32 results about "Fuzzy object" patented technology

The invention discloses a liver-clearing and vision-improving boxthorn leaf traditional Chinese medicine tea. The traditional Chinese medicine tea comprises the following raw material medicines in parts by weight: 10-30 parts of boxthorn leaf, 10-20 parts of polygonum cuspidatum, 10-20 parts of Chinese angelica, 10-20 parts of jasmine, 10-20 parts of radix ophiopogonis, 10-20 parts of codonopsis pilosula, 10-20 parts of schisandra chinensis, 10-20 parts of radix polygonati officinalis, 10-20 parts of cuscuta chinensis, 10-20 parts of wolfberry fruit, 10-20 parts of prepared rhizome of rehmannia, 10-20 parts of prepared fleece flower root, 10-20 parts of eucommia ulmoides, 10-20 parts of glossy privet fruit, 10-20 parts of rhizoma polygonati, 10-20 parts of white paeony root and 10-20 parts of semen cassiae. The boxthorn leaf is cool in nature, bitter in taste and non-toxic and has the effects of tonifying deficiency, replenishing vital essence, clearing away heat, quenching thirst, dispelling the wind, improving the vision, treating hot eyes and pain, treating heat toxin sore and the like, and by adopting a modern process, the boxthorn leaf tea making is systematically researched, the prepared boxthorn leaf tea is good in mouthfeel and high in aroma, does not have green odor and can be accepted by consumers. For the boxthorn leaf tea, traditional Chinese medicines with the effects of nourishing liver and kidney and mild in nature and flavor are added, and the tea has obvious effects on dry and unsmooth eyes and unclear and fuzzy object observation caused by liver-kidney deficiency and excessive eyes.

A system and method for recovering three-dimensional (3D) particle systems from two-dimensional (2D) images are provided. The system and method of the present invention provide for identifying a fuzzy object in a two-dimensional (2D) image; selecting a particle system from a plurality of predetermined particle systems, the selected particle system relating to a predefined fuzzy object; generating at least one particle of the selected particle system; simulating the at least one particle to update states of the at least one particle; rendering the selected particle system; comparing the rendered particle system to the identified fuzzy object in the 2D image; and storing the selected particle system if the comparison result is within an acceptable threshold, wherein the stored particle system represents the recovered geometry of the fuzzy object.

The invention discloses a personalized recommendation method based on a fuzzy object language concept lattice, which can solve the problems of fuzzy recommendation interpretation and cold start and avoid information loss, and comprises the following steps: data processing: initializing a collected training data set into a fuzzy object language form background; constructing fuzzy object language concepts and concept lattices; calculating evaluation differences between the to-be-recommended user and other users; performing preliminary processing on the training data set according to the evaluation difference; constructing a cognitive system of the training data set; constructing a sufficient knowledge base and a fuzzy object language knowledge simulation lattice of the training data set; constructing a necessary knowledge base and a fuzzy object language knowledge simulation lattice of the training data set; calculating a frequent fuzzy object language concept or frequent fuzzy object language knowledge; calculating a fuzzy object language association rule; and calculating a recommendation rule base and carrying out recommendation.

The invention provides a picture fuzzy processing method and system. The picture fuzzy processing method includes obtaining a target picture; determining a fuzzy object of the target picture according to a preset fuzzy object and determining a fuzzy area corresponding to the fuzzy object; and performing fuzzy processing on the fuzzy area. The picture fuzzy processing method provided by the invention can automatically identify the fuzzy area corresponding to the fuzzy object in the picture, does not need a user to operate for multiple times, and reduces the risk of privacy leakage on the premise of ensuring that the picture is beautiful.

The invention discloses a moving object observation and fuzzy restoration method which comprises the following steps: a plurality of cameras of the same model that have a continuous shooting function are used for capturing photographs of moving objects in the same view field in a fixed manner, observation images are obtained; the obtained observation images are respectively subjected to moving fuzzy function estimation and segmentation extraction operation, and corresponding moving fuzzy object images are obtained; the obtained moving fuzzy object images and moving fuzzy functions are used, and clear object images can be obtained via a space domain combination restoration algorithm. The moving object observation and fuzzy restoration method has low requirements for hardware equipment, and only a plurality of digital cameras, popular in markets, that have a continuous capture function are required for solving a problem of high speed moving object observation and fuzzy restoration.

The invention discloses a method for fast carrying out edge extraction on a fuzzy object. According to the method, after being modulated by a spatial light modulator (SLM), a light source is projected on the target; after the light source is reflected/transmitted by the target, a detector receives fuzzy image information under the action of a scattering medium; a projected random speckle is moved in four directions according to Roberts and gradient operators, the detector records four data and calculates the partial derivatives of the image in each direction; after a plurality of times of sampling is performed, second-order correlation calculation is performed, and the strong correlation information of the four partial derivatives is obtained, and gradient information is obtained; and two pieces of gradient information are added, so that rapid reconstruction of the information of edges which incline to any directions can be realized. With the method of the invention adopted, the quantity of reconstruction information can be decreased, the difficulty of the recognition of the target can be decreased, and the computation speed of a system can be improved; the fuzzy image can be accurately recognized; and the number of times of sampling required by the reconstruction of a target image is small, and the quantity of data required to be processed by the system is decreased, and hardware implementation can be easier.

The invention relates to a low-voltage risk assessment system based on neural network identification and fuzzy analysis, the neural network identification system comprises an input layer, a hidden layer and an output layer, the input layer of the neural network identification system is used for receiving data of an external database, and the hidden layer is connected with the input layer and is used for receiving a transmission signal of the input layer; the hidden layer node is formed by taking a Gaussian function as a primary function, the primary function in the hidden layer node locally responds to an input signal; when the input quantity is close to the central range of the primary function, the hidden layer generates relatively large output, the signal outputs a low-voltage risk index through the output layer, and the fuzzy analysis system comprises a fuzzy controller. The input end of the fuzzy controller is connected with the database, the output voltage indexes screen and output low-voltage risk indexes, and fuzzy objects are quantitatively described by using a membership function method; The system has the advantages of accurate evaluation and combination of a neural network identification system and a fuzzy analysis system.

The invention discloses a blurred image rendering method and device, a storage medium and an electronic device. The method comprises the steps of obtaining a current to-be-processed target object froma target image frame to be rendered by a mobile terminal; under the condition that the target object is a fuzzy object on which a fuzzy processing script is mounted, obtaining a blurred map matched with the target image frame, the blurred map being a map generated by performing blurring processing on a target object set in the target image frame, and all objects in the target object set being objects rendered before the first blurred object in the target image frame; adjusting the fuzzy map according to the display position of the target object in the target image frame to obtain an object fuzzy map matched with the target object; and displaying the target image frame containing the object blurred map. The blurred image rendering method and device solve the technical problem that a blurred image rendering method provided in the prior art is large in processing expenditure.

The invention relates to the technical field of medical care informatics, in particular to the technical field of information specially used for treating or processing medical or health data, and discloses a disease diagnosis and prediction system for deriving a fuzzy object language concept lattice based on attributes. The system comprises: a data collecting module which is used for acquiring symptom language expressions of patients with known disease diagnosis results, and determining an attribute export fuzzy object language concept set through a fuzzy object language form background; a rule extraction module which is used for extracting an attribute export fuzzy object language three-way decision rule from the attribute export fuzzy object language concept set to obtain an attribute export fuzzy object language three-way decision rule set; and a disease diagnosis module which is used for acquiring the symptom language expression of the patient to be predicted, exporting a fuzzy object language three-way decision rule set according to the attributes, and determining a disease diagnosis result of the patient to be predicted. According to the invention, language information problems in decision analysis can be processed, and intelligent medical diagnosis results are provided.

The invention discloses a method for designing an event-triggered DOFF quantization controller under spoofing attack. The method comprises the following steps of: A, establishing a T-S fuzzy object model, a discrete event trigger model and a logarithmic quantizer model; B, establishing a spoofing attack model and a DOFF quantization controller model, and establishing a closed-loop T-S fuzzy system augmentation model organically fusing spoofing attack, a discrete event trigger, a logarithm quantizer and network induced delay parameters; and C, designing a DOFF quantization controller design condition organically fusing spoofing attack, a discrete event trigger, a logarithmic quantizer and network induced delay parameters, solving a positive definite matrix and a gain matrix, and obtaining a DOFF quantization controller suitable for the T-S fuzzy system under the influence of the spoofing attack and the event trigger by using a threshold parameter. According to the method, the problem that the existing T-S fuzzy system cannot be stable under the influence of spoofing attack, data quantization and network induced delay can be solved.

The invention discloses a fuzzy identification method based on a fuzzy ellipsoid book. The fuzzy identification method comprises the following steps: step 1, obtaining a to-be-identified target or observation data sets of l standard categories; Step 2, respectively calculating the mean value of each feature of the observation data set of the to-be-identified target and the standard category in thestep 1; and step 2, respectively calculating the mean value of each feature of the observation data set of the to-be-identified target and the standard category in the step 1. step 3, constructing afuzzy n-cell number in a standard category, and constructing fuzzy n-cell numbers of the to-be-identified target; step 4, respectively calculating centroids of the fuzzy n-cell number of the to-be-identified target and the fuzzy n-cell number in each standard category, calculating the Euclidean distance through the centroid of the to-be-identified target and the and the centroid of each standard category; step 5, according to the Euclidean distance value obtained in the step 4, judging the pattern classification of the to-be-identified target by using a minimum distance principle. According tothe invention, the fuzzy mode identification accuracy of the fuzzy object is improved.

The invention discloses a fuzzy identification method based on a fuzzy ellipsoid book. The fuzzy identification method comprises the following steps: step 1, obtaining a to-be-identified target or observation data sets of l standard categories; 2, calculating the mean value u, the left dispersion degree L and the right dispersion degree R of each feature of the standard category in the first step;3, constructing a standard type of parabolic fuzzy ellipsoid number u; 4, calculating the membership degree of the to-be-identified target corresponding to each standard category by using a joint membership degree approximation algorithm; and 5, judging the pattern classification of the to-be-identified target according to a maximum membership degree principle. According to the method, the membership function of the fuzzy object can be reasonably constructed, and fuzzy mode recognition can be accurately carried out on the recognition problem of the fuzzy object.

The invention discloses a personalized commodity recommendation method based on fuzzy object language concept lattice clustering, and the method comprises the following steps: collecting and processing user behavior data, and initializing the collected data into a fuzzy object language form background; constructing a fuzzy object language concept and a concept lattice; searching a clustering reason; hierarchical clustering is carried out; a hierarchical clustering result corresponds to the reason; obtaining required concepts and hierarchies, and completing clustering; and recommending commodities to the user by adopting Top-N recommendation according to a clustering result. And a concept construction problem can be optimized while a local optimal hierarchy of hierarchical clustering can be found.

The invention discloses a static processing method and device of a video and a computer readable storage medium, the static processing method is applied to a mobile terminal, and the static processingmethod comprises the following steps: obtaining the video of the mobile terminal; determining a fuzzy object according to the video; and performing static processing on the fuzzy object. The video shooting object is subjected to related processing, the definition of the shooting object is enhanced, and the user experience is improved.

The invention discloses a distortion target identification and judgment method based on discrete orthogonal moments, which comprises the following steps of inputting a distortion image to be detected,and identifying and judging a distortion target according to matrix transformation of the discrete orthogonal moments of the distortion image; and giving an optimal order reference value participating in calculation according to the image parameters. Experimental results show that the method has stronger robustness, and has advantages in the aspects of fuzzy object judgment and identification ofsimilar and complex objects under a distortion condition; and the given optimal order reference values suitable for the images of different sizes give consideration to the requirements of reducing thecalculation amount and improving the robustness, and have a very good matching effect with newly proposed parameters.