13results about How to "Informative" patented technology

The application discloses a camellia seed oil adulteration identification method based on triglyceride isomers; the method comprises the following steps: collecting the data of the first mass spectrum information and the second mass spectrum information of glycerides of the oil sample standard by an ultra-high performance liquid chromatography-quadrupole time-of-flight-high resolution mass spectrometer; extracting the peak area content of the top 27 glyceride compounds; identifying and extracting the isomer content of the top 27 glyceride compounds; screening characteristic isomer compounds according to variable projection importance; establishing an adulteration identification model according to the principal component analysis and the least square-discriminant analysis of the isomer content; and identifying the sample in the adulteration identification model. The adulteration identification model can not only distinguish camellia seed oil and high-oleic oil, but also has a highest prediction accuracy of 100% for the type of high-oleic oil, and has a minimum identification adulteration concentration of 2%, thereby significantly improving the identification accuracy of camellia seed oil.

The application discloses a deep learning DOA estimation method based on original IQ data. The application uses I and Q components of the original signal as the input of the model to improve the performance. The application aims to solve the DOA estimation problem of a single signal source, and models the single signal source DOA estimation problem as a single-label multi-classification problem. By discretizing the DOA range, the possible directions of arrival are taken as corresponding labels. A convolutional neural network is designed to adapt to different numbers of snapshots, and accurate DOA estimation can be adaptively obtained for input signals of different lengths. Experimental results show that, compared with existing deep learning DOA estimation methods based on covariance matrix as input, the scheme has more excellent performance, and can provide a more reliable solution for array signal processing.

The invention provides aluminum foil tensile strength testing equipment, which comprises a rack, a first testing mechanism, a material pulling mechanism and a second testing mechanism, and is characterized in that two guide rods are symmetrically arranged on the rack; the first testing mechanism comprises two fixing assemblies, a first electric telescopic rod and a tension meter, and the two fixing assemblies are slidably connected with the two guide rods respectively; the first electric telescopic rod is detachably arranged between the rack and one fixing assembly, and the tension meter is detachably arranged between the rack and the other fixing assembly; the material pulling mechanism comprises two electric driving modules and a material pulling assembly, the two electric driving modules are symmetrically arranged on the rack, and the material pulling assembly is connected with the two electric driving modules. According to the aluminum foil tensile strength testing equipment, the tensile strength of an aluminum foil material can be tested when the aluminum foil material is flatly laid or wound and conveyed, so that the actual production environment is better fitted, and obtained testing data are more referential.

This application provides a method for inverting the dynamic changes of the seabed sedimentary environment based on three-phase potential, including in-situ data processing, electrical signal calibration, mathematical model establishment, and dynamic change analysis. By deploying a seabed electrode array, signals such as spontaneous potential, resistivity, and redox potential are acquired in real time. An inversion algorithm is used to deduce seabed sedimentary environment characteristics, such as seabed interface location, suspended particle concentration, sediment density, porosity, and water content. This method, combined with multi-frequency signal analysis, can monitor changes in the seabed sedimentary environment in real time and identify sediment movement. Compared with existing technologies such as CN118153411B and CN110411923B, this invention covers richer inversion content, and the complementarity of potential signals improves monitoring accuracy and efficiency, exhibiting stronger adaptability and reliability. This method has lower equipment and maintenance costs, is suitable for large-scale, long-term monitoring, and can provide innovative solutions in fields such as deep-sea monitoring, environmental assessment, and resource exploration.

This invention relates to the field of distribution network fault location technology and discloses a fault location decision method for an off-site distributed fault location device. It utilizes an off-site power grid sensor acquisition system to collect current information from each outgoing line and busbar, establishing typical fault samples and forming a typical fault sample space. Then, a zero-sequence network diagram is constructed based on the current information collected by the power grid sensor acquisition system. Circuit equations are written based on the zero-sequence network diagram, and the adiabatic equivalent of the circuit equations is applied using the servicing principle. Finally, the sequence parameters that indicate the phase transition development trend of the system are solved. This off-site distributed fault location device fault location decision method can correctly solve the uncertainty between fault symptoms and faulty lines, achieving fault location for low-current grounding systems. Furthermore, the correlation analysis-based fault diagnosis requires fewer samples, involves less computation, and is easy to master.

This invention discloses a heuristic dynamic memory weighting and recall reordering method, comprising six steps: initial impression value calculation, Ebbinghaus forgetting curve modeling, recall depth parameterization, multi-dimensional independent sorting, multi-dimensional result fusion, and recall-triggered impression value inversely proportional increment. This invention simulates the natural decay of memory through an 8-stage Ebbinghaus forgetting curve, achieves a recall-as-reinforcement effect through an inverse impression value increment mechanism, comprehensively considers semantic similarity, temporal freshness, and memory importance through multi-dimensional reordering, and flexibly adjusts the breadth and depth of recall through recall depth parameterization. This invention provides two forgetting curve implementation methods: discrete-stage lookup table method and continuous curve calculation method (including exponential decay type and power-law decay type), suitable for different application scenarios. This invention significantly improves the intelligence level and anthropomorphism of AI long-term memory systems, enabling AI to perform natural memory recall like humans.

The application discloses a wind power daily scene generation method based on a tensor self-organizing mapping neural network, and comprises the following steps: obtaining a daily scene data set of multiple wind farms; clustering the daily scene data set by using a tensor self-organizing mapping neural network; constructing a variational autoencoder for each cluster respectively to extract hidden features from the daily scene data; performing random simulation and sampling on the daily scene data of each cluster by using the hidden features to obtain a daily scene hidden variable data set; decoding and reconstructing the daily scene hidden variable data set to obtain reconstructed daily scene data of each cluster; and aggregating the reconstructed daily scene data of each cluster to obtain a reconstructed daily scene data set. After clustering the daily scene samples, the application respectively reduces the dimension and reconstructs the samples of each cluster, improves the accuracy of the reconstructed daily scene data, significantly reduces the spatial and temporal correlation error of the reconstruction by considering the time and space correlation of the scene data, and strengthens the feature expression ability of the generated scene.

The application discloses a digital microfluidic chip integrating myocardial tissue culture and multimode detection, and belongs to the microfluidic chip field.The digital microfluidic chip comprises a lower substrate, an upper substrate and a sealing gasket; the lower substrate and the upper substrate are connected through the sealing gasket around the lower substrate and the upper substrate, and a space for liquid drop flow is formed in the middle; the lower substrate is provided with a functionalized partition electrode array, which comprises a liquid storage area, an electrical physiological acquisition area and a biochemical detection area; the electrical physiological acquisition area adopts an MEA electrical physiological acquisition array and is used for acquiring field potential signals of myocardial tissue in situ; a recess for accommodating a solid-phase carrier is formed in an electrode of the biochemical detection area, and a myocardial injury marker is captured through the solid-phase carrier; the upper substrate is made of a transparent material and is provided with a channel communicating with the liquid storage area and the electrical physiological acquisition area; and the digital microfluidic chip drives liquid drops through a dielectric wetting effect, integrates myocardial tissue culture, electrical physiological signal monitoring and biochemical marker detection on the same chip, and realizes in-situ synchronous detection of electrical physiology and biochemistry in a dual-mode manner.

The invention belongs to the technical field of biology, and particularly relates to a honeysuckle SSR molecular marker development method based on honeysuckle genome sequencing and application of the developed SSR molecular marker in honeysuckle germplasm resource identification. The SSR molecular marker developed by the invention has the characteristics of good stability and high effectiveness. Eight pairs of SSR molecular marker primers are obtained by taking honeysuckle as a material through genome sequencing, SSR site analysis and identification, SSR primer design, SSR-PCR reaction system establishment, SSR-PCR amplification and effectiveness detection, and 30 honeysuckle germplasm resources can be effectively identified. The SSR molecular marker obtained by the invention can be used for germplasm resource identification, genetic diversity analysis and molecular-assisted new variety breeding of honeysuckle.

This invention relates to the field of artificial intelligence technology and discloses a method for identifying marine environmental risks based on big data. The method includes: standardizing marine environmental monitoring data to obtain a standardized dataset of the ocean; performing correlation analysis on the standardized dataset based on a pre-set risk knowledge base to obtain risk correlation features of the ocean; constructing a topology of the risk correlation features based on historical risk events of the ocean to obtain an environmental risk feature map of the ocean; tracking the trajectory of the environmental risk feature map to obtain the spatiotemporal evolution path of the ocean; simulating the spatiotemporal evolution path to obtain a risk evolution trajectory report of the ocean; and comprehensively evaluating the risk evolution trajectory report to obtain the risk level and early warning information of the ocean. This invention can improve the efficiency of marine environmental risk identification based on big data.

The application relates to the technical field of plasma detection, in particular to a double-channel ion energy probe based on a magnetic deflection effect. The application provides a double-channel ion energy probe based on a magnetic deflection effect, which comprises an upper permanent magnet plate, a lower permanent magnet plate, a double-channel collimator and a double-channel probe; the double-channel collimator penetrates into a shell; the double-channel collimator comprises a scanning channel and a reference channel; the scanning channel and the reference channel are connected with different values of voltage; the double-channel collimator is used for enabling ions to enter the shell along the channel direction; the upper permanent magnet plate and the lower permanent magnet plate are fixed at the upper and lower ends of the shell respectively so as to form a uniform magnetic field; the double-channel probe is arranged in the shell and comprises a scanning collection probe and a reference collection probe which are respectively coplanar with the scanning channel and the reference channel; and the scanning collection probe and the reference collection probe are respectively connected with different values of voltage. The scheme can provide an ion current probe with high signal-to-noise ratio.

The application discloses a training method and device of a time sequence application model based on medical time sequence data, and relates to the fields of medical information and artificial intelligence. The method comprises the following steps: obtaining medical training samples; generating soft labels for the medical training samples based on classification judgment auxiliary information corresponding to the medical training samples, wherein the classification judgment auxiliary information represents continuous variables for distinguishing and quantifying the feature difference degrees of different samples in the same category; the soft label is a continuous distribution probability value, and the soft label is used for reflecting the confidence degree of the medical training sample belonging to a target category; and the neural network is iteratively trained according to the medical training samples and the soft labels of the medical training samples, so that a time sequence model is obtained. The application solves the technical problem that the robustness of the trained model is poor due to the excessively simple classification of the training labels in the prior art.

This invention relates to a multi-channel micro-area angle-resolved monitoring system and method. The monitoring system includes: a relatively movable incident arm and a receiving arm, the incident arm having at least two incident channels and the receiving arm having at least two receiving channels; an ellipsoidal beam path assembly and a holographic interference beam path assembly selectively and positionally adjustable in the incident and receiving channels; and a light source, a photodetector, and a large numerical aperture lens arranged in coordination. The ellipsoidal beam path assembly can be used to measure the ellipsoidal deviation of the sample, and the holographic interference beam path assembly can be used to perform off-axis digital holographic measurement of the sample. Furthermore, the relative movement of the incident arm and the receiving arm, combined with the large numerical aperture lens, allows for adjustment of the incident angle of the incident beam, thereby acquiring more comprehensive multi-dimensional optical signals and more accurately monitoring the optical properties of the sample.