed of meter-wave radar height measurement is increased, a computational process for height measurement is simplified, and real-time signal processing of the meter-wave radar is easier.

The invention provides a GNSS multipath effect correction method based on a BP neural network technology. The method sequentially comprises the following steps of multipath error extraction, sample pre-selection, sample preprocessing, sample training, network topology determination, data storage, data post-processing and model updating, wherein modeling is carried out on multipath errors by meansof forward propagation of signals and reverse propagation of the errors of a BP neural network algorithm, a motion condition of a satellite, a satellite elevation, a signal-to-noise ratio and multipath effect errors caused by ambient conditions around monitoring points can be effectively weakened, and meanwhile, periodic fluctuations in directions X, Y and Z of a monitoring station are remarkablyimproved through trained results, a model is dynamically updated according to the new multipath error data, and the periodic errors caused by multipath effects on the monitoring points can be weakenedto the maximum extent.

A fuel cell system includes a fuel cell; a voltage measuring portion that measures a voltage of the fuel cell; an electric current adjusting portion that adjusts an electric current flowing in the fuel cell; an electric current-voltage characteristic information obtaining portion that controls the electric current adjusting portion to change the electric current, and obtains electric current-voltage characteristic information that is information indicating a correspondence relation between an electric current value and a voltage value measured by the voltage measuring portion; and a negative voltage cause determining portion that determines, if the voltage of the fuel cell is a negative voltage, a cause of the negative voltage of the fuel cell, based on the obtained electric current-voltage characteristic information.

The invention discloses a turn-on voltage testing method of a switching tube. The testing method comprises the following steps that: an initial testing voltage upper limit, an initial testing voltage lower limit, and a target current are set; the testing voltage is set as a value that is obtained by multiplying a sum of the initial testing voltage upper limit and the initial testing voltage lower limit by a compressibility coefficient, and a drain terminal current of the switching tube is tested; a relation between the drain terminal current and the target current is determined; if the drain terminal current is larger than the target current, the testing voltage is used as a new testing voltage upper limit, and if the drain terminal current is less than the target current, the testing voltage is used a new testing voltage lower limit; the above steps are repeated by using the new testing voltage upper limit or the new testing voltage lower limit and preset repletion times so as to obtain a first testing voltage upper limit and a first testing voltage lower limit; and a turn-on voltage of the switching tube is tested and obtained in a range from the first testing voltage lower limit to the first testing voltage upper limit. Compared with the traditional testing method, the provided method has the high testing efficiency.

The embodiment of the invention discloses a method and device for adjusting test parameters and a computer readable storage medium, and the method comprises the steps: setting current parameters of 3Dsweep frequency load according to a pre-defined current value; and performing 3D frequency sweeping and loading on the memory to be tested to obtain a pull-load current. The value range of the current is greatly reduced through the pull-load current obtained after 3D frequency sweeping, the pull-load current serves as a test parameter, the test time can be saved, and the test accuracy is improved. The more accurate the rise time is, the more accurate the test result is. the rise time of the signal generator is determined according to the corresponding relationship between the voltage and thepull-load current and the voltage conversion rate. The pull-load current obtained through 3D frequency sweeping and the rising time obtained through calculation serve as test parameters of the signalgenerator, the test parameters better conform to the actual test range of the to-be-tested memory, and therefore the signal generator conducts pull-load test on the to-be-tested memory according to the test parameters, and the obtained test result is more accurate.

The embodiment of the invention discloses a memory erasing method and device. The memory erasing method comprises the following steps: detecting a power-on signal of a memory; and controlling a plurality of memory blocks in the memory to carry out over-erase verification operation at the same time, so that the threshold voltages of all memory units in the memory are greater than zero voltage. According to the technical scheme provided by the embodiment of the invention, the problems of serious electric leakage of the storage unit and influence on normal use caused by abnormal power failure ofthe existing memory can be solved.

The invention relates to a gas turbine dynamic working condition database sample point selection method. A gas turbine is a three-shaft type gas turbine with an adjustable power turbine guide vane. The gas turbine adopted in the method is structurally characterized in that the power turbine inlet guide vane is adjustable, and the gas turbine is provided with a high pressure shaft, a low pressure shaft and a power turbine shaft to form the three-shaft type structure. The method comprises the steps of calculating and analyzing the influence caused by turbine rotating speed on the gas turbine, calculating and analyzing the correspondence of rotating speeds of the high and low pressure shafts corresponding to the guide vane angles under the steady-state condition, calculating the changing range of the low pressure shaft rotating speed in the acceleration and deceleration process, according to the calculation result, selecting sample points, utilizing a gas turbine simulation model for obtaining sample points, forming a gas turbine dynamic working condition database and the like. According to the gas turbine dynamic working condition database sample point selection method, the power turbine guide vane angle changing phenomenon is considered by means of operating characteristics of the gas turbine, the sample point valuing range is reasonable reduced, the sample scale is greatly reduced, and workloads are reduced.

The invention discloses a water environment mathematical model water quality parameter uncertainty and sensitivity analysis method, which comprises the following steps of: screening water quality parameters to be analyzed and prior distribution characteristics, and taking a value range; extracting a proper amount of parameter groups by using a Latin hypercube sampling method; calculating a water quality simulation result of the water quality parameter through the water environment mathematical model; selecting a specific confidence interval as an upper boundary and a lower boundary for judgingan uncertainty interval, defining an interval coverage rate, an uncertainty interval width and a relative uncertainty interval width, and evaluating the uncertainty of the water environment mathematical model; measuring the sensitivity of the water quality parameters through a sensitivity analysis chart; and finally, optimizing the value range of the sensitive water quality parameters through parameter posteriori distribution. Water quality parameter evaluation is more comprehensive, and the problem of same effect of different parameters is effectively solved; parameter sensitivity is quantified, and parameter posteriori distribution is obtained, and the value range of sensitive parameters is narrowed, and a basis is provided for parameter constant value selection and model sensitivity improvement.

The invention provides a classifier generation method, an atrial fibrillation detection device and a storage medium. The atrial fibrillation detection device and the storage medium carry out the atrial fibrillation/non-atrial fibrillation classification detection on detected signals through the classifier. The generation method of the classifier comprises the following steps of extracting a cardiac interval of a cardiac signal segment of a pre-labeled type; calculating a variation value of the heart beat interval; grouping the plurality of heart beat interval variation values, and extracting the frequency of each group of data as a feature vector; and carrying out machine learning training on the mark type and the feature vector to generate the classifier. According to the classifier generation method provided by the invention, the dimension of the original feature can be reduced, the discrimination of the sample and the robustness to noise are improved, and the classifier can be quickly obtained through training by adopting a simple machine learning model.

The invention provides a material demand scheme design method for equipment manufacturing industry. The method comprises the steps of determining material demand time points according to a material demand data set and clustering a time point set: clustering the material demand time point set by utilizing an improved K_Means algorithm, and clustering the demand time points suitable to combine material demands for centralized purchase into a category; according to material demand information and material types, determining initial purchase schemes, and establishing optimization constraints of the purchase schemes; selecting the purchase schemes meeting the requirements to form a purchase scheme set; obtaining the fitness of the schemes; according to the fitness of the purchase schemes, optimizing the purchase scheme set by utilizing a GWO optimization algorithm; performing variation on the optimized purchase scheme set, checking the varied purchase schemes by utilizing the optimization constraints, calculating the fitness of new purchase schemes, and performing comparison and replacement with the fitness of the initial purchase schemes; and ending the process until satisfied purchaseschemes are obtained. The data support is provided for purchase of production resources, so that the production cost is reduced.

Provided is a method and apparatus for providing information related to density of a PT-RS in a wireless communication system. The operation method of a base station may include: configuring a phase tracking reference signal (PT-RS) corresponding to a terminal; and transmitting time and frequency density information of the PT-RS to the terminal, and the time and frequency density information of the PT-RS is configured based on threshold values of time and frequency density of the PT-RS which are compressed based on at least one of a uniform sampling grid scheme, a non-uniform sampling grid scheme, a combinational indexing scheme, or a scheme of imposing a limit on a threshold value. Other embodiments may be possible.

An incoming direction estimation apparatus estimates an incoming direction of a radar wave using three or more sensors or antennas simultaneously. An arithmetic expression for estimating an incoming direction of a radar wave is configured as sin⁻¹((½πa)*tan⁻¹b). The “a” is d/λ determined by an antenna interval and by a wavelength λ of a carrier, or such, carrying the radar wave. A simultaneous use of three antennas makes it possible to set the a as a value depending on a value d₀=a₀λ based on the interval between first antennas and value d₁=a₁λ based on the interval between second antennas. Therefore, if a wide field of vision of an incoming direction is needed, it is only necessary to adjust (d₁−d₀)/λ=(a₁−a₀), in place of the absolute interval of antennas a=d/λ, thereby making it possible to lessen a limitation on the design of antennas and set a field of vision of the incoming direction appropriately.

The invention discloses a rapid optimization method of a cycloidal gear tooth profile modification coefficient. The method comprises the steps that firstly, by means of the characteristics that the initial meshing clearance of a cycloidal gear and a pin gear is a minimum value at the position and is a minimum value in the interval of [0, pi], derivation operation is conducted, the value range of tooth profile modification coefficients delta rp and delta rrp is narrowed, and the calculated amount for screening the optimal tooth profile modification coefficients delta rp and delta rrp is reduced; secondly, in the optimization process of the tooth profile modification coefficient, when the step of calculating the stress Fmax of the tooth with the maximum stress in the teeth transmitting the force at the same time is carried out, a warning parameter warn is introduced to represent the convergence state of the Fmax, according to the counting condition of the warning parameter warn, the non-solution condition that the Fmax cannot converge is rapidly eliminated in the optimization process, and the optimization speed is further increased.

The embodiment of the invention provides a red envelope amount allocation method and device, a computer device and a readable storage medium, which can reduce the difference between the luck red envelopes. The embodiment of the invention obtains the amount of red envelopes to be distributed and the number n of red envelopes to be distributed; Determining a first candidate value of the maximum amount distributable this time according to the amount of red envelopes to be distributed, the number n of red envelopes to be distributed and the preset minimum amount of single red envelopes, and determining a second candidate value of the minimum amount distributable this time according to the amount of red envelopes to be distributed, the number n of red envelopes to be distributed and the presetmaximum amount of single red envelopes; Thereby, determining the smaller of the first candidate value and the preset single red packet maximum amount as the upper limit value of the allocatable amount, and determining the larger of the second candidate value and the preset single red packet minimum amount as the lower limit value of the allocatable amount; and any value between the lower limit value and the upper limit value is determined as the target amount of the red envelope.

The invention discloses a roughness partition calibration method and system based on posteriori distribution. The method comprises the steps that 1, dividing a target water body into k subareas; 2, determining the roughness value range of the target water body; 3, equally dividing the determined roughness value range into n intervals, and simultaneously extracting a plurality of roughness combinations in each sub-region; 4, selecting a target likelihood function to perform normalization calculation to obtain likelihood values of the sub-regions; 5, calculating the probability of the likelihoodvalue of each sub-region on each sub-interval, and obtaining the roughness posteriori distribution of each sub-region; 6, repeating the steps 3 to 5 for iteration once on the premise that the roughness posteriori distribution of each sub-region replaces the roughness uniform distribution of each sub-region; and 7, obtaining the maximum likelihood value probability of each subarea and the optimalroughness value of each subarea corresponding to the maximum likelihood value probability. According to the invention, the roughness partition calibration precision can be improved.

The invention creatively combines an improved seagull algorithm with an unmanned aerial vehicle group, and discloses a multi-unmanned aerial vehicle gas leakage source positioning method based on the improved seagull algorithm. Compared with a traditional traceability method depending on a fixed monitoring station or a ground mobile robot, the method takes the unmanned aerial vehicle cluster as a traceability platform of gas leakage, and has the advantages of low cost, wide search range, high flexibility, high traceability efficiency and the like. Compared with a standard seagull algorithm, the gas leakage source positioning method based on the improved seagull algorithm has the following innovativeness: the value range of a balance operator B is reduced, the step length is greatly reduced, and the global search capability is greatly improved; the spiral radius r is set to be a fixed value, it is guaranteed that no disordered moving direction occurs in the iteration process of an individual, and the stability of the algorithm is improved; the foraging behavior is introduced, so that other individuals can explore themselves while following the optimal individual, and local optimum is avoided.

Transient connected regions are tracked in a video sequence, marking them as foreground layer or background layer when the transient regions become stable. A stack of background/foreground Gaussian mixer models is maintained for each pixel. The decision to mark a stable region as background layer or a new or existing foreground layer is done by matching the region with each model in the model stack. If the new region matches an existing model, then the layers above the matched layers are purged, or else the new region is pushed as a new foreground layer with a new model.