8results about How to "Reduce input dimensionality" patented technology

The present application relates to a unit multi-physical field coupling monitoring system based on a Relief-FCC algorithm, and belongs to the technical field of electric digital signal processing, aiming to solve the problems of ignoring multi-field coupling effect, high fault omission rate and extensive control strategy of existing pumped storage unit monitoring. The system collects unit electromagnetic field, temperature field and mechanical vibration field operation data through a sensor network, introduces an inter-field coupling coefficient FCC in the Relief feature weight to construct a Relief-FCC algorithm, and selects dominant coupling features; the inter-field coupling coefficient is used to complete multi-field coupling consistency cross-validation, distinguish unit body and sensor abnormalities, and complete adaptive correction; based on the dominant coupling features, fault identification, health assessment and failure root cause analysis are realized, and dynamic optimization control of the unit is simultaneously realized. The present application significantly improves the fault prediction accuracy of pumped storage units and ensures efficient and safe operation of the units.

This invention discloses a method for constructing a sinusitis regulation analysis model based on nasal polyp cell inhibition data, belonging to the field of CRSwNP disease analysis technology. The method includes the following steps: collecting Lund-Kennedy scores and corresponding nasal polyp cell inhibition data from CRSwNP patients to obtain raw sample data; screening the raw sample data to remove unqualified raw sample data from CRSwNP patients to obtain standard sample data; performing index preprocessing based on the standard sample data and obtaining the optimal lag order to obtain reference sample data; constructing an LK analysis prediction model based on the reference sample data and analyzing and predicting the Lund-Kennedy scores of CRSwNP patients. This invention addresses the problem that existing CRSwNP disease analysis technologies cannot provide clinicians with objective and reliable analytical and predictive tools when analyzing and predicting the Lund-Kennedy scores of CRSwNP patients, thus failing to help clinicians reliably predict the disease progression of CRSwNP patients during treatment.

The application relates to the field of safety early warning processing, in particular to a video monitoring early warning method based on AI analysis, which comprises the following steps: constructing a human body key point detection model by using historical image data, extracting key point positions and change sequences, labeling the sequences according to behaviors, calculating the importance of the key point sequences to each behavior, screening important key points, constructing a time sequence prediction model, determining an input length, at the same time, constructing a region division model, dynamically adjusting a detection threshold according to the safety early warning grades of different regions to behaviors, inputting a real-time image into the model, extracting key point sequences, predicting behavior probabilities and comparing the behavior probabilities with a threshold, judging abnormal behaviors and early warning. The application can automatically extract key point position information and change sequences to accurately identify personnel behaviors by constructing a human body key point detection model and a time sequence prediction model, and can dynamically adjust a detection threshold by setting safety early warning grades of different regions, thereby improving the efficiency, accuracy and flexibility of a monitoring system.

The application discloses a microalgae filtration membrane system performance prediction method based on a machine learning algorithm, relates to the microalgae filtration membrane system performance prediction field, and comprises the following steps: constructing a microalgae filtration membrane system performance dataset, introducing a feature selection and integrated machine learning modeling method, screening and modeling analysis on key operating variables in the system, and establishing a prediction model of microalgae recovery rate, pollutant removal rate and membrane permeation performance on the basis. In combination with an interpretable machine learning analysis method, the contribution degree of different operating parameters in model prediction is quantified, the influence law and nonlinear action characteristics of factors such as microalgae concentration, operating time, membrane pore size parameter and operating pressure on the system performance are revealed, so that the precise prediction of the microalgae filtration membrane system performance and the identification of key influence factors are realized, and reliable technical support is provided for the optimized operation and engineering application of the microalgae filtration membrane system.

The invention relates to the crossing field of public health and geographic information technology, and discloses a plague risk assessment method and system combined with a MaxEnt model, and the method comprises the steps: obtaining a multi-source environment variable, and carrying out the standardization; reducing a colinear variable through variance threshold preliminary screening, a Pearson's correlation coefficient and plague point significance test; performing variable importance sorting by using recursive feature elimination and a support vector machine, and dynamically determining an optimal variable subset; and inputting a MaxEnt model to train ecological niche probability distribution, and dividing risk levels based on historical occurrence point probability quantiles. The system comprises corresponding function modules. According to the system, through a three-stage automatic variable screening mechanism, the generalization ability, interpretability and prediction precision of the model are remarkably improved, and reliable support is provided for accurate prevention and control of plague.

The invention relates to the technical field of overhead transmission lines, and discloses a meteorological environment factor-based insulator surface contamination prediction method, which comprises the following steps of: firstly, acquiring multi-source heterogeneous data of insulator body attributes, a micro-meteorological environment and atmospheric chemical components, and constructing a historical sample database; obtaining a normalized time sequence matrix through data cleaning and feature screening; constructing a hybrid neural network model, extracting local features through a convolutional neural network, generating attention masks based on relative humidity by using a dynamic weighted chemical component feature fusion network, performing dynamic weighting on feature vectors, and inputting the feature vectors into a long-short-term memory network to perform time sequence evolution prediction; and finally, outputting predicted values of equivalent salt density and insoluble deposition density of the surface of the insulator by utilizing the model for training convergence. By introducing a humidity attention mechanism, deep fusion of the meteorological environment and the chemical component characteristics is realized, and the prediction precision of insulator dirt retention in a complex environment is remarkably improved.

The invention relates to the technical field of motors, in particular to a driving motor NVH performance evaluation method and device, and the method comprises the following steps: S1, obtaining the time domain electromagnetic force distribution data of each skewed pole section stator tooth of a target motor through electromagnetic simulation; s2, performing spatial domain equivalent synthesis on the electromagnetic force of each skewed pole section to generate a global equivalent electromagnetic force time domain signal; s3, performing frequency domain-order conjoint analysis on the global equivalent electromagnetic force time domain signal, identifying a target frequency point having significant influence on NVH performance, and extracting a main space order and a main time order corresponding to the target frequency point to obtain a corresponding synthetic excitation force amplitude; s4, on the basis of the time domain electromagnetic force distribution data, under the target frequency point, the main space order and the main time order, calculating the electromagnetic force phase difference between the at least two groups of skewed pole sections, and generating a phase difference characterization quantity; and S5, inputting the target frequency point, the synthesized exciting force amplitude and the phase difference representation quantity into an NVH performance evaluation model, and outputting an NVH performance index.

The invention relates to the technical field of network security and machine learning, and particularly discloses an intrusion detection method fusing feature optimization and two-stage pruning optimization, which comprises the following steps: screening each feature of an original traffic sample in a training set based on an information gain and grey wolf optimization algorithm to obtain an optimal feature subset and a common feature subset; performing two-stage pruning on the random forest classifier, in the first-stage pruning, performing hierarchical feature sampling according to a fixed proportion, and adopting a Gini index as a measurement index of node splitting purity to control a node splitting process of a single decision tree; in the second stage of pruning, marginal contribution degrees of the current decision tree in different decision tree subsets are quantified by a Shapley value, dynamic smooth updating is performed on the marginal contribution degrees in combination with a sliding window mechanism, and decision trees with low contribution degrees are eliminated. According to the method, on the premise of ensuring high detection precision, the model complexity and calculation overhead are reduced, and current complex and changeable network security threats are dealt with.