3 results about "Radial basis function kernel" patented technology

This invention relates to the field of computational toxicology, and in particular to a precise method for comparing the consistency of animal and non-animal toxicity evaluation results. This method acquires in vivo baseline data from animals and in vitro test data from non-animals. It employs a dynamic time warping algorithm and radial basis function kernel function to construct correction coefficients and perform equivalent mapping to generate a converted sequence. Based on a binary classification mapping of toxicity thresholds, a confusion matrix is ​​constructed to calculate sensitivity, specificity, and predicted values. Each indicator is treated as an independent source of evidence, and the Mahalanobis distance is calculated using the covariance matrix to generate a basic probability allocation function. The D-S evidence theory combination rule is applied for fusion, and a secondary factor allocation is introduced when there is conflict. Finally, the result is compared with a preset threshold to determine the feasibility of substitution. This invention adaptively eliminates the nonlinear misalignment difference between in vivo and in vitro dose responses, providing an objective quantitative judgment standard for toxicological substitution verification.

This invention discloses a self-learning judgment method for gas sensing data based on NB-IoT, comprising the following steps: acquiring and preprocessing data through a gas sensor to generate standardized samples; extracting features using a sparse autoencoder and training and updating the model using unlabeled data; initializing an improved ridge regression model based on labeled data, and constructing an initial dictionary and regression coefficients using a radial basis function kernel; during real-time data input, the model quickly outputs gas state judgment results, while updating the memory matrix using a moving average; and recursively updating the regression coefficients and dictionary using labeled data. This invention's method has high real-time performance, accuracy, and adaptability, effectively improving the intelligence level of gas monitoring.

This invention relates to the field of data processing, specifically to an intelligent early warning method and system for sintering furnaces used in powder metallurgy. The method includes: collecting historical normal operation data and dividing it into multiple windows; extracting multi-dimensional features for each window, including a structural feature vector based on the singular values ​​of the autocorrelation matrix, a coupling feature vector based on the cross-correlation matrix, an inertial-coupling dominant feature, and an inertial-coupling consistency vector; using the structural feature vector as the first sub-vector and the remaining features as the second sub-vector, employing a cosine kernel and a radial basis function kernel respectively, and determining the weights of the two kernels to construct a combined kernel function to train a single-class support vector machine; collecting data in real time and extracting identical features, and substituting them into a decision function to determine whether an early warning is triggered. This invention, through multi-dimensional feature extraction and adaptive combined kernel functions, can effectively distinguish between normal fluctuations and abnormal precursors, improving the accuracy of early warnings for sintering furnace anomalies.