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

Piezoelectric digital seismometer on land

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.
Owner:INST OF GEOLOGY & GEOPHYSICS CHINESE ACAD OF SCI +1

Image processing method, equipment, computer storage medium and server

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.
Owner:TENCENT TECH (SHENZHEN) CO LTD

Radar range profile statistics and recognition method based on PPCA model in strong noise background

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.
Owner:XIAN CETC XIDIAN UNIV RADAR TECH COLLABORATIVE INNOVATION INST CO LTD

Radar range profile statistics and recognition method based on FA model in strong noise background

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.
Owner:XIAN CETC XIDIAN UNIV RADAR TECH COLLABORATIVE INNOVATION INST CO LTD

Underwater stereoscopic vision system calibration method

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.
Owner:HUST WUXI RES INST

BP (Back-Propagation) neural network-based human body behavior recognition method

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.
Owner:ZHEJIANG UNIV OF TECH

Fixation point track description method and system based on video analysis

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.
Owner:ANHUI UNIVERSITY

Prestack seismic attribute-based carbonatite reservoir phase forecast method and device

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.
Owner:CHINA PETROLEUM & CHEM CORP +1

Description model and parameter identification method for intrinsic characteristic parameters of MOSFET device

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.
Owner:CHINA THREE GORGES UNIV

Radar range profile statistics and recognition method based on FA model in strong noise background

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.
Owner:XIAN CETC XIDIAN UNIV RADAR TECH COLLABORATIVE INNOVATION INST CO LTD

Network data detection method and device, computer equipment and storage medium

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.
Owner:TENCENT TECH (SHENZHEN) CO LTD

Fragmented knowledge intelligent aggregation method

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.
Owner:TIANJIN UNIVERSITY OF SCIENCE AND TECHNOLOGY

A matching method for automatically analyzing request parameters

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.
Owner:卓集送信息科技(武汉)有限公司
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