768 results about "Characteristic matrix" patented technology

A method of simulating aerial images of large mask areas obtained during the exposure step of a photo-lithographic process when fabricating a semiconductor integrated circuit silicon wafer is described. The method includes the steps of defining mask patterns to be projected by the exposure system to create images of the mask patterns; determining an appropriate sampling range and sampling interval; generating a characteristic matrix describing the exposure system; inverting the matrix to obtain eigenvalues as well as the eigenvectors (or kernels) representing the decomposition of the exposure system; convolving the mask patterns with these eigenvectors; and weighing the resulting convolution by the eigenvalues to form the aerial images. The method is characterized in that the characteristic matrix is precisely defined by the sampling range and the sampling interval, such that the sampling range is the shortest possible and the sampling interval, the largest possible, without sacrificing accuracy. The method of generating aerial images of patterns having large mask areas provides a speed improvement of several orders of magnitude over conventional approaches.

An image processing apparatus includes a characteristic region detecting section that detects a plurality of characteristic regions in an image, a condition storing section that stores thereon assignment conditions differing in accordance with characters of characteristic regions, so that different compression strengths are assigned in accordance with the characters of the characteristic regions, a compressing section that respectively compresses a plurality of characteristic region images which are images of the plurality of characteristic regions, and a compression control section that controls compression strengths at which the compressing section respectively compresses the plurality of characteristic region images in accordance with characters of the plurality of characteristic regions, with reference to the conditions stored on the condition storing section. Also provided is an image processing apparatus that includes an encoding manner storing section that stores encoding manners in association with quantities of characteristics of objects, a characteristic region detecting section that detects a plurality of characteristic regions from an image, and a compressing section that compresses the images of the plurality of characteristic regions by encoding manners stored in the encoding manner storing section in association with the quantities of characteristics of objects included in the plurality of characteristic regions respectively.

In one embodiment, a method includes receiving a digital image captured by a mobile device; and using a processor of the mobile device: generating a first representation of the digital image, the first representation being characterized by a reduced resolution; generating a first feature vector based on the first representation; comparing the first feature vector to a plurality of reference feature matrices; and classifying an object depicted in the digital image as a member of a particular object class based at least in part on the comparing.

The invention provides a prediction method of a road traffic flow based on a graph convolution network. The method comprises the following steps of collecting vehicle GPS data in the past period of time; integrating the vehicle GPS data with actual road network information data to obtain a road traffic flow characteristic matrix, and converting a road and a road intersection point in the actual road network through line graph conversion to generate a road adjacency matrix; and based on the road adjacency matrix and the road traffic flow characteristic matrix, acquiring a predicted value of traffic flow data of the road in the next time period by using a spatial characteristic and a time characteristic of traffic flow data of the integrated road of a GCN network and an LSTM network. Through integrally using the spatial characteristic of the road traffic flow data extracted by GCN and the time characteristic of the road traffic flow data extracted by LSTM, and combining a time period characteristic and a weather characteristic, the road traffic flow data is predicted, and a prediction effect is better than the prediction effect acquired through using only time characteristic or the spatial characteristic.

The invention is directed to an article with a liquid-impregnated surface, the surface having a matrix of features thereupon, spaced sufficiently close to stably contain a liquid therebetween or therewithin, and preferable also a thin film thereupon. The surface provides the article with advantageous non-wetting properties. Compared to previous non-wetting surfaces, which include a gas (e.g., air) entrained within surface textures, these liquid-impregnated surfaces are resistant to impalement and frost formation, and are therefore more robust.

The invention relates to a method for identifying facial expressions from a human face image sequence, belonging to the technical field of analyzing and identifying human facial expressions. The method of the invention comprises the following steps of: firstly, adopting a method for tracing feature points, sequentially extracting the displacement amount of the normalized facial key point and the length of the special geometrical characteristic for each frame image of the expression image sequence, and combining the data to form a characteristic column vector; secondly, sequentially arranging all characteristic column vectors of the sequence to form a characteristic matrix, wherein each characteristic matrix represents a facial expression image sequence; finally, comparing the similarities among the characteristic matrixes by using a canonical correlation analysis method, thereby determining the human face images to be identified into one of the basic expressions of happiness, sadness, fear, hate, surprise and anger. In the invention, the canonical correlation analysis method is successfully applied to identifying the human facial expressions, the dynamic information in the expression generation course is utilized effectively and the higher recognition rate and the shorter CPU computation time are acquired.

The invention discloses a significant object detection method based on sparse subspace clustering and low-order expression. The method comprises the steps of: 1, carrying out super pixel segmentation and clustering on an input image; 2, extracting the color, texture and edge characteristics of each super pixel in clusters, and constructing cluster characteristic matrixes; 3, ranking all super pixel characteristics according to the magnitude of color contrast, and constructing a dictionary; according to the dictionary, constructing a combined low-order expression model, solving the model and decomposing the characteristic matrixes of the clusters so as to obtain low-order expression coefficients, and calculating significant factors of the clusters; and 5, mapping the significant value of each cluster into the input image according the spatial position, and obtaining a significant map of the input image. According to the invention, the significant objects relatively large in size in the image can be completely and consistently detected, the noise in a background is inhibited, and the robustness of significant object detection of the image with the complex background is improved. The significant object detection method is applicable to image segmentation, object identification, image restoration and self-adaptive image compression.

The invention relates to a system and method for detecting the quality of a metal cap based on machine vision, used for detecting the quality defect of the metal cap and rejecting the inferior quality product. The detection system comprises an optical imaging device, an image processing device, a rejecting device and a conveying belt, wherein the optical imaging device comprises a planar array industrial camera, a first optical detection sensor and a light source; the image processing device is used for processing the image; and the rejecting device comprises a control circuit board, a second optical detection sensor, a solenoid valve and an injection tube. The method for detecting the quality of the metal cap based on machine vision comprises the following steps of: acquiring an interested maximum outline scale of a template cap image; establishing a rotational invariance characteristic template matrix for the template cap image; acquiring a round outline of the cap to be detected according to the interested maximum outline scale; calculating the rotational invariance characteristic matrix in the coverage of the round outline of the cap to be detected; and matching the rotational invariance characteristic matrix with the rotational invariance characteristic template matrix, so as to judge whether the cap to be detected is a qualified product or an inferior product.

The invention discloses a mobile phone advertising method based on a user consumption feature vector, and belongs to the field of mobile phone data mining and analyzing. The method includes the steps of establishing a user keyword to represent a vector according to browsing contents of a user mobile internet; establishing a user consumption feature vector of a corresponding user according to the browsing contents of the mobile internet; extracting a keyword from an advertisement experience text database and setting a keyword weight, and constructing a characteristic matrix for each user feature vector; computing each component value of the user consumption feature vector of each user; setting the mapping relations between a user consumption tag and each component of the consumption feature vector of the user within different threshold ranges; labeling a corresponding consumption tag for each user and then conducting advertising according to each component of the current consumption feature vector of the user and the mapping relation. The mobile phone advertising method can improve precision of advertising, reduces manual interference, and effectively lowers platform running cost.

The invention provides a wind turbine bearing fault diagnosis method based on PCA and KNN density algorithm. The method comprises the following steps: obtaining vibration signals of a wind turbine bearing under different working states; carrying out pretreatment on the vibration signal data; calculating time domain and frequency domain statistical parameters of each sample, and constructing a characteristic matrix of the wind turbine bearing signals; carrying out dimensionality reduction on the multi-characteristic matrix of the wind turbine under different working states by utilizing a PCA algorithm, extracting characteristic input and serving the input as a training sample set of a fault diagnosis model; carrying out modeling on training samples through a support vector machine (SVM); carrying out optimization on parameters of the support vector machine through the KNN density classification algorithm; and displaying the final diagnosis result in a human-computer interaction interface. The method can accurately carry out classification on fault types, thereby improving wind turbine bearing fault classification precision; and the method provides guarantee for safe and reliable operation of the wind turbine, so that power grid dispatching can be optimized, and safe, stable and economical operation of the power grid is realized.

The invention provides a method for predicting the service life of a screw pair of a numerical control machine on the basis of a performance degradation model. The method comprises the following steps of: acquiring vibration signals, carrying out time-frequency domain analysis, extracting the sensitive characteristic data vectors of the performance degradation of the screw pair, and forming a sensitive characteristic matrix in a time-sequence manner; calculating the load Pi of the screw pair and recording the operating time ti at the same time; calculating the rated life time Lhi, the total time t' that the screw pair has run under the current working condition, and the expected residual life LDi according to Pi, and forming an expected residual life vector T of the expected residual life in a time-sequence manner; and fitting the mapping relation between the inputted sensitive characteristic matrix and the expected residual life vector by using a degradation model formed by a double-layer dynamic fuzzy neural network, and outputting the prediction result of the service life. By taking the impact on the performance degradation of the screw pair caused by the load change thereof under various working conditions of the numerical control machine into consideration, the method of the invention can achieve the prediction of the residual life when the screw pair is used, and ensure the high prediction accuracy and high value in actual application.

The invention provides a method for tracking a moving object. The method comprises the following steps of 1, accurately detecting the moving object by a rapid moving object detection method based on a codebook model; 2, initializing a weak classifier group of an online Adaboost algorithm to obtain a strong classifier, fusing local direction histogram characteristics and color characteristic in selection of characteristics of the moving object; 3, computing a characteristic matrix of an online Adaboost tracking algorithm and the weak classifier to obtain a confidence chart, updating the weak classifier according to the obtained moving object position by a CamShift tracking algorithm on the confidence chart, and finally obtaining a tracking result of a whole segment of video sequence. The method for tracking the moving object capable of effectively tracking the moving object which largely deforms within a short period of time, generates shelter even large-area shelter, is similar to the colors of the other moving objects in background, rapidly changes and accelerates is provided by the invention.

The invention discloses a polarized SAR image classification method based on a nonsubsampled contourlet convolutional neural network, and mainly at solving the problems that influence of speckle noises is hard to avoid and the classification precision is low in the prior art. The method comprises the steps that a polarized SAR image to be classified is denoised; Pauli decomposition is carried out on a polarized scattering matrix S obtained by denoising; image characteristics obtained via Pauli decomposition are combined into a characteristic matrix F, and the characteristic matrix F is normalized and recorded as F1; 22*22 blocks surrounding the F1 are taken for each pixel point to obtain a block based characteristic matrix F2; a training data set and a test data set are selected from the F2; the nonsubsampled contourlet convolutional neural network is established to train the training data set; and the trained nonsubsampled contourlet convolutional neural network is used to classify the test data set. The polarized SAR image classification method improves the expression capability and the classification precision of the features of the polarized SAR image, and can be used for target identification.

The present invention is applicable to the technical field of computers, and provides a recommended method, apparatus and device, and a storage medium. The method comprises: obtaining history score data of a user, a to-be-scored item, and text content of the to-be-scored item; according to the history score data of the user, the to-be-scored item, and the text content of the to-be-scored item, training the preset stack noise reduction self-encoder and the preset probability matrix decomposition model to obtain the user characteristic matrix, the item characteristic matrix, the corresponding item latent characteristics and user latent characteristics; according to the user characteristic matrix, the item characteristic matrix, the corresponding item latent characteristics and the user latent characteristics, calculating the predicted score of the to-be-scored item; and according to the predicted score, generating a recommendation list, and outputting the recommendation list to the user, so that when recommending the item to the user, the item characteristics are combined with the user characteristics, the recommended accuracy is effectively improved, and the recommended efficiency of the item is further improved.

The invention discloses a robust mechanism research method of characteristic significance in image quality evaluation. The robust mechanism research method comprises the following steps: firstly, determining a target function of characteristic selection in the image quality evaluation, and initializing a model parameter; secondly, adding an optimal characteristic into a characteristic matrix, and removing a characteristic disturbance term; thirdly, calculating the significance of the characteristic selection in an image quality evaluation system; fourthly, judging whether the significance meets a system robust requirement or achieves an upper limit of a characteristic number; and finally, verifying a model classification effect. The characteristic significance is measured through an imported system characteristic signal to noise ratio, a constrained optimization problem of a smooth convex function in the image quality evaluation system is solved, interference on a classification face by non-significant characteristics is effectively lowered, the robustness of the image evaluation system is improved, and the self-adaptive optimization problem of characteristic attribute selection on the basis of an image quality evaluation network of a learning mechanism is solved.

The invention discloses a recognition method of a critical geometrical error source of a machine tool and belongs to the technical field of machine precision designing. The recognition method of the critical geometrical error source of the machine tool is characterized by comprising the steps that the machine tool is abstracted into a multi-body system according to the structure and motion characteristics of the machine tool, relevance of parts of the machine tool is described by a topological structure and a low-order body array, a generalized coordinate system is built in the multi-body system, coupling relationship of error amounts of parts of the machine tool is described by a homogeneous transformation matrix, a characteristic matrix and a motion equation of the relative movement between two adjacent bodies of the machine tool are elicited, a precision model of a machining center is built, an ordinary mathematical model used for error sensitivity analysis of a four-shaft machine tool is built with a matrix differential method according to the precision model of the precision horizontal machining center, influence degrees on the whole space error of all error elements are compared by calculating the geometrical error sensitivities of all parts, and finally the critical error source influencing the machining precision of the machine tool is recognized.

A multilayered nano transparent conductive film is of alternate structures of dielectric layer and metal layer, and the metal layer is between two dielectric layers which are symmetric in structure or non-symmetric in structure. Its preparing method is to first work out a computer analog program for photoelectric performance of multilayer film according to the characteristic metrix calculation formula of multilayer, the theory of plasma resonance and the electronic transport in metal continuous film, to optimize the design of D/M/D multilayer film structure including selecting and matching ofmaterial quality, film thickness, reflecting rate and conductive rate, predicting of several kinds for D/M/D multilayer film structure, and then to use vacuum vaporation coating method to make D/M/D multilayer film by using accurate film thickness detection system to record film thickness

A system and method for interactively generating a family of mesh models is provided for use in engineering analysis. The system includes a user computer system having a memory, a processor, a user input device and a display device. The system also includes a remotely located computer system having a processor, a memory, and a data storage means and in communication with the user computer system. The system further includes a computer-generated geometric mesh base model stored in the data storage means, a computer-generated mesh feature stored in the data storage means, and a new mesh model including the base mesh model with a mesh feature that is stored in the data storage means. The method includes the steps of selecting a base model from a model library maintained in the data storage means. The method also includes the steps of creating a mesh feature that is defined by a modifiable set of parameters and saving the mesh feature in a mesh feature library maintained in the data storage means. The method further includes the steps of establishing a feature matrix by the user containing instructions for selectively applying the mesh feature to the base mesh model and using the feature matrix to selectively apply the mesh feature to the base model to generate a new mesh model that is available for further analysis.

The embodiment of the invention discloses an object detection method and an object detection device. The method comprises the steps of acquiring characteristic information of a to-be-detected object,wherein the characteristic information includes an RGB (Red Green Blue) image, a depth image and three-dimensional point cloud information; determining a shape characteristic matrix of the to-be-detected object according to the depth image and the three-dimensional point cloud information; and determining a category of the to-be-detected object according to the RGB image and the shape characteristic matrix. By use of the embodiment, the accurate rate of object detection can be effectively improved.

The invention discloses a video significance detecting method based on area segmentation, wherein the method mainly settles a problem of low detecting accuracy by an existing video significance detecting method. The video saliency detecting method comprises the steps of 1, performing linear iteration clustering on video frames, thereby obtaining a super-pixel block, and extracting the static characteristic of the super-pixel block; 2, by means of a variational optical flow method, obtaining the dynamic characteristic of the super-pixel block; 3, fusing the static characteristic and the dynamic characteristic for obtaining a characteristic matrix, and performing K-means clustering on the characteristic matrix; 4, performing linear regression model training on each cluster, thereby obtaining a regression model; and 5, reconstructing a mapping relation between a test set sample and a obtaining the significance value of a test set super-pixel block, and furthermore obtaining the significance graph of a testing sequence. Compared with a traditional video significance algorithm, the video significance detecting method has advantages of improving characteristic space and time representation capability, and reducing effect of illumination to detecting effect. The video significance detecting method can be used for early-period preprocessing of video target tracking and video segmenting.

The embodiment of the invention provides a product recommendation method and device. The method is used for determining whether to recommend a to-be-recommended product to a target user. The method comprises the steps that multi-field information associated with the target user is acquired, wherein the information comprises purchase data of the target user in the product field of the to-be-recommended product and purchase data in other product fields; a user feature matrix of the target user is constructed according to the multi-field information; for one to-be-recommended product, a user feature matrix of multiple users who purchase the to-be-recommended product is acquired, and a product feature matrix of the to-be-recommended product is obtained based on feature values in the matrix; the user feature matrix and the product feature matrix are input into a machine learning model to obtain user preference vectors and product preference vectors; a selection assessment value between theto-be-recommended product and the target user is obtained according to the user preference vectors and the product preference vectors; and when the selection assessment value is greater than a predetermined recommendation threshold, it is determined that the to-be-recommended product is recommended to the target user.

A transmitter includes an antenna array having a plurality of antennas for a wireless communication with one or more receivers, and a precoder connected to the antenna array, the precoder to apply a set of beamforming weights to the antenna array, the set of beamforming weights selected from a codebook to form by the antenna array one or more transmit/receive beams or nulls pointing in selected directions. The codebook includes a plurality of sets of beamforming weights for a plurality of directions. The beamforming weights in the codebook are based on a first antenna array response matrix, the first antenna array response matrix defined by a second antenna array response matrix and one or more characteristic matrices. The first antenna array response matrix contains first array response vectors of the antenna array, the second antenna array response matrix contains second array response vectors of another antenna array, the other antenna array being different from the antenna array, and the one or more characteristic matrices describing characteristics of the antenna array.

The invention relates to the voltage control of the electric power field and especially relates to a reactive voltage partitioning method based on spectral clustering. A topologic matrix with a weight is used to construct a simplified power grid model. According to a spectral clustering definition, a Laplace matrix is acquired. Through an improved K-means clustering algorithm, clustering is performed on different characteristic vectors in a characteristic matrix. During clustering, modularity Q is introduced to be taken as an index of measuring an area partitioning quality. A partitioning scheme with a largest modularity Q value is selected as an initial partitioning scheme. Connectivity verification and reactive verification are performed on each area of the initial partitioning scheme. If the area can not simultaneously satisfy two conditions of area static state reactive balance and an enough reactive reserve margin, under the condition that a value of partitioning modularity Q does not change greatly, node adjusting is performed till that all the verification conditions are satisfied. In the invention, a topology structure of a complex power grid is embodied, calculating complexity is reduced, an integration evaluation system is established based on the modularity, the reactive balance and a reactive reserve index, and integration verification is performed on a partitioning result so as to ensure feasibility of the partitioning scheme.