34results about How to "Keep useful information" patented technology

The invention discloses a method and a system for denoising partial discharge signals of power cables and extracting effective signals. A new threshold function is proposed to perform threshold processing on wavelet coefficients after wavelet decomposition. The problems existing in an existing hard threshold method and a soft threshold method for processing signals can be solved. Denoising processing and extraction of the effective signals can be performed on cable operation data laid in the cable trench or directly buried underground. Accurate positioning of cable faults is realized, and fault points are effectively found out in time, thereby guaranteeing safe and effective operation of electric power facilities.

The invention provides a method for utilizing a terahertz time-domain spectroscopy technology to detect amino acid content in grain. The method comprises the following steps: grinding and then performing tableting on a grain sample to be detected, directly utilizing a terahertz time-domain spectroscopy system to detect the sample subject to tableting in a transmission measurement mode and under the nitrogen condition to obtain terahertz time-domain spectroscopy signals as sample signals, under the same conditions, acquiring the terahertz time-domain spectroscopy signals under the nitrogen condition as reference signals, then obtaining an absorptivity spectrum of the grain tableting sample to be detected, at last dividing the absorptivity spectrum of the grain tableting sample to be detected into a calibration set sample absorptivity spectrum and a verification set sample absorptivity spectrum, utilizing a partial least squares regression method to establish a quantitative analysis mode, and obtaining the quantitative detection value of the grain sample to be detected. The method disclosed by the invention is simple in preparation of the sample, is not required to process any pretreatment, and can practically and effectively realize fast and accurate quantitative detection on the amino acid in the grain or food.

The invention discloses a fatigue driving recognition method and system based on multiple features, and the method and system carry out the preprocessing of an image, filter noise, and avoid problemsthat the image quality is poor and the detection precision is low because the image is affected by an external environment factor. The AdaBoost algorithm is adopted to stably, quickly and efficientlydetect the human face, so that the complexity of human face detection is reduced. According to the scale space-based face target tracking algorithm, a self-adaptive high-confidence updating strategy is adopted, and when an error occurs in a target tracking stage, the confidence of target detection is relatively low, and a model is not updated, so that the risk of drifting of the tracking algorithmis effectively reduced, and the tracking precision is improved. The SVM classifier is adopted for eye state recognition, the precision of eye state recognition is improved, and therefore the method is high in recognition precision and high in adaptability to the environment.

The invention discloses a face image normalizing method. The method comprises the following steps of 1, normalizing a target face image to acquire an initial face gray image; and 2, adjusting the gray value of the initial face gray image, wherein the gray value of a relatively flat face area is relatively reduced, and the gray value of a relatively un-flat face area is relatively increased. The scaling factor of a Weber face is adaptively adjusted according to a space flatness mask, intrinsic information of different space positions of a face base image under normal lighting condition can be used, and when shadow is caused by illumination in a target image, the shadow part has relatively small scaling factor, so that large response is not caused, and the problem that a shadow edge cannot be effectively processed by the primary Weber face method is effectively solved. Complementary information under different scales can be effectively used by a multi-scale adaptive Weber fusion method provided by the invention, so that more useful information for face identification/authentication is kept.

The invention relates to a word splitting method and device aiming at a URL. The method comprises the steps of 1, dividing a URL address according to an internal layer structure of the URL address toobtain several layer parts; 2, conducting symbol dividing and regular expression filtering on the layer parts in sequence; 3, dividing a character string obtained after processing conducted in step 2to obtain a URL word splitting sequence. The URL address is divided into five layer parts in step 1, namely, a protocol type, a free domain name, a secondary domain name, a primary domain name and a path; a bidirectional maximum matching algorithm and a probability model are utilized to divide the character string in step 3. The layer structure of the URL itself is made full use of, the URL can beefficiently divided, useful information in the URL address is maximally reserved, the obtained URL word splitting sequence can be used for feature analysis in tasks like webpage classification and angling URL detection, and the task accuracy can be effectively improved.

The invention provides a method for detecting amino acid content in grain by use of terahertz frequency-domain spectrum technology. The method comprises the steps of grinding grain samples to be tested and then tableting the grain samples to be tested, directly testing the grain samples to be tested by use of a terahertz time-domain spectrum system in a transmission measurement mode under a nitrogen atmosphere to obtain the terahertz time-domain spectrum signals of the samples, performing Fourier transform on the time-domain spectrums to obtain the frequency-domain spectrums of the samples, performing normalization spectrum preprocessing on all the obtained frequency-domain spectrums, and finally, dividing the preprocessed frequency-domain spectrums of the grain tableted samples to be tested into calibration set frequency-domain spectrums and verification set frequency-domain spectrums, and establishing a quantitative analysis model by use of the partial least squares regression method so as to obtain the quantitative detection values of the various grain samples to be tested. The method for detecting the amino acid content in the grain by use of the terahertz frequency-domain spectrum technology is simple in sample preparation, simple to operate, and capable of truly and effectively realizing fast and accurate quantitative detection on the amino acids in the grain.

The present invention discloses a three-dimensional CBCT (cone-beam computed tomography) image denoising method on the basis of improved nonlocal means. The three-dimensional CBCT image denoising method on the basis of the improved nonlocal means comprises: obtaining projection data of three-dimensional CBCT images having different angles, projection data of a three-dimensional CBCT image having an angle corresponding to a set of projection data of the three-dimensional CBCT images, calculating edge information of the three-dimensional CBCT images, and dividing background subblocks and texture subblocks of the three-dimensional CBCT images; respectively calculating a noise standard deviation of a background region and a mean of the average gradient values of the texture subblocks in the three-dimensional CBCT images; respectively calculating filtering intensity values of the projection data of the three-dimensional CBCT images having different angles according to the edge information, the noise standard deviation of the background region and the mean of the average gradient values of the texture subblocks in the three-dimensional CBCT images; and searching for other pixel points in the three-dimensional CBCT images similar to filtered pixel points, and calculating the similarity among other pixel points similar to the filtered pixel points to achieve the three-dimensional CBCT image denoising according to the filtering intensity values of the projection data of the three-dimensional CBCT images having different angles.

The invention provides an audio processing method and device based on artificial intelligence, electronic equipment and a computer readable storage medium, and relates to a cloud technology and an artificial intelligence technology; the method comprises the following steps: acquiring an audio clip of an audio scene , and the audio clip comprises noise; performing audio scene classification processing based on the audio clip to obtain an audio scene type corresponding to noise in the audio clip; and determining a target audio processing mode matched with the audio scene type, and applying the target audio processing mode to the audio clip of the audio scene. According to the invention, the audio quality can be improved based on the targeted audio processing of the audio scene.

The invention provides a special-shaped radar combined decision target detecting method. A high threshold and a low threshold are utilized for performing CFAR detection on original video data of eachradar. After performing high-threshold detection and low-threshold detection on each data, a distance condensation point trace is extracted from the data. Normalization is performed on the point traceamplitude according to distance point trace local signal clutter/noise ratio and the noise substrate of each radar. Combined detection and decision is performed on the low-threshold detection condensation point traces of all radars according to a radar distance measurement error. Furthermore the high-threshold detection condensation point traces of multiple radars are combined, thereby finishingoriginal-video-grade multiple-radar combined target detection processing. According to the method of the invention, the high threshold and the low threshold are utilized, thereby improving target detection probability. For each radar, the distance point trace is extracted and then combined determining is performed, thereby preventing a problem of direct fusion processing incapability caused by different multiple-radar sampling rates, different resolutions, different noise substrates and the like.

A handheld molecular imaging navigation system comprises a multi-spectral light source module configured to provide light in a plurality of different spectrum bands in a time division control manner according to a control signal sequence to irradiate an inspected object; a time division control module configured to generate the control signal sequence; an optical signal acquisition module configured to acquire a near-infrared fluorescence image and a visible light image of the inspected object in a time division control manner according to the control signal sequence provided by the time division control module; and a processing module configured to perform image processing on the acquired near-infrared fluorescence image and visible light image according to the control signal sequence to fuse the visible light image and the fluorescence image and output the fused image, and output a feedback signal according to the acquired near-infrared fluorescence image and visible light image.

The invention discloses an earth surface deformation prediction method and system based on an InSAR technology and behavior characteristic deep learning, is applied to the technical field of earth surface deformation monitoring of the InSAR technology, and is used for obtaining image data, obtaining a prediction model based on an earth surface time sequence deformation prediction method combining the InSAR technology and a 3D-CNN network, and carrying out earth surface deformation prediction by combining noise reduction processing. According to the method, the ground surface settlement is monitored in a large range and with high precision through the InSAR technology, the economic investment of ground surface time sequence settlement monitoring can be effectively reduced, then the 3D-CNN network is utilized to learn ground surface time sequence deformation characteristics, the future ground surface deformation trend is predicted, and auxiliary decision making and technical support are provided for preventing ground surface settlement disasters; the method does not need to select a stable region to estimate deviation on a regional scale, carries out statistical analysis operation on the image after removing null values and abnormal values, can globally and automatically correct strip noise, and can retain useful information of a landslide deformation detection result to the greatest extent.

The invention provides a partial discharge signal denoising method for an oil-immersed transformer, and the method comprises the steps: inputting a to-be-denoised signal, and decomposing the to-be-denoised signal into K components through a VMD algorithm; determining a final decomposition component number K according to the energy loss coefficient; screening components containing more noises according to the warpage and permutation entropy of each component; using a wavelet packet denoising algorithm to denoise the component with more noise; and reconstructing the processed component into a partial discharge signal. According to the method, two types of noise contained in the partial discharge signal can be effectively removed, and useful information of the partial discharge signal is reserved to a greater extent.

The invention discloses a rapid image repairing method based on a rough set. The rapid image repairing method comprises the following steps: establishing a rough set model for an acquired digital image, and abstracting a knowledge representation system; then performing brightness grade division according to an equivalence relation in the knowledge representation system; solving illumination brightness layers of a discourse domain (an image) through upper approximation and lower approximation according to the division; then introducing approximate classification accuracy and system parameter importance, and calculating the variation trend of a value along with the division layer number; finally, feeding back optimal division according to the convergence of the approximate classification accuracy and the system parameter importance so as to self-adapt to the brightness layers, dividing all the brightness layers into an excessive region, a normal region and an over-dark region, and restoring the brightness layer by layer by using the normal region as the reference. A clear image with high visibility and outstanding details is obtained finally. The rapid image repairing method is high in adaptability and high in processing speed, has certain online timeliness, is of great realistic significance, and has a great practical value; various severe illumination environment conditions can be overcome.

The invention relates to an image feature recognition method, in particular to an image feature recognition method for a soil drought region, which comprises the following steps of: acquiring an image, storing, processing the image, and transmitting a processing result to an edge server module of a cloud server; the cloud server module is in wireless communication connection with the edge server module and used for storing soil basic information and providing computing power, and the user side module is in wireless communication connection with the cloud server module and used for controlling and checking the edge server module, judging an area needing irrigation and taking irrigation measures. According to the technical scheme for monitoring the crop area and identifying the drought area, the defect that the agricultural production cost is greatly increased due to the fact that a traditional crop irrigation technology is backward, can only be manually carried out, cannot intelligently monitor the crop condition and cannot carry out fixed-point and fixed-area precise irrigation can be effectively overcome.