1811results about How to "Fully extracted" patented technology

In addition to dedicated channel signal formation units 101-1 to 101-N, control information channel signal formation unit 110 is provided and this control information channel signal formation unit 110 forms control information for carrying out uplink packet transmission. The control information channel signal formation unit 110 multiplexes control information (RG information, ACK / NACK, etc.) directed to a plurality of communication terminals through a channel encoding section 111 according to a multiplexing rule preset between the base station apparatus and each communication terminal and spreads the control information using a spreading code common to the communication terminals through a spreading section 113 and thereby forms a control information channel signal for uplink packet transmission.

Capsule for the preparation of a beverage in a beverage machine comprising an enclosure (20) containing one or more beverage ingredients, a filtering wall (22) delimiting at least one filtering side of the enclosure, wherein the capsule further comprises an overflow wall (3B) that is positioned in the path of the brewed liquid after the filtering wall and which comprises at least one overflow aperture or is associated with at least one puncture means (8A, 8B) or, respectively, puncture indication means capable of producing or, respectively, indicating at least one overflow aperture. The capsule of the invention is designed for delivering a brewed liquid at relatively low pressure and is more particularly suited for tea beverages.

A device is arranged to detect a configuration of withdrawal and return devices (1, 14, 111, 112, 211, 212, 702, 703, 802, 803) coupling an extracorporeal blood flow circuit (20) to a cardiovascular system of a subject. The device comprises a signal processor (29), which is configured to receive a primary measurement signal obtained by a primary pressure sensor (4a, 4b, 4c) in the extracorporeal blood flow circuit (20). The device is further configured to process the primary measurement signal for extraction of primary pressure data originating from a subject pulse generator (3′) in the cardiovascular system or extracorporeal blood flow circuit (20), the primary pressure data comprising at least a part of a first pulse from the subject pulse generator (3′). The device is also configured to calculate a parameter value from the primary pressure data and to determine the configuration based at least partly on the parameter value.

The invention discloses a method for extracting peony seed oil. In the method, peony seed powders are put in a supercritical CO2 extractor for supercritical extraction, and oil is collected to obtain the provided peony seed oil. In the method, the supercritical extraction temperature ranges from 30 to 50 DEG C, 45 DEG C is optimized, the pressure for supercritical extraction ranges from 20 to 35MPa, and 30MPa is optimized; time for supercritical extraction ranges from 0.5 to 2 hours, and 1 hour is optimized. The grain diameter of the peony seed powders ranges from 20 to 60 screen meshes. The provided supercritical CO2 extracting method for preparing peony seed oil needs no organic reagents, has simple and convenient extracting and separating method, the extraction yield can reach 27 percent, and obtained unsaturated fatty acid can reach 90 percent; and compared with the prior organic solvent extraction method, the contents of linolenic acid and linoleic acid in peony oil are increased to certain extent, and the extraction is completer.

The invention relates to an object elevation vectorization method and system based on three dimensional laser scanning. The method comprises the steps of regional dividing: functional regions are extracted from the cloud data of three dimensional laser points on the surface of the object through a method of small plane dividing-polymerizing; boundary extracting: geometric mutation is detected through a directional image, and then regional boundaries are used for supplementing to extract the geometric edge information of the functional regions included in the three dimensional laser points; and boundary correcting: a convex decomposition method is used for correcting the boundaries of the functional regions to complete the reconfiguration of the object elevation. The system comprises a laser scanning unit, a regional dividing unit, a boundary extracting unit, a boundary correcting unit and a three dimensional model mapping unit. The regional dividing unit extracts the functional regions from the obtained cloud data of the three dimensional laser points; the boundary extracting unit extracts the boundary information of the functional regions included in the cloud data of the three dimensional laser points; and the boundary correcting unit fits the functional regions again to correct the boundaries.

It is provided a video distribution system including a video distribution module, a control module, and a bookmark management module. The bookmark management module manages a first type bookmark whose status is not changed by viewing of the video and a second type bookmark whose status is changeable after the viewing of the video. The control module generates screen data that enables one of the first type bookmark and the second type bookmark to be selected with regard to the video stored in the library, transmits the generated screen data to the one of the receiving terminals, and sets one of the first type bookmark and the second type bookmark, which is selected by a user using the one of the receiving terminals, for the video and record the one of the first type bookmark and the second type bookmark in the bookmark management module.

The invention relates to a method for preparing a health-care food (therapy) product (nutrient juice wine) by use of extracts from cordyceps militaris and cocoon, aiming at extracting multiple nutrient substances with natural activity from cordyceps militaris and cocoon wastes to produce different types of dietary supplements for fully improving the utilization rate of cordyceps militaris and cocoon. The method is characterized in that the nutrient components can be extracted from the cordyceps militaris and the cocoon through freezing and thawing, combined enzymolysis, and a water extraction and alcohol precipitation step-by-step extraction method, and the health-care food (therapy) product has three formulas (in part by weight) according to different functions (requirements): A. nutritive extract of cordyceps militaris and cocoon 85% and assistant 15%; B. nutritive extract of cordyceps militaris and cocoon 95% and assistant 5%; and C. nutritive extract of cordyceps militaris and cocoon 98% and assistant 2%, wherein the assistant can be starch, beta dextrin and the like, can be combined with one of A, B and C formulas to produce the health-care food (therapy) product with different efficacies through the processes such as sterilization, enzymolysis, precipitation, residue filtering, saccharification and fermentation.

The invention provides a preparation method for high-activity lepidium meyenii extract. The preparation method is characterized by comprising the following steps of: crushing fresh or dry lepidium meyenii roots, and performing primary ultrasonic reinforced extraction, primary solid-liquid separation and primary low-temperature supernatant concentration under the conditions of certain liquid / solid ratio and 20 to 50 DEG C by taking water as a solvent; performing secondary ultrasonic reinforced extraction, secondary solid-liquid separation and secondary low-temperature supernatant concentration on filter residues under the conditions of certain liquid / solid ratio and 20 to 50 DEG C by using an ethanol-water solution; and mixing the two concentrated solutions, and drying the mixture at low temperature or quickly to obtain powdery lepidium meyenii extract. The preparation method has the characteristics of high speed, high efficiency, completeness of functional component extraction, capability of preventing the damage of heat-sensitive functional components of lepidium meyenii, high bioactivity of the extract, low energy consumption, low cost and the like. The lepidium meyenii extract prepared by the preparation method provided by the invention has the effects of eliminating fatigue, resisting pressure, improving immunity, alleviating climacteric syndromes, resisting prostatic hyperplasia, enhancing sexual function, and the like.

The invention discloses a hyperspectral image classification method based on a convolutional neural network and spatial spectrum information fusion. The method comprises the steps that S1 X and Y axiscoordinates of each pixel point of a hyperspectral image are extracted as spatial information, and the spatial information and spectral information are combined as the feature information of a sample; S2 training set and test set data are randomly divided; S3 training set samples including the spatial information and the spectral information are put into a one-dimensional convolutional neural network to train a classification model; and S4 the training set samples including the spatial information and the spectral information are put into the classification model for classification prediction. The convolutional neural network uses convolution kernels of different sizes to carry out convolution operation, which can effectively extract feature information with different resolutions in the spectral dimension of a hyperspectrum. In addition, the spectral dimension information and the spatial dimension information are input into the neural network to learn simultaneously. The feature of dual high resolutions of the hyperspectrum is fully used. The algorithm structure is simple, and the classification accuracy can be significantly improved.

The invention discloses a real-time learning debutanizer soft measurement modeling method on the basis of Gaussian mixture models (GMM). The real-time learning debutanizer soft measurement modeling method includes training process Gaussian mixture models to acquire various Gaussian component parameters and building corresponding sub-models; computing posterior probabilities of to-be-predicted samples and local Mahalanobis distances of various Gaussian components by a Bayesian process so as to obtain weighted sample similarity definition indexes; reasonably selecting similar samples by the aid of the new similarity indexes for local modeling. The posterior probabilities indicate whether the to-be-predicted samples belong to the various Gaussian components or not. The real-time learning debutanizer soft measurement modeling method has the advantages that problems of process non-Gaussianity and nonlinearity can be effectively solved, characteristics of the to-be-predicted samples can be sufficiently extracted, the similar samples can be reasonably selected for real-time learning modeling, and accordingly the real-time learning debutanizer soft measurement modeling method is favorable for improving the model prediction precision.

The invention relates to a method for preparing nutrient (liquid) beverages and granules of healthcare food (therapy) products by using extractives of cordyceps militaris and cocoons and aims to meet market requirements by producing nutrient healthcare products with different types by using various nutrient substances extracted from waste cocoons of the cordyceps militaris. The method is characterized by comprising the following steps of: firstly extracting the nutrient substances from the cordyceps militaris and the cocoons by adopting freeze thawing, combined enzymolysis and a water-extraction alcohol-precipitation sequential extraction method, and producing nutrient liquors and granules with different functions according to different functions (requirements) and different proportions. According to the method for preparing the nutrient (liquid) beverages and the granules of the healthcare food (therapy) products by using the extractives of the cordyceps militaris and the cocoons, the synergistic effects among nutritional ingredients are adequately exerted, and the use ratios of the cordyceps militaris and the cocoons are improved.

The invention discloses a method for preparing health food capsules by using cordyceps militaris and cocoon extracts, and aims to extract various nutrient substances with natural activities from the cordyceps militaris and cocoon to produce different types of nutrient health products, so that the utilization rate of the cordyceps militaris and cocoon is fully improved, and the market demand is met. The method is characterized by comprising the steps of extracting the nutrient substances from the cordyceps militaris and cocoon by performing freeze thawing, combined enzymolysis and aqueous alcohol precipitation sub-step extracting methods; adding a proper quantity of auxiliary materials into the nutrient substances to fully and evenly stir and sieve to prepare the particles; filling the particles into the capsules; wiping off residual powder adhered to the capsule shells; waxing; polishing; and sterilizing the capsules in a radiation mode after the capsules are packaged so as to obtain the finished product.

The invention relates to a method for preparing high-purity rice protein and high-purity rice peptide. Rice or broken rice is used as a raw material in the method; based on crude rice protein prepared by an alkali extraction and acid precipitation method, non-protein substances in the crude rice protein are removed by the impurity removing effect of an efficient composite enzyme consisting of amylase, cellulase and lipase, and then the high-purity rice protein with protein content of more than 90 percent is prepared; and based on the high-purity rice protein, the hydrolysis degree of the rice protein is controlled under the action of the composite enzyme by an alternated enzymolysis ultra-filtration coupling technology, and the high-purity rice peptide with protein content of over 90 percent, polypeptide content of more than 70 percent and 200 to 1,200Da oligopeptide content of over 50 percent is prepared by separation.

The invention relates to a non-linear industrial process fault detection method based on Bayes kernel slow feature analysis. After normalization processing of training data and test data, kernel functions of different types are adopted based on a conventional kernel feature analysis method, and the various kernel functions are configured with different kernel functions, and therefore a series of basic KSFA models are established. Non-linear slow features are more fully extracted from the normalized training data and the normalized test data by using the basic KSFA models, and the basic KSFA models are respectively used to monitor the process. The non-linear industrial process fault detection method is provided with Bayesian inference, and the test data monitoring results of the series of basic KSFA models are weighted in a combined manner by adopting a probability way, and finally the integrated monitoring result of a plurality of models is acquired, and therefore a fault detection result is improved, and a fault detection rate is improved.

A catheter assembly (1) comprising a catheter (8) having a proximal end (10) adapted to be inserted into a bodily cavity and a distal end (11) having a drainage outlet (13), a catheter package (2) having a cavity for accommodating the catheter and an opening for withdrawing the catheter from the package, and extracting means (17) that are coupled to the catheter for extracting the distal end of the catheter out of the package.

Disclosed is a fat emulsion injection of 'Xingnaojing' and its preparing process, which comprises using supercritical extraction technology and macroscopic adsorption resin technology for isolating and purifying the water soluble and fat soluble parts in the medicinal materials including musk, cape jasmine, and curcuma root, finally utilizing emulsion technology to obtain the end product.

A six-DOF high-precision calibrating method for an articulation type coordinate measuring machine comprises the following steps of: using the articulation type coordinate measuring machine as a calibrated machine, using a three-coordinate measuring machine with higher precision as a standard machine, and disposing the calibrate machine into the measurement space of the standard machine; respectively obtaining the standard coordinates of probes in a standard coordinate system and the measurement coordinates of the probes in a calibrated coordinate system; using the standard coordinates and the measurement coordinates as sampling data, and obtaining the error parameter of the calibrated machine with least square method. The method is characterized in that the sampling density is that each joint of the calibrated machine at least locates at two equally divided angle positions in each circle (360 DEG), and the placement attitudes of the calibrated machine formed by the combination of the six joints with different angle positions are at least 64 types. The inventive method can further improve parameter identification precision, and improve the entire precision of the measuring machine.

The invention discloses a herba portulacae extract and a preparation method thereof. The preparation method comprises the following steps of: 1, carrying out extraction and enzymolysis on fresh herba portulacae and / or dried herba portulacae with water and hydrolysis enzyme; 2, carrying out ethanol extraction on a feed solution obtained in the step 1, carrying out solid and liquid separation to obtain an extraction solution A and filter residue; 3, carrying out ethanol extraction on the filter residue obtained in the step 2, carrying out solid and liquid separation to obtain an extraction solution B and filter residue; 4, extracting the filter residue obtained the step 3 by using water, carrying out solid and liquid separation to obtain an extraction solution C; and 5, mixing the extraction solutions A, B and C obtained the step 2, 3 and 4, removing ethanol, concentrating, standing, and filtering to obtain filtrate to obtain the herba portulacae extract. The invention also discloses the herba portulacae extract solution and the preparation method thereof. A herba portulacae extract or extract solution can be used as an anti-stimulation and antianaphylaxis function component to be applied to cosmetics. The preparation method disclosed by the invention can be used for preparing the herba portulacae extract with higher effective component content, with high yield.

A process for preparing the ganoderma spore powder includes such steps as removing impurities, drying, sieving, breaking wall of spore, adding alcohol solution, extracting, filtering, sieving the filtrate, vacuum concentrating, and drying.

The invention provides a human face recognition method based on supervision isometric projection. The human face recognition method comprises the human face sample training process and the human facesample testing process. The human face sample training process comprises the following steps: firstly carrying out pretreatment on a human face training image, adopting Gabor wavelet for filtering theimage, proposing a new distance formula for calculating an adjacency matrix of a training sample, calculating a shortest path distance matrix D in the training sample by the adjacency matrix DG of the training sample, calculating a low-dimensional projection matrix describing data of the human face training sample, calculating the projection of the training sample in low-dimensional space througha projection conversion matrix A and the like; and the human face sample testing process further comprises the following steps; carrying out the pretreatment on a human face testing image, adopting the Gabor wavelet for filtering the image, calculating the projection of the testing image in the low-dimensional space, adopting a nearest neighbor algorithm for judging the type of a testing sample and the like. The human face recognition method is characterized by stronger description of the structure of the sample data, elimination of high-order redundancy and small calculation cost, thereby being more applicable to mode classification tasks and the like.

The invention discloses a lightweight image super-resolution reconstruction method based on multi-dimensional knowledge distillation. The method comprises the following steps: preprocessing a teacher network; preprocessing the data set to generate a low-resolution image; inputting the low-resolution image into the constructed student network, and outputting a super-resolution image reconstructed by the student network; inputting the low-resolution image into a plurality of teacher networks to obtain a reconstructed super-resolution image group; respectively carrying out L1 loss calculation and perception loss calculation on the super-resolution image group of student network reconstruction and the super-resolution image group of teacher network reconstruction, and carrying out update training of back transmission to obtain a final student network model; and inputting the low-resolution image into the final network model, and outputting a super-resolution image. According to the super-resolution training mode, the parameter quantity is reduced, indexes and visual effects which are comparable to those of a fully-supervised teacher network trained by paired data are obtained, meanwhile, the size of the model is effectively reduced, and compared with a traditional training mode, the model is obviously improved.

The invention discloses a face identification algorithm under different illumination conditions, comprises the steps that: (1) the inputted face original image F(x, y) is subjected to Gaussian filter so as to eliminate noise point in the image and smooth the image, thereby obtaining a new image F'(x, y); (2) the F' (x, y) is subjected to feature extraction by using a Weber local operator description; (3) an edge is extracted from a new image WF(x, y) by LoG operator operation; (4) the inputted image is converted to a principal component space by using fast PCA algorithm; (5) in the PCA transform space, a inter-class scatter matrix is calculated (img file='DDA0000447013750000011.TIF' wi ='45' he='65' / ); (6) the obtained new image feature WF'(x, y) is projected on a feature projection vector group Phi; (7) finally, a nearest neighbor classifier carries out classification. The invention carries out feature extraction of the image operated by the LoG operator, more complete identification information of the original imagely can be extracted, the data from the above experiment shows that the new algorithm can obtain the prominent face identification rate from all three face databases, and also completely meet the real-time requirements in the speed.

The invention discloses a steam turbine rotor fault diagnosis method based on LSTM, and belongs to the technical field of mechanical fault diagnosis. Firstly, multi-point acquisition sensors are deployed and controlled, and vibration signals of various typical turbine rotor faults are collected as a training set and a verification set. Secondly, the steam turbine rotor vibration signals are extracted from a power plant DCS system to serve as a testing set. Thirdly, the training set, the testing set and the verification set realize fusion of multi-point signal data and data enhancement throughsignal division, stacking and other operations. Fourthly, a neural network based on the LSTM is constructed, the training set and the verification set are used for completing training of the network,and finally, maintenance of a diagnostic model is achieved in cooperation with an actual diagnostic task, and finally the steam turbine rotor fault diagnosis is realized on the testing set.

The invention discloses an autonomous navigation method of an AUV (Autonomous Underwater Vehicle) based on a FastSLAM (Simultaneous Localization and Mapping) algorithm. The autonomous navigation method comprises the steps that 1) the AUV acquires initial pose and position information through the GPS and a navigation sensor on the water surface; 2) predicting the pose and position and an environmental road sign of the AUV by adopting unscented particle filtering according to latest control variables inputted into the AUV and observation variables of the sensor; 3) generating a proposal distribution function for parameter adaptive adjustment by adopting fading adaptive unscented particle filtering, and sampling in the proposal distribution function; 4) associating the latest observation environment information according to each particle, and updating estimation for each characteristic by adopting unscented Kalman filtering; 5) performing resampling on a particle set by adopting an adaptive partial system resampling method; and 6) performing AUV positioning and map building. The autonomous navigation method can improve the particle sampling efficiency of the Unscented FastSLAM algorithm and reduce the degradation degree of the particles through improving the proposal distribution function and the resampling process of the Unscented FastSLAM algorithm, thereby enabling the consistency of AUV pose and position estimation and the accuracy of autonomous navigation to be greatly improved.

The invention belongs to a medlar general flavone extracting and purifying technology and a medlar general flavone detecting method as well as applications of medlar general flavone. At present, the method for preparing medlar general flavones comprises a reflux extracting method, an ultrasonic wave extracting method and a microwave extracting method. According to the extracting and preparing method of flavones in medlar leaves disclosed in 201010542616.3, a colloid breaking-in technique, a high-pressure homogenizing technique and a membrane separation and combination application technique are adopted, and the preparation is complex and long time is consumed. The invention aims to research the medlar general flavone extracting and purifying technology, the medlar general flavone detecting method as well as applications of the medlar general flavone. The technical scheme provided by the invention comprises the following steps of conducting an extracting technology: drying and crushing medlar, adding alcohol, extracting and combining filter liquor, concentrating, and extracting, thereby obtaining a medlar flavone rough extract; and conducting an extracting and purifying technology: purifying the rough extract by adopting porous resin D101, and further purifying the flavones by adopting polyamide. The medlar general flavone extracting and purifying technology, the medlar general flavone detecting method as well as applications of the medlar general flavone provided by the invention have the effects that general flavones are extracted as well as polysaccharide is obtained, effective ingredients of medlar are extracted fully, and foundation for large-scale industrial production is laid.

The invention discloses a preparation method for extracting, separating and purifying hydroxysafflor yellow A from a Chinese medicinal material safflower at high yield and purity. The method comprises the following steps of: performing ultrasonic water extraction on the safflower medicinal material, separating by using HZ801 macroporous adsorption resin, performing LH-20 sephadex chromatographic separation, performing ultrafiltration, and performing freeze drying. The content of the prepared hydroxysafflor yellow A dry powder is 99.8 percent, and the yield of the hydroxysafflor yellow A is 62.7 percent.

The present invention provides a microbial fermented composite antibiotic-free feed containing natural antibacterial components. The feed comprises a fermented product based on the united segmented fermentation of aerobic-anaerobic composite strains, composite natural antibacterial components, a non-plant source protein / polypeptide compound nutrient supplement agent, and an additive comprising atleast a soybean protein amino acid / polypeptide compound chelate. The composite strains comprises at least lactic acid bacteria, bacillus subtilis, yeast selected from candida utilis or saccharomyces cerevisiae, and enterococcus faecalis. The fermented product based on the united segmented fermentation is prepared by carrying out secondary fermentation on fermentation raw materials through the aerobic-anaerobic composite strains. The combination of the microbial fermented feed and the natural antibacterial components is beneficial to the balance of intestinal flora of animals, and can prevent the dysbacteriosis of digestive tracts, improve the immunity functions of animal organisms, reduce or replace the use of feed antibiotics, and significantly improve the digestion and absorption efficiency of the feed and the meat quality of animals.

The invention relates to a preparation method of a health-care appetizing eucommia ulmoides beverage. The raw materials comprise folium cortex eucommiae, medicated leaven, malt, hawthorn, sugar, honey, coffee essence and the like. The preparation method comprises the steps of preparing raw materials, extracting, melting sugar and blending; the drink has special fragrance of folium cortex eucommiae and faint coffee flavor and is similar to coffee in appearance, color, luster and taste, and the health-care appetizing eucommia ulmoides beverage has the effects of tonifying spleen, appetizing, enhancing appetite, restoring consciousness, refreshing and resisting fatigue and is suitable for sub-health crowds who work on intellectual activities for a long time.

The invention discloses a cardiovascular disease risk predicting method based on an electronic medical record. The method comprises the steps of acquiring electronic medical record data and dividing the electronic medical record data into a training set and a testing set; arranging the electronic medical record data for forming a training set sequence and a testing set sequence; inputting the training set sequence into the embedding layer of the predicting model for generating an embedding vector; performing representation learning on the embedding vector by means of an LSTM module based on attention mechanism for obtaining a representation vector; splicing the representation vector and obtaining a preliminary cardiovascular disease risk predicting model through softmax layer prediction; performing testing and correcting on the preliminary cardiovascular disease for obtaining a final cardiovascular disease risk predicting model; and performing cardiovascular disease risk prediction onthe to-be-predicted patient by means of the final cardiovascular disease risk predicting model. The cardiovascular disease risk predicting method can efficiently and comprehensively capture characteristic information of the electronic medical record data and realizes more accurate disease risk prediction and higher model accuracy.