8 results about "Random search" patented technology

This invention relates to the field of computing power scale prediction technology, specifically disclosing a method and system for predicting urban computing power scale based on a sled dog-optimized MLP. This invention constructs a parameter optimization architecture for an MLP driven by a sled dog optimization algorithm, encoding the number of hidden layer neurons, truncation quantiles, and year-specific switch variables as decision vectors. It designs a multi-objective fitness function that integrates training set error, validation set error, generalization gap penalty, model complexity penalty, and stability penalty. This simulates the dynamic selection, movement, obstacle avoidance, disorientation, training, and retirement behaviors of a sled dog population through iterative optimization. This solves the problems of traditional grid search and random search easily getting trapped in local optima, and the reliance on human experience for key MLP parameter configuration. It also overcomes the shortcomings of insufficient fitting of statistical models and overfitting of conventional neural networks in small sample scenarios, achieving improved accuracy in urban computing power scale prediction and enhanced model generalization performance.

The present application relates to the technical field of pipeline structure reliability analysis, in particular to a pipeline structure reliability analysis method under interval uncertainty, comprising: collecting basic parameters affecting the vibration fatigue life of the pipeline structure; establishing a function function in reliability analysis; standardizing the input vector and the function function; establishing a reliability model of the pipeline structure under interval uncertainty with the standard function function as a constraint; introducing an intermediate variable for equivalent conversion; solving by using the dichotomy combined with the random search feasibility judgment method to obtain the reliability index of the pipeline structure. The present application solves the problems of difficulty in solving the black box function and difficulty in ensuring global convergence in the vibration fatigue life analysis of the engine pipeline structure by using the interval reliability analysis framework of the dichotomy combined with the random search feasibility judgment.

This invention relates to the fields of artificial intelligence and medical clinical decision support, specifically a method and system for predicting postoperative delirium risk in elderly patients based on machine learning. The method includes: acquiring perioperative data of the patient to be predicted, including clinical indicators from the preoperative, intraoperative, and postoperative stages; preprocessing and preliminary feature screening of the data to obtain a structured feature set; constructing and optimizing a machine learning-based postoperative delirium prediction model, forming a modeling pipeline by combining multiple feature selection methods with a classifier, determining the optimal hyperparameters using random search and k-fold hierarchical cross-validation, and selecting the best pipeline based on feature stability assessment and multiple evaluation indicators; training and evaluating the performance of the final model; outputting the postoperative delirium risk prediction results and providing model interpretation. This invention is applicable to scenarios such as perioperative risk assessment of elderly patients, early warning of high-risk patients with postoperative delirium, individualized intervention plan formulation, and clinical auxiliary decision systems, providing reliable technical support for reducing the incidence of postoperative delirium and optimizing the allocation of medical resources.

This invention proposes a method for predicting tree fall specifications based on historical meteorological data and UAV post-disaster inspection surveys. It integrates historical typhoon data, UAV inspection data, and geographic information data to form a comprehensive and multi-dimensional dataset. Through in-depth analysis of various influencing factors, key features are identified, and advanced machine learning methods such as regression and deep learning models are used to construct the model. Simulation optimization is employed to handle complex data relationships, improving prediction accuracy and reliability. Scientific evaluation indicators such as accuracy, recall, F1 score, and mean squared error are set, and k-fold cross-validation is used to comprehensively evaluate the model's generalization ability. Grid search and random search methods are used to fine-tune the model parameters. The constructed real-time monitoring and early warning system, along with post-disaster response and recovery strategies, effectively reduces tree fall damage and plays a positive role in protecting urban greening and the ecological environment.

The application provides an optimization design method of additive manufacturing process parameters based on machine learning, which can realize rapid and accurate prediction of product forming quality under any process parameters for a wide range of material systems by establishing an additive manufacturing process parameter-material performance gradient boosting regression tree (GBDT) model and double optimizing the model hyperparameters by using random search (RS) and K-fold cross validation (K-CV) algorithms, so as to quickly and accurately determine the best process parameters, and solve the problems of high calculation and test cost and long cycle in the optimization of the process parameter window of laser additive manufacturing.

The application relates to an aorta CT image center line determination method and system, electronic equipment and a computer readable storage medium, and relates to the field of CT image processing.The method comprises the following steps: inputting a three-dimensional aorta CT image to be detected into an aorta CT image key point detection model, and outputting a key point extraction result; the aorta CT image key point detection model is determined according to training of a U-Net network; the extracted key points are sequentially connected by using a progressive optimal fast random search tree algorithm; in the connecting process, a correction process is performed on sampling candidate points generated by random sampling; if the distance between the sampling candidate points located between two key points and any point outside the aorta is less than a set threshold value, an aorta inscribed sphere containing the sampling candidate points is formed, and the sphere center is taken as a connecting point between the two key points. The application improves the determination accuracy and efficiency of the aorta CT image center line.

A feedback generative adversarial network (GAN) with channel-space attention mechanism for agent path planning is proposed. First, an environment map containing obstacles, start and end points is acquired and divided into training and test sets. Second, an improved fast random search tree (FSRS) algorithm is input into the training set to generate realistic map paths. Third, a concatenated channel-space feedback attention model is constructed. Finally, a feedback GAN with channel-space attention mechanism is built. After the network is built, the training set is input into the network, the weights are saved after training, and the test set is input into the network to obtain the optimal path. This invention fully considers timeliness and accuracy, enabling rapid path finding and improving efficiency. The generated paths include the optimal path and are mostly concentrated near the optimal path, improving accuracy. The addition of the attention mechanism enhances the correlation between features, significantly improving the quality of the generated paths.