41results about How to "Improve the detection rate" patented technology

The application provides a kind of shipboard aviation equipment composite material damage D-S evidence theory nondestructive evaluation method, belongs to the field of shipboard aviation equipment nondestructive evaluation, it includes the following steps: at time, the triaxial damage area characteristics and triaxial vibration frequency characteristics of the composite material structure of shipboard aviation equipment are synchronously acquired and stored;The triaxial damage area characteristics and triaxial vibration frequency characteristics at time are compared respectively, and the corresponding triaxial damage area change degree and triaxial vibration frequency change degree are obtained;The identification framework of D-S evidence theory is established, and the triaxial mass function of the composite material structure is calculated respectively;According to the created D-S damage judgment model, the classification judgment and early warning of the composite material damage of shipboard aviation equipment are carried out.The application can realize the real-time classification evaluation of the dynamic damage of the composite material component of shipboard aviation equipment, and provide data support for the condition-based maintenance of shipboard aviation equipment.

The application provides application of dimethyl carbonate as a chronic obstructive pulmonary disease diagnostic marker and belongs to the technical field of biomedical detection. The application first finds that the detection rate of dimethyl carbonate in exhaled air is significantly related to chronic obstructive pulmonary disease, and the detection rate of dimethyl carbonate in exhaled air of a chronic obstructive pulmonary disease patient is significantly higher than that of a healthy person. The ROC curve analysis result shows that dimethyl carbonate has extremely high specificity as a biomarker for diagnosing chronic obstructive pulmonary disease, and therefore can be used as a detection target for the diagnosis of chronic obstructive pulmonary disease patients, and has good practical application value.

This invention relates to a membrane tension compensation method based on the detection of uniformity in the thickness of water-soluble membranes produced by dishwashing pods. Addressing the problem that membrane thickness distribution is sensitive to nonlinear tension disturbances under high-speed operation conditions during water-soluble membrane production, and that traditional thickness control methods struggle to achieve precise closed-loop compensation, this invention proposes a method that continuously collects cross-sectional thickness information of the membrane material, constructs a time-space thickness sequence, extracts thickness gradient topological features, combines a multidimensional tension sensitivity feature vector with a dynamic thickness gradient topological graph, and uses a layer-by-layer sparsely connected lightweight graph convolutional network to infer the tension compensation amount. After median filtering and denoising, the result drives the actuator to adjust in real time, achieving high-precision closed-loop compensation for the uniformity of the water-soluble membrane thickness. This method improves the sensitivity and stability of thickness control under nonlinear disturbance environments, while also possessing both real-time performance and computational efficiency.

This invention relates to the field of jujube sorting technology, specifically to a high-speed, non-destructive jujube sorting system with a flexible material handling structure and its working method. The system includes a feeding inlet, a feeding conveyor belt, sorting rollers, variable-pitch spiral guide ribs, a full-field transparent detection chamber, a finished product collection bin, and a foreign matter collection bin. The feeding inlet feeds jujubes into the sorting rollers via the feeding conveyor belt, arranging them in single-grain intervals. The full-field transparent detection chamber on the CoreXY planar motion mechanism detects and sorts the jujubes into different finished product collection bins. Diseased fruits and foreign objects such as leaves and branches enter the foreign matter collection bin. This invention achieves efficient, non-destructive, and accurate jujube grading through a closed loop of "flexible upper material handling—full-field perception—non-destructive jujube grading—destruction and compression of diseased fruits and foreign objects."

The application discloses a PCB defect detection method and device, computer equipment and a storage medium. The method comprises the following steps: collecting resistance data of multiple test points in a to-be-detected area of a PCB to form a resistance data set; sorting the resistance data set and determining the median of the resistance data set as a data median Med; calculating the absolute deviation of each data in the resistance data set from the data median Med to form an absolute deviation set; sorting the absolute deviation set and determining the median of the absolute deviation set as an absolute deviation median MAD; constructing a robust detection threshold for defect determination; and comparing each resistance data in the resistance data set with the robust detection threshold to obtain a PCB defect detection result. The method of the application can realize PCB resistance defect detection with high anti-interference, high detection rate and low false alarm rate, and improve the quality control level of the PCB manufacturing process.

This invention relates to the field of printing machinery manufacturing technology and discloses a printing roller rounding machine with machine vision flaw detection. The machine includes a base with a groove on its top. A movable seat is slidably connected to the inner side of the groove. A flaw detection mechanism is located on the top right side of the movable seat. The flaw detection mechanism includes a bracket, which is fixedly connected to the rear end of the top right side of the movable seat. A hydraulic rod is fixedly connected to the inner side of the bracket, and a connecting rod is rotatably connected to the top of the hydraulic rod. An arc-shaped plate is rotatably connected to the front side of the bracket. This invention uses a motor to drive a lead screw to rotate, which in turn moves the movable seat, enabling the flaw detection mechanism and the rounding mechanism to move freely along the printing roller axis. It can perform full-coverage scanning of printing rollers of different lengths and specifications, and uses a polarizing filter in conjunction with a lamp inside a hollow plate to supplement lighting, eliminating specular high-gloss reflections on the surface of the metal printing roller.

This invention discloses an underwater target detection device and operating method based on forward-looking sonar partitioning, relating to the field of underwater acoustic signal processing technology. This invention, by displaying the measurement range, divides the beam domain into grids from two dimensions: beam and range, forming multiple local detection grids. It compresses and images the corresponding beam domain data to form image domain data, and determines the detection threshold for each local detection grid based on the signal-to-noise ratio of the corresponding image domain data to identify underwater targets. By dividing the beam domain data of the detection area into grids according to beam and range, this invention can reasonably divide the beam domain data generated by underwater targets, underwater reverberation, and acoustic ray changes in the beam domain data corresponding to the signal domain. It compresses and images the beam domain data of each local detection grid, and accurately identifies the parameters corresponding to underwater targets, underwater reverberation, and acoustic ray changes from the perspective of obtaining the detection threshold from the signal-to-noise ratio of the image domain data, thus accurately identifying underwater targets.

This application relates to the field of medical image processing technology, specifically to a hemoglobin emphasis device and method for endoscopes. The device includes a light source unit; an imaging unit for acquiring reflected light signals from a target area under multispectral illumination and outputting a raw Bayer format image or an RGB color image; and an image processing unit for receiving and processing the output Bayer format image or RGB color image, comprising at least: a spectral reconstruction module for calculating and reconstructing a single-band image signal using a spectral reconstruction model; a hemoglobin concentration calculation module configured to calculate a relative hemoglobin concentration based on the image signal; and an image enhancement module configured to perform pixel-level gain adjustment on the raw color image or the processed image, convert it to a set color space for contrast mapping, and finally output a hemoglobin-emphasized image. The image output by this invention can significantly improve the contrast between blood vessels and surrounding tissues.

The application discloses a defect detection method based on physical rendering image adaptive restoration applied to the field of industrial production defect detection, comprising S100, an image parameter acquisition step, S200, an image restoration step and S300, a defect prediction step; S100 comprises: S110, acquiring an industrial field image; S120, calculating an image gradient and splicing the original image to obtain a multi-channel input set; S130, decomposing the multi-channel input set frequency domain feature and then reconstructing the frequency domain to obtain a reconstructed feature map; S140, inputting the reconstructed feature map into an image parameter prediction model to output an interference parameter; S200 comprises: S210, inputting the industrial field image into an image restoration model and performing defogging enhancement based on the interference parameter; S220, performing brightness enhancement on the defogging image to obtain a final restoration image J; S300: inputting the final restoration image J into a defect detection network to output defect recognition information. The application can perform online casting surface defect detection based on the real-time acquired industrial field production monitoring image of the ingot production system.

The application discloses a coal mine railway inspection early warning method and system based on rail surface and flaw detection cooperation, collects images containing rail surface and structured light band, and preliminarily divides the rail surface region and the light band region; the binary method is used to calculate the pixel-level accurate position of the light band center in the segmented light band region, so as to realize dynamic detection of the light band deviation; the block defect detection is carried out based on the rail surface region, and the severity of the block is automatically and quantitatively graded according to the shadow rate and length continuity characteristics of the block; the surface defect information fusion and risk area identification are carried out based on the dynamic deviation detection result and the severity of the block; the area needing to be checked is determined according to the severity grading result of the block; and the flaw detection system is cooperatively controlled to strengthen the monitoring of the corresponding section based on the potential high-stress risk area and / or the area needing to be checked, so that the accurate guidance and prospective inspection early warning of the internal damage detection of the surface defect are realized.

The application provides a greenhouse armyworm plate pest image acquisition and identification method based on deep learning, and belongs to the technical field of image processing. The method comprises the following steps: obtaining an armyworm plate image through a field acquisition device; performing a pretreatment operation on each initial armyworm plate image to obtain a plurality of sample armyworm plate images; inputting the plurality of sample armyworm plate images into an initial pest detection model, generating a candidate region by using a region proposal network, performing pooling and classification regression on each candidate region to obtain sample pest type and sample pest position information; calculating a region proposal network loss and a classification regression network loss according to the sample pest type and the sample pest position information, and determining total loss information according to the region proposal network loss and the classification regression network loss; inputting a to-be-detected armyworm plate image into a target pest detection model, sequentially passing through a region proposal network and a classification regression network to obtain target pest type and target pest position information.

The application belongs to the cross field of mobile communication technology and artificial intelligence predictive maintenance, and discloses a maintenance method and device of a relay communication equipment of a mobile communication system, the device comprises a cross-domain data sensing module, which is used for synchronously collecting physical state data and communication service quality data; a context self-adaptive analysis module, which is used for generating an environmental interference context factor based on the communication service quality data, and dynamically adjusting the analysis algorithm parameters of the physical state data by using the factor; a fusion prediction and decision module, which is used for combining the context self-adaptive physical state characteristics and the communication service quality data to perform fault prediction; and a closed-loop self-optimization module, which is used for quantifying the feedback of the execution effect of the maintenance task to automatically optimize the prediction model. The application helps to significantly improve the signal-to-noise ratio and accuracy of fault prediction under complex environments, realizes the evolution from passive prediction to intelligent self-adaptation, and guarantees the high reliability of the communication system under extreme working conditions.

This invention discloses a method for detecting minute defects in cigarette boxes based on a CDM-YOLOv8 network, comprising: Step 1, acquiring an image of the cigarette box surface and constructing a defect detection dataset; Step 2, performing feature extraction enhancement on the input image, extracting defect-related features through multi-branch convolution, deformable convolution, and spatial attention mechanisms; Step 3, dynamically fusing features at different scales, and performing frequency domain banding processing on the fused features during the downsampling stage, employing a differentiated downsampling strategy to preserve defect edge and texture information; Step 4, outputting the category and location information of the defects based on the detection head. This invention solves the problems of easy loss of minute defects in cigarette boxes during downsampling and easy missed detection in complex backgrounds. By enhancing feature representation capabilities and preserving high-frequency defect information while ensuring computational efficiency, it improves the detection accuracy of minute defects in cigarette boxes and reduces the missed detection rate.

The invention discloses a blade defect detection method based on progressive feature enhancement and Inner-IoU, and relates to the technical field of computer vision and image processing, and the method comprises the following steps: constructing and preprocessing a blade defect data set, and adopting a joint data enhancement JDA strategy to expand a training data set; a ResNet50 backbone network of an integrated channel self-adaptive weighted ACAW module is constructed, after the preprocessed image is input into the ResNet50 backbone network, feature maps of different scales are generated, and for the feature map of each scale, the system executes an attention-based channel self-adaptive weighted ACAW processing flow; in the encoding and decoding stage, a progressive feature input PFI encoder is adopted; according to the method, the detection speed is remarkably improved, the calculation amount is greatly reduced through the combination of the PFI strategy and the light-weight feed-forward network LW-FFN, the model can meet the real-time detection requirement of an industrial assembly line, and through the synergistic effect of the ACAW module and the Inner-IoU loss function, it is ensured that tiny defect features are not submerged by the background and can be accurately positioned.

This invention discloses a graph-augmented time-frequency dual-path anomaly detection method and system for multivariate time series data. The method includes: cleaning and standardizing the original multivariate time series data; modeling the sensor network topology using a graph convolutional autoencoder to extract spatially enhanced sequence representations; inputting the sequence representations into a time-domain generation path and a frequency-domain discrimination path, respectively. The time-domain generation path combines a Transformer and a hierarchical variational autoencoder to model the temporal dynamics and generate a time-domain anomaly score, while the frequency-domain discrimination path analyzes the frequency domain distribution through context-aware spectral transform and generates a frequency-domain anomaly score; finally, the final anomaly score is obtained by fusing the time-domain and frequency-domain anomaly scores, thus achieving anomaly determination. This invention achieves high accuracy while possessing broad applicability, enabling precise detection of different types of industrial data, especially communication system data.

This invention relates to a method for mobile vision detection of construction workers' protective gear. The method uses YOLO11m as the basic architecture to build a detection model. The backbone of the detection model includes an SPFF module, and a reflection-sensitive feature channel is introduced at the end of the backbone. The head of the detection model incorporates a conditional attention mechanism, which activates local attention weights in the region below the safety helmet in the image when a safety helmet is detected. The SPFF module can fuse deep semantics and shallow detail features for small targets at 1K+ resolution through multi-scale dilated convolution and channel attention, overcoming the bottleneck of traditional FPN in losing features of small targets and significantly enhancing the feature representation capability of small targets.

This invention provides a prototype infrared detector with integrated metalens, its fabrication method, and an optoelectronic instrument. The prototype infrared detector with integrated metalens, its fabrication method, and the optoelectronic instrument of this invention offer an effective and feasible solution for the industrialization of infrared detectors with integrated metalens, and are beneficial to the development of optoelectronic devices. The prototype infrared detector with integrated metalens of this invention has the advantages of miniaturization and high performance, and is expected to be widely used in scenarios such as integrated miniaturized infrared detection and infrared detection in low-light environments.

The application relates to a kind of A-D-A'-D-A type evaporable near-infrared electron acceptor materials and a preparation method and organic photoelectric detector application thereof, which have the following structure: A' is electron-deficient unit, D is electron donor unit, and A is electron acceptor unit.The evaporable near-infrared acceptor small molecule provided in the application effectively enhances intramolecular ICT effect, improves the quinoid content of conjugated skeleton, promotes the delocalization of electron cloud on the conjugated skeleton, thereby extending the light response range to the near-infrared band;It is applied in the field of organic photoelectric detector, and has high responsivity and high detection rate, larger cut-off bandwidth and wide linear dynamic range in the near-infrared region.The application fills the technical gap of evaporable high-performance near-infrared organic electron acceptor materials, and the material provides a key material basis for the industrialization application of flexible near-infrared imaging chip, medical diagnosis and industrial detection, etc., and promotes the progress of intelligent consumer electronics technology.

The present application relates to the technical field of disease risk prediction, and particularly relates to a ten-year risk prediction method, system and device for sudden cardiac death, the method comprising: obtaining 48-hour electrocardiogram data of an individual sample and a risk classification evaluation result based on a China-PAR risk assessment model; splitting and combining the electrocardiogram data to obtain a preset multi-dimensional waveform graph; constructing a convolutional neural network model, taking the waveform graph as input and the corresponding risk classification evaluation result as output to train the model and obtain a sudden cardiac death risk prediction model; inputting the processed waveform graph of 48-hour electrocardiogram data of a subject into the prediction model, and outputting the result as a ten-year sudden cardiac death risk prediction level result. The present application can create a non-linear, high-precision, and iterative sudden cardiac death risk deep learning model for the general population and doctors, improve the detection rate of sudden cardiac death, and reduce the recurrence rate and mortality.

This invention relates to an interface layer for optoelectronic devices, its fabrication method, and its application, belonging to the field of optoelectronic device technology. The hole interface layer prepared by this invention is based on a perovskite precursor, and the material is homologous to perovskite, resulting in better interfacial compatibility between the hole interface layer and the perovskite layer. This facilitates perovskite layer growth, reduces the interfacial defect density between the perovskite and hole interface layers, effectively suppresses detector dark current, and achieves high detectivity and device stability. The perovskite precursor of the hole interface layer specifically incorporates Sn. 2+ This invention can oxidize and form an oxide protective layer during heat treatment, suppressing the hole interface layer from being damaged by subsequent steps. Compared to the PEDOT:PSS transport layer, the hole interface layer of this invention can reduce the dark current of perovskite optoelectronic devices by an order of magnitude and achieve better device stability. This invention allows for flexible adjustment of the electrical parameters of the hole interface layer, enabling targeted optimization for specific applications.

The application discloses a reverse transcriptase, a nucleic acid molecule and a method for synthesizing cDNA, and relates to the technical field of reverse transcriptase mutants. The amino acid at the 322th position of a wild-type M-MLV reverse transcriptase is mutated into Val, so that the thermal stability of the reverse transcriptase is increased. The thermal stability experiment and the heat shock stability experiment prove that the reverse transcriptase with the mutation has higher thermal stability and heat shock stability. Compared with the wild-type reverse transcriptase, the reverse transcriptase provided by the application has higher sensitivity and higher detection rate in reverse transcription amplification detection.

This invention discloses a method for target recognition in low-altitude remote sensing images based on a neighborhood self-attention spatiotemporal model, belonging to the field of low-altitude remote sensing image target recognition technology. The method includes the following steps: constructing a low-altitude remote sensing image target recognition model based on a neighborhood self-attention spatiotemporal model, the low-altitude remote sensing image target recognition model including an encoder module, a decoder module, and a target recognition module connected in sequence; training the low-altitude remote sensing image target recognition model using historical time-series data of the low-altitude remote sensing image to obtain a trained low-altitude remote sensing image target recognition model; and obtaining the low-altitude remote sensing image target recognition result based on real-time time-series data of the low-altitude remote sensing image and the trained low-altitude remote sensing image target recognition model. This invention effectively solves the problems of frame loss, distortion, boundary blurring, and missed detection of small targets caused by high dynamic range and low contrast in low-altitude remote sensing images, improving the accuracy and robustness of target recognition.

The application discloses an otoacoustic emission signal detection system and method, and belongs to the technical field of hearing function detection. The system adopts a modular architecture and comprises a signal emission module, a signal receiving module, a core processing module, an intelligent interaction module and a low-power supply module. The modules are interconnected through an SPI bus. The method can realize high-precision extraction and analysis of otoacoustic emission signals through a dynamic noise source classification and inhibition algorithm combined with a space-time multi-scale feature fusion network. The application can solve the problems of poor portability, weak anti-interference ability, simple signal preprocessing, complex operation and insufficient endurance of existing devices, realize integrated design of 'high-precision detection + convenient operation + long endurance', and meet the needs of primary medical treatment, family health monitoring and outdoor emergency scenes.

The application provides a reagent and kit for quantitatively detecting chicken egg protein allergen specific IgE. The kit comprises biotin-labeled chicken egg protein allergen and a preservative solution, the biotin-labeled chicken egg protein allergen comprises Sulfo-NHS-biotin-labeled ovomucin, Sulfo-NHS-LC-biotin-labeled ovalbumin, Sulfo-NHS-biotin-labeled ovotransferrin and Sulfo-NHS-PEG4-biotin-labeled lysozyme with a mass ratio of (0.5-1):1:(0.5-1):0.5, and the preservative solution is a phosphate buffer solution containing 1-2 wt% BSA, 1-3 wt% glycerol and 2-5 wt% mannitol. The reagent has higher detection rate, detection sensitivity and reliability, the detection result has higher consistency with clinical symptoms, and the batch consistency of the finished product is better.

This invention provides a method, apparatus, and equipment for detecting bolt defects in power facilities, relating to the field of power technology. The method includes: acquiring inspection images of bolts in power facilities; downsampling the inspection images to extract original features; inputting the original features into a feature extraction network constructed by a high-frequency detail-guided feature enhancement module and a decoupled context and saliency pyramid pooling module to extract multi-scale features; performing cross-scale feature fusion on the multi-scale features through an asymmetric guided fusion pyramid network constructed by the high-frequency detail-guided feature enhancement module, an asymmetric path splitting module, and a detail-guided fusion module to obtain fused features; and processing the fused features through a detection head to obtain the bolt defect detection result. This invention can solve the problems of lost detail information, insufficient utilization of context, and semantic conflicts in multi-scale feature fusion that exist in existing methods when detecting bolts.

This invention provides a kit for detecting the pathogen of turtle mumps disease and its usage method, belonging to the field of biodetection technology. The kit includes a tissue pathogen release buffer, magnetic macroporous SiO2 microspheres, nucleic acid extraction solution, and detection reagents for the turtle mumps disease pathogen. This invention uses the tissue pathogen release buffer to release pathogens from tissues, magnetic macroporous SiO2 microspheres for pathogen enrichment, and nucleic acid extraction reagents to obtain a crude extract that can be directly used for LAMP amplification and qPCR amplification detection.

This invention relates to a hybridoma cell line, monoclonal antibody, test strip, reagent kit, and their applications targeting feline leukemia virus (FLV), belonging to the field of immunology. The invention utilizes FLV P27 protein immunization of mice to isolate specific hybridoma cell lines FeLV-06D8F10 and FeLV-02B6H12 from mouse ascites fluid. Monoclonal antibodies are then isolated and purified and applied to the detection of FLV. The test strip described in this application employs the colloidal gold immunochromatographic principle. The reagent kit method described in this invention is simple in procedure, highly operable, and can simultaneously detect FLV antigens in different media, including feline saliva and blood, enabling detection of FLV antigens in various media. Since pet cat saliva is easier to collect in a home, this method allows for home testing of pet cats and has broad application prospects.

This invention discloses a method, equipment, and medium for testing the corrosion resistance of gas pipelines, relating to the field of pipeline corrosion resistance testing technology. The method includes the following steps: obtaining the zinc bath temperature during hot-dip galvanizing of the gas pipeline to obtain galvanizing temperature data, and screening samples for inspection based on the galvanizing temperature data; acquiring images of the sampled gas pipelines to obtain initial image data, and conducting corrosion resistance tests; acquiring images of the sampled gas pipelines after testing to obtain test image data, and performing image corrosion analysis processing to obtain corrosion image data; obtaining the corrosion area of ​​the gas pipeline to obtain the pipeline corrosion resistance test result. This invention addresses the problem that existing pipeline corrosion resistance testing technologies, when detecting the white rust area in salt spray tests on gas pipelines, cannot dynamically set thresholds based on the surface characteristics of the gas pipeline to accurately separate the white rust from the background in the image, resulting in inaccurate white rust area measurements and a low detection rate of defective products.

The application relates to the field of lighting control, in particular to a high-voltage cable channel lighting intelligent control method based on data analysis. The high-voltage cable channel lighting intelligent control method based on data analysis comprises the following steps: S1, acquiring equipment information of a high-voltage cable channel, and screening a reflective equipment sequence according to the equipment information; S2, processing historical operation and maintenance data of the high-voltage cable channel to obtain inspection target information and inspection position information; S3, acquiring lamp information of the high-voltage cable channel, constructing a lighting direct radiation area graph based on the lamp information, and constructing a direct radiation dead angle target position graph according to the lighting direct radiation area graph and the inspection target information; and S4, obtaining a reflection area position and a reflection range through a lamp light reflection model according to the lamp information and the reflective equipment sequence. The application can realize effective light compensation by utilizing the reflection characteristics of equipment in the channel, and can inhibit the interference of adverse reflection on detection sensors.

This application discloses a method and system for giant panda data detection based on learnable motion saliency modulation, belonging to the fields of endangered animal protection and intelligent infrared image processing technology. It can be applied to the rapid screening of long-term continuous monitoring data from fixed-position infrared cameras. The method constructs a multi-channel temporal difference input for continuous infrared image frames and generates a motion saliency weight map. The weights are embedded as conditional control signals into the convolution calculation process of a lightweight feature backbone network, enabling differentiated feature extraction between motion-saliency regions and static background regions. At the detection output, suppressive modulation is applied to the confidence score to reduce false alarms in static backgrounds. Simultaneously, perturbation frames are identified, and weight updates are maintained or limited to suppress false activations across the entire image and confidence score jitter across consecutive frames. The technical solution of this application solves the problem of balancing efficiency and stability caused by target sparsity, high background ratio, and pseudo-motion interference.