77 results about "Morphological trait" patented technology

The invention relates to the technical field of medical image processing and computer vision, in particular to a parasitic ovum microscopic image detection method and system based on polymorphic prior, and the method comprises the steps: obtaining a to-be-detected microscopic image, and carrying out the feature extraction of the to-be-detected microscopic image through a convolutional neural network, and obtaining an initial feature map; constructing a polymorphic convolution kernel library based on preset biological morphological characteristics of the parasitic ova; performing deep convolution and feature fusion operation on the initial feature map by using a polymorphic convolution kernel library to generate a space attention map; performing feature enhancement processing on the initial feature map by using the spatial attention map to obtain an enhanced feature map; and performing bounding box regression and category prediction on the enhanced feature map to obtain a parasitic ovum detection result. According to the method, morphological priori and attention mechanisms are introduced, so that the problems of egg form similarity, background interference and the like are solved, accurate and robust automatic detection is realized, and the clinical diagnosis efficiency is remarkably improved.

Disclosed in the present invention is a method for identifying morphological features of an underwater pile group of a bridge on the basis of a three-dimensional sonar point cloud, comprising the following steps: collecting point cloud data of the three-dimensional morphology of underwater pile foundations of a bridge: using a three-dimensional real-time sonar system-mounted survey vessel to perform multi-directional scanning around the underwater pile foundations of the bridge and performing real-time stitching, so as to acquire point cloud data of the complete morphology of the underwater pile foundations; on the basis of the point cloud data of the complete morphology of the underwater pile foundations, performing pile foundation extraction by means of a point cloud minimum cut method, and on the basis of a single pile foundation extracted after segmentation, projecting the pile foundation point cloud to an XOY plane to obtain a planar pile foundation point cloud; on the basis of the planar pile foundation point cloud, performing pile foundation morphological feature identification by means of an iterative MAD method; and on the basis of the identification result, mapping a complete pile foundation morphological feature point cloud back to an original three-dimensional space on the basis of elevation information so as to obtain a three-dimensional pile foundation point cloud having complete morphological features. The present invention can implement accurate identification of complete pile foundation point cloud morphological features.

Systems and methods are provided for classifying microbial cells according to morphological features of microcolonies. A dark-field objective is employed to acquire a dark-field image of a microcolony during a microcolony growth phase that is characterized by phenotypic expression of microcolony morphological features which evolve with time and are differentiated among classes of microbial cell types. The dark-field image is processed to classify the microcolony according to two or more microbial cell types, such as Gram status and / or speciation. The dark-field objective may have a numerical aperture selected to facilitate the imaging of microcolony morphological features, residing, for example, between 0.15 and 0.35. A set of dark-field images of a microcolony may be collected during the microcolony growth phase and processed to classify the microcolony. Classification may be performed according to a temporal ordering of the dark-field images, for example, using a recurrent neural network.

The invention discloses a morphological feature-based cell segmentation and cell nucleus identification method for a turned unstained bone tissue pathological image. The method comprises the following steps of: 1, eliminating tool marks by adopting a tool mark elimination method combining local frequency domain analysis and directional suppression; 2, performing cell segmentation by using a K-means method, and performing morphological expansion and topological analysis on a segmented single cell image to identify a cell nucleus in the single cell image; step 3, calculating morphological characteristic indexes of each region; the method comprises the following steps: establishing a multi-dimensional Gaussian mixture model according to existing bone cell labeled sample information, performing outlier detection according to statistical data analysis, and removing results which do not conform to cell morphology; classifying different regions, and removing non-cell regions; by calculating morphological characteristic indexes of each region, different regions are distinguished according to the indexes, and cells are preliminarily screened. According to the method, high-precision cell segmentation and cell nucleus identification can be carried out on the cut undyed bone tissue pathological image.

The invention discloses a hyperspectral and morphological feature fused rice planthopper stage and sex identification method, and aims to improve the classification accuracy of the variety, age and sex of rice planthoppers in the initial stage of nymphs. Spectral data of rice planthoppers are obtained through a hyperspectral imaging technology, morphological characteristics are extracted by combining a morphological image processing technology, and the spectral data and morphological characteristic data are combined by adopting a multi-modal data fusion technology. And training and classifying the fused data by using a machine learning algorithm, and accurately identifying the type, age and gender of the rice planthopper. In order to solve the problem of data imbalance, an SMOTE algorithm is adopted to carry out sample oversampling, and class distribution balance is ensured. The severity of insect pests is evaluated in real time through automatic counting and population density generation, and support is provided for prevention and control measures. The screening of characteristic wave bands is expected to provide key technical support for the development of special multispectral cameras. In addition, the model interpretability is improved by adopting SHAP analysis, and the contribution of the features to the classification result is quantified.

The invention relates to a method for evaluating the anti-shedding capability of kelp and application thereof, and belongs to the technical field of seawater algae culture, the method comprises the following steps: 1) sample collection and preparation: collecting kelp samples, dividing the samples into two groups of samples, and counting the seedling shedding condition on a culture seedling rope at the same time; 2) mechanical property testing: applying a tensile load to a group of samples until the samples are fractured to obtain a stress-strain curve; 3) morphological characteristic detection: after softening the other group of samples in a low-specific-gravity seawater culture solution, expanding the rhizoid into a planar form, and measuring and analyzing the morphological characteristics of the rhizoid; and 4) data analysis and model construction: performing principal component analysis on the mechanical and morphological characteristic parameters, constructing a multiple regression model in combination with the seedling storage rate of the culture seedling rope, and calculating the comprehensive score of the anti-shedding ability of the kelp. Based on the comprehensive score, evaluating the anti-shedding capability of leaves of different kelp strains, and providing a theoretical basis for breeding of anti-shedding kelp varieties.

The invention discloses an evaluation method and device for morphological characteristics of a Mars barrier impact crater and a storage medium, relates to the technical field of planetary remote sensing, and mainly aims to improve the evaluation efficiency and the evaluation precision of the morphological characteristics of the Mars barrier impact crater. The method comprises the steps of obtaining position information of a target barrier impact pit in response to a morphological characteristic evaluation signal of the target barrier impact pit; based on the position information, determining a remote sensing image to which the target barrier impact crater belongs, and matching a digital elevation model homologous with the remote sensing image; based on the remote sensing image and the digital elevation model, segmenting the target barrier impact crater and a contour area of a corresponding sputtering object in the remote sensing image; determining geometrical characteristic parameters of the contour area, and evaluating the morphological characteristics of the target barrier impact crater based on the geometrical characteristic parameters. The method is suitable for the scene of evaluating the morphological characteristics of the Mars barrier impact crater.

A method for detecting the extent of microplastic accumulation in bivalve organisms in extreme deep-sea environments is provided. This method is performed through sampling of biological communities to represent the community structure of that biological bed layer, then morphological characterization statistics are conducted to classify the age stage data of individual bivalves, multivariate factor analysis is used to obtain the individual bivalves with the greatest degree of contribution in each classified age stage, subsequently tissue-specific microplastic extraction is performed, the morphology of seafloor microplastics is restored and streamlined identification of full-size microplastics is considered, carbon-14 dating is used to trace the duration of microplastic adsorption by each individual bivalve, dynamic accumulation curves for seafloor bivalves are constructed by connecting each bivalve's survival duration, and finally this is scaled up to the entire bivalve bed to derive the microplastic accumulation rate and historical accumulation of the entire extreme ecosystem.

The invention discloses a data-driven traditional courtyard morphological characteristic comfort influence evaluation method, which comprises the following steps: S1, data collection and arrangement: collecting morphological characteristic data of a traditional courtyard and thermal comfort data related to the morphological characteristic data, and constructing a morphological index evaluation system; s2, numerical simulation: constructing an ENVI-met model in combination with the data collected in S1, simulating microclimate of typical days in winter and summer to obtain a UTCI thermal comfort index, and calculating an average value of a simulation domain and time to represent the thermal comfort level of each courtyard; s3, building and analyzing a machine learning model: performing regression analysis on the relationship between the courtyard morphological characteristics and the thermal comfort index by using an XGBoost model; and S4: interpretability analysis: by means of an SHAP interpretation method, checking the importance and local dependency relationship of the morphological characteristics, visualizing a nonlinear correlation mode of a single variable, and understanding the direct influence degree of different morphological characteristics on thermal comfort and the cooperation or inhibition effect between the different morphological characteristics through an SHAP value.

The invention provides a tailing particle classification method based on multi-dimensional morphological feature compression and clustering, and belongs to the technical field of geotechnical engineering, mineral processing and particle classification, and the method comprises the following steps: S1-S6, obtaining an STL file database of tailing particles; s7, calculating and extracting morphological parameters to form an original parameter data set; s8-S10: obtaining a three-dimensional morphological parameter set which retains the original morphological feature information; s11-S12, determining an optimal clustering number by adopting an elbow rule, a contour coefficient and Gap statistics, and performing particle morphology clustering grouping on the three-dimensional morphology parameters by adopting a K-means algorithm; and S13, carrying out stacking test simulation on the clustered particle categories by using a discrete element method, analyzing the stacking density and the average coordination number of different categories of particles, and verifying the physical significance of a classification result. The tailing particle classification method can systematically and efficiently integrate particle multi-dimensional form information, can directly associate the information with macroscopic physical and mechanical properties, and is data-driven, clear in mechanism and explainable in result.

An automated method for assessing the ZP binding capacity of sperm from morphological characteristics of sperm in assisted reproduction using deep learning is disclosed. The invention also provides a method for predicting fertilization success based on ZP binding capacity of sperm in clinical assisted reproduction.

The invention relates to the technical field of fungus bag cover taking, in particular to an automatic edible fungus bag cover taking method for a fungus bag cover taking machine, and the method comprises the steps: obtaining the morphological characteristic information and quality of each edible fungus bag, recognizing the relative position of a fungus bag cover and the upper edge of the edible fungus bag, determining the clamping stability characterization parameter of the edible fungus bag, and obtaining the clamping stability characterization parameter of the edible fungus bag. Determining a cover taking deformation type according to the clamping stability characterization parameter; determining the form stability level of the edible fungus bag according to the fungus bag surface point cloud data; determining a cover taking mode of the edible mushroom bag according to the cover taking deformation category and the form stability level, and determining a cover clamping correction mode of the edible mushroom bag according to the relative position and the cover taking deformation category; the success rate of the current cover taking mode and the cover clamping correction mode is determined, whether the automatic cover taking requirement is met or not is judged, whether the cover taking mode is adjusted or not is determined according to the judgment result, the cover taking method is suitable for fungus bags of different forms and densities, and the automation level and the success rate of edible fungus bag cover taking are improved.

The present invention provides a method and system for encrypted storage and inference of DNA barcodes of rare and endangered species, which relates to the field of encrypted inference technology, including: obtaining a rare and endangered species monitoring data set and a corresponding biological sample set; constructing an encrypted database based on the monitoring data set to obtain an anti-parsing database framework; encrypting the DNA barcodes based on the biological sample set to obtain an encrypted barcode set; generating a single valid sequence based on a verification sequence generated by the DNA sample to be identified; performing collaborative inference verification based on the single valid sequence and the encrypted barcode set to generate a species identification code; performing a comprehensive judgment based on the species identification code and the monitoring data set, and outputting an identification result by correlating morphological feature changes with geographic distribution density. By constructing an anti-parsing encrypted database framework and a functionally limited DNA barcode set, the present invention achieves active protection of rare species genetic data and solves the key hidden danger of sensitive biological information being used for poaching tracing.

The invention discloses a fish body morphological index measuring device, which relates to the technical field of fish body morphological characteristic measuring equipment, and comprises a rack, a measuring mechanism and a conveying mechanism, the measuring mechanism can measure the body weight and the fish body shape indexes, and the fish body shape indexes comprise one or more of the body length, the body height, the head length, the kiss length, the caudal peduncle length and the caudal peduncle width; the conveying mechanism is arranged on the machine frame, an outlet of the conveying mechanism is communicated with the measuring mechanism, the conveying mechanism can convey to-be-measured fishes to the measuring mechanism, and the machine frame and the walking mechanism at the bottom of the machine frame are arranged, so that the fish body shape index measuring device can be flexibly transferred to adapt to fish body shape index measuring and collecting in the field on-site environment. The interference on the fish wild protection population caused by transferring the fish sample to a laboratory to carry out species identification and morphological characteristic index determination is avoided.

The invention discloses an SNP (Single Nucleotide Polymorphism) marker related to a tabebuia chrysantha seed morphology and application thereof. The SNP marker comprises an SNP1, an SNP4, an SNP8, an SNP18 and an SNP24, and the SNP molecular marker is used for typing morphological characters of seeds of tabebuia chrysantha. The seed morphological character is at least one of winged length, winged width, seed length and seed width. According to the application, the SNP marker is used for typing seed morphological characters of tabebuia chrysantha, or is used for predicting the expression quantity of seed morphological related genes in tabebuia chrysantha. The SNP marker related to the seed morphology is excavated from the genome DNA of the tabebuia chrysantha through a whole genome association analysis technology, and a scientific basis is provided for genetic improvement, classification and breeding of the tabebuia chrysantha.

The invention provides a primer group, a kit and a method for identifying Yunnan No.2, and belongs to the field of germplasm resource identification. The primer group comprises six primer pairs, each primer pair comprises a forward primer and a reverse primer, and the forward primer of the first primer pair, the reverse primer of the first primer pair, the forward primer of the sixth primer pair and the reverse primer of the sixth primer pair are sequentially shown as SEQ ID NO: 1 to SEQ ID NO: 12 in a sequence table. According to the primer group and the corresponding method provided by the embodiment of the invention, the uncertainty of variety identification based on external morphological characteristics of mangoes can be overcome, the new variety Yunnan No.2 of mangoes can be accurately identified, the operation is simple and convenient, the cost is low, the efficiency is high, and the result is visual and reliable. Scientific technical guarantee is provided for protection of the new mango variety Yunnan No.2, and popularization and application of the mango variety are facilitated.

The invention relates to the technical field of biological information processing and automatic sorting, and discloses a chick gender automatic identification method and system based on multi-modal fusion, and the method comprises the steps: collecting original three-dimensional point cloud data through a three-dimensional form sensing module, and calculating the key region coordinates of chicks; based on the coordinates, controlling the addressable micro sampling array to execute active sampling adaptation, and obtaining measurement response time sequence data of the target volatile matter; acquiring background response time sequence data by utilizing a background channel sensor array; and performing differential processing on the two to generate a differential biochemical response time sequence vector. And then, respectively extracting morphological feature vectors and biochemical fingerprint feature vectors, inputting the morphological feature vectors and the biochemical fingerprint feature vectors into respective independent classifiers to obtain prediction categories and confidence coefficients, finally, judging a bimodal prediction result through preset cross validation gating fusion logic, outputting a final decision, and controlling a sorting execution module to complete physical sorting. And the accuracy of biochemical sampling is improved through active sampling adaptation.

The present invention discloses a primer for identifying Epinephelus coioides and its application, the nucleotide sequence of the primer is shown in SEQ ID NO: 5-6; the application is an application in identifying Epinephelus coioides. The molecular specific marker primer provided by the present invention can perform rapid molecular identification of Epinephelus coioides. The method is simple, accurate, and sensitive, and is an effective auxiliary means for distinguishing Epinephelus coioides by morphological characteristics. Compared with the traditional survey method, which cannot perform effective identification due to incomplete organisms, the detection method of the present invention only needs to collect environmental samples, and can identify whether Epinephelus coioides exists by detecting whether the D-loop region fragment of Epinephelus coioides exists in the environment. It has little damage to the ecosystem, is conducive to the rapid identification of Epinephelus coioides, and has broad application prospects in the protection of germplasm resource diversity and germplasm ecological survey of Epinephelus coioides.

This invention provides a tailings particle classification method based on multi-dimensional morphological feature compression and clustering, belonging to the fields of geotechnical engineering, mineral processing, and particle classification technology. The method includes: S1-S6: acquiring an STL file database of tailings particles; S7: calculating and extracting morphological parameters to form an original parameter dataset; S8-S10: obtaining a three-dimensional morphological parameter set retaining the original morphological feature information; S11-S12: determining the optimal number of clusters using the elbow rule, silhouette coefficient, and Gap statistic, and using the K-means algorithm to cluster the three-dimensional morphological parameters into particle morphology groups; S13: using the discrete element method to conduct a stacking test simulation on the clustered particle categories, analyzing the stacking density and average coordination number of different particle categories, and verifying the physical meaning of the classification results. This tailings particle classification method can systematically and efficiently integrate multi-dimensional particle morphological information and directly correlate this information with macroscopic physical and mechanical properties. It is data-driven, has a clear mechanism, and the results are interpretable.

This invention belongs to the field of biological monitoring, specifically a method for detecting the viability of Poria cocos strains, comprising the following steps: Step S1: acquiring images of Poria cocos strains; Step S2: preprocessing the images acquired in Step S1; Step S3: extracting features from the preprocessed images, extracting key growth characteristics of the hyphae, including growth rate, biomass, and morphological characteristics; Step S4: quantitatively evaluating the viability of Poria cocos strains by combining the extracted growth rate, biomass, and morphological characteristics. In this application, the growth rate, biomass change rate, and morphological characteristic values ​​predicted by the ARIMA model can generate viability curves for future time periods. By performing time-series analysis on these predicted viability values, the viability trend of the strains over a future period can be determined, enabling early detection of the risk of decreased viability and timely adjustment of environmental conditions to maintain optimal growth. This also helps in selecting suitable temperature environments to maintain the optimal viability of Poria cocos strains.

The invention discloses a regenerated rice yield prediction method, device and equipment and a medium. The method comprises the following steps: acquiring a current first time period hyperspectral image and a current first time period color image of regenerated rice in a target area; according to the hyperspectral image in the current first time period, calculating the surface reflectance of the hyperspectral image in the current first time period to obtain a vegetation parameter corresponding to the hyperspectral image in the current first time period; determining morphological characteristics of the regenerated rice according to the color image in the current first time period; according to the morphological characteristics and a morphological characteristic library, comparing rice varieties in the morphological characteristics and the morphological characteristic library, and determining the variety type of the regenerated rice; and inputting the variety type and the vegetation parameters into a pre-trained regenerated rice yield prediction model to obtain the predicted yield of the regenerated rice in the current second time period. According to the embodiment of the invention, the efficiency and accuracy of regenerated rice yield prediction can be improved.

Described herein are methods and devices (801) for determining whether an R-wave detection should be classified as a false R-wave detection due to T-wave oversensing (TWO) or P-wave oversensing (PWO). One such method includes comparing a specific morphological characteristic (e.g., peak amplitude) associated with the R-wave detection to the specific morphological characteristic associated with each R-wave detection in a first set of earlier detected R-wave detections to thereby determine whether first TWO or PWO morphological criteria are met, and in a second set of earlier detected R-wave detections to thereby determine whether second TWO or PWO morphological criteria are met, wherein the second set differs from the first set but may have some overlap with the first set. The method also includes determining whether to classify the R-wave detection as a false R-wave detection, based on whether one of the first or second TWO or PWO morphological criteria are met.

The invention provides a sperm midsection morphological defect analysis method, electronic equipment and a storage medium, and the method comprises the following steps: carrying out the semantic segmentation of a dyeing image containing a sperm head and a midsection through a semantic segmentation network, and obtaining a binary segmentation result marked with the sperm head and the sperm midsection; fitting the sperm head into an ellipse according to the obtained binary segmentation result of the sperm head, and solving a long axis endpoint connected with the middle section of the sperm; refining the obtained binary segmentation result of the middle section of the sperm to obtain a middle section center line; and calculating sperm midsection morphological characteristics, and identifying sperm midsection morphological defects. The method can quantitatively calculate the characteristic result, is high in interpretability, does not need to depend on professional knowledge and experience of doctors or researchers, and improves the accuracy and efficiency of sperm midsection morphological defect recognition.

The present invention discloses a method, device and storage medium for evaluating the morphological characteristics of a Martian barrier crater, which relates to the field of planetary remote sensing technology, and is mainly capable of improving the evaluation efficiency and accuracy of the morphological characteristics of a Martian barrier crater. The method comprises: in response to a morphological characteristic evaluation signal of a target barrier crater, obtaining the position information of the target barrier crater; based on the position information, determining the remote sensing image to which the target barrier crater belongs, and matching a digital elevation model homologous to the remote sensing image; based on the remote sensing image and the digital elevation model, segmenting the contour area of ​​the target barrier crater and its corresponding sputtering material in the remote sensing image; determining the geometric characteristic parameters of the contour area, and based on the geometric characteristic parameters, evaluating the morphological characteristics of the target barrier crater. The present invention is applicable to scenarios for evaluating the morphological characteristics of Martian barrier craters.

The invention relates to the technical field of edible mushroom cultivation, in particular to an indoor cultivation method and system for edible mushrooms and a storage medium. The method comprises the steps that differential monitoring frequencies are set according to different growth and development stages in the fruiting stage, and pileus diameter, pileus height and pileus expansion degree morphological characteristic data of sporocarps at the current monitoring time point are collected; the growth rate is calculated by comparing the morphological characteristic data of the adjacent time points; preliminarily judging whether the growth is stagnated according to the growth rate, if so, further judging whether the growth is mature according to a maturity standard, and triggering a harvesting prompt; if not, predicting the harvesting time length; and if the growth is stagnated but is immature, continuously monitoring and identifying growth abnormality through a secondary verification mechanism. Through dynamic monitoring and intelligent analysis of multi-dimensional morphological characteristics, accurate judgment of harvesting opportunity and early warning of abnormal growth are realized, the management difficulty of household users is effectively reduced, and the yield and quality of mushrooms are improved.

This invention discloses a bird-inspired wing-shaped deformable aircraft that can move across media. Using birds such as pelicans, which possess transmedia capabilities, as biomimetic prototypes, and based on research into their water-entry and exiting behaviors and morphological characteristics, an innovative structural design scheme for a biomimetic deformable wing aircraft is proposed. Based on the biomechanical mechanisms of multi-joint linkage in bird skeletons and the hydrophobic properties of feathers, the aircraft employs a deformable wing and tail design; combined with lightweight materials and a multi-degree-of-freedom actuation strategy, it achieves efficient coordination between dynamic morphological switching and transmedia motion.

The invention relates to a grazing rate prediction data processing method based on plant morphological characteristics, and belongs to the technical field of electric digital data processing and machine learning application. The method solves the problems that the grazing response signal is weak due to the dependence on the overall characteristics of the vegetation community, and the reproducibility is poor due to the lack of a standardized process. The method comprises the following steps: in a model training stage, receiving a plant individual form measurement value containing a plant height and a crown diameter and a grazing rate reference value, calculating an individual form index based on the plant height and the crown diameter, dividing a space unit and calculating spatial statistical characteristics of the form index in the unit, and training to obtain a grazing rate estimation model; in the model application stage, the same index calculation and space statistics steps are executed on the new plant morphology measurement value, the result is input into the trained model, and the estimated grazing rate is output. According to the method, through building individual index calculation, space unit statistics, model training and application, effective prediction of the grazing rate based on plant morphological characteristics is realized.

The present invention relates to a method of identifying and / or verifying a security article, the method comprising probing and / or analyzing (120) in at least a portion of the security article one or more markers representative of the presence, content and / or morphological characteristics of a calcium carbonate filler of bio-based origin, and determining the identity and / or authenticity of the security article based at least on a comparison of the one or more markers with one or more reference information (130).

The invention relates to the technical field of species identification, in particular to a cactus plant germplasm identification method and system based on morphological characters and a medium. The method comprises the following steps: constructing a morphological character data matrix of different cactus plants by adopting a standardized measurement rule, and screening to obtain key morphological characters by adopting an improved principal component analysis method based on the morphological character data matrix; carrying out standardization processing on each key morphological character; constructing an identification reference database containing standard species vectors based on the standardized key form vectors; key morphological character measurement data of a to-be-measured plant sample is collected and standardized, and a to-be-measured morphological vector is obtained; and calculating the spatial similarity between the to-be-detected morphological vector and each standard species vector in the identification reference database, and judging the cactus plant germplasm corresponding to the to-be-detected plant sample based on the calculation result of the spatial similarity. Therefore, a morphological identification technology system is optimized, and a scientific basis is provided for rapid, accurate and standardized identification of cactus plants.