10 results about "Information gain ratio" patented technology

The present application relates to power industry safety domain boundary fitting technology, in particular to a kind of high-dimensional static security domain boundary fitting method of power system, considering system generator output constraint, node load value sampling is carried out, through power flow calculation, the sample in the security domain is filtered out, and initial sample set is formed.Boundary sample search algorithm is proposed, for each sample in initial sample set, find a sample in the security domain, a sample outside the security domain, and satisfy the distance between the two is less than the set distance threshold value.The data in the boundary sample set in the original power injection space is converted to new three-dimensional feature space by deep neural network model.Extract the boundary in feature space by the weighted tilt decision tree algorithm based on information gain ratio, and evaluate the boundary performance, select the optimal boundary.The method can realize the fitting of high-dimensional boundary of power system security domain, and reduce the error with actual boundary.

The invention relates to the technical field of natural language processing and information retrieval, and discloses a retrieval enhancement generation method and device based on self-feedback driving, and the method comprises the steps: determining a to-be-queried problem through a pre-obtained query request; retrieving in the knowledge base according to the to-be-queried question, and generating an answer of the round of retrieval; calculating an information quality index of the current round of retrieval according to the answer and the to-be-queried question, and if the current round of retrieval is not the first retrieval, calculating an information gain rate of the current round of retrieval according to an information quality index of the previous round of retrieval and the information quality index of the current round of retrieval; and if the information gain ratio does not meet the preset condition, returning to determine the to-be-queried question in combination with the pre-acquired query request to perform the next round of retrieval, and stopping iteration and outputting the final answer until the information gain ratio meets the preset condition. The problems that an existing retrieval enhancement generation method depends on a fixed retrieval threshold value and lacks dynamic judgment capacity are solved, and the information utilization efficiency and the answer accuracy are improved.

The application discloses a liquid cooling adaptive control method and system based on a single-phase immersion liquid cooling data center, relates to the technical field of liquid cooling control, and comprises the following steps: reading real-time power consumption data of the data center, synchronously starting a temperature monitoring sensor to read chip temperature data and environment temperature data; configuring a short-time predictor by using a historical load sequence, performing information gain ratio and prediction uncertainty analysis of a preset candidate granularity; performing matching evaluation of the single-phase immersion liquid cooling and the data center by using a calibrated prediction granularity; if a trigger threshold is met, performing adaptive optimization under local-global control coordination constraints of single-phase immersion liquid cooling control, and outputting a liquid cooling response scheme. The technical problems of low matching degree of cooling capacity of single-phase immersion liquid cooling and load demand, response lag and insufficient energy consumption control in the prior art are solved, adaptive optimization control based on real-time and prediction data is achieved, and the technical effects of reducing energy consumption, improving heat dissipation efficiency and temperature control precision are achieved.

This invention discloses a feature selection method for early screening of coronary heart disease based on soft path cost accumulation, belonging to the field of medical data processing technology. The method includes: S1, selecting a candidate early coronary heart disease feature from the candidate feature set and adding it to the selected feature set with the goal of maximizing the soft path cost accumulation function, and then removing the selected candidate early coronary heart disease feature from the candidate feature set; S2, calculating the difference between the information gain ratio of the current selected feature set to the entire dataset and the information gain ratio of the selected feature set before its addition to the entire dataset, using it as the stopping scoring function; S3, repeating S1-S2 until the stopping scoring function obtained k consecutive times is less than 0, where k is a set hyperparameter threshold, or the candidate feature set is empty; S4, taking the current selected feature set corresponding to the maximum stopping scoring function as the optimal early coronary heart disease feature set. This method can improve the accuracy and efficiency of early coronary heart disease screening prediction models.

The invention relates to the technical field of data processing, in particular to an operation and maintenance fault monitoring method based on multi-modal feature extraction. The method comprises the following steps: for each monitoring period of each fault source, classifying multi-modal data in the monitoring period layer by layer according to each attribute of the fault source to obtain a decision tree of the fault source in the monitoring period; the step of determining the target attributes for classification at each node of the decision-making tree comprises the following sub-steps: according to the information gain ratio after classification through various attributes existing in a fault source and the similarity between the classified samples, determining the target attributes of the multi-modal data to be classified at the node, and determining the target attributes of the multi-modal data to be classified according to the similarity between the classified samples, selecting a target attribute from various attributes existing in the fault source; determining the fault occurrence rate of each fault source according to the similarity of the data of each layer of nodes in each decision tree of each fault source in different monitoring periods; and determining a target fault source according to the fault occurrence rate of each fault source. The method improves the accuracy of fault detection.

The invention discloses a display screen production process management system and method based on artificial intelligence, and belongs to the technical field of production management. According to the method, a component type library and a production database are constructed, a process flow decision tree for bearing component productivity parameters is created, trunk / cotyledon nodes are marked, and a production process topological graph is generated; tasks are divided according to production batches, two types of training sample sets with or without incomplete components are constructed, and a node comprehensive sample set is separated after standardization processing; and calculating a feature information gain ratio based on a C4.5 algorithm to screen an optimal feature set, constructing a C4.5 decision tree-based learning device through bootstrap resampling, integrating the C4.5 decision tree-based learning device into a double-branch random forest model by adopting a weighted voting method, and inputting real-time feature data of a current batch to output a productivity prediction result. According to the method, the productivity prediction accuracy and the process suitability of display screen production batches are improved, and the problem of productivity imbalance of multi-component and multi-batch production is effectively solved.

This invention provides a method for constructing sensitive APIs for malware detection, comprising: collecting and labeling Android software samples to obtain multiple APK files; reverse-engineering static features from the multiple APK files; constructing a candidate sensitive API list; and constructing a simplified sensitive API set based on the candidate sensitive API list using a logistic regression classification model combined with an information gain ratio statistical method. This invention can solve the technical problem of the large number of sensitive APIs listed in the current Android official documentation, which is inconvenient to use. Experimental data verification shows that although the constructed simplified sensitive API set significantly reduces the number of sensitive APIs compared to the original set, it still retains the effectiveness in identifying malware, with an accuracy rate of over 90%.

This invention belongs to the field of mechanical equipment condition monitoring and fault prediction technology, specifically relating to a method for constructing a health index for rolling bearings based on deep reinforcement learning. The method includes: acquiring features to be fused from sensor data of the mechanical equipment and performing normalization processing; utilizing deep reinforcement learning technology to simultaneously perform two stages of tasks: feature selection and feature weight allocation, to construct a comprehensive health index; wherein the action space of deep reinforcement learning includes discrete feature selection actions and continuous feature weight allocation actions, and the reward function is based on the information gain ratio of the health index; by training a deep reinforcement learning agent, it learns to select the most effective subset of features and allocate optimal weights, thereby generating a health index. This invention can automatically and adaptively construct health indices, avoiding the problems of relying on expert knowledge and manual design in traditional methods, and improving the quality and generalization ability of health indices.

The application discloses a display screen production process management system and method based on artificial intelligence, and belongs to the technical field of production management. The method constructs a component type library and a production database, creates a process flow decision tree carrying component capacity parameters, marks main stem / leaflet nodes and generates a production process topology graph; divides tasks according to production batches, constructs two types of training sample sets with or without defective components, separates node comprehensive sample sets after standardization processing; calculates the feature information gain ratio based on the C4.5 algorithm to screen the optimal feature set, constructs a C4.5 decision tree base learner through bootstrap resampling, integrates it into a double-branch random forest model by using a weighted voting method, and inputs current batch real-time feature data to output capacity prediction results. The application improves the capacity prediction accuracy and process adaptability of display screen production batches, and effectively solves the capacity imbalance problem of multi-component and multi-batch production.

This invention provides a method for assessing the situation in 1v1 close-range UAV games based on a multi-criteria random forest. First, it collects state parameters such as position, attitude, and velocity of both friendly and enemy UAVs and labels the situation categories. Then, based on these state parameters, it constructs four types of advantage features: distance advantage, azimuth advantage, velocity advantage, and altitude advantage, and obtains advantage feature vectors through normalization. Next, it uses a bagging ensemble strategy to generate multiple training subsets, randomly assigning one or more splitting criteria from information entropy, information gain ratio, Gini impurity, and chi-square test to each decision tree. Feature selection and node splitting are then performed according to the corresponding criteria, forming a multi-criteria random forest model composed of multiple splitting criteria. Finally, the real-time collected game state data is converted into advantage feature vectors and input into the multi-criteria random forest model. The game situation category and corresponding probability distribution are obtained through the voting results of multiple decision trees, achieving real-time intelligent assessment of the 1v1 close-range UAV game situation. This invention significantly improves situation recognition accuracy, adaptability to unbalanced situation distributions, and reliability in identifying dangerous situations, while maintaining low computational complexity.