36results about How to "Reduce marking costs" patented technology

The invention belongs to the technical field of pedestrian re-identification, and discloses an unsupervised image video pedestrian re-identification method and system based on a migration network, andthe method comprises the steps: carrying out the feature extraction of an image and a video data set in a source domain through employing an improved triple network; training the generative adversarial network by using the source domain data set and the target domain training set; generating a depth feature by using the trained generative adversarial network according to the pedestrian image Itito be identified in the target training set; calculating the Euclidean distance between the depth feature of the image and the depth feature of the video in the target domain; and selecting a video which is closest to the query image, and marking a class mark which is the same as the image. According to the invention, an unsupervised method is used to eliminate a gap between an image and a video,so that the marking cost is greatly saved, and the pedestrian re-identification efficiency is improved.Through carrying out unsupervised deep learning on different modal images and videos, the cross-mode recognition efficiency is effectively improved.

The invention provides a machine learning-based automatic application interface test method and test system. The test method comprises the steps of converting a UI into a picture when it is judged that the obtained UI of a to-be-tested application occurs for the first time; inputting the obtained picture to a preset UI classifier to obtain a type of the UI, wherein the UI classifier is a UI classifier based on a GAN (Generative Adversarial Networks) and a CNN (Convolutional Neural Nework); according to the type of the UI, obtaining a corresponding test outline script from a preset test outlinescript library; matching the test outline script with a layout of the UI to obtain a test script corresponding to the UI; and testing the UI through the test script. The picture tagging cost can be effectively reduced; and the test scene validity and the test efficiency are greatly improved.

The invention discloses a field in-situ continuous low-abundance 13CO2 isotope labeling device and method, relates to a simulator for detecting crop rhizosphere respiration and labeling plants, and solves the problems of low labeling precision and high cost of a pulse labeling mode of a traditional C element labeling device or method for the plants. A labeling box is a transparent box with a through hole in the side wall, a combustion end of a combustor penetrates the through hole of the labeling box and extends into the labeling box, the tail end of the combustor is simultaneously communicated with a gas outlet of a liquefied gas bottle and a gas outlet of an oxygen bottle, an electronic fire maker is arranged at the combustion end of the combustor, a fan is fixed to the middle of the inner top of the labeling box, an air conditioner is fixed to the upper portion of the side wall of the labeling box, a CO2 measurement and control device and a humidifier are positioned in the labeling box, a dehumidifier is fixed to the lower portion of the side wall of the labeling box, and the fan, the air conditioner, the CO2 measurement and control device, the electronic fire maker, the dehumidifier and the humidifier are controlled by an automatic control system. The device and the method are used for performing continuous low-abundance 13CO2 labeling for crops.

The invention provides an intelligent paper inspection system for multiple question types. The system comprises a test paper obtaining module, a test paper preprocessing module, a question judging andscoring module and a result output module, wherein the test paper obtaining module is used for obtaining questions and question answering contents of test paper and standard answers of the questions;the test paper preprocessing module is used for preprocessing the received questions and question answering contents of the test paper: carrying out type classification on each question so as to obtain a question type, and carrying out question answering content segmentation, extraction and format standardization on the question answering content corresponding to each question according to the question type and a preset first rule, so as to obtain preprocessed question answering contents; the question judging and scoring module is used for carrying out question judging and scoring on the preprocessed question answering contents according to the question types and the standard answers and a preset second rule; and the result output module is used for outputting received scoring results. According to the intelligent paper inspection system for multiple question types, automatic paper inspection of multiple question types is realized, so that the working efficiency of paper inspection isimproved and the paper inspection cost is reduced.

The invention discloses an individual labeling method for selective breeding of tilapia. Firstly, an appropriate plastic pin, a tagging gun, a closed loop plastic tag and so on, are selected; secondly, Tricaine methane sulphonate with a concentration of 0.33g / L is used to anesthetize the tilapia; and then a fish body is labeled. The tilapia is kept a posture of swimming during the labeling and faces toward the left side; an insertion point is positioned at the front end on the back of the fish body; the closed loop plastic tag is sleeved on a gun needle; the plastic pin directly passes through the fish body to fix a label, and the length of the plastic pin exposed outside the fish body is between 2 and 4cm; and finally the labeled tilapia is placed into a 10 percent povidone iodine solution for soaking disinfection. The method has the advantages of simple operation, low cost, and high automation, has less damage degree to the fish body, ensures that the retention rate of the label is more than 90 percent and the effective recognition rate is more than 90 percent, can not affect the normal activity of the tilapia, and can meet the need of large-scale labeling of selective breeding of the tilapia.

The invention provides an electrical chemiluminescence immunoassay method based on gold magnetic particles, which realizes the quantitative detection on the content of antigen / antibody in human serum, blood plasma, cell culture supernatant or other related biological liquid so as to solve the problems of high cost, complicated steps, poor reaction controllability, no generality and the like in the prior art. The method mainly comprises the steps of 1) preparing immunomagnetic beads by gold magnetic particles; 2) labeling the antigen / antibody by Ru(bpy) <2+>3; 3) establishing a standard curve for quantitatively detecting the antigen / antibody; and 4) detecting a sample to be detected. A novel electrical chemiluminescence immunoassay technology is adopted, and the electrical chemiluminescence immunoassay method based on gold magnetic particles has the advantages that the operation is simple and convenient, the valence of antibody can not be affected in the process that the antibody is labeled by Ru(bpy) <2+>3, the labeling cost is low, and the universality is realized.

The embodiment of the invention discloses a multi-scale detection method and device for tumors in gastrointestinal endoscopic images. The method comprises the following steps: carrying out tumor imagemarking on each gastrointestinal endoscopy image sample, and carrying out tumor property grade marking on the gastrointestinal endoscopy image sample marked as a tumor image according to a preset tumor property grade; On the basis of the gastrointestinal endoscopy image sample subjected to tumor property grade marking, obtaining a gastrointestinal endoscopy image detection model by training; Obtaining an endoscope image to be detected; And obtaining a detection result of the to-be-detected endoscopic image according to the gastrointestinal endoscopic image detection model, the detection result comprising one or more of a first classification result, a second classification result and a multi-scale tumor region detection result. By implementing the embodiment of the invention, the markingcost can be reduced, and meanwhile, the tumor region in the gastrointestinal endoscopy image can be accurately positioned.

The invention relates to the technical field of the isotope labeling and in particular to a circular system for a cultivating abundant isotope carbon and nitrogen double-labeling plant sample. The circular system for the cultivating abundant isotope carbon and nitrogen double-labeling plant sample comprises a sealed cultivation chamber, a CO2 injector, a gas balancing device, a nutrient solution injector and a nutrient solution circular system. The sealed cultivation chamber comprises a transparent cover and a base. The transparent cover is internally provided with a semiconductor condensation dehumidification device. The transparent cover is connected with the CO2 injector and the gas balancing device. A cultivation basin, a liquid storage chamber, an external peristaltic pump, an ultraviolet sterilizing device, a circular peristaltic pump and a converging pool are successively connected to form a circular loop of the nutrient solution circular system. The converging pool is connected with the semiconductor condensation dehumidification device and the nutrient solution injector. The cultivation basin and the liquid storage chamber are installed in the base. The top of the cultivation basin is communicated with the transparent cover. Silica sand is installed in the cultivation basin. The circular system for the cultivating abundant isotope carbon and nitrogen double-labeling plant sample is capable of simultaneously labeling the carbon and nitrogen isotope, and realizing the cyclic utilization. The pollution of normal 12 C and 14 N is avoided, and an abundant carbon and nitrogen isotope double-labeling plant can be obtained.

The invention discloses a laser preparing method of glass black markers. The method includes the following steps of firstly, fixing a graphite sheet to a working table, fixing a glass substrate to the upper portion of the graphite piece so that the glass substrate can make close contact with the graphite sheet; secondly, making laser beams irradiate the interface of the glass substrate and the graphite sheet, making laser beams and the working table for installing workpieces combined and linearly move in the Z direction, and conducting scanning through a galvanometer in the X / Y direction; thirdly, cleaning the glass substrate through an ultrasonic cleaning machine after marking is completed, and removing graphite residues on the surface of glass. The method has the advantages that the black markers of glass are obtained through the graphite back etching method, and the markers of high contrast can be prepared; the marking method is small in grooving heat effect and good in marking quality and has a great effect on increasing the recognition degree of the glass material markers; in addition, through the laser preparing method, the machined markers are resistant to corrosion, high temperature, water and friction and basically cause no feelings when touched by hand, and stability and durability of the markers are effectively achieved; finally, the marking method adopts the graphite sheet as the consumption material, one graphite sheet can be repeatedly used, cost is low and no pollution is caused.

The invention discloses a semi-supervised collaborative training analog circuit early fault diagnosis method, which can classify and identify analog circuit faults without using a large amount of labeled data so as to realize early fault diagnosis. The method comprises the following steps: firstly, carrying out Fourier transform on a known analog circuit normal signal and a fault signal; carryingout spectrum partitioning, empirical wavelet transform on the frequency spectrum; obtaining AM-FM components, carrying out hilbert transformation on the data; obtaining a plurality of EWF components;superposing the energy of the plurality of EWF components; finally, through a semi-supervised collaborative training algorithm, utilizing a small amount of labeled data fully for learning and utilizing a large amount of unlabeled data fully, thus the accuracy of marking actions is guaranteed to a certain extent, a large amount of marking cost is saved, and therefore early fault diagnosis of theanalog circuit based on semi-supervised collaborative training is achieved.

The invention relates to an otolith daily increment labeling method for game fish, and relates to an otolith daily increment labeling method for fish. The invention solves the problems that: the prior labeling method takes time and labor, and has expensive labeling cost, harm to fish bodies and complex operation; and the label is easily lost and the labeling quantity is limited. The method comprises the following steps: controlling the water body temperature in the game fish incubation period so as to change the otolith daily increment appearance feature of the game fish and form an recognizable game fish otolith daily increment label; and setting the water body temperature in the incubation period 2 to 4 DEG C higher or lower than the normal water temperature in the game fish incubation period. The method has the advantages of time and labor reduction, safety, simple operation, lifelong reading, no harm to the fish bodies, and a large quantity of labels. According to the quantity and category of the labeled fish, the process and the method with different labeling spectrums are designed. The application of the method can reach 100 percent of colony labeling.

The invention discloses a hyperspectral image classification method based on correction prototype learning, and the method comprises the steps: selecting a hyperspectral scene image, and randomly extracting a part of samples as a training set; randomly selecting a support set and a query set; building a deep network model, and calculating an initial class prototype in the learning measurement space; building a convolutional neural network with a residual block; training the deep network model; selecting a test data set, randomly selecting supervision samples from the test data set, and performing correction standardization processing on distribution of the supervision samples; and calculating the Euclidean distance between a test set sample and the test class prototype. According to the invention, the hyperspectral image is classified by using the hyperspectral classification method based on correction prototype learning, and the support set and the query set are selected, so that the use of a large number of labeled samples is avoided, and the sample labeling cost is saved. Compared with a traditional prototype network, the method obtains better classification precision, and has important application value in the aspects of hyperspectral image earth surface fine classification and the like.

The invention relates to a method for marking larimichthys crocea by using alizarin red. The method comprises the steps that 1, an alizarin red is prepared for mother solution preparation, a mother solution is diluted to reach application concentration, then the salinity is regulated with refined salt and the like to reach 1.5% or above, and an artificial seawater dye liquor is obtained; 2, larimichthys crocea fry to be dyed is soaked in the dye liquor for dyeing, the fry is transferred to natural seawater for rinsing after dyeing is completed, and then larimichthys crocea marked with the alizarin red is obtained. By the adoption of the method, the influence of calcium ions on alizarin red dissolution and precipitation are avoided, the effectively concentration and a dip dyeing effect of the alizarin red are improved, and the problem of massive dead due to the fact that suspending precipitates easily block the gills of the larimichthys crocea is solved.

The invention provides a neural network model training method based on a pyramid pooling and long-term memory structure. The neural network model training method comprises the following steps: a, establishing a neural network model based on the pyramid pooling and long-term memory structure; b, pre-training the model; c, carrying out adaptive training on the model; according to the method providedby the invention, a new neural network structure model is provided, the model can extract and store feature information of each sample in a sample image in the source domain, and pre-training and adaptive training are provided for the model; meanwhile, a common task knowledge vector t * is introduced between the source domain encoder Ms and the target domain encoder Mt to serve as an auxiliary variable, and the auxiliary variable is used for storing feature information of the source domain sample images and used for calibrating feature output of all the sample images; and then calibrating thefeature information of the sample images in the target field in the adaptive training step, thereby improving the similarity of image distribution in different fields.

The invention discloses an automatic marking device for cylindrical neodymium iron boron magnetic steel. The automatic marking device comprises a supporting table, a vibration material feeding disc, a material feeding hose, a first optical fiber sensor, a guide channel, a rotating disc, a supporting disc, a workbench, a driver, a code spraying machine, a material discharging pipeline, an air blowing pipe and a material receiving hose used for packaging the cylindrical neodymium iron boron magnetic steel. The automatic marking device for the cylindrical neodymium iron boron magnetic steel has the advantages that the cylindrical neodymium iron boron magnetic steel can be conveyed after being erected, so that polarity marking can be achieved through an existing assembly line type automatic code spraying marking technology, the marking efficiency is high, the marking cost is low, the automatic marking device is suitable for batch production, made marks are uniform, the cylindrical neodymium iron boron magnetic steel is guaranteed to have high consistency, the labor intensity of workers is low, and the cylindrical neodymium iron boron magnetic steel can be automatically packaged after being marked, the position of the cylindrical neodymium iron boron magnetic steel will not be placed reversely, the actual polarity of the cylindrical neodymium iron boron magnetic steel after follow-up magnetization is guaranteed to be the same as the marked polarity, and the production qualification rate of the cylindrical neodymium iron boron magnetic steel is increased.

The invention relates to a mark generation method and device, a model training method and device, a marking method and device, a storage medium and equipment. The mark generation method comprises the following steps: acquiring a to-be-marked graph of a target object; obtaining a conversion graph corresponding to the to-be-marked graph; acquiring a reference map of the target object; associating the conversion graph with the reference graph to obtain a conversion relation or a transformation relation; and mapping the fiducial mark based on the conversion relation or the transformation relation to obtain a mark. According to the technical scheme, automation of image marking is achieved.

The invention discloses a sample classification method based on active learning and a neural network, and belongs to the field of machine learning in intelligent science and technology. According to the method, the uncertainty of a neural network model to sample points is taken as a reference, three traditional uncertainty indexes of Least content, Margin and Entry are calculated respectively, the three indexes are used for voting samples, the sample with the highest vote number is the finally screened sample points, the sample points are the most uncertain samples of the model, and then training of the neural network model is facilitated. The number of sample points needing to be marked can be effectively reduced, the marking cost is reduced, and the classification precision of the model is improved.

The invention provides a rice plant carbon isotope field in-situ calibration system, which comprises the following components: a carbon marker release device, which comprises a motor, a screw, a sliding member and an injector, wherein the motor is in transmission connection with the screw and is used for driving the screw to rotate, the sliding part is connected with the screw rod and the injectorand is used for sliding under the driving of the screw rod so as to extrude and release the carbon marker in the injector; a marking box, wherein the marking box is hollow, an opening is formed in the bottom of the marking box, and the marking box is communicated with the injector through a carbon marker release tube; and a motor controller connected with the motor through a first signal line andused for sending a signal for driving the screw to rotate to the motor. The invention further provides a rice plant carbon isotope field in-situ calibration method. According to the rice plant carbonisotope field in-situ calibration method, 13C in-situ calibration is conducted on rice plants under the field in-situ condition, the process is controllable, measurement is accurate, human interference is reduced, and the 13C marking cost is reduced.

A method of manufacturing a light-emitting diode module comprises the steps of: coupling an electronic device with a circuit board such that a distinguishing resistor is coupled adjacent to a light-emitting diode; baking the circuit board by a reflow oven; and testing the baked circuit board, whereby the related information of the light-emitting diode including the manufacturer, the product cycle, the product code, and the grade can be obtained via the distinguishing resistor. Accordingly, the marking accuracy will not be affected by the subsequent processes and the marking cost can also be reduced.

The invention provides electrochemical luminescence mark liquid. The electrochemical luminescence mark liquid is prepared by mixing tris bipyridine ruthenium, buffer substances and distilled water. The electrochemical luminescence mark liquid is mainly prepared from, by weight, 0.22-0.52 g of tris bipyridine ruthenium, 1.88-3.02 g of disodium hydrogen phosphate and 2.06-3.18 g of monopotassium phosphate, the distilled water is added till the volume is kept at 300 ml, and the PH value is adjusted to range from 6.5 to 7.6. The electrochemical luminescence mark liquid is composed of a single solution, 2,2':6',2''-Terpyridine[4'(N-succinyl oxo carbonyl propyl)-2,2':6',2''-terpyridyl](2,2':6',2''-terpyridyl)ruthenium(II) is adopted as a marker, and the unit luminous quantity is greatly increased; by means of the high conjugation effect of molecules, higher detection sensitivity is achieved; trisodium citrate is added, the solubility of labeled protein is improved, the marking rate is increased, the marking cost is reduced accordingly, and meanwhile the luminous quantity of unit concentration reactants is increased.

The invention relates to the technical field of isotope labeling, in particular to a circulation system for cultivating high-abundance isotope carbon and nitrogen double-labeled plant samples, including a closed culture room, CO 2 Syringe, gas balance device, nutrient solution injector and nutrient solution circulation system; the airtight culture room includes a transparent cover and a base, and a semiconductor condensation dehumidification device is installed in the transparent cover, and the transparent cover is connected to the CO 2 Syringes and gas balance devices; culture basins, liquid storage chambers, outflow peristaltic pumps, ultraviolet sterilizers, circulating peristaltic pumps, and confluence pools are connected in sequence to form a circulation loop of the nutrient solution circulation system; the confluence pool is connected to the semiconductor condensation dehumidification device and the nutrient solution A syringe; a culture basin and a liquid storage chamber are arranged in the base, the top of the culture basin is connected with a transparent cover, and quartz sand is arranged in the culture basin. The invention can mark carbon and nitrogen isotopes at the same time, and realize their recycling, without ordinary 12 C and 14 N pollution, and high-abundance carbon and nitrogen isotope double-labeled plants.

The invention discloses a denoising method and a denoising device for linear interference, and relates to the technical field of computers. A specific embodiment of the method includes: preprocessing the original image to obtain a binarized image; determining the coordinate interval corresponding to each character in the binarized image; determining the corresponding first and last points of the character according to the coordinate interval corresponding to the character The coordinates of the points; solve the noise curve expression according to the coordinates of the first end point and the end point corresponding to each character; use the noise curve expression to predict and remove the noise. This embodiment can accurately solve the noise curve expression in sections, better fit the noise curve, realize more accurate character recognition, and further improve the recognition success rate of the entire verification code.

The invention belongs to the technical field of pedestrian re-identification, and discloses an unsupervised image video pedestrian re-identification method and system based on a migration network, andthe method comprises the steps: carrying out the feature extraction of an image and a video data set in a source domain through employing an improved triple network; training the generative adversarial network by using the source domain data set and the target domain training set; generating a depth feature by using the trained generative adversarial network according to the pedestrian image Itito be identified in the target training set; calculating the Euclidean distance between the depth feature of the image and the depth feature of the video in the target domain; and selecting a video which is closest to the query image, and marking a class mark which is the same as the image. According to the invention, an unsupervised method is used to eliminate a gap between an image and a video,so that the marking cost is greatly saved, and the pedestrian re-identification efficiency is improved.Through carrying out unsupervised deep learning on different modal images and videos, the cross-mode recognition efficiency is effectively improved.

The invention discloses an automatic marking device for cylindrical NdFeB magnets, which comprises a support table, a vibrating feeding tray, a feeding hose, a first optical fiber sensor, a guide channel, a rotating disc, a supporting disc, a workbench, a driver, and a spray nozzle. coding machine, feeding pipe, blowing pipe and receiving hose for packaging cylindrical NdFeB magnets; the advantage is that the cylindrical NdFeB magnets can be transported after being erected, so that the existing assembly line can be used Automatic coding marking technology realizes polarity marking, high marking efficiency, low marking cost, suitable for mass production, uniform marking, high consistency of cylindrical NdFeB magnets, low labor intensity of employees, and After marking, the cylindrical NdFeB magnets can be automatically packaged, and the orientation of the cylindrical NdFeB magnets will not be reversed, so as to ensure the actual polarity and marked polarity of the cylindrical NdFeB magnets after subsequent magnetization Similarly, improve the production pass rate of cylindrical NdFeB magnets.

The present application provides a training method, device, computer equipment and storage medium for a semantic segmentation model, wherein the method includes: constructing a training sample set, the training sample set includes objects of the first category and objects of the second category, wherein the first category The object is marked with a bounding box and a segmentation mask, and the second category object is marked with a bounding box; the training sample set is input into the deep network model for training, and the first bounding box parameters of the first category object are trained , the first mask parameter and the second bounding box parameter of the second category object; the first bounding box parameter and the first mask parameter are input into the weight transfer function for training, and the bounding box prediction mask parameter is trained; A semantic segmentation model is constructed based on the parameters trained above. This application greatly reduces the marking cost of the training sample set, reduces the pressure on subsequent training samples and training calculations, and improves training efficiency.

The invention discloses a breast cancer data classification method based on a single category. According to the breast cancer data classification method, breast cancer data classification is effectively carried out only by utilizing single-class sample data and unlabeled sample data; a probability graph model is adopted to describe the relation among the data characteristics, the observation tagsand the real labels, and a corresponding joint probability distribution model is given, and then an EM algorithm is used for solving the model, and then test data are classified. The breast cancer data set used in the breast cancer data classification method is only composed of a single-class sample and an unlabeled sample, so that the sample labeling cost is reduced, and the problem that negative-class sample data are lacked in breast cancer diagnosis is solved.

The embodiment of the invention discloses a model training method, a model training device, a storage medium and electronic equipment. According to the embodiment of the invention, the method comprises steps of after a to-be-trained model of a source domain, a first image set and a second image set of a target domain are obtained, training the to-be-trained model by taking each first image as an input and taking an object recognition frame corresponding to each first image and the category of the object recognition frame as training targets, meanwhile, taking each second image as an input, obtaining outputs of the second images in different layers of the to-be-trained model so as to train the attention discrimination model and the feature discrimination model, thereby responding to convergence of a detection loss function of the to-be-trained model, an attention loss function of the attention discrimination model and a feature loss function of the feature discrimination model, and obtaining a training result;determining the trained to-be-trained model as a target model. According to the embodiment of the invention, the to-be-trained model can be trained without marking the target domain image, the object detection capability of the model for the target domain is improved, and the marking cost is reduced.

The invention provides electrochemical luminescence mark liquid. The electrochemical luminescence mark liquid is prepared by mixing tris bipyridine ruthenium, buffer substances and distilled water. The electrochemical luminescence mark liquid is mainly prepared from, by weight, 0.22-0.52 g of tris bipyridine ruthenium, 1.88-3.02 g of disodium hydrogen phosphate and 2.06-3.18 g of monopotassium phosphate, the distilled water is added till the volume is kept at 300 ml, and the PH value is adjusted to range from 6.5 to 7.6. The electrochemical luminescence mark liquid is composed of a single solution, 2,2':6',2''-Terpyridine[4'(N-succinyl oxo carbonyl propyl)-2,2':6',2''-terpyridyl](2,2':6',2''-terpyridyl)ruthenium(II) is adopted as a marker, and the unit luminous quantity is greatly increased; by means of the high conjugation effect of molecules, higher detection sensitivity is achieved; trisodium citrate is added, the solubility of labeled protein is improved, the marking rate is increased, the marking cost is reduced accordingly, and meanwhile the luminous quantity of unit concentration reactants is increased.

The present invention provides a multi-question type intelligent marking system, comprising: a test paper obtaining module, used to obtain test paper questions, answer content and standard answers to the questions; a test paper preprocessing module, used to process the received test paper questions and Preprocessing the answer content; including classifying the type of each question to obtain the question type; segmenting, extracting and standardizing the format of the answer content corresponding to each question according to the question type and the first preset rule to obtain the pre-processed answer content. The processed answer content; the judgment and scoring module, for each question, according to the question type and standard answer, according to the second preset rule, judge and score the pre-processed answer content; the result output module, the received Scoring results are output. The multi-question type intelligent marking system provided by the present invention realizes multi-question type automatic marking, thereby improving the work efficiency of marking and reducing the cost of marking.