13 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 invention discloses an open environment radiation source individual identification method based on manifold learning, and belongs to the technical field of radiation source individual identification. According to the method, firstly, high-dimensional signal data are mapped to a low-dimensional Grassmann manifold space, and the internal structure of the data is kept while redundant features in the high-dimensional data are removed; and then aligning the open manifold structures of the source domain and the target domain in the low-dimensional manifold space, so that the method can measure the distance between the prototypes of the source domain and the target domain in the unified manifold space. And finally, performing cross-domain prototype matching by replacing Euclidean distance with geodesic distance on a Grassmann manifold, labeling a pseudo-label for target domain data according to a matching result, and jointly using source domain label data and target domain pseudo-label data as a reference set of a nearest neighbor classifier so as to obtain a nearest neighbor classifier. And accurate identification of known and unknown types of radiation source individuals in an open environment is realized through a nearest neighbor classification strategy.

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 application relates to the technical field of feature selection learning. The glass type recognition method provided by the application obtains a feature optimal subset by using a constructed semi-supervised feature algorithm to perform feature selection on obtained glass feature data, filters data to form a training set by using the feature optimal subset, trains a nearest neighbor classifier, and performs type recognition on unknown glass data. In the constructed semi-supervised feature algorithm, the weight of features in a feature data set is obtained by calculating a constraint pair, the change of sample features in a weighted feature space is considered, more effective local information is obtained, and the classification performance of the model on glass is improved on the premise of saving cost.

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 a therapeutic peptide prediction method based on a nearest neighbor classifier, and belongs to the field of biological information. According to the method, peptide sequence features are extracted from QSP740 and CPP400 data sets by using a UniRep model, a kernel risk sensitive loss function is fused on the basis of a K-nearest neighbor algorithm to construct an objective function, and a multi-Laplacian matrix is constructed in combination with three similarity matrixes of an RBF function, cosine similarity and a Pearson's correlation coefficient to enhance model robustness. And finally, classifying the peptide sequence to be predicted by minimizing the objective function and evaluating the performance. According to the method, features are automatically extracted through deep learning, the model robustness is improved through a kernel risk loss function, and the uncertainty of data is processed by means of multi-view Laplace regularization, so that compared with an existing method, the treatment peptide type can be more accurately predicted, and the classification precision and the anti-noise capability can be improved.

This invention discloses a method, system, device, and medium for feature extraction from medical clinical data, relating to the field of data processing technology. It includes: acquiring a medical clinical dataset; replacing the moss reproduction mechanism of the moss optimization algorithm with a biogeographical learning strategy, removing the cryptic effect of the moss optimization algorithm, and introducing a bimodal propagation search to obtain a bio-learning moss optimization algorithm; using the bio-learning moss optimization algorithm to find the subset of clinical features most relevant to sample classification during the feature extraction process of the medical clinical dataset; and using a K-nearest neighbor classifier to classify the subset of clinical features most relevant to sample classification, thereby obtaining the optimal subset of clinical features for the medical clinical data. This invention overcomes the deficiency of the moss optimization algorithm in its insufficient ability to exploit local optimal solutions, effectively improving the algorithm's convergence accuracy.

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 invention discloses a sparse null space and norm-based Chinese wolfberry origin identification model construction method and application, Chinese wolfberry of the same origin is regarded as a category, and near infrared spectrum data of Chinese wolfberry samples of a plurality of origins are collected; preprocessing the acquired near infrared spectrum data to eliminate noise and scattering influence in the spectrum data; carrying out dimension reduction processing on the preprocessed near infrared spectrum data, and compressing the dimension of the spectrum data; performing multi-time projection processing on the spectral data in a sparse null space and a norm; and training a K-nearest neighbor classifier by using a sample obtained after projection, constructing a Chinese wolfberry origin identification model, and performing testing. The wolfberry samples can be classified by using the constructed wolfberry production place identification model, and a production place identification result is output; and the identification stability, accuracy and efficiency are improved.

The invention discloses a method and device for classifying carbon emission data of a thermal power plant and electronic equipment, and the method comprises the steps: obtaining a plurality of weighted Euclidean distances, and screening out candidate Euclidean distances meeting a preset distance condition from the weighted Euclidean distances; performing trend weighting processing on the candidate Euclidean distance to generate a corresponding trend weighted Euclidean distance; performing matching processing on the candidate Euclidean distance and the trend weighted Euclidean distance, and determining a target Euclidean distance corresponding to the first time sequence and the second time sequence; and based on the target Euclidean distance, using a nearest neighbor classifier to divide the subsequence categories of the thermal power plant carbon emission data, realizing classification of the thermal power plant carbon emission data, and based on a classification result, analyzing and controlling the production data related to the thermal power plant carbon emission. The device comprises a processor and a memory. The electronic equipment comprises a processor, a memory, a network interface, a display screen and an input device which are connected through a system bus. According to the method, different types of thermal power plant carbon emission data can be classified, and the safety and reliability of industrial production are improved.

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.