66results about How to "Improve description accuracy" patented technology

The invention relates to a piezoelectric digital seismometer on land to solve the problem of poor interference resistance of the conventional seismometer. The seismometer consists of a piezoelectric seismometer, a digitalizing unit and a data communication unit which are connected sequentially, wherein the piezoelectric seismometer and the digitalizing unit are directly connected and integrated into a whole; a piezoelectric material of the piezoelectric seismometer consists of a multi-layer piezoelectric device; and the data communication unit provides the function of data communication with a seismic instrument host system. In the piezoelectric digital seismometer, a core body comprises a cylindrical shell, a substrate, the multilayer piezoelectric device, a mass block and extraction electrodes, wherein the substrate is arranged on the bottom of the shell and is in rigid connection with the shell; the substrate is provided with the piezoelectric material; the piezoelectric material is provided with the mass block perceiving seismic wave movement; and the extraction electrodes are arranged on the upper side and the lower side of the piezoelectric material respectively. The piezoelectric digital seismometer has the advantages of small volume, light weight, high sensitivity, small resistance and strong interference resistance.

A method for determining the configuration of a hardware item use with an online auction system is disclosed. The configuration is determined by sending a unique code to the manufacturer that manufactured the hardware and receiving configuration data corresponding to the hardware. The configuration is further determined by executing configuration utility software that analyzes the hardware using diagnostic routines as well as analyzing the operating system registry for hardware information. Re-use of components in the old computer system in a new computer system is also disclosed for maximizing total value of the purchase of a new computer system by re-using high value components. The method retrieves the street price corresponding to the old computer system and subtracts the price from the new computer price without the reused components in order to minimize the cost of the transaction. A computer system for receiving configuration data and submitting an auction description to an online auction site is further disclosed.

An embodiment of the invention discloses an image processing method and equipment, a computer storage medium and system, wherein the method is applied to the cage. The method comprises the following steps of: The cage acquires M groups of image features of the image to be processed from the encoder, and acquires first image representation information corresponding to each group of image features in the M groups of image features. According to each group of image features and first image representation information corresponding to each group of image feature, M image representation informationsets are generated, wherein, a set image features corresponding to an image representation information set generated, and an image representation information set includes at least one second image representation information. The second image representation information included in the M image representation information sets is merged to acquire target image representation information, and the target image representation information is output to the decoder. According to the image processing method and equipment, the computer storage medium and system, it helps to improve the natural statement description accuracy of the image, and optimize the quality of the image content understanding service.

The invention discloses a radar range profile statistics and recognition method based on a PPCA model in the strong noise background, which relates to the technical field of radar automatic target recognition and mainly solves the problem that the current statistics and recognition methods based on the PPCA model are not robust to noises. The training phase comprises the following steps: framing, translating, aligning and strength-normalizing radar HPPR continuously, learning the parameters of each azimuth frame of the PPCA model by adopting the processed HRRP and storing a template. The test phase comprises the following steps: first strength-normalizing, translating and aligning the samples to be tested and then estimating the range of the signal-to-noise ratios (SNR) of the samples; computing the distance value of each frame of each target and deciding the category attribute if the SNR is more than 30dB, and rewriting the distance value, solving the noise energy under SNR condition by minimizing the distance value, finally computing the distance value of each frame of each target and deciding the category attribute if the SNR is less than 30dB. The method has the advantages of robustness to noises and less computation and is applied to identifying radar targets.

The invention provides a carbonate rock microfacies type identification method and a sedimentary facies description method thereof; the microfacies type identification method comprises the following steps: a microfacies classifying step: carrying out microfacies classification for a single well coring segment so as to obtain a plurality of microfacies types; a microfacies identification step: obtaining features of the microfacies types, and identifying each microfacies type according to the features of the microfacies types, wherein the microfacies type features comprise at least one of the following features: imaging logging image feature and earthquake reflection composition feature. In a microfacies types identifying process, the method combines the imaging logging image feature with the earthquake reflection composition feature, thus more accurately identifying microfacies of the carbonate rocks.

The invention discloses a method and a device for modeling a reservoir physical property parameter. The method comprises the following steps: firstly, constructing a three-dimensional sedimentary facies model of a reservoir, and establishing a three-dimensional reservoir petrophysical facies model under the restriction of the three-dimensional sedimentary facies model; secondly, establishing a three-dimensional reservoir physical property parameter model under the restriction of the three-dimensional reservoir petrophysical facies model. Compared with the prior art, the method and the device have the advantages that when a reservoir petrophysical property parameter model is constructed, sedimentary facies restriction is considered, and the influence of reservoir petrophysical facies is also considered, so that a modeling result can reflect a combined action of a reservoir sedimentation action, a diagenesis action and a later-stage construction action, thereby improving the modeling accuracy of the reservoir physical property parameter.

The invention discloses a radar range profile statistics and recognition method based on a FA model in the strong noise background, which relates to the technical field of radar automatic target recognition and mainly solves the problem that the current statistics and recognition methods based on the FA model are not robust to noises. The training phase comprises the following steps: framing, translating, aligning and strength-normalizing radar HPPR continuously, learning the parameters of each azimuth frame of the FA model by adopting the processed HRRP and storing a template. The test phase comprises the following steps: first strength-normalizing, translating and aligning the samples to be tested and then estimating the range of the signal-to-noise ratios (SNR) of the samples; computing the distance value of each frame of each target and deciding the category attribute if the SNR is more than 30dB, and rewriting the distance value, solving the noise energy under SNR condition by minimizing the distance value, finally computing the distance value of each frame of each target and deciding the category attribute if the SNR is less than 30dB. The method has the advantages of robustness to noises and less computation and is applied to identifying radar targets.

The invention discloses an underwater stereoscopic vision system calibration method. The method comprises the following steps of: S101, in underwater environment, shooting images of a plurality of plane calibration plates by using a stereoscopic vision system formed by internal parameter-known cameras, extracting feature points of the image, and carrying out distortion correction on the feature points; S102, constructing a cost function according to a geometric relationship of refraction light paths, randomly selecting a camera in the stereoscopic vision system and calibrating refraction parameters of the camera; and S103, calibrating refraction parameters of the non-calibrated cameras in the stereoscopic vision system and external parameters of the stereoscopic vision system on the basisof the cost function according to the refraction parameters of the calibrated camera. By using the plane calibration plates, the calibration precision is improved; and on the basis of a multilayer refraction model, no system error is caused, the description precision is high, and the multilayer refraction model can be used for calibrating the refraction parameters of a single underwater camera orcalibrating the refraction parameters and external parameters of underwater stereoscopic vision systems and underwater multi-camera systems.

The invention discloses an image subtitle generation method of feature reconstruction ASCA-FR of a joint attention mechanism based on multi-scale learning MLL and adjacent time nodes. The invention mainly solves the problems of inaccurate generated caption description and unsmooth expression caused by the fact that the output of an attention model at a certain moment only considers the feature setof an image and the word vector at the previous moment and only uses a cross entropy loss function to train a network in the prior art. The method comprises the following specific steps: (1) generating a natural image test set and a training set; (2) extracting feature vectors; (3) constructing ASCA-FR network; (4) training ASCA-FR network; (5) obtaining natural image subtitles; according to themethod, the MLL loss function pair is utilized to train the constructed ASCA-FR network, so that the generated subtitles are accurately described and smoothly expressed.

A strong maneuvering target tracking method based on multiple variable structure models relates to the field of target tracking, and aims at solving the problem of low target tracking accuracy duringtracking of a near-space high-speed strong maneuvering target. The method comprises the steps of 1, constructing a dynamic tracking model set by utilizing dynamic characteristics of a target aircraft,and then obtaining a state equation set of a maneuvering target tracking system; 2, establishing a system measurement model, and obtaining a measurement equation and measurement noise of the system according to the established system measurement model; and 3, based on the state equation set of the system, the measurement equation of the system and the measurement noise, recursively estimating themotion state and the aerodynamic parameters of the target aircraft. According to the method, the dynamic tracking model set is constructed based on the dynamic characteristics of the target aircraft,the description precision of target motion is improved, and the target tracking precision is improved by adopting an improved variable structure multi-model tracking algorithm.

The invention discloses a target positioning method applicable to a vehicle-mounted photoelectric watching-aiming system with atmospheric refraction and belongs to the technical field of vehicle-mounted photoelectric observation. By adopting the target positioning method, geographical location information of a target can be solved in real time by modifying light deflection with atmospheric refraction and implementing coordinate conversion calculation of different coordinate systems according to geographical location information of a carrying vehicle, posture angles of the carrying vehicle, angles of a photoelectric turret, laser ranging values, the altitude of a target point and atmospheric parameters of a carrying vehicle point. By adopting the target positioning method, target positioning errors caused by atmospheric refraction can be modified on the basis of a conventional vehicle-mounted target positioning way, that is, vehicle-mounted target positioning precision is improved through innovation of techniques, and the method has the characteristics of being high in calculation precision, simple to operate, and the like.

The invention discloses a seismic response simulation method based on a VTI anisotropic propagation matrix. The method comprises steps that VTI medium elastic parameters are obtained through rock physical modeling according to logging data; a frequency-varying reflection coefficient Rf of corresponding reflection waves is calculated according to the generalized propagation matrix theory of an inelastic thin interbed reflection coefficient; an amplitude spectrum Uf of the corresponding reflection waves is solved according to the frequency-varying reflection coefficient Rf; inverse Fourier transform of the Uf is performed to obtain a reflection wave waveform ut. Compared with a seismic synthetic record obtained by a conventional convolution model, the seismic synthetic record obtained by themethod is advantaged in that waveform description precision is improved.

The invention discloses a system for marking positions along a road in a map and a method thereof. The system is characterized by comprising a position marking module, a position obtaining module, a position data conversion module and a position information display module. The marking is performed in the map and is virtual, so that the sign erection on the spot is not needed; the manufacturing cost is low; the manufacturing and updating speed is high; the sharing can be realized; the updating and maintaining cost is very low; the system and the method are suitable for the position marking by using all roads. The road name, the distance from the road starting point and the vertical distance from the center line of the road are used for marking, so that the direct convenient-to-express method capable of being used for marking surface positions can be realized.

The invention relates to a BP (Back-Propagation) neural network-based human body behavior recognition method. The method includes human body behavior modeling processing and human body behavior recognition processing. The human body behavior modeling processing includes the following steps that: a training dataset is acquired; basic feature information is extracted on the basis of a filtering type feature selection method; and hierarchical clustering analysis processing is performed on an extracted basic feature information dataset, so that a human body behavior classifier can be generated. The human body behavior recognition processing comprises the following steps that: a BP neural network model is constructed; human body behavior classification data are imported into the neural network, a quasi-Newton back-propagation method is adopted to perform training; the BP neural network algorithm is adopted to continuously improve and optimize the human body classifier; and discretization processing is performed on an output result, and a human body behavior recognition processing result is obtained. The method of the invention has the advantages of simplicity, high efficiency, high recognition accuracy, high expansibility, stable and reliable working performance and wide application.

The invention provides an electrochemical process-based lithium ion battery equivalent model in a full SOC range, and relates to the field of lithium ion battery equivalent models. The model comprises a lithium ion battery real-time SOC simulation model composed of capacitors Ccapacity, Cds1 and Cds2, resistors Rds1 and Rds2, a first current source and a second current source, and a lithium ion battery terminal voltage response simulation model composed of two capacitors Cdl and Cconc, an impedance Zctr, a voltage source UOCV and a terminal voltage Ut. Ut is equal to UOCV (USOC_surf)-IRohm-eta ctr-eta conc. The model is high in simulation precision in the full SOC range of the lithium battery, is high in calculation efficiency, avoids the solving of a partial differential equation, and is more suitable for being applied to a BMS.

The invention provides an object information display method and a mobile terminal. The method comprises the steps of obtaining depth information of an object; Establishing an object model of the target object according to the depth information and the size information of the target object; Establishing a virtual scene comprising a target reference object model and a target object model; Displayinga virtual scene, According to the invention, the virtual scene comprising the target object model and the target reference object model is established by using the depth information of the object; According to the technical scheme, the reference object model and the object model are displayed on the mobile terminal, so that a user can perceive and judge the size of the object through visual omparison of the reference object model and the object model in the scene, the size accuracy of the object model established according to the depth information and the size information of the object is higher, and the description accuracy of the size of the displayed object is improved.

The invention discloses a fixation point trajectory description system based on video analysis. The fixation point trajectory description system comprises a data acquisition and preprocessing module,a pupil positioning module, a fixation point calibration module and a fixation point trajectory description module. The invention further discloses a fixation point track description method based on video analysis. A video eye movement image is collected; pretreatment operation is carried out, the coordinates of the pupil center and the corneal reflection facula center in an eye pattern are solvedthrough a pupil coarse positioning method and a pupil fine positioning method, the three-dimensional space mapping relation between a vector formed by the pupil center and the corneal reflection facula center and a fixation point is solved in combination with a dynamic head compensation model, and fixation point track description is conducted through the mapping function. According to the invention, the fixation point track of the user is obtained on the basis of establishing the fixation point three-dimensional space mapping relation, the pre-judgment capability of people on the region of interest of the user is improved, the optimization of advertisement webpage layout can be effectively supported, and the method has the advantages of being simple to use, high in method precision, largein application potential and the like.

A spacecraft formation flying method in a weak-stability boundary area of the invention comprises the following steps: (1) solving out an earth-moon low-energy transfer orbit of a main spacecraft based on weak stability boundary properties under a sun-earth-moon-spacecraft four-body model; (2) deriving a relative dynamic equation of an accompanying spacecraft under the sun-earth-moon-spacecraft four-body model; (3) designing a Hamiltonian structure shape maintenance controller; (4) solving out a bounded relative flying orbit of the accompanying spacecraft; and (5) planning a main spacecraft and accompanying spacecraft formation flying orbit based on the earth-moon low-energy transfer orbit of the main spacecraft obtained in step (1). The precision of a formation flying basic dynamic model is improved. The factors of gravity environment change are taken into consideration in formation control. Moreover, a formation flying control method based on a Hamiltonian structure shape maintenance controller is put forward to generate a bounded relative flying orbit in the earth-moon low-energy transfer process.

The invention discloses a time-varying fractional FCOR multi-axis coordinated control system performance evaluation method which includes the following steps: S1, data acquisition; S2, spatial motion track reconstruction; S3, fractional model structure estimation; S4, fractional model parameter estimation; S5, output-noise model estimation; S6, variable structure whitening filtering; and S7, time-domain and frequency-domain correlation analysis. In the continuous operation process of a multi-axis coordinated control system, an external system performance evaluation module continuously implements the steps S1 to S7. Compared with a built-in system evaluation algorithm, the external system performance evaluation module and algorithm of the invention is independent of an evaluated system (namely, higher reliability), and thus, the data meets the 'single input and single output' requirement of an FCOR algorithm; and the method can achieve higher model description precision and a better whitening effect.

The invention discloses a video processing method, which comprises the steps of editing a to-be-edited video according to a scene, and obtaining a target video; obtaining characteristic parameters ofthe target video; generating keywords of the target video according to the feature parameters; and storing the keyword and the target video in an associated manner. The invention further discloses a video searching method, terminal equipment and a computer readable storage medium. According to the invention, editing is carried out according to the scene change, the target video can be ensured to be in the same scene, the accuracy of identifying the feature parameters in the target video can be effectively improved, and the corresponding keywords are generated according to the feature parameters of the target video, so that the target video and the keywords have the beneficial effects of strong relevance and high description accuracy.

The invention relates to influence maximization methods of theme interest, in particular to an influence maximization method in a social network and based on theme interest, and aims to solve the problem of low information dissemination prediction accuracy caused by the fact that existing IC models do not take features of dissemination items into consideration and suppose probability of influenceon all dissemination items to be equal and existing TIC models do not take interest distribution of users into consideration and cannot accurately describe information dissemination laws. The method includes: step 1, building a dissemination model TI-IC based on theme interest; step 2, utilizing an EM algorithm to learn parameters of the TI-IC model and theme distribution vector of a new dissemination item; step 3, putting forward an influence maximization algorithm for the TI-IC model based on the step 2. The method is used in the field of influence maximization problems of the social network.

The invention provides a prestack seismic attribute-based carbonatite reservoir phase forecast method. The prestack seismic attribute-based carbonatite reservoir phase forecast method comprises the steps of inverting a carbonatite prestack reservoir elastic parameter; building a logging reservoir parameter information-based carbonatite reservoir phase prior probability model; and achieving reservoir three-dimensional reservoir phase forecast of carbonatite reservoir by a Bayesian classification method according to phased achievements of the first step and the second step. According to the prestack seismic attribute-based carbonatite reservoir phase forecast method, logging reservoir information-based reservoir phase division of carbonatite reservoir and carbonatite reservoir phase prior probability model is used as a basis, excellent reservoir phase forecast of the carbonatite reservoir is achieved by the prestack seismic reservoir elastic parameter inversion and reservoir multi-parameter Bayesian classification method, the carbonatite excellent reservoir description accuracy can be improved, the exploration and development risk of the carbonatite risk is reduced, and meanwhile, adecision basis is provided for well location deployment of the carbonatite reservoir.

The invention relates to a description model and a parameter identification method for intrinsic characteristic parameters of an MOSFET device. The description model is a fractional order model for PNjunction capacitance bias voltage, and the parameter identification method of the description model is a fractional order multi-target offline parameter identification method based on a differentialevolution algorithm. The method comprises the following steps: 1) obtaining data that the capacitance value of the junction capacitor changes along with bias voltage according to a data manual of a certain type of MOSFETs; 2) according to the fractional order model, obtaining the relationship of the junction capacitance with the bias voltage; 3) taking the average absolute percentage error of thefractional order model CE and the corresponding capacitance value in the data manual as a target function of a parameter identification method based on differential evolution to carry out data fitting. The description model provided by the invention can accurately describe the change curve of the intrinsic characteristic parameter PN junction capacitance of the MOSFET device along with the bias voltage, so a reference basis can be provided for the design and reliability analysis of a circuit system containing the element.

The invention discloses a radar range profile statistics and recognition method based on a FA model in the strong noise background, which relates to the technical field of radar automatic target recognition and mainly solves the problem that the current statistics and recognition methods based on the FA model are not robust to noises. The training phase comprises the following steps: framing, translating, aligning and strength-normalizing radar HPPR continuously, learning the parameters of each azimuth frame of the FA model by adopting the processed HRRP and storing a template. The test phasecomprises the following steps: first strength-normalizing, translating and aligning the samples to be tested and then estimating the range of the signal-to-noise ratios (SNR) of the samples; computing the distance value of each frame of each target and deciding the category attribute if the SNR is more than 30dB, and rewriting the distance value, solving the noise energy under SNR condition by minimizing the distance value, finally computing the distance value of each frame of each target and deciding the category attribute if the SNR is less than 30dB. The method has the advantages of robustness to noises and less computation and is applied to identifying radar targets.

The embodiment of the invention provides a network data detection method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining a tunnel request segment, obtaining at least two character strings contained in the tunnel request segment, and generating a character distribution matrix corresponding to the at least two character strings, wherein the character distribution matrix comprises elements corresponding to characters in the at least two character strings respectively, the elements comprise character distribution frequencies, and the character distribution frequencies are determined based on distribution positions of the characters in the at least two character strings; calculating abnormal request evaluation values respectively correspondingto at least two to-be-detected sub-matrixes in the character distribution matrix according to the character distribution frequencies, and selecting an abnormal request evaluation value meeting a numerical threshold value from the at least two abnormal request evaluation values as a target abnormal request evaluation value; and generating evaluation reference information for evaluating the abnormaltrend of the tunnel request segment according to the target abnormal request evaluation value. By adopting the embodiment of the invention, the accuracy of network data detection can be improved.

The invention discloses a production line efficiency automatic monitoring and analysis system and method, and the system comprises a production line data collection module, an OEE calculation module, and a result display and output module. The OEE calculation module further comprises a theoretical time utilization rate calculation unit, a production scheduling utilization rate calculation unit, a time utilization rate calculation unit, a configuration utilization rate calculation unit, a program point efficiency calculation unit, a performance utilization rate calculation unit, a product qualification rate calculation unit and a data summarization and OEE calculation unit. According to the invention, OEE automatic monitoring of an industrial production enterprise is realized in an automatic and systematic manner, so that the human input cost is saved, and the OEE management efficiency is remarkably improved.

The invention relates to a fragmented knowledge intelligent aggregation method. The method comprises the steps that step 1, knowledge element ontologies are defined; step 2, fragmented knowledge ontology associated aggregation is defined; step 3, associated aggregation rules based on ontology implication are established; step 4, the determination of aggregation associated rules is conducted; step5, the determination of fragmented knowledge associated rules based on the knowledge element ontologies is conducted; step 6, fragmented knowledge aggregation associated discovery is conducted; step 7, fragmented knowledge aggregation is achieved. According to the method, the associated relationship between two or more knowledge element ontologies is determined through supporting and confidence level determination, and fragmented knowledge aggregation is achieved through a strong associated method; fragmented knowledge characteristics are analyzed by the method, facing the demands of online study, original solid knowledge structures are repartitioned and dynamically aggregated into a knowledge cluster with a self-organizing capability to finally complete fragmented knowledge aggregation, and learners are guided to fully utilize fragmented time to accurately acquire meaningful knowledge contents.

The invention relates to a coherence story generation system and method based on vision and theme collaborative attention. The method comprises the following steps: 1) extracting photo album feature vectors and time dynamic information; 2) obtaining topic probability distribution of each description statement and predicting topic distribution information in each image in the photo album; 3) generating an image description statement with theme coherence based on vision and theme collaborative attention; and 4) carrying out phrase beam search on the image description statement through a phrase beam search algorithm considering n-gram diversity, so that the accuracy and diversity of visual story description expression are improved. Compared with the prior art, the invention has the advantages that the theme coherence of the description statement is enhanced, the expression diversity of the story text is improved, and the generation quality of the visual story is optimized.

The invention discloses a matching method for automatically analyzing request parameters. The matching method comprises the following steps of creating an annotation identifier, creating an analysis module and processing JSON parameters; in order to improve the development efficiency and accuracy, analyzing a whole introduced JSON string into a plurality of input parameters according to the number, names and the types of the development receiving parameters, matching the input parameters a with the input parameters established through development, and assigning automatically, so that the characteristic that the input parameters of the method are clear is reserved, the description accuracy of the method is improved, and therefore development errors and development efficiency reduction caused by the fact that actual parameter items in an input parameter object are not known in development are reduced. Moreover, the method avoids the situations that JavaBean corresponding to various typesis compiled for analyzing data corresponding to JSON, the amount of compiled codes and unnecessary class definitions are reduced, and the memory space is occupied, so that a data receiver can know the number and meaning of the received parameters more clearly, the understanding deviation is reduced, and the code accuracy and the readability are improved.