47results about How to "Suitable for practical application" patented technology

The invention provides a bidirectional quantum time synchronization method based on frequency entangled light sources. Two entangled light sources with same wavelength are placed at a place A and a place B respectively, and signal lights generated by the entangled light sources pass through an optical fiber circulator, are transmitted in opposite directions through a same optical fiber, and are detected by detectors at the opposite places; idle lights generated by the entangled light sources are detected by a detector at a uniform place of the entangled light sources; a clock a at the place arecords the time (tij} and time (t2j} for photons emitted by the entangled light source at the place a to reach the detectors at two places, and a clock b at the place b records time (t3j} and time (t4j} for photons emitted by the entangled light source at the place b to reach the detectors at two places, wherein j is representative of jth photon; the (tij} and (t2j} are subjected to cross-correlation operation to obtain t2-t1; the (t3j} and (t4j} are subjected to cross-correlation operation to obtain t4-t3; the clock difference t0 between the clock a and the clock b is equal to half of the difference of (t2-t1) and (t4-t3). The bidirectional quantum time synchronization method based on the frequency entangled light sources is high in measurement precision, and an applied device is simplein structure.

The invention discloses a similarity-weight-semi-supervised-dictionary-learning-based human behavior identification method. With the method, a problem of low human behavior identification rate of the existing supervision method in the prior art can be solved. The identification method comprises: (1), an inputted data set is divided into test samples and training samples; (2), local feature detection is carried out on all samples and local features with the labeled samples are selected randomly to obtain an initialized dictionary; (3), according to the initialized dictionary, dictionary learning is carried out by using a semi-supervised method; (4), group sparse coding is carried out on all samples by using the learned dictionary to obtain a coding matrix of each sample; (5), vectorization is carried out on the coding matrix of each sample to obtain a final expression; and (6), testing sample classification is carried out by using the final expression of each sample and a sparse representation classification method to complete human behavior identification in the testing samples. Therefore, discrimination of dictionary learning is enhanced; the human behavior identification rate is improved; and the method can be used for target detection in a video.

The invention provides a coronary artery stenosis detection method and device, computer equipment and a storage medium. The coronary artery stenosis detection method and device are characterized in that a detection result is obtained by inputting an obtained to-be-detected image into a coronary artery stenosis detection model; the coronary artery stenosis detection model comprises a trunk network,a segmentation network and a stenosis analysis network, and the output of the trunk network is connected with the input of the segmentation network and the input of the stenosis analysis network; thedetection result comprises a coronary artery segmentation result and a coronary artery stenosis result. Due to the structure of the coronary artery stenosis detection model, the coronary artery stenosis detection model comprises a segmentation network and a stenosis analysis network. Therefore, the coronary artery stenosis detection model is a multi-task model, can segment the input images to bedetected at the same time and conduct stenosis detection on the input image to be detected, so as to obtain detection results including at least two types, namely the coronary artery segmentation result and the coronary artery stenosis result.

The bacterium agent in this invention is used to biologically strenghen dephosphor- ization process in urban sewage treatment plant. It has mainly two uses: quickly start the biodephosphorization ability of new sewage treatment plant; resume the strong biodephosphorization ability of exasperate dephosphorization system. It is made from high efficient ployphospher bacterial strain GM6, and it is odor Pseudomonas putida from appraising result. Its main biocharacteristic is G-. Its thalli is rhabditiform with the size of 0. 7í‡2. 5ª–m, and it is facultative anaero-bic; it is positive in indole reaction, oxidase and sulfate reduction reaction; while negative in methyl red test, V-P reaction, citrate test and hydrogen sulfide reaction; the number of the strain 16S rDNA is DQ133506. The strain can be used to sewage dephosphorization.

The invention provides a rare-earth oxide and microencapsulated red phosphorus synergic flame retardance PA6 composite and a preparation method thereof. The rare-earth oxide and microencapsulated red phosphorus synergic flame retardance PA6 composite is characterized by being prepared from PA6, microencapsulated red phosphorus and rare-earth oxide. Rare-earth oxide is introduced into a red phosphorus flame-resistant polymer material system, rare-earth oxide can catalyze dehydrated carbonization of red phosphorus, a more compact and continuous charcoal layer structure can be formed, the mass transfer process of volatile products generated by polymer degradation to a gas phase is blocked, feedback of heat generated by gas phase combustion to a condensed phase is also blocked, and flames are prevented from propagating and spreading.

The invention discloses a black smoke vehicle detection method based on pixel adaptive segmentation and Bayesian model. The black smoke vehicle detection method comprises the following steps: detecting a moving target by using a PBAS model, dividing an image into grids, and marking all small squares where a foreground target is located; for each foreground target grid, extracting POEM histogram orLDP histogram to characterize the spatial information, for each foreground target grid, extracting HOOF histogram or MOH histogram to characterize the temporal information, for each foreground targetgrid, extracting the STH feature and depicting the structure information, fusing different types of histogram features, using the characteristics of non-smoky vehicles being much higher than smoky vehicles in the actual scene to add a priori knowledge, classifying the small squares of each foreground target in each frame by Bayesian model, and comprehensively analyzing multi-frame recognition ofsmoky vehicles. The invention can automatically identify the black smoke vehicle from the traffic flow, improves the detection rate, reduces the false alarm rate, and has robustness to shadows.

The invention provides a subject community mining method for an online social networking service.The subject community mining method is based on nonnegative matrix factorization (NMF).User node link information and content information can be integrated in a unified mode through an NMF model, an affiliation matrix between user nodes and communities and a correlation intension matrix between communities and subject feature words are obtained with the matrix approximative decomposition method, and then subject community mining can be directly conducted by means of matrix decomposition information.By the adoption of the method, user node link information and content information can be processed with a unified model, mining is easier and more efficient, mining quality is higher, and therefore the method is more suitable for being actually applied to mining of subject communities in the online social networking service.

The present invention discloses an SOM neural network-based malicious user discrimination method in cognitive radio. The method of the present invention utilizes an SOM neural network to learn the distribution characteristics of an input energy matrix, effectively classifies the input according to the learning results, and comprises the step of firstly introducing a suspicious degree concept, wherein the suspicious degree is distributed according to the number of the secondary users of each type after every time training. In order to eliminate the defects of the conventional SOM neural network, the present invention further provides an average suspicious degree concept. The concrete steps comprise obtaining an energy matrix, utilizing an SOM neural network algorithm to train the energy matrix to obtain a classification matrix, calculating the suspicious degree of each secondary user, constructing an index matrix and repeating the training process, averaging the suspicious degrees obtained at each time, namely obtaining the average suspicious degree, utilizing the average suspicious degree to classify the secondary users, and identifying the malicious users or the normal users.

The invention discloses a composite soft form board liquid material plasma spray method for preparing a gas-sensitive coating of a hollow micro-nano structure and belongs to the technical fields of engineering and material science. The spray method comprises the specific steps that suspension liquid of a hollow spherical shell structure serves as a liquid material, wherein the suspension liquid issynthesized through a composite soft form board method; the gas-sensitive coating of the hollow micro-nano structure is prepared in a manner of liquid material plasma spray; and high response is madeto specific gas under the conditions of the low temperature even the room temperature, and the response/recovery time of the gas-sensitive coating is shorted, and long-time stability of the gas-sensitive coating is good. The gas-sensitive coating prepared through the method is controllable in structural appearance, high in specific surface area, good in gas-sensitive performance and low in cost.

The invention provides a method for predicting a saturated permeability coefficient of a clay mineral in an electrolyte solution. The method comprises: step one, establishing a relationship model between types and concentrations of electrolyte solutions and permeability coefficients of the clay minerals; step two, carrying out collection to obtain a physical parameter of the model and a clay surface charge density parameter; step three, calculating surface charge densities of different types of clay minerals in a polar solution; step three, carrying out fixed-water-head testing to obtain permeability coefficients of different types of clay minerals certain designated electrolyte solutions with different concentrations and calculating relevant parameters needed by the model based on the relationship model and the surface charge densities of the clay mineral in the polar solution; and step four, on the basis of the relationship model with relevant parameters introduced and an actually measured electrolyte solution concentration, predicting a permeability coefficient of a clay mineral in the electrolyte solution with the concentration. Compared with the prior art, the invention provides a method capable of predicting the permeability coefficient of the clay mineral accurately theoretically.

The present invention pertains to the field of firefighting equipment, concretely relates to an automatic foam proportional mixing digital display controlling module and consists of microcomputer control unit, sensor unit and driver unit. The microcomputer control unit consists of circuit board component, electric signal connector, display and operating mechanism, and housing. They are overlapped in the housing. The circuit board component consists of microprocessor, electric signal input interface, electric signal output interface and digital communication interface. The electric signal connector and the display and operating mechanism are inside the housing; the sensor unit includes water flowmeter, foam flowmeter, and one or several types of level sensor or pressure sensor; the driver unit includes one or several types of DC brushless motor driver and electric valve driver; the electric signal input interface, electric signal output interface and digital communication interface of the electric circuit component are connected to the sensing component of the sensor unit and the driving component of the driver unit with electric signal connector. The present invention may greatly raise the automation degree of proportional foam mixing, and realize accurate control and adjustment of less than 1% mixing proportion.

The invention discloses a multispectral imaging method and device based on an RGB camera and a deep neural network, and the method comprises the following steps: designing a convolutional neural network which takes a mosaic image as an input and takes a corresponding multispectral image as an output; training the convolutional neural network by using the existing multispectral data set to obtain an optimal network model; and shooting by adopting an RGB color camera to obtain a current mosaic image of an actual scene, and taking the mosaic image as the input of the optimal network model to obtain a target multispectral image. According to the method, calculation imaging from mosaic images to multispectral images can be directly completed by utilizing the deep neural network, and the methodis more suitable for practical application and is simple and easy to implement.

The embodiment of the invention provides a yield prediction method, device and equipment based on a convolutional coding dynamic sequence network. The yield prediction method comprises the steps of obtaining static data and dynamic data corresponding to each target horizontal well, wherein the dynamic data comprises yield data corresponding to each time point in the preset time; dividing the dynamic data of each target horizontal well into first dynamic data and second dynamic data based on the prediction time point to obtain a sample data set, wherein the prediction time point is within the preset time; and training the convolutional coding dynamic sequence neural network by using the sample data set to obtain a target horizontal well yield prediction model. According to the embodiment of the invention, the accuracy of productivity prediction of the fractured horizontal well can be improved.

The invention discloses a planar phased array-based two-dimensional beam scanning method. According to the prior art, when a liquid crystal spatial light modulator is adopted to deflect a two-dimensional beam, only a limited deflection angle can be realized; a one-dimensional phase modulation diagram needs to be rotated; an entire scanning range is in sparse and uneven concentric circle distribution; and as a scanning angle increases, diffraction efficiency is seriously degraded, and as a result, problems such as a problem of failure to meet the practical requirements of capture, tracking andaiming (ATP) in spatial optical communication which require beam continuous scanning can be solved. With the method adopted, the problems of scanning range discreteness and uneven distribution can besolved, diffraction efficiency can be improved, and the requirements of practical application can be better met.

The invention discloses a multi-target unlabelled aquatic organism identification tracking method and system; the method comprises the following steps: 1, using an aquatic organism culturing system toculture aquatic organisms; 2, using two cameras, arranged on the top and a side face of the aquatic organism culturing system, to respectively gather videos of the aquatic organisms, and sending thegathered videos to a controller; 3, data processing: using the controller to process the video images gathered by the two cameras, thus finally obtaining aquatic organism three dimensional tracks. Theproblems that in existing organism tracer technology the organism individual distinguish capability is low, and too much video storage space is taken are solved via the unlabelled identity identification technology and video three dimensional processing.

The invention relates to the technical field of battery capacity prediction, and in particular, relates to a battery capacity estimation model and method based on a double-tower deep learning network. The battery capacity estimation model comprises a first input layer used for inputting a constant current charging characteristic sequence, a first fully connected network for processing the constant current charging characteristic sequence and generating a first output, a second input layer used for inputting a random discharge characteristic sequence, a second fully connected network for processing the random discharge characteristic sequence and generating a second output, and an output layer used for combining the first output and the second output to generate an estimated battery capacity. The method is realized based on the model. The method has higher battery capacity estimation precision.

The invention discloses a novel nanometer flame-retardant composite material. The composite material is characterized by comprises the following substances by weight: 35 to 70 parts of glass fiber reinforced polyethylene terephthalate ester, 15 to 23 parts of rare earth phenyl phosphonate, 10 to 20 parts of aluminum hydroxide, 3 to 8 parts of ethylene acrylate, 1 to 9 parts of high density polyethylene, 5 to 9 parts of diammonium phosphate, 3 to 11 parts of hexabromo water dispersion, 4 to 12 parts of superoxide dismutase, 10 to 18 parts of tetra(3,5-di-tert-butyl-4-hydroxy)pentaerythritol phenylpropionate, 20 to 31 parts of methyl benzoate, 5 to 17 parts of polyvinyl alcohol and 10 to 24 parts of chlorobenzene. The novel nanometer flame-retardant composite material has a wide application scope, reduces the usage amount of a fire retardant, enables cost to be saved, accords with the trend of environmental protection and conservation, is simple and easy to prepare and process, exerts an obvious effect, accords with practical application conditions and is quite possible to be marketed.