7 results about "Nearest neighbor classifier" patented technology

The invention discloses a small sample automatic modulation identification method and system based on a multi-stage regularization Y-shaped frame in the technical field of wireless communication signal processing. The method comprises the following steps: carrying out Gramer angle field transformation on a received I / Q signal sequence to obtain a two-dimensional GAF image set; performing data enhancement on the two-dimensional GAF image set, dividing the enhanced GAF images with known modulation category labels in the enhanced GAF image set into a support set, and dividing all the remaining enhanced GAF images with unknown modulation category labels into a query set; performing feature extraction on the support set and the query set by using a trained feature extraction model based on a channel space attention relation network CSARN to obtain a to-be-identified feature vector of the support set and a to-be-identified feature vector of the query set, and performing modulation mode classification by using a nearest neighbor category mean classifier to obtain a to-be-identified feature vector of the support set and a to-be-identified feature vector of the query set; and obtaining modulation identification results of the enhanced GAF images of all the unknown modulation category labels.

The application provides a small sample named entity recognition model training method, comprising the following steps: S1, obtaining a training set, a training set type description set, a support set and a support set type description set; S2, mining clue words in each sample on the training set and the support set respectively and performing clue word labeling to obtain the training set and the support set containing named entity labels and clue word labels respectively; S3, performing multi-round iterative training on a basic named entity recognition model until convergence by using the training set and the training set type description set processed in step S2; and S4, performing migration training on the basic named entity recognition model trained in step S3 until convergence by using the support set and the support set type description set processed in step S2, to obtain a small sample named entity recognition model composed of an encoder and a nearest neighbor classifier.

The invention discloses a peanut kernel producing area identification method based on a near infrared spectrum. The method comprises the following steps: (1) collecting near infrared diffuse reflection spectrum data of peanut kernel samples from different producing areas by using a portable near infrared spectrometer; (2) carrying out preprocessing on the spectral data by utilizing a multiple scatter correction (MSC) method and a Savitzky-Golay smoothing filter; (3) extracting identification information of the spectral data by using a fuzzy identification analysis method; and (4) classifying by using a K neighbor classifier. According to the method, linear identification information of the near infrared spectrum value domain space and the null space of the peanut kernels can be extracted, the K-nearest neighbor classifier is adopted for classification, and the peanut kernel producing area can be rapidly and accurately identified. The peanut kernel producing area identification method based on the near infrared spectrum can be used for accurately identifying the peanut kernel producing area.

The invention specifically discloses a rotary machine fault diagnosis method and equipment based on multi-graph cooperation of an intrinsic model, and belongs to the technical field of fault diagnosis. According to the method, while multi-manifold structure features of a data set are fully considered, a graph embedding thought is introduced, dimensionality reduction is performed on a fault feature set, and a processed low-dimensional feature set is input into a K-nearest neighbor classifier, so that fault mode identification is realized. According to the method, while high-dimensional nonlinear fault feature information is effectively extracted, the difficulty of equipment fault classification is reduced, and the accuracy of fault identification is improved. Besides, the constructed diagnosis model keeps good fault identification accuracy in different noise environments and training environments, the fault identification capability is effectively improved, and the method has good dimension reduction effect, noise resistance and stability.

The invention discloses an anti-cancer peptide recognition method and system based on multi-feature fusion and double-layer integrated learning. The method comprises the steps of S1, data preprocessing; s2, feature extraction and feature fusion based on a protein language model; s3, feature extraction; s4, performing dimension reduction processing on the high-dimensional features; s5, inputting each feature vector in the multi-source feature set into a corresponding XGBoost classifier for training and prediction, wherein each classifier outputs a peptide sequence as a preliminary prediction probability of the anti-cancer peptide; combining the preliminary prediction probabilities output by all classifiers into a probability feature vector; s6, inputting the probability feature vectors obtained from the upper layer into a K-nearest neighbor classifier and a soft voting classifier at the same time; the KNN outputs a first prediction probability, and the soft voting integrator outputs a second prediction probability; s7, calculating an arithmetic mean value of the first prediction probability and the second prediction probability as a prediction probability; and comparing the peptide sequence with a preset threshold value, if the peptide sequence is greater than or equal to the threshold value, determining that the peptide sequence is an anti-cancer peptide, otherwise, determining that the peptide sequence is a non-anti-cancer peptide.

A KNN-based lateral stability control method for heavy-duty AGVs is disclosed, characterized by the following steps: 1. Classifying AGV operating conditions into five categories based on different loads, and collecting motion state data of the AGV under different operating conditions using simulation software; 2. Establishing a K-nearest neighbor (KNN) classifier, and training and validating it using the collected dataset; 3. Designing a set of nonlinear sub-controllers based on fuzzy PID, calculating the required yaw moment, and distributing the torque to the four drive wheels according to the torque distribution rule; 4. Introducing an error judgment strategy, activating the controller based on the centroid sideslip angle error to control its lateral stability. This invention establishes a load KNN classifier for heavy-duty AGVs, which can monitor the centroid sideslip angle error in real time. If the error exceeds a threshold, the controller is activated, the classifier's classification result is matched to the corresponding sub-controller, and an additional yaw moment is calculated and applied to the AGV.

The purpose of this invention is to address the problems of poor adaptability to multiple postures, distortion of the combined acceleration waveform, and insufficient accuracy caused by fixed thresholds in existing smartphone gait detection technologies. It relates to the fields of pedestrian dead reckoning and motion perception technology, and provides a smartphone multi-posture gait detection method based on a dual adaptive mechanism, comprising the following steps: acquiring three-axis data from the smartphone accelerometer; calibrating noise parameters using Allan variance and constructing a Kalman filter for noise reduction; extracting temporal features and identifying the phone's carrying posture in real time using a K-nearest neighbor classifier; entering the evaluation stage, calculating the peak-normalized fluctuation coefficient and trough-normalized fluctuation coefficient of the three-axis acceleration signal respectively, and selecting the axis with the smallest total fluctuation coefficient as the optimal detection axis. This invention effectively solves the industry problems of low accuracy and poor robustness in gait recognition under multiple smartphone carrying conditions.