6 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.

The invention relates to the field of machine learning, in particular to a residual service life prediction method and system based on a deep Gaussian process and meta-learning, a medium and equipment, and the method comprises the steps: obtaining original data to construct a multi-task data set; a multi-layer depth Gaussian process model is constructed based on a multi-task data set, a radial basis function kernel is used as a covariance function, and a multi-task Gaussian likelihood function is used for modeling uncertainty of RUL prediction; meta-training is carried out on a PHM data set, the model is optimized through internal circulation and external circulation, and an Adam optimizer is adopted to learn cross-task generalization weights; transferring the last layer of parameters and likelihood function parameters obtained by meta-training to a fan gearbox data set and an NASA aero-engine data set based on the same meta-learning model framework, and performing fine tuning on a support set of the test data set; and performing RUL prediction on the test sets of the fan gear data set and the NASA aero-engine gear data set by using the weights obtained after fine tuning, and outputting a prediction mean value, a variance, a confidence band and a prediction band.

The invention discloses an SPR (Surface Plasmon Resonance) signal classification system based on a support vector machine algorithm, and the system comprises the following modules: a signal collection module which is used for collecting the intensity of reflected light and environmental parameters; the preprocessing module is used for resampling, denoising, correcting and normalizing the signals; the feature extraction module is used for extracting time domain, frequency domain and Fresnel model-based physical features; the model training module is used for training by adopting a radial basis kernel function containing a physical constraint term to obtain an optimal classification model; the online self-adaptive updating module is used for monitoring distribution change based on a sliding window and dynamically updating model parameters; and the classification and output module is used for executing classification reasoning and outputting signal categories and alarm control. According to the SPR signal classification method, the physical constraint kernel function is introduced, and an online self-adaptive updating mechanism is combined, so that the physical interpretability and dynamic stability of SPR signal classification are realized, and the classification accuracy and real-time performance are remarkably improved.

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.

The invention discloses a protein function discrimination and similarity calculation method based on cooperation of a protein language model and a support vector machine, and belongs to the field of bioinformatics and artificial intelligence. The method comprises the following steps: firstly, carrying out standardized pretreatment on a protein sequence, converting the protein sequence into a space partition format, filtering non-standard amino acid characters, and then generating an input tensor and an attention mask tensor through filling and truncation; inputting the tensor into a pre-trained protein language model, extracting the hidden state of the last layer of Transform module, and performing mean pooling to obtain a global feature vector; constructing a data set in combination with the feature vectors and category labels, and performing stratified sampling to divide the data set into a training set and a verification set; training a support vector machine model with a radial basis function kernel by using the training set, optimizing hyper-parameters through grid search, and evaluating performance by using the verification set; and repeating preprocessing and feature extraction on a to-be-detected sequence, inputting a trained model output function category, and quantifying the similarity with known protein by using a decision function value.

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.