786results about How to "The result is obvious" patented technology

The invention discloses an image super-resolution reconstruction algorithm based on a shallow convolution neural network and a deep convolution neural network. The method comprises the following steps: 1) selecting a training sample and a testing sample; 2) performing extracting, mapping, upsamping and multi-scale conversion to the deep network characteristics; 3) extracting the characteristics of the shallow network; and 4) combining the shallow network and the deep network. Compared with the prior art, with regard to the single image reconstruction and video sequence reconstruction, the algorithm of the invention can accurately and effectively reconstruct the model of a high-resolution image so as to obtain a good reconstruction effect. In addition, multi-scale detail characteristic extraction is achieved, and the algorithm has better results than that of other existing algorithms. The reconstruction speed is also fast.

The invention discloses a microblog user interest recognizing method based on text mining, and belongs to the field of text mining and natural language processing. The method includes the steps of collecting the newest topical microblog text data of a microblog text set and microblog text data of a designated user, standardizing the collected microblog text data, recognizing the newest microblog words and renewing a new word dictionary for the standardized topical microblog text data through the microblog new word recognition method, conducting Chinese character word separation on the standardized microblog text data of the designated user through the new word dictionary word separation method to achieve text vector expression, clustering the microblog text data, expressed through text vectors, of the designated user, recombining original microblog text data, extracting new text set features through a topic model, presetting topic dictionaries, calculating the weight of each topic dictionary based on the new text set features to obtain the final topic, and enabling the final topic to serve as the microblog user interest recognition, thereby improving accuracy of feature extraction.

The invention belongs to the field of video and image processing. The objective of the invention is to further improve the reconstruction effect and reconstruction precision of a high-resolution image, promote the structure of the generative adversarial network and the improvement of a loss function; the invention discloses a dense connection generative adversarial network single image super-resolution reconstruction method. A generation network and an adversarial network are included. A basic framework of a residual dense network RDN is adopted by the generation network; the adversarial network adopts a deep convolution generative adversarial network DCGAN discriminator network framework; the low-resolution image is used as an input and is sent into a generation network for processing; and the obtained output is sent to the adversarial network for judgment, a judgment result is fed back to the generative network through a loss function, the steps are repeated until the adversarial network is judged to be qualified, the generative network can generate a clear image, and then super-resolution reconstruction of a low-resolution image is completed by using the trained generative network. The method is mainly applied to image processing occasions.

The invention discloses a simulation system and a simulation method for three-dimensional comprehensive situations of a space mission. The simulation system comprises a space satellite trail and posture modeling module, a satellite frame modeling module, a coverage area analysis and modeling module, a data tracking and receiving modeling module, a time management module, a coordinate system management module and a three-dimensional visualizing simulation module, wherein the time management module is used for providing simulation time and various time standards of astronomical time for simulation of the space mission; the coordinate system management module is used for providing various coordinate systems such as a celestial coordinate system, an earth fixed connection coordinate system, a moving coordinate system and the like to describe moving rules of moving objects; and the three-dimensional visualizing simulation module is used for three-dimensional visual display, playback and deduction for situation analysis and modeling results, capable of controlling acceleration, deceleration, pause and start of a simulation process according to the simulation mission or a demonstration script, and capable of providing a powerful guarantee for display of the space comprehensive situations and analysis and demonstration of a focused simulation stage.

The invention discloses a method for automatically extracting gingiva curves on a three-dimensional digital dentition model. The method comprises the steps of model positioning, gingival feature line search, gingival feature line partitioning and inter-tooth space gingival line interference analysis. The method is high in automatic degree, enclosed gingival curves around each tooth on the dentition model can be constructed rapidly and accurately, the problems of low efficiency, poor accuracy and the like existing in the conventional manual design are solved effectively, and the efficiency and attractiveness of oral rehabilitation are improved.

The invention discloses a protein secondary mass spectrometric identification method based on a probability statistic model. The method comprises the following steps of: firstly, virtualizing an enzymolysis protein database array, and establishing a peptide section database and a peptide section database index for peptide sections processed by the enzymolysis according to the mass number of the peptide sections; secondly, finding out standby peptide sections meeting the requirements from the peptide section database according to a nuclear-cytoplasmic ratio of parent ions in an experiment map to be analyzed, and generating a theoretical map meeting the requirements by all the standby peptide sections; thirdly, removing isotopes and noises from the experiment map to be analyzed; matching the processed experiment map to be analyzed and the theoretical map of each standby peptide section and grading, and selecting the standby peptide section with the highest score as an identification result of the experiment map; and finally, carrying out whole false positive control according to all the experiment map identification results. According to the invention, the quantity of effective massspectrums and the quantity of the protein peptide sections are higher than those of an existing algorithm; and the method has the advantages of capability of dynamically selecting peaks and fast operation speed.

The invention discloses a hydroxylated tung oil and an ester group-aminated preparation method, belonging to the technical field of organic chemosynthesis. In the method, under a certain condition, ester groups in the tung oil and diethanol amine are subjected to aminolysis reaction on the premise of reserving conjugated double bonds, thus the tung oil is hydroxylated to mainly produce a compound containing terminated hydroxyl and three conjugated double bonds. The structural formula of the compound is shown in the specification. The aminolysis reaction product plays an important role in developing and producing processes of leading the tung oil into waterborne polyurethane, not only can combine the advantages of the tung oil and the polyurethane, but also can improve the utilization ratio of tung oil resources as special local products in China and decrease the dependence on petroleum products; and simultaneously, the method has the advantages of simplicity, practicability, high conversion rate and convenience in industrialized production.

The invention relates to an improved double-threshold cooperative spectrum sensing method, which comprises the following steps that: a cognitive user in a cognitive radio system senses a local spectrum, compares and classifies sensed main user energy values, if the energy values are beyond two thresholds, can directly perform local judgment to obtain a hard judgment result whether a main user exists and send the result to a fusion center, if the energy values are between the two thresholds, the cognitive user needs to send the sensed main user energy values and signal to noise ratios to the fusion center, and the fusion center calculates a weight factor of each cognitive user according to the received signal to noise ratios and performs fusion judgment according to the received energy values to obtain a soft judgment result whether the main user exists; and the fusion center comprehensively processes the hard judgment result and the soft judgment result, and makes a final judgment whether the main user exists by using an 'or' rule. According to the method, the advantage of double-threshold cooperative spectrum sensing can be fully realized; and the spectrum sensing performance is improved.

The invention relates to an uncooled double-field-of-view infrared optical system and belongs to the technical field of optics. A first positive meniscus lens, a second double-concave negative lens, a diaphragm, a third double-convex positive lens, a fourth double-convex positive lens, a fifth negative meniscus lens and a detector are coaxially arranged from an object space to an image space, wherein the second double-concave negative lens can axially move between the first lens and the diaphragm to form a narrow-field-of-view light path and a wide-field-of-view light path respectively. The system is provided with two optical fields of view, namely a wide field of view and a narrow field of view, wherein the wide field of view is used for target detection, and the narrow field of view is used for target identification; due to an aspheric design, the double-field-of-view optical system has higher degree of freedom, alternative variables for optimization design are increased, and an optical aberration design is easy to obtain high image quality; and because transformation and focusing compensation of the two fields of view are realized due to movement of the same lens, movement mechanisms in the system are reduced, the rest components in the light path are fixed parts which are easy to assemble and debug, and the assembling and debugging difficulty of the system is reduced greatly.

Disclosed is an optical film with adhesive, which is obtained by forming an adhesive layer on the surface of an optical film. The adhesive layer is improved in antistatic properties, while having excellent durability. Specifically, the optical film with adhesive is obtained by forming an adhesive layer on the surface of an optical film. The adhesive layer is made from a composition containing 100parts by weight of a specific acrylic resin (A), 0.01-5 parts by weight of an ionic compound (B), 0.05-5 parts by weight of a hexamethylene diisocyanate crosslinking agent (C), and 0.03-1 part by weight of a silane compound (D). The acrylic resin(A)is mainly composed of a hydroxyl group-containing acrylic resin having a weight-average molecular weight of 500,000-2,000,000, which contains a structural unit derived from a (meth)acrylate represented by the formula (I) below and a structural unit derived from a hydroxyl group-containing monomer in such an amount that the structural unit derived from a hydroxyl group-containing monomer is 0.5-5 parts by weight per 100 parts by weight of the entire resin. In the formula (I), R1 represents a hydrogen or a methyl; and R2 represents a C1-14 alkyl or aralkyl.

The invention provides a time series anomaly detection method for key performance index data, which is used for solving the problems of low time series data anomaly detection efficiency and low accuracy in the prior art. The a time series anomaly detection method comprises the following steps: correcting missing values and abnormal values in acquired time series data; extracting features to splicethe data; dividing the spliced data into a training set and a test set; performing training on the basis of optimizing a target function to obtain an anomaly detection model; performing testing and evaluating to obtain an evaluation standard; and performing time series anomaly detection on the data to be detected according to the evaluation standard and the anomaly detection model. According to the invention, anomaly detection is carried out on different time series data with periodicity; features of different dimensions are extracted from the time series data to ensure that the correlation of the data in different dimensions can be learned by the model, so that the cost caused by abnormal labeling is reduced, and meanwhile, the method is suitable for a scene with extremely non-uniform positive and negative samples, and the detection efficiency is improved.

The invention discloses a method for fusing sensor grid data based on grid clustering, which comprises the following steps: allocating tasks to all sensor nodes of each wireless sensor network, and then running an adaptive genetic algorithm based on the grid clustering in each wireless sensor network; according to perceptive data of all the sensor nodes, finding out an optimal sensor node and then finding out an optimal fusing routing with the minimum communication cost; primarily fusing data by a mobile agent along the optimal fusing routing and then sending the primarily fused result to nodes of convergent points of the network; and adopting an adaptive weighted fusion algorithm by a grid layer to carry out deep fusion on the data so as to obtain the final fused result which is providedfor a user for making decisions. The method can be applied to a sensor grid environment and is used to process the data sufficiently so as to obtain the good fused result. The grid is used for deeplyfusing the data, thus reducing energy consumption of the wireless sensor networks and prolonging the service life of sensor grids.

The invention provides a commodity three-dimensional reconstruction system and a reconstruction method, wherein, the method comprises the following steps: S1, obtaining at least three point cloud images of commodities from different angles of view; S2, based on SAC-IA-ICP algorithm registers the point cloud of two adjacent point cloud images in order to eliminate the deviation between adjacent point cloud images. S3, adopting the improved Poisson surface reconstruction algorithm to reconstruct the complete three-dimensional surface model of the merchandise for all the point clouds after registration; S4, color rendering and illumination rendering are carried out on the commodity three-dimensional curved surface model by using an illumination renderer to obtain a three-dimensional commoditymodel that can be displayed. The three-dimensional commodity model constructed in the above method is more prominent in detail and more obvious in geometrical features. The effect is more real and the global shape and detail features of the commodity are truly restored.

The invention discloses a sentiment analysis method based on a Skip-gram model fusing part-of-speech and semantic information. The method includes five steps of data preprocessing, part-of-speech information modeling, word vector representation, semantic information modeling and sentiment analysis. Data preprocessing includes filtering, word segmentation and part-of-speech labeling. Part-of-speechinformation modeling contains context-based modeling on part-of-speech information. A word vector representation module carries out vector training on the Skip-gram model fusing the part-of-speech information. The semantic information modeling module includes text representations fusing sentiment semantic information. Compared with the prior art, the method considers the part-of-speech information and the sentiment semantic information of words, fully uses the part-of-speech information of the words for helping in word vector training and the sentiment semantic prior-information for helping in learning of text vectors on the basis of modeling on the part-of-speech information of the words and fusion of the semantic information, enables the represented text vectors to more accord with linguistic features, and has good results for sentiment analysis.

The invention discloses a fluorescence quantitative PCR detecting system based on bottom scanning detection, comprising a temperature changing metal module capable of being inserted with a plurality of test tubes, a semiconductor freezer and a radiating device which are arranged into a whole in sequence from up to bottom. The system is characterized in that test tube slots of the temperature changing metal module are through holes running through the temperature changing metal module, the semiconductor freezer and the radiating device; the radiating device is a thermotube radiator, the thermotube extends to the outside of the whole structure, the extending end is connected with a radiator fin; a photoelectric detector is positioned at the bottom of the radiating device of the whole structure and is driven to scan by an electric motor; and the reagent fluorescence is detected from the bottom of the test tubes. The adoption of the system contributes to improving the luminous efficiency, obtaining higher fluorescence sensitivity and the fluorescence signal intensity, and improving the stability, ensures that the detection result is more accurate, realizes the rapid temperature rise and temperature reduction as well as the accurate temperature control of the temperature changing metal module, and greatly prolongs the service life of the temperature changing metal module.

A conjoint analysis method for electroencephalograph and electromyography signals based on autonomous movement and imagination movement comprises the steps of performing system setup, and using a LabVIEW 8.6 to generate square wave pulse signals; respectively collecting electroencephalograph signals and electromyography signals including electroencephalograph signals and electromyography signals in autonomous movement modalities and in imagination movement modalities; performing noise removal pretreatment on collected original data; performing electroencephalograph and electromyography time-domain signal analysis in the autonomous movement and imagination movement modalities on electroencephalograph and electromyography signal time-domain pictures which are performed with noise removal pretreatment in the autonomous movement and imagination movement modalities; performing time-frequency signal analysis on electroencephalograph and electromyography signals performed with noise removal pretreatment and in the autonomous movement and imagination movement modalities based on Morlet wavelet transformation; and performing partial directional coherence analysis, and in particular adopting granger causality to perform the partial directional coherence analysis. The conjoint analysis method provides new evaluation parameters for monitoring recovery auxiliary equipment and assessing organism movement level.

The invention discloses a method for optimizing electric-quantity-constrained monthly unit commitment and based on an induction target function and belongs to the technical field of power dispatching automation. The method includes building a mathematical model of a monthly unit commitment plan according to unit commitment basic data, building a mathematical model of a loose monthly unit commitment plan and calculating the model to obtain the loose monthly unit commitment plan, counting the number of start-stop variables with the value as 0 or 1 in the loose monthly unit commitment plan, building the induction target function of the mathematical model of the loose monthly unit commitment plan, conducting iteration optimization till the number of the start-top variables with the value as 0 or 1 stops increasing, adopting the induction target function to obtain the monthly unit commitment plan, conducting further optimization if a deviation factor of the monthly unit commitment plan is larger than a deviation threshold and finishing optimization otherwise. The method can be used for optimizing the electric-quantity-constrained monthly unit commitment plan, calculation efficiency is remarkably improved, and the method has important practical significance and good application prospects.

The invention discloses an alternating current and direct current interconnected power grid wind and fire coordination dynamic economy scheduling optimization method and belongs to the technical field of electric power scheduling automation. The method comprises the following steps: dynamic economy scheduling optimization fundamental data is obtained, a main model for dynamic economy scheduling optimization and sub-models for a plurality of periods of time are constructed; the main model for dynamic economy scheduling optimization is subjected to solving operation; an optimal solution is obtained; a calculation result is transmitted to the sub-models for the plurality of periods of time; the optimal solution for the main model is substituted into the sub-models and serves as a known condition for constructing a sub-model constraint, optimization objects of all the sub-models are subjected to parallel computing and verifying operation; if the optimization objects omega of all the sub-models are 0 or reach a maximum number of iterations, the obtained result is used as an optimal solution for the alternating current and direct current interconnected power grid wind and fire coordination dynamic economy scheduling optimization. The method disclosed in the invention can be used for optimizing a current electricity generating plan for an alternating current and direct current interconnected power grid containing wind power and thermal power; calculation precision and calculation efficiency are effectively improved, safety of a power generation and transmission plan is safeguarded, and important practical significance and good application prospects can be realized.

The invention discloses a method for collaboratively diagnosing the fault of a rolling bearing based on vibration signals of cartridge receiver. The method includes the following steps: S1. arranginga sensor on a cartridge receiver to acquire vibration signals; S2. based on smallest entropy convolution, enhancing the impact part of a to-be-detected signal; S3. based on wavelet transformation, extracting the resonance waveband of the rolling bearing; S4. suppressing the non-periodic part within the resonance waveband by performing autocorrelation analysis; and S5. based on Hilbert transformation envelope demodulation, positioning the fault of the rolling bearing. According to the invention, the method herein can effectively eliminate influence on signal transmission path of the rolling bearing and interference by complex background noises, highlights the spectral peaks corresponding to the feature frequency of the fault, can position the fault which causes damage to the surface of therolling bearing, and addresses difficult diagnosis, low precision and poor robustness in monitoring the fault of the rolling bearing based on vibration signals that are tested on test points of the cartridge receiver.

The invention provides motion identification system by simulating a spiking neuron of primary visual cortex. The system comprises a video image pretreatment module, a feature extraction module, and a motion identification module. According to the invention, firstly, a 3D gabor space time filter simulates a simple cell in primary visual cortex, so that a video image is processed to obtain motion information being sensitive to a motion speed and a direction; secondly, a neuron in the primary visual cortex is stimulated by employing an integrated and fire spiking neuron model and the obtained motion information is converted into a pulse train responded by the neuron; thirdly, a motion feature vector is extracted according to an average firing rate and a behavior feature of the pulse train and identification is carried out on the motion in the video image by employing a method of support on a vector machine. According to a test result, in the test environment of a Weiziman database, the system provided in the invention not only enables an accuracy of the motion to be improved but also enables an identification rate is accelerated substantially.

The invention relates to a cementing material of steel slag tailings and composite slag micro powder, which comprises the composite slag micro powder and the steel slag tailings, and the weight ratio of the composite slag micro powder to the steel slag tailings is between 1 to 1 and 1 to 3; the specific surface area of the composite slag micro powder is larger than 350 m / kg, the composite slag micro powder comprises a combined material and the slag micro powder, wherein the combined material accounts for 8 to 35 percent of the composite slag micro powder by weight, and the slag micro powder accounts for 65 to 92 percent of the composite slag micro powder by weight; and the combined material comprises gypsum. The cementing material has the advantages that: 1, the method is an effective method utilizing large amount of waste slag; 2, the raw material of the composite slag micro powder has wide source; 3, the steel slag tailings are natural-grade tailings after steel slag wet-type ball milling magnetic separation particles are toughened, and the settled tailings do not need to be dried and are directly used as an auxiliary material; and 4, the steel slag tailings account for large proportion, and can fully utilize waste slag resources.

The invention provides a joint sight line direction calculation method of left and right eye images of human eyes. The method includes: a two-eye information extraction model, in which a human eye image is input, and left eye and right eye information features contained in the image are respectively and automatically extracted through the two-channel model; and a joint human-eye information feature extraction model, in which two-eye images of a user are input, and two-eye information is combined to extract joint human-eye information features by the model. According to a joint algorithm provided by the invention, a three-dimensional sight line direction is calculated and obtained through inputting feature information. One of the applications of the method is virtual reality and human-machine interaction, and a principle thereof is to calculate the sight line direction of the user through shooting the eye image of the user, and thus carry out interaction with an intelligent system interface or a virtual-reality object. The method can also be widely used in the fields of training and cultivating, games and entertainment, video surveillance, medical care and the like.

The invention discloses a discloses a positron emission tomography (PET) and computed tomography (CT) cross-modality medical image fusion method based on wavelet transform, comprising the following steps shown in the figure 1: fixing a patient in a locating frame; acquiring a PET image according to the frame and simultaneously utilizing the locating frame to acquire a CT image; respectively extracting the PET image and the CT image; registering the extracted PET image and the extracted CT image according to locating points obtained in the locating frame; carrying out wavelet decomposition and variation solving fusion on the rectified PET image and the registered PET image and the registered CT image; and finally carrying out wavelet inverse transformation on the PET image and the CT image which are subject to fusion to generate a fused image. The invention has the advantages that rectification is carried out on the acquired PET image and the acquired CT image by an external label point-based method, and a variation method is innovatively proposed to be applied to a wavelet region, thus realizing the accurate fusion of PET and CT cross-modality medical image; and the method is simple in operation and accurate in results, and has important application value and economic benefits in clinical applications.

The invention relates to an image overdivided optimization method based on multi-object evolutionary clustering and space information for applying the space information of the multi-object evolutionary clustering algorithm to the image overdivided optimization which includes steps as follows: (1) processing watershed pre-divide to an input image for obtaining overdivided result image; (2) stating characteristic of each area in the overdivided result image as data of the multi-object evolutionary clustering; (3) actualizing the multi-object evolutionary clustering, optimizing an object function added the space information by using the multi-object evolutionary clustering method, processing clustering to each area characteristic of the overdivided result image for obtaining a non-dominated solution set; (4) integrating the non-dominated solution set for obtaining final solution; (5) outputting the optimization result of the overdivided image according with final solution. The method introduces the multi-object evolutionary algorithm into the image dividing field for making the image clustering dividing method more stably and having better adaptability; the method uses the image space information that makes the image optimization dividing inner is more compact.

The invention relates to an enterolobium cyclocarpum knot nematode loop-mediated isothermal amplification (LAMP) rapid detection method and applications. The method comprises the following steps: designing 5 LAMP primers: MEF3, MEB3, MEFIP, MEBIP and MELB in a sequence conversed domain according to an enterolobium cyclocarpum knot nematode ITS sequence of the clone sequencing; configuring a LAMP reaction system; detecting isothermal amplification and amplification product color developing of the enterolobium cyclocarpum knot nematode through extracting the DNA of the enterolobium cyclocarpum knot nematode, thus rapidly detecting the enterolobium cyclocarpum knot nematode. The detection method provided by the invention has strong specific, high sensitivity, low cost and simple operation, and has high application value in the aspects of spot rapid detection and the early-stage diagnosis of the enterolobium cyclocarpum knot nematode.

The invention discloses a Chinese text keyword extraction method based on document theme structures and semantics, and relates to keyword extraction. The method includes the steps: text preprocessing;Chinese segmentation and part-of-speech tagging; stop word filtering and part-of-speech filtering; keyword extraction. The basic conception of text keyword extraction, Chinese segmentation and English segmentation differences and a common Chinese text keyword extraction method are introduced. A method based on the document theme structures and a method based on semantics are researched, and the principle and an existing implementation scheme are analyzed. In order to overcome difficulty in new word identification in Chinese segmentation, Chinese segmentation effects are continuously improvedby the aid of a dynamically updated segmentation dictionary. The method based on the document theme structures is improved, and global keywords are extracted. Semantic similarities of Chinese words are taken into account, and an algorithm is further improved. The improved algorithm is verified in a self-built data set, good results are acquired by verification experiments and comparison experiments, and keyword extraction effects can be improved by the improved algorithm.

The invention provides a hand vein recognition method based on the fusion of structure coding characteristics and texture coding characteristics, belonging to the technical field of intelligent monitoring in computer vision. The method comprises the following steps of: step 1, acquiring and preprocessing an image; step 2, extracting the structure coding characteristics; step 3, extracting the hand vein texture coding characteristics; step 4, fusing the structure coding characteristics with the hand vein texture coding characteristics; and step 5, performing recognition through a sorter, thereby obtaining a result. The invention provides the hand vein recognition method based on the fusion of the structure coding characteristics and the texture coding characteristics, wherein binary coding is performed on the extracted structure characteristics and texture characteristics, which is advantageous for the retention of information in the characteristic fusion; the result obtained by fusing the characteristics is far better than the result of recognition obtained by only employing the structure coding characteristics and the result of recognition obtained by only employing the texture coding characteristics; and the robustness for image distortion and wrong segmentation is high, therefore, the hand vein can be correctly recognized in the presence of certain image distortion and wrong segmentation.

The invention relates to a multi-feature-combined Hash information retrieval method. The method is characterized by comprising the following basic steps that 1, an objective function is set up, data distribution of an object space is protected, compact matrix basis in an NMF is obtained, and redundancy is reduced; 2, alternative optimization is carried out, U and V are optimized through an iterative process, and updating rules of the base operator U and low-dimension data V are obtained; 3, global convergence is carried out, and alternating iteration is carried out through the original objective function; 4, a Hash function is generated, and finial results are obtained by calculating the hamming distance between training data and a test sample, namely XOR operation; 5, complexity analysis is carried out on the methods in the step 1 to step 4. By means of the method, probability distribution of data can be effectively protected, redundancy of low-dimension data is reduced, and therefore a Hash embedded function can be learned, wherein through the Hash embedded function, multiple expressions obtained from multiple sources can be fused, and RKNMF can be used for protecting high-dimension joint distribution and obtaining orthogonal basis.

The present invention relates to compounds of the formula (1) which are suitable for use in electronic devices, in particular organic electroluminescence devices.