55results about How to "Keep the original information" patented technology

The invention provides an unmanned aerial vehicle airborne LiDAR point cloud filtering method based on adaptive gradient. The method comprises the following steps: obtaining point cloud data in a test region and carrying out filtering on the point cloud data; 2) establishing index for the point cloud data; 3) carrying out calculation and keeping an optimal plane in a grid; 4) selecting an elevation lowest point in a preset window as an initial seed point of a ground point; 5) calculating the distance between the optimal plane and the ground point and gradient from the ground point to the center of the optimal plane, and obtaining a ground point set according to gradient threshold; 6) serving a point in the initial ground point set as a base point, and generating a digital terrain model through an inverse distance weighted interpolation method; and 7) through the newly-generated digital terrain model, obtaining update value of the gradient threshold, repeating the steps 4)-6) and obtaining a final ground point set. The filtering method based on adaptive gradient improves algorithm operation efficiency.

The invention discloses a parking payment system, which is characterized in that a vehicle owner terminal registers on a parking platform, logs in a platform account and opens an ETC payment functionof a specified license plate number; a toll collection system comprises a license plate recognition camera and a control server, the license plate recognition camera monitors the motion condition of avehicle on a lane, captures a vehicle head when finding that the vehicle is about to enter a parking area, acquires a vehicle image, performs image recognition on the vehicle and a license plate, generates a license plate number and sends the license plate number to the control server; the control server calculates the parking duration according to the vehicle parking time and generates a parkingfee payment order, and the control server sends the license plate number and the payment order to the parking platform for ETC payment; and the parking platform matches the license plate number withthe platform account, judges whether the platform account corresponding to the license plate number opens an ETC payment function, and judges whether the license plate number is in an ETC blacklist.

The invention relates to a novel noise image fusion method based on a cycle spinning (CS) technology and Contourlet domain context hidden Markov model (CS-CT-CHMM). First, a source image containing a certain level of Gauss white noise is de-noised by use of the CS-CT-CHMM, wherein the structure of the context is calculated based on neighborhood entropies of a father node, two nearest cousin nodes and a current node of a Contourlet decomposition coefficient; then, Shearlet transform is performed on the de-noised image, and a fusion rule is designed by adopting a weighted average fusion strategy for low-frequency sub-band coefficients and adopting an improved pulse-coupled neural network (IPCNN) model for high-frequency sub-band coefficients; and finally, reverse Shearlet transform is performed to obtain a fused image. The CS-CT-CHMM de-noising method can protect detailed information of the image and suppress the pseudo Gibbs effect while effectively removing noise, while the fusion method combining Shearlet transform and IPCNN is advantageous in terms of image contrast enhancement and the amount of information. Compared with the traditional fusion method, the quality of the fused image is greatly improved.

Provided is a panorama image splicing method based on edge vertical distance matching. The method comprises the following steps: (1), extracting the edges of two images by use of a CANNY algorithm; (2), screening a matched pixel column; (3), performing matching according to variances of edge pixel ordinates; (4), performing grouping according to variance values and reflected image relative positions; (5), performing the edge vertical distance matching on each group, and determining that a smallest group distanced from statistical values is an image splicing position; and (6), performing image stitching processing. According to the invention, through matching the edge vertical distances of two neighboring images, automatic splicing of a panorama image is realized. A matching position is screened in advance so that computing amount is reduced, computing time is saved, and computing precision is improved. A vertical distance coupling algorithm can help to find out an optimal seam, i.e., the seam position is disposed at a position with smaller information content in an image, and an information key position is avoided as likely as possible; and an original image is completely reserved in most area of a splicing image so that information of the original image can be reserved to the maximum.

A method used for extracting an unearthed bamboo slip and silk character pattern image and based on digital image processing belongs to the technical field of computer image processing of unearthed bamboo slip and silk. The method is characterized in that Photo shop digital image processing software is used for extracting a digital image of the unearthed bamboo slip and silk, and steps of capturing of single character pattern, re-distribution of gray level between each character stroke ink mark and a bamboo slip and silk background according to original ratio, distinguishing of radical selecting area of a single character and a stroke selecting area in each radical by using an original basic stroke table and a basic radical table area, reserving of all stroke images in each radical selecting area and deleting of non-stroke ink mark image are conducted sequentially, and extracting of a single character pattern is achieved. By means of the method, basic radical separation is used as a basis, thorough separation of the unearthed bamboo slip and silk character patterns and a bamboo slip and silk background is achieved, character pattern images which reserve character original information and are clear and distinguishable are presented, and the technical problem of great distortion of images caused by copying the unearthed bamboo slip and silk through a traditional writing method at present is solved.

The invention provides a credit card simulation test method under multiple transaction channels. The method comprises the following steps: receiving credit card information configured on a preset configuration interface; generating a simulation message according to configured credit card information; determining a target transaction channel and a target communication strategy according to a presetrule; packaging the analog message according to the target communication strategy to obtain a response message; obtaining a test case set corresponding to the target transaction channel, wherein thetest case set is a test case library formed after desensitization processing according to credit card transaction data in a production environment; performing simulation test on the response message according to the test case set; and outputting a test result. The invention also provides a credit card simulation test device under multiple transaction channels, a terminal and a storage medium. According to the invention, a plurality of credit card transaction channels and communication strategies under different credit card transaction channels are configured through the automatic test platform, and simulation of credit card transaction information under various transaction channels can be supported at the same time for the first time.

The invention discloses a method for detecting a P wave and T wave in an ECG signal based on Gaussian function fitting. Firstly, baseline drift removal is carried out on an original signal, and then according to QRS wave group labeling of the existing ECG signal, the signal is divided into a series of single heartbeat signals; secondly, for each heartbeat cycle, search intervals where the P wave and the T wave roughly and possibly appear are divided respectively through approximate ranges of the P wave and T wave, the wave crest direction and suspected wave crest appearance point of each search interval are determined through numerical integration; according to the information, a Gaussian function is used and appropriate initial parameters are configured for fitting; finally, the wave crests of Gaussian function fitting results and starting and stopping points of the wave crests are calculated and taken as the wave crests and starting and stopping points of the P wave and T wave of a current heartbeat cycle. The method for detecting the P wave and T wave in the ECG signal based on Gaussian function fitting has the advantages that the interference of noise signals is effectively avoided, and the robustness of an algorithm is enhanced, so that the accuracy of the detection of the P wave and T wave is improved.

The invention discloses a detection method for odor substances in exhaust gas of the perfume and essence industry. According to the invention, exhaust gas discharged by the perfume and essence industry is directly or indirectly acquired, then solid-phase micro-extraction (SPME) or solid-phase adsorption-thermal desorption technology are employed for extraction of a sample; and extraction temperature and extraction time are adjusted and GC-MC is cooperatively used for analysis of the exhaust gas sample. The detection method is high in efficiency, simple to operate and applicable to analysis of odor substances with any concentration. An airbag method and negative pressure sampling technology are cooperatively used, an appropriate sampling point is selected and a representative gas sample is acquired, so original information of the sample is maintained to a greatest extent. A sampling bag has good airtightness, and a sample in the bag can be maintained for more than one month. SPME pretreatment technology is employed, so the advantages of greenness, rapidness, no pollution and repeated usability can be obtained. The detection method is applicable to both exhaust gas discharged in the perfume and essence industry and to detection of exhaust gas of industries like petroleum, the chemical industry, food, medicine and cosmetics.

The invention discloses an attribute-level sentiment analysis method based on a hierarchical attention mechanism and a gate mechanism, and aims to make full use of the relationship between contexts and attribute words to make the contexts and the attribute words fully associated. Highlighting the importance of different words in the context and the attribute words so as to improve the attribute-level sentiment analysis precision; the method enriches information in context and attribute word representation, and adopts the scheme that comment corpora are preprocessed, and a context word embedding matrix and an attribute word embedding matrix are obtained through GloVe word vector indexing; inputting the context and the attribute words into the GRU to obtain a context hiding state and an attribute word hiding state; obtaining a context vector representation 1, an attribute word vector representation and a context vector representation 2 through self-attention; splicing to obtain an overall vector representation, obtaining distribution of sentiment polarities, corresponding to contexts, of attribute words through a classifier, analyzing differences, and adjusting parameters in the model; the method belongs to the field of sentiment analysis in natural semantic processing.

The invention relates to a method for rapidly predicting macromolecule energy gap. The method is based on a first principle in combination with a support vector machine, and comprises the following steps: looking up the structure of macromolecule and an experiment value of an energy gap from literatures; computing the energy gap according to different first principle methods, and selecting an optimal method; looking up 4 module macromolecule structures of repetitive units from the literatures and optimizing the 4 module macromolecule structures; computing the energy gap of the 4 module macromolecules; generating a descriptor through Dragon software, and dividing data into a modeling set and a testing set; performing variable screening through maximum correlation minimum redundance algorithm, and establishing a model through the support vector machine; rapidly predicating the energy gap of the macromolecules to be detected through the established model. The method is based reliable literature data and modeling methods, and the predicating model for the macromolecule energy gap has the advantages of being convenient, rapid, low in cost and free of pollution.

According to the pavement use performance weight analysis method based on the modified analytic hierarchy process, firstly, all single items are subjected to importance degree overall sorting, the importance degrees of adjacent indexes are compared in sequence, then a judgment matrix is constructed, the judgment matrix of all the indexes is calculated, and the weights of all the single items of indexes are obtained. The judgment matrixes obtained through the improved analytic hierarchy process are all consistency matrixes, and the situation that consistency inspection is not passed is avoided.The result shows that the importance degree of each single index in experts in different technical fields is comprehensively evaluated by analyzing the weight of the pavement use performance index byadopting an improved analysis hierarchy analysis method, and the obtained weight result is more accurate, comprehensive and reliable, and more conforms to the actual situation of the weight of the analyzed local pavement use performance index.

Embodiments of the present invention provide an image processing method, a processing device, and processing equipment, which implement image conversion by using a generated neural network in combination with image content features, so that the converted output image includes both the conversion features and the consistency with the input image. The image processing method includes: acquiring an input image; performing image conversion processing on the input image according to the input image by using a generated neural network to output the converted output image, wherein the generated neural network includes a plurality of processing levels, wherein The output result of the i-th processing level is input to the i+1th processing level and the jth processing level, and the jth processinglevel further receives an output result of the j-1th processing level, and the output result of the j-1th processing level is The output of the i-th processing level has the same size, where: i is less than j-1, and i and j are positive integers.

The invention relates to a single image rain removing method and device based on dark channel and fuzzy width learning. The method comprises the steps: S1, carrying out defogging preprocessing on an original image; S2, carrying out high-low frequency separation on the image after defogging pretreatment, taking a high-frequency part to carry out color space conversion, and converting the RGB colorspace into a YCbCr color space; S3, taking the Y channel of the YCbCr color space corresponding to the image of the training set part as the input of fuzzy width learning to carry out model training;S4, taking the Y channel of the test set part as the input of fuzzy width learning to obtain a rain-free graph of the Y channel; S5, combining the high-pass layer rain removal graph with the low-passbase layer to obtain a preliminary rain removal effect graph; and S6, based on the defogged and preprocessed image, carrying out optimization processing on the preliminary rain removal effect pictureto obtain a final rain removal effect picture. Compared with the prior art, the method has the advantages of high color rendition degree and the like.

The invention relates to a phase attention mechanism network model-based circuit breaker residual life prediction method, which comprises the following steps of: firstly, acquiring a vibration signal in an opening process, then optimizing a VMD algorithm, decomposing the vibration signal by using the optimized VMD algorithm, and selecting a modal component with relatively high kurtosis for reconstruction; then, according to the energy-entropy ratio, a contact breaking vibration segment is extracted from the reconstructed vibration signal; and finally, a prediction model fusing a stage attention mechanism is established, the prediction model takes a one-dimensional convolutional neural network and a GRU network as a trunk network, the stage attention mechanism is divided into two stages, the first stage is a distributed attention mechanism applied to the one-dimensional convolutional neural network, weighting is performed on an input sample in time and feature dimensions, and the second stage is a distributed attention mechanism applied to the GRU network. And in the second stage, weighting is carried out on the time dimension again by applying a time step attention mechanism of the GRU network. According to the method, the contribution degree of important information on the time dimension and the feature dimension to the prediction result is enhanced, and the prediction precision is improved.

The invention relates to the technical field of computer software, in particular to a heterogeneous software workload estimation method based on deep learning, which comprises the following steps: S1,establishing data sets including a source data set and a target data set; wherein the target data set is a data set owned by the user; wherein the source data set is a data set having heterogeneity with the target data set; S2, training an auto-encoder by using the source data set and the target data set; and S3, extracting data features from the auto-encoder, inputting the data features into a convolutional neural network training predictor, and generating a predicted value of the software workload by using the predictor. The convolutional neural network can automatically extract the high-level meaning of the data, so that the software workload estimation work is more efficient, and resources are saved.

The invention discloses an image thinning and characteristic classification method used for product defect detection and quality control. A pixel of an image is subjected to binaryzation to form a pixel value set area corresponding to the image; end point removal is carried out before an image is skeletonized to eliminate noise influence; an image thinning direction is judged, image skeletonization is carried out, an image is subjected to continuous iteration thinning successively from the upper, lower, left and right pole positions of the image to the inside, and strict image skeletonization is carried out, wherein the strict skeletonization is carried out is characterized in that continuous iteration thinning is carried out successively from the north, the south, the west and the east points of the image. When the image is not thinned or reaches thinning frequencies, redundant end points are removed, and the image is smoothly processed. A round area forms a point after being thinned, and a skeleton keeps original information; the method consumes time like a Davies two-step method; and after an END point is defined, end point interference can be eliminated, and an improved thinning principle is cooperated to guarantee judgment validity.

The invention discloses a label feature near-duplicate video detection method based on convolutional neural network semantic classification, and aims to solve the problems of large feature storage space, low retrieval efficiency and the like in the existing near-duplicate video retrieval field. The method comprises the following steps: firstly, extracting dense semantic classification label features from a video by utilizing a deep convolutional neural network model; removing redundancy according to repeatability among the video frame label features to obtain semantic classification label features of the video; carrying out similarity matching on the feature vectors of the query video and the library video; and finally, measuring the similarity of the two videos by calculating a Jaccard coefficient so as to achieve the detection of the nearly repeated videos, wherein the two steps of video tag feature redundancy elimination and feature matching have two implementation modes of a videolevel and a frame level, namely, nearly repeated video detection based on semantic classification tag features can be achieved through two different levels of methods. According to the invention, near-repetitive video detection can be rapidly realized, and the method has certain robustness for video editing transformation and noise.