18results about How to "Increase sample size" patented technology

The application provides a child skull fracture automatic detection method and device based on generated data augmentation. The method comprises the following steps: acquiring a first child brain CT image set, and performing pixel labeling and preprocessing to obtain fracture data, suture data and normal data; obtaining a trained fracture lesion generation model and a suture generation model according to the fracture data, the suture data and the normal data; randomly sampling Gaussian noise for multiple times, inputting the Gaussian noise into the fracture lesion generation model and the suture generation model to obtain generated fracture CT patch data and generated suture CT patch data; training a candidate fracture detection model according to the fracture data, the suture data, the normal data, the generated fracture CT patch data and the generated suture CT patch data, and outputting a fracture detection model; and acquiring a second child brain CT image, and detecting the second child brain CT image according to the fracture detection model. The fracture lesion data and the suture data are augmented, so that the resolution of fractures and sutures can be improved.

The application provides a frequency standard measurement method, device, computer equipment and storage medium, the method comprises: frequency source output measurement signal is carried out frequency division processing, obtains low frequency pulse signal; using high-precision time interval counter to low frequency pulse signal is carried out delay measurement and time interval counting, and is output in the form of time stamp; by setting a sliding window, using sliding average algorithm to time stamp is carried out sliding average filtering processing, all time stamps are carried out sliding difference to obtain time difference, time difference is grouped according to order and is taken average in group to obtain average time difference; by increasing the length of the sliding window, the number of samples is increased to reduce the statistical error; using average time difference, calculate frequency measurement value, relative frequency deviation and allan variance. The method of the application increases the sliding window, and the frequency measurement result obtained by the algorithm of grouping and averaging the time difference has higher measurement precision and stability.

The application relates to the technical field of deep learning, in particular to a traffic scene image small target detection method and device, equipment and a storage medium. The method comprises the following steps: acquiring a traffic scene image and a detection result of a small target in the traffic scene image, generating a training data set according to the traffic scene image and the detection result of the small target in the traffic scene image; constructing a convolutional neural network based on a feature extraction module, a feature fusion module, a sample distribution module and a positioning classification module, and optimizing the sample distribution module and the positioning classification module of the convolutional neural network to obtain an optimized convolutional neural network; training the optimized convolutional neural network by using the training data set, updating network parameters of the optimized convolutional neural network based on gradient back propagation, and stopping the training until a preset training stop condition is met; and detecting small targets in the traffic scene image by using the trained convolutional neural network. Thus, the problems of small target sample imbalance and poor positioning capability in the related art are solved.

The application discloses a co-entropy ceramic protective coating sintering temperature and aerodynamic thermal load cooperative control system and relates to the technical field of sintering temperature control. The system comprises an information acquisition module: acquiring previous sintering information, wherein the previous sintering information comprises sintering temperature and performance results, and the performance results are the performance of aerodynamic thermal load. The application considers coating material and structure information and environmental information to formulate a reference directory under different basic information in the whole process, and the sintering temperature is regulated according to the aerodynamic thermal load of the coating material structure and environmental manufacturing demand, and then the density, interface bonding and residual stress distribution of the coating are realized through the controllable sintering temperature curve at the preparation end, so that the aerodynamic thermal load of the coating in use is ensured, the sintering temperature is designed and optimized, the response characteristics of the coating to the aerodynamic thermal load in use are within the user demand, and the closed-loop design of optimizing the response of the service end by controlling the manufacturing end is embodied.

This invention discloses a training method and apparatus for a 3D meandering river recognition model, and a method for identifying meandering rivers. The method includes: acquiring several interpretation results and corresponding seismic data volumes in a target area; initializing a preset label data volume based on the interpretation results and the seismic data volume; masking the label data volume to update it; inputting each seismic data point in the seismic data volume as sample data into a pre-constructed image segmentation model for training; calculating a loss function value based on the output of the image segmentation model and the updated label data volume; adjusting the parameters of the image segmentation model inversely based on the loss function value; iteratively training until the loss function reaches a preset convergence condition, stopping the training, and obtaining a trained 3D meandering river recognition model. This model enables the identification of meandering rivers in a target area using sparse labels, improving the efficiency of seismic data interpretation.

The utility model discloses a kind of equipment for seaweed quality detection, a kind of equipment for seaweed quality detection, including lye prestorage tank, seaweed processing tank body, lye recovery tank;Wherein, lye prestorage tank is communicated with seaweed processing tank body by lye pipeline;Seaweed processing tank is communicated with lye recovery tank.The beneficial effect of the utility model is: compared with conventional seaweed detection method, conventional seaweed detection method can only handle 1 batch of seaweed at a time, each batch of seaweed is detected separately, and artificial efficiency is low.New seaweed detection equipment and method can handle 16 batches of seaweed at a time, greatly improve artificial efficiency.

The invention belongs to the technical field of nuclear radiation detection, and particularly relates to an ESEM-oriented radon solid track image counting method and system, and the method and system are composed of a solid track detector, an environment scanning electron microscope, a high-resolution camera, an image analysis processing system, a fixed frame and a communication module, and achieve the automatic image preprocessing and image edge detection. The method comprises the steps of image binarization threshold segmentation, morphological processing, connected component analysis, feature extraction, body track automatic counting, result output and the like, automatic collection and processing of a solid track image of a radon environment are completed, a marked image is obtained, the marked image comprises the marked solid track position and the serial number of a connected component, the marked image can be directly used for observing a sample, and the detection accuracy is improved. The requirements on chemical fixed trace and vacuum observation environment are reduced, the influence of charging effect and high vacuum environment on the sample is avoided, and real-time detection can be carried out.

This invention discloses a well-seismic combined seismic data overlay method, relating to the field of geophysical exploration processing and interpretation technology in oil and gas field exploration. The method includes the following steps: S1, acquiring acoustic and density logging data, calculating the wave impedance curve and converting it to reflection coefficients, selecting a wavelet close to the seismic data, convolving it with the reflection coefficients to form a conventional synthetic record, and performing well-seismic calibration; S2, determining the target wavelet for overlay, and convolving it with the reflection coefficients to form a broadband synthetic record; S3, combining well-side seismic data, conventional synthetic records, and broadband synthetic records to construct a sample set, and balancing the number of samples; S4, expanding the sample set through mutation; S5, establishing a nonlinear relationship for overlay based on the sample set; S6, applying the nonlinear relationship to conventional seismic data to obtain high-resolution seismic data. This invention utilizes the advantages of wide bandwidth of well logging data and seismic frequency bands to enhance high-frequency seismic data and broaden the seismic frequency band, thereby improving seismic resolution.

The invention provides a code retrieval data generation method and device and a code retrieval method and device, and relates to the technical field of computers.The method comprises the steps that basic code data and a plurality of preset retrieval scenes are obtained; respectively combining the basic code data with each retrieval scene to generate a plurality of pieces of first code retrieval data; and rewriting the first code retrieval data in at least one mode of replacing the basic code data, replacing the retrieval scene and replacing the language type to generate second code retrieval data. According to the embodiment, combination and rewriting are carried out on the basis of the preset retrieval scene and the basic code data, and the high-quality code retrieval data capable of being used for model training is obtained. Specifically, code rewriting is carried out on the basis of various modes of basic code data, retrieval scenes and language types, code retrieval data can be effectively expanded, and the number of samples of the code retrieval data is greatly increased.

The invention relates to the field of explosive performance evaluation equipment, and discloses a thermal explosive heat resistance evaluation device, system and evaluation method. The thermal explosive heat resistance evaluation device comprises a constant-temperature drying box, the interior of the constant-temperature drying box is provided with a containing cavity, and the constant-temperature drying box is used for responding to a received environmental parameter adjusting instruction, adjusting environmental parameters in the containing cavity and controlling the duration of the adjustment; wherein the environmental parameters comprise temperature and / or humidity; the heating medium bearing container is arranged in the constant-temperature drying box and is used for bearing a heating medium; the sample container is arranged in the heating medium bearing container, the heating medium is uniformly filled outside the sample container, and the sample container is used for bearing explosives and powders; and the temperature acquisition module is in contact with the sample container and is used for acquiring the temperature of the sample container. The actual use condition of the explosive can be simulated, and meanwhile, the test device is simple, convenient to construct and relatively high in efficiency.

This invention relates to the field of in vitro diagnostics, and particularly to a combined diagnostic kit for prostate diseases. The invention provides reagents including pH, peroxidase, elastase, lecithin bodies, citric acid, zinc, and acid phosphatase under mild reaction conditions, enabling rapid differentiation of inflammatory and non-inflammatory prostatitis, benign prostatic hyperplasia, and non-prostatic genital tract infections using a single test card. Through extensive experimentation, this invention has developed a one-step reaction scheme suitable for the dry chemical determination of acid phosphatase and lecithin bodies in seminal plasma. This method eliminates the need for a stop solution, achieving a one-step reaction under mild conditions that will not cause damage to operators or fully automated instruments. The samples used are seminal plasma or semen, allowing for large sample volumes and easy acquisition, significantly improving diagnostic efficiency for doctors and patient comfort.

This invention relates to the field of intelligent driving technology, and provides a method, system, and computer for generating virtual fatigue driving samples. The method includes: assigning several facial key point labels and several posture angle labels to training images; training a key recognition model based on a facial recognition algorithm; constructing several 3D mesh head models based on several facial features; acquiring several experimental fatigue images and inputting them into the key recognition model to obtain a set of fatigue key point coordinates and a set of posture angles; forming several fatigue 3D mesh head models using radial basis function interpolation; generating several virtual fatigue images based on the several fatigue 3D mesh head models, and then generating several virtual fatigue videos based on the several virtual fatigue images. This method generates a large number of virtual fatigue videos with good individual differences, which is beneficial for accurately and comprehensively testing the accuracy of fatigue driving detection products.

The invention relates to the technical field of sorting mechanisms, and discloses a ginseng grading device which comprises a feeding mechanism, a deep learning recognition system, a sorting mechanism and a control module which are sequentially arranged in the ginseng conveying direction. The deep learning recognition system comprises a horizontal conveying belt located between the feeding mechanism and the sorting mechanism, a high-definition camera fixed to the upper portion of the horizontal conveying belt and a convolutional neural network model. The convolutional neural network model comprises a preprocessing module, a multi-branch structure re-parameterization module, a CA attention mechanism module, a multi-scale feature fusion module and an efficient reasoning design module; the control module controls the feeding mechanism to convey ginseng to the horizontal conveying belt, after ginseng image information is obtained through the high-definition camera, a real-time grading result is obtained through recognition of the convolutional neural network model, the ginseng is conveyed to the sorting mechanism through the horizontal conveying belt to be sorted, and therefore the environmental adaptability, generalization ability and grading efficiency are improved.

The application discloses a multi-modal residual value evaluation method and system for mobile terminal pre-set-off, and belongs to the field of electric digital processing, comprising the following steps: migratable feature acquisition, migratable feature reliability evaluation, residual value evaluation result verification and mobile terminal pre-set-off. The application analyzes migratable features by inputting static data into a migration learning model, synchronously evaluates the reliability of the migratable features, determines whether to take reliability adjustment measures based on the evaluation results, then performs residual value evaluation to output residual value analysis results for credibility verification, pre-sets off the mobile terminal to be evaluated based on the residual value analysis results after credibility verification, and determines whether to end the lease period based on the reservation mechanism triggered by the mobile terminal to be evaluated after the preset lease period, thereby improving the accuracy of pre-set-off data and solving the problem of reduced accuracy of pre-set-off results due to reduced reliability of residual value evaluation results in the prior art.

The invention belongs to but not limited to the technical field of environment monitoring and image processing, and discloses a system for analyzing the particle size and type of micro-plastic in a water sample by using a convolutional neural network. The system acquires a sample image of the micro-plastic in the water sample through the image acquisition device; the image preprocessing module carries out enhancement and standardization processing on the sample image so as to improve the image quality; the feature extraction module adopts a pre-trained convolutional neural network model to automatically extract morphological features of the micro-plastic from the preprocessed image; the classification and recognition module judges the polymer type of the micro-plastic according to the extracted features; the particle size calculation module calculates particle size distribution of the micro-plastic based on an image analysis technology; and the result output module is used for integrating and displaying the analysis result. According to the invention, the problems of low efficiency and strong subjectivity of the existing micro-plastic analysis technology are solved, and rapid, accurate and high-throughput automatic analysis is realized.

The application discloses a time service link intrusion detection system for power dispatching, and relates to the technical field of power dispatching. The system comprises an information acquisition module, which acquires information interfaces of time service links to obtain target interfaces, and the target interfaces are detection points. The application independently executes a double-layer detection process of rapid detection and deep detection for each segment, and realizes comprehensive judgment of the whole link through segment superposition, so that the accuracy and robustness of detection are improved. The output information of each segment is compared through the introduction of a third node storage to form an independent and tamper-proof evidence chain, the trustworthiness and auditability are enhanced, the impact of single-point failure and attack on the clock continuity and consistency of the system is reduced, the segment superposition double-layer detection mechanism can improve the timeliness and accuracy of detection, improve the coverage ability of complex and distributed attacks, enhance the consistency and traceability across segments, and realize the non-repudiation of evidence under the resistance attack through decentralization comparison.

The invention discloses an SOP chip pin upwarp defect detection method and system based on optical tomography and a Yov5 model, and the method comprises the steps: scanning a chip pin through employing a tomography technology, obtaining an interference signal, carrying out the image reconstruction of the collected interference signal, and making a data set; the data set is input into a pre-constructed Yolov5 model for training, and an SOP chip pin defect detection model based on the Yolov5 model is obtained; and inputting the sample into the SOP chip pin upwarp defect detection model based on the Yolov5 model to obtain a detection result of the sample pin, so that the SOP chip pin upwarp defect detection efficiency and accuracy are improved.

The invention provides a liver and gall disease prediction method and system based on an eye OCT image. The method comprises the steps that an original fundus OCT image is acquired; performing image preprocessing on the original fundus OCT image to obtain a standardized image; inputting the standardized image into a preset image recognition model to enable the image recognition model to sequentially perform feature extraction and global average pooling on the standardized image, outputting corresponding prediction labels through a plurality of binary classifiers in a full connection layer, and further generating a liver and gall disease prediction result; wherein the image recognition model is obtained based on a pre-trained neural network model and a plurality of historical fundus OCT images by adopting a cross entropy loss function with category weights and transfer learning training, and the category weights are set in an inverse proportion according to the proportion of each category in the plurality of historical fundus OCT images; and the accuracy of predicting liver and gall diseases by the artificial intelligence model and the use experience of the user are improved.