7 results about "Compositional data" patented technology

This invention discloses a dynamic error control method for automatic drilling and riveting equipment based on an error mechanism model, comprising: acquiring production data and classifying the production data according to the causes of errors to construct a processing dataset; constructing a mathematical model based on the error generation mechanism, inputting the processing dataset into the mathematical model, and obtaining optimal parameters affecting processing quality based on the principle of minimum parameters; labeling the optimal parameters based on processing quality, and combining the labels and optimal parameters into a dataset; constructing an initial model and training it using the dataset to obtain a predictive model for predicting the processing quality of the automatic drilling and riveting equipment. This invention also provides a dynamic error control device for automatic drilling and riveting equipment. The method provided by this invention can solve the problem of poor product processing quality caused by processing errors.

This invention discloses a two-phase flow identification system and method based on critical elevation maps, comprising the following steps: acquiring relative position data of all equipment, pipes, and valves in the pipeline system to be analyzed, from the starting equipment to the final equipment, as well as operating temperature, operating pressure, and medium composition data of each material flow point; drawing a critical elevation map based on the collected data; and analyzing the impact of pressure and temperature changes at each material flow point on the medium phase state using the critical elevation map, combined with the thermodynamic properties and flow characteristics of the medium, to identify locations prone to gas-liquid two-phase flow. This invention, by establishing critical elevation maps, can identify pipelines prone to two-phase flow in advance, preventing gas-liquid two-phase flow in process equipment pipelines, which can lead to long-term pipeline vibration, resulting in pipeline fatigue, support structure failure, weld cracking, and flange leakage. Furthermore, for pump inlet pipelines, it avoids the presence of two-phase flow that could cause pump cavitation and damage to the pump equipment.

A method encodes compositional data structures by receiving component sparse distributed representation arrays having a predetermined array length and target sparsity level, applying position-specific permutation transformations to encode ordinal position information, combining position-encoded arrays through bitwise union to generate an intermediate array, and processing the intermediate array through a dual-phase sparsity reduction procedure comprising a coarse additive phase and a fine subtractive phase controlled by a sparsity overshoot threshold to generate a composite encoded array. The dual-phase procedure converges in substantially constant iterations for 4 or more components with final sparsity tightly controlled around the target. A decoding method applies inverse position-specific transformations to generate position-decoded arrays, computes overlap scores with candidate components through bit counting operations, and determines component identities based on threshold comparison. Scalable decoding may use triadic associative memory. Applications include searchable compression, privacy-preserving analytics, and efficient neural network embeddings.

The application discloses a fuel property prediction method and device, equipment and a storage medium, and the method comprises the following steps: analyzing a to-be-tested fuel sample by means of a two-dimensional gas chromatograph to obtain hydrocarbon composition data of the to-be-tested fuel sample, inducing a plurality of key chemical component variables and constructing a component feature vector; performing standardization processing and principal component analysis on the component feature vector to obtain principal components meeting preset conditions and calculating a principal component score vector; inputting the principal component score vector into a trained nonlinear regression prediction model to obtain a predicted value of a physical and chemical property of the to-be-tested fuel sample; wherein the nonlinear regression prediction model comprises linear terms, square terms and interaction terms of the principal component score vector; wherein the square terms and the interaction terms are used to represent the nonlinear effect of the components on the physical and chemical property and the synergistic effect between the components. Thus, the problem of large fuel sample consumption and long test period can be solved, and the fuel property can be accurately predicted.

The application provides a kind of based on nonparametric density ratio's component data classification method and system, it is related to data processing technical field, method includes: obtaining component data;Through L2 norm normalization, each variable in component data is mapped to non-negative sphere;Through finite reflection group, non-negative spherical data is expanded to entire sphere;Based on kernel density function, estimate the class conditional probability density of spherical data;Using pullback operation, obtain the class conditional probability density of component data;Through log marginal density ratio transformation, obtain the feature enhancement data set of each component type variable;SVM model is used to train feature enhancement data set and corresponding class label;Target feature enhancement data set of target component data is input into the SVM model after training, and output component data classification result.

The present application relates to a kind of based on the improved YOLOv7 clean energy field key equipment defect method, comprising: obtaining preprocessed image and composition data set;YOLOv7 model is improved, and the improved YOLOv7 model is obtained;Get trained model, i.e. The target detection model;Substation equipment to be detected image is input into target detection model, and the position of substation equipment to be detected image and defect information are output.The present application effectively suppresses the noise interference in complex background, so that the network can accurately focus on the defect features of small targets, while reducing the computational redundancy, significantly improving the detection accuracy and feature extraction ability of the model;By introducing auxiliary frame to fine constraint on original bounding box, the regression process is optimized, and a dynamic adjustment factor is designed to balance the contribution weight of high-quality and low-quality samples to the loss function, which greatly enhances the recognition ability of small target defects with low resolution, insufficient information and susceptible to noise interference.

This application discloses an automated control method and system for a catalytic reaction analysis instrument, relating to the field of catalytic reaction analysis technology. The method includes the following steps: real-time acquisition of compositional data of reaction products; acquisition of catalyst activity indicators based on the compositional data; calculation of the rate of change of the activity indicators; during the reactor heating phase, when the rate of change exceeds a preset activation threshold, determining that an activity activation event is triggered, locking the reactor temperature at the temperature point at which the activity activation event is detected, and maintaining a preset isothermal duration; during the reactor isothermal operation phase, when the rate of change is less than a preset deactivation threshold, determining that a rapid deactivation event is triggered, increasing the flow rate of the feed gas to shorten the average residence time of the reaction products in the reactor; and increasing the frequency of compositional data acquisition in response to the determination of an activity activation event or a rapid deactivation event. This application can improve the adaptability of experiments, the effectiveness of data acquisition, and the accuracy of result interpretation.