262 results about "Nearest neighbor classifier" patented technology

The invention discloses a night pedestrian identification method and a system thereof based on thermal infrared gait. The steps of the method comprise detecting and dividing the pedestrian from infrared video images to obtain image sequences of the thermal infrared gait, employing an average gray image to describe videos of the thermal infrared gait to obtain features of the thermal infrared gait and identifying the thermal infrared gait of the pedestrian in the thermal infrared video images, based on a nearest neighbor classifier and taking the average gray image as the description of identity. The system comprises an acquisition module of the thermal infrared video images and an analysis processing module for the infrared images. Experiments on a CASIA night infrared gait data bank and an NLPR day visible light modal gait data bank prove that the invention has effectiveness for identification of the night pedestrian and strong identification ability of gait representation mode. The invention is applied to security monitoring of special occasions.

The invention discloses a face recognition method based on weighted diagnostic sparseness constraint nonnegative matrix decomposition, and mainly aims to solve the problem that the method in the prior art is not robust to an obscured face and is of low recognition rate. According to the technical scheme, the method comprises the following steps: (1) constructing a nonnegative weight matrix according to the obscured area of a test image; (2) introducing the weight matrix into a general KL divergence objective function, applying a sparseness constraint to a basis matrix, and applying intra-class and inter-class divergence constraints to a coefficient matrix to get a weighted diagnostic sparseness constraint nonnegative matrix decomposition objective function; (3) solving the objective function, and decomposition-training a data matrix to get a basis matrix and a coefficient matrix; (4) projecting a test data matrix on the basis matrix to get a corresponding low-dimensional representation set, and taking the low-dimensional representation set as final test data; and (5) using a nearest neighbor classifier to classify the test data by taking the coefficient matrix as training data, and outputting the result. By using the method, the effect of obscured face recognition is improved. The method can be used in identity recognition and information security.

The invention discloses an effective micro-expression automatic identification method which comprises the steps of micro-expression frame sequence preprocessing, micro-expression information data study and micro-expression identification. The method for micro-expression frame sequence preprocessing comprises the steps that frames of obtained micro-expression sequences are detected, data of an image of each frame are extracted so that graying processing can be conducted on the data, and all the micro-expression sequences are interpolated into the frame of the unified number through the linear interpolation method. The method for micro-expression information data study comprises the steps that the micro-expression sequences obtained in the preprocessing stage are written in a tensor mode, then, the intra-class distance of the same class of micro-expressions is minimized in a tensor space through the discriminating analysis method of tensor expression and the between-class distance of different classes of micro-expressions is maximized, so that data dimension reduction is achieved, and characteristic data are ranked in a vectorized mode according to a class discriminating capacity descending order. A nearest neighbor classifier is used for micro-expression identification. Compared with the methods of MPCA, GTDA, DTSA and the like, the effective micro-expression automatic identification method has the advantages of being high in rate of identification, low in computer performance requirement and easy to achieve.

The invention provides a probabilistic latent semantic model object image recognition method with the fusion of a significant characteristic of a color, belonging to the field of image recognition technology. The method is characterized by: using an SIFT algorithm to extract a local significant characteristic of an image, adding a color characteristic simultaneously, generating a HSV_SIFT characteristic, introducing TF_IDF weight information to carry out characteristic reconstruction such that the local significant characteristic has discrimination more, using a latent semantic characteristicmodel to obtain an image latent semantic characteristic, and finally using a nearest neighbor KNN classifier to carry out classification. According to the method, not only is color information of theimage considered, but also the distribution of a visual word in a whole image set is fully considered, thus the local significant characteristic of an object has discrimination more, and the ability of recognition is raised.

The invention provides an identification method for a radar disoperative target based on a mixed model, which is used for solving the problem that the disoperative target is low in identification rate. The method comprises the following steps: establishing a standard body model library of a refined scattering point model; structurally decomposing the disoperative target according to the standard body model library to generate a first scattering point model; shielding the first scattering point model to obtain an effective scattering point model; calculating RCS intensity for the effective scattering point to obtain intensity information and combining the intensity information to generate a scattering point matrix; adding a statistic characteristic into the scattering point matrix to obtain a second scattering point model of the disoperative target containing coordinate information and the RCS intensity information; carrying out multi-scattering point radar return simulation on the second scattering point model to establish a high resolution one-dimensional range profile template library; and identifying the tested high-resolution one-dimensional range profile by adopting a K near neighbor classifier by virtue of the high resolution one-dimensional range profile template library. According to the method provided by the invention, the target identification performance of the radar system can be improved.

The invention discloses a single sample face recognition method based on face sparse descriptors. The method includes the first step of carrying out alignment and normalized preprocessing on all available face images in a reference image set, the second step of calculating a group of scale spaces and differential scale spaces on each face image to carry out key point detection, the third step of selecting the local area using the key point as the center, carrying out statistics on the histogram of the area in the gradient direction, and using the histogram as the local feature description corresponding to the key point, the fourth step of calculating non-similarity measures of the images to be tested and each image in the reference image set on the basis of local match, and the fifth step of using the nearest neighbor classifier to carry out classification and recognition on the input images to be tested according to the non-similarity measures. The single sample face recognition method can quite effectively solve the problem of single sample face recognition on the conditions of sheltering, changing of expression and postures and the like.

The invention provides a gait recognition method based on a partitioned matrix. Firstly, extracting single-frame images from a video, then carrying out grey scale transformation on the single-frame images, using the background subtraction method to extract person body targets, using mathematical morphology to fill the holes of binary images, and extracting profiles of the person by means of single connection analysis so that the person bodies are positioned in the middle and are uniformly in the size of 64 * 64 pixels; observing the periodic change of the gait according to elliptical short axis and eccentricity fitted in image regions after the standard centralization of each frame image in a gait video sequence; using a gait energy diagram to extract the integral characteristic of the gait in the one period, dividing GEI into sub-blocks by means of the partitioned matrix, eliminating the sub-blocks which are useless to classification in a self-adapting manner, and adopting the method, which combines the two-dimensional principal component analysis of a sub-block mode with the two-dimensional linear discriminant analysis, to further extract local characteristics; and integrating the characteristics of each effective sub-block into a whole during the classification recognition, and adopting a nearest neighbor classifier to perform identification judgment. The method is effective for the recognition of the gait of knapsack change.

The invention discloses a human face identification method based on manifold learning, and belongs to the technical field of image processing. The method solves the problem of excessive resource consumption of the traditional method for directly processing high-dimension images. The method is combined with two kinds of methods including the nearest characteristic sub space classifier method and the local linear embedding method for realizing the dimension reducing processing on human face images, then, the nearest classifier is adopted for identifying the data subjected to dimension reduction, firstly, the human face image high-dimension data is firstly built, and the human face image samples are stretched into one-dimension vectors in lines; then, the built human face image high-dimension data is subjected to dimension reduction processing, and the low-dimension expression of all obtained human face images is obtained; and finally, the data is embedded into the space at the low dimension. Through the training on the images, the images to be tested are collected in real time, the human face identification is carried out, the method is more reasonable than a local linear embedding method based on Euclidean distance, the identification accuracy is higher, the method has lower operation complexity than a method of directly adopting high-dimension data for identification, and the method is simpler and more convenient.

A human face recognition method for eliminating an effect of a local obstruction belongs to the technical field of artificial intelligence and mode recognition. The method comprises: based on a BP neural network algorithm, performing preliminary human face recognition according to a local feature; then in an RGB space, performing image fusion and reconstruction on a preliminarily recognized human face image and a to-be-recognized human face to remove an obstruction; and finally, performing human face classification recognition on a fused image by using a linear discriminant analysis method and a nearest neighbor classifier. By the method, the obstructed human face can be more effectively recognized and the human face can be better reconstructed compared with the existing method, so that the method is more applicable to human face recognition in a natural environment, and has higher robustness.

The invention discloses a three-dimensional face reconstruction method based on gray scale and depth information. The three-dimensional face reconstruction method includes the steps of identifying face gray scale information, identifying face depth information, conducting multimodal face identification, matching by a 3D model and conducting face 3D reconstruction. Feature areas of face data are located, and registering and feature extraction are carried out through feature points. Adaboost algorithm is used to select the most effective feature for classification. A nearest neighbor classifier is used to calculate matching scores to achieve multimodal face identification. Finally, face reconstruction is completed by matching the local 3D model, the. The performance and efficiency of a face recognition system are improved through a fusion strategy. 3D cascade regression is used, dense 3D point set is selected, and a face is completely marked to avoid the logo position change. The problems of inconsistency of action change position points and self-closure are solved, and the calculation cost is reduced. The method is highly universal, and the real-time effect is good.

The invention discloses a palm print palm vein image layer fusion method based on wavelet transformation and a Gabor filter. The method comprises the following steps: 1, acquiring palm print and palm vein images, and then respectively extracting regions of interest (ROI); 2, performing image layer fusion based on the wavelet transformation and Gabor filtering on input ROI images, i.e., performing the wavelet transformation on the palm print palm vein images after ROI extraction by use of a two-dimensional Mallat algorithm, enhancing textures of high-frequency subimages by use of the Gabor filter, and finally obtaining images after fusion by reconstructing the images by use of a two-dimensional Mallet rapid algorithm; 3, performing subspace-based feature extraction on the obtained fusion images; and 4, performing classification by use of a nearest neighbor classifier. The palm print palm vein image layer fusion method based on the wavelet transformation and the Gabor filter, provided by the invention has the advantages of quite high security and quite high recognition rate.

The invention discloses a method for extracting and identifying handwriting stroke features. By means of introduction of low-rank matrix recovery and sparse projection, a handwriting image is divided into low-rank main component stroke features, remarkable stroke features and stroke errors; encoding of the main component features, extraction of the remarkable stroke features and automatic stroke error correction are achieved through a convex optimization technology, and similarity of the remarkable stroke features is kept. Obtained sparse projection shadows can be used for extracting the remarkable stroke features of handwriting training samples, and can also be used for embedding operation of test samples and extraction of identification features so as to generate a training set and a test set, the remarkable stroke features are input into a nearest neighbor classifier to obtain class information of the test samples according to similarity between the test samples and the training samples and the class of the training samples, and the most accurate handwriting identifying result is obtained. Due to the fact that low-rank and spare encoding is introduced, the main component stroke features and the remarkable stroke features with identification performance are obtained, wrong strokes can be detected, and the handwriting description and identification capacity is effectively improved.

The invention belongs to the technical field of pattern recognition, and in particular relates to a single sample face identification transfer learning method based on LPP feature extraction. The method is different from a traditional global face recognition method based on generalization ability improvement and a traditional local face recognition method depending on image segmentation. The method provided by the invention comprises the steps that whitening cosine similarity is used to screen a migration source to acquire a selected sample source; feature projection is respectively carried out on source feature and target feature faces in the selected source by using LPP, a feature migration matrix is solved, and mapping relationship approximating is carried out; the feature migration matrix is imposed on a training sample, and the original macro feature is migrated to a goal macro feature; and face recognition with high accuracy is realized by using a nearest classifier. According to the invention, a number of source samples correlated with a target single sample are effectively used; rational screening and macro feature migration are carried out; the problem of difficult single sample training is solved to a large extent; and high face recognition accuracy can be acquired.

The invention discloses a multi-mode face identification device and method fusing grey information and depth information. The method mainly comprises the following steps: identifying the face grey information; identifying the face depth information; splicing all characteristics to form a characteristic pool after obtaining a characteristic of each data source by utilizing a characteristic layer fusion strategy, then picking out a characteristic most effective to classification from the characteristic pool by utilizing an Adaboost algorithm, and finally calculating a matching score by utilizing a nearest neighbor classifier based on the characteristics obtained by multi-mode characteristic layer fusion to realize multi-mode face identification, wherein a weak classifier is created for each characteristic in the characteristic pool. With the adoption of the scheme, a multi-mode system overcomes certain inherent weakness of a single-mode system by acquiring the two-dimensional grey information and the three-dimensional depth information, utilizing the advantages of the two-dimensional grey information and the three-dimensional depth information and applying the fusion strategy, so that the performance of a face identification system is effectively improved and face identification is more accurate and quick.

The invention discloses a method for automatic target recognition of an SAR. The automatic target recognition of the SAR mainly comprises three steps such as SAR image preprocessing, feature extraction and target classification, and the method is applicable to feature extraction and target classification of the automatic target recognition of the SAR and solves the problem that effective identification information can not be extracted from high-dimensional SAR images. According to the method for the automatic target recognition of the SAR, a manifold structure theory is introduced, and the method is based on a neighborhood identification embedding criterion. The method comprises the steps of A, initializing; B, constructing a similarity matrix and a difference matrix; C, calculating a target matrix on the basis of a maximum margin criterion; D, calculating a projection matrix; E, conducting feature extraction on training samples according to the projection matrix to obtain training sample features; E, conducting feature extraction on SAR images to be classified to obtain test sample features; and F, classifying SAR images to be tested according to a nearest neighbor classifier, wherein Step A-Step E belong to the feature extraction phase, and Step F belongs to the target classification phase. By the aid of the method, the probability of correct identification of targets can be improved.

Disclosed is a super-resolution face recognition method based on multi-manifold discrimination and analysis. During the training phase, a mapping matrix from a low-high-resolution face image multi-manifold space to a high-resolution face image multi-manifold space is acquired by multi-manifold discrimination and analysis. An intra-class similar graphs and aninter-class similar graph are constructed in an original high-resolution face image multi-manifold space, a discrimination bound term is constructed by utilizing the two neighbor graphs, and a most optimization method is to acquire the mapping matrix by reconstructing a cost function composed of a bound term and the discrimination bound term. During the recognition phase, a low-resolution face image to be recognized is mapped o the high-resolution face image multi-manifold space by the mapping matrix acquired by offline learning, and a high-resolution face image is acquired. Classification and recognition are achieved by a nearest-neighbor classifier according to the Euclidean distance principle in the high-resolution face image multi-manifold space. Compared with a traditional super-resolution method, the super-resolution face recognition method has greatly improved face recognition rate and operation rate.

The invention provides a method for recognizing a rock type. The method comprises the following steps of: (1) preprocessing a formation microresistivity imager (FMI) color image of a rock to be recognized and converting the FMI color image into an FMI gray scale image; (2) setting three threshold values for performing Canny edge detection on the FMI gray scale image; (3) performing three times of Canny edge detection on the FMI gray scale image by using the three threshold values; (4) evaluating three box dimension characteristics of the rock to be recognized by a box dimension characteristic computing method; and (5) recognizing the type of the rock to be recognized according to the evaluated box dimension characteristics of the rock to be recognized by using a nearest neighbor classifier. The method has the advantages of laying a basis for the intelligent recognition of the FMI image, promoting the research of an intelligentized high-efficiency FMI image recognition system and indirectly facilitating the popularization of FMI logging technology and can be widely applied to the aspects of oil exploration, FMI image processing, development of intelligent logging instruments and the like.

The invention discloses a face identification algorithm under different illumination conditions, comprises the steps that: (1) the inputted face original image F(x, y) is subjected to Gaussian filter so as to eliminate noise point in the image and smooth the image, thereby obtaining a new image F'(x, y); (2) the F' (x, y) is subjected to feature extraction by using a Weber local operator description; (3) an edge is extracted from a new image WF(x, y) by LoG operator operation; (4) the inputted image is converted to a principal component space by using fast PCA algorithm; (5) in the PCA transform space, a inter-class scatter matrix is calculated (img file='DDA0000447013750000011.TIF' wi ='45' he='65' / ); (6) the obtained new image feature WF'(x, y) is projected on a feature projection vector group Phi; (7) finally, a nearest neighbor classifier carries out classification. The invention carries out feature extraction of the image operated by the LoG operator, more complete identification information of the original imagely can be extracted, the data from the above experiment shows that the new algorithm can obtain the prominent face identification rate from all three face databases, and also completely meet the real-time requirements in the speed.

The invention discloses a fully-autonomous on-line study method based on a random fern classifier. With the method, on-line study targeted for a classifier of the target class can be carried out by selecting a target in a video frame for only once. The method comprises the following steps: carrying out affine transformation on the selected target to obtain an initial positive sample set, and extracting a small number of negative sample sets in a non- target area of the video to train an initial random fern classifier; then, carrying out target detection in the video frame by using the classifier; in the detection process, using a nearest neighbor classifier to collect on-line study new samples and automatically judging the class of the samples; and finally, applying the new samples to on-line training of the random fern classifier, updating random fern posterior probability, improving the precision of target detection of the classifier gradually, and realizing fully-autonomous on-line study of a target detection system.

The invention relates to a multi-work-condition rotary machine fault diagnosis method. The method includes the following steps that: S1, the original vibration data matrix of a rotary machine are acquired; S2, Fourier transformation and normalization processing are sequentially performed on the original vibration data, so that a normalized vibration data matrix can be obtained; and S3, the normalized vibration data matrix is inputted into a fault diagnosis model, so that a diagnosis result is obtained, wherein the fault diagnosis model is established by means of the series connection of a convolutional neural network and a K-nearest neighbor classifier. According to the method of the present invention, the capacity of the diagnostic model to extract invariant features under variable work conditions can be enhanced by means of the convolutional neural network; the classification capacity and robustness of the diagnostic model for nonlinear fault features can be improved by means of theK-nearest neighbor classifier; and on the basis of the convolutional neural network and the K-nearest neighbor classifier, the accuracy of the diagnostic model in the fault diagnosis of the rotary machine under complex working conditions can be improved, and powerful support can be provided for online intelligent fault diagnosis.

The invention relates to a human-face identification method based on a local contrast pattern (LCP) and belongs to the technical field of pattern identification. The human-face identification method based on the LCP includes that human-face database images are preprocessed firstly so as to weaken influences of illumination on feature extraction; calculation of LCP feature matrixes is performed on training set images and testing set images respectively; the obtained LCP feature matrixes are converted into a column diagram; calculation of feature similarity of testing samples and training samples is performed on the column diagram by adopting a chi-square (X<2>) distance function; and the testing samples are classified and identified by using a nearest neighbor classifier. The human-face identification method based on the LCP has high human-face identification accuracy.

The invention discloses a nuclear norm regularization based low-rank image characteristic extraction identification method and system. Firstly an original training image is subjected to similarity learning to construct a reconstruction weight coefficient; and secondly a nuclear norm measurement based neighborhood reconstruction error is minimized and a projection matrix is subjected to nuclear norm regularization processing to obtain a low-rank projection matrix capable of directly extracting two-dimensional image characteristics, so that the topological structure and correlativity among image pixels can be effectively kept. In addition, it can be ensured that low-rank salient image characteristics are obtained by optimization. An original test image is directly embedded in the low-rank projection matrix obtained by training, low-rank salient characteristics of the image are output, classification is performed by utilizing a nearest neighbor classifier based on low-rank salient characteristics in a training set, and category labels of training image samples with highest characteristic similarity with test image samples are obtained, thereby finishing the classification of the test image samples. By introducing nuclear norm regularization, the robustness of noises in a characteristic extraction process can be effectively ensured and the system performance is better.

The invention discloses a method for identifying a palm print on the basis of fusion of a local feature and a global feature. The method comprises the following steps of: 1 extracting a texture feature, a principal line feature, and an overall feature by means of two-dimension Gabor phase coding, an modified finite Radon transformation (MFRAT), and a bidirectional principal component analysis method respectively; 2, performing classified selection on the texture feature by means of Hamming distance in order to obtain a texture selected feature, performing classified selection on the principal line feature by means of dot pair area bidirectional matching in order to obtain a principal line selected feature, and performing classified selection on the overall feature by means of a minimum Euclidean distance algorithm in order to obtain a overall selected feature; 3 performing a K nearest neighbor classifier method on the texture selected feature, the principal line selected feature, and the overall selected feature in order to obtain k nearest classification results of a sample T to be identified, performing information fusion on the classification results by means of a Borda voting strategy decision fusion rule in order to achieve palm print multi-feature fusion identification. The method has advantages of accurate identification and good robustness.

The invention discloses a video sequence face identification method based on an AAM model. The method comprises the training step (1) and the identification step (2), wherein the training step (1) comprises PCA protection and (1-2) LDA protection, projection is carried out on feature vectors which are subjected to dimensionality reduction of the PCA protection through a WLDA, and therefore the best classification feature of each training picture is obtained; the identification step (2) comprises (2-1) Adaboost detection, (2-2) AAM tracking and gesture correction, (2-3) PCA projection, (2-4) LDA protection aiming to obtain the best classification feature of a face picture to be identified, (2-5) nearest-neighbor classifier decision which judges the face picture of types where gamma1 is located to the be the identification result, and the gamma1 is the smallest euclidean distance between the best classification feature of the face picture to be identified and the best classification feature of all training pictures. The video sequence face identification method has the advantages of being capable of accurately identifying a face under the condition that the face gesture is variable, and is strong in robustness.

The invention provides a method and system for face identification. The method comprises the following steps: collecting multi-band training images of visible, near-infrared, intermediate-infrared, far-infrared, and thermal-infrared lights; carrying out evaluation and selection on extracted face image characteristics of all bands based on a sparse regularization method so as to obtain new characteristics of the face images of all the bands and corresponding new characteristic evaluation indexes after dimensionality reduction; carrying out evaluation, selection and fusion on a first characteristic set based on the parse regularization method so as to construct a second characteristic set finally expressing a face and a corresponding second characteristic evaluation index; obtaining a second characteristic set for finally expressing a face of a to-be-tested person from a third characteristic set according to a second characteristic evaluation index; and employing a nearest neighbor classifier to obtaining a classification result. According to the invention, on the basis of fusion of enough face image information, a characteristic set for finally expressing a face is ensured to have the low dimensionality, thereby ensuring the speed of face identification and the low data volume that an individual needs to store and thus improving identification precision.

The invention discloses a face identification method based on Gabor wavelet and SB2DLPP. The face identification method based on the Gabor wavelet and SB2DLPP mainly includes pre-treatment, feature extraction, feature dimension reduction and classification identification, and to be specific, the face identification method includes that (1) pre-treating all the face images in a known face database, wherein the pre-treatment includes scale normalization and histogram equalization; (2) using the Gabor wavelet to extract features of the pre-treated face images; (3) leading in class information, and using a supervised bidirectional two-dimensional local preserving projection (SB2DLPP) algorithm to reduce the dimensions of the high-dimensional image features extracted through the step (2) to extract feature matrices mapped to a low-dimensional sub-space; (4) using a nearest neighbor classifier to perform classification identification. The face identification method based on the Gabor wavelet and SB2DLPP uses the Gabor wavelet and improved SB2DLPP algorithm to identify images, the problems that a traditional face identification method is easy to be influenced by light, expression and the like external factors are overcame, and the face identification rate is effectively improved.

The invention discloses an industrial process fault classification method based on self-adaption integrated semi-supervision Fisher discrimination. The method comprises the steps of when off-line modeling is conducted, firstly conducting off-line modeling on unlabeled data, and constituting a semi-supervision random training subset by combining labeled data with the unlabeled data; when iteration training is conducted on a sub classifier each time, conducting semi-supervision Fisher dimensionality reduction to obtain a Fisher discrimination matrix, and obtaining a posterior probability matrix, a combined weight of the sub classifier and a sample weight of the labeled data during next time iteration with the labeled sample data after dimensionality reduction according to a Bayesian statistics method; adopting the posterior probability matrix of the labeled data and a label of the matrix as a training set of a fusion algorithm K near neighbor; during online classification, calling each sub classifier to obtain the posterior probability matrix of an online sample to be detected, and inputting the posterior probability matrix into a fusion K near neighbor classifier with the weight to obtain a final result. Compared with an existing method, the industrial process fault classification method based on the self-adaption integrated semi-supervision Fisher discrimination improves the fault classification result of an industrial process, and more facilitates automated implementation of the industrial process.

The invention discloses a gait identifying method with robustness to walking gait changes. A distance metric expression among gait feature matrixes of different walking states is established, samples of different walking states of a same individual in a training stage are related and are coupled into a same image space through a machine learning strategy, and after training is completed, projection matrixes of the samples themselves under the different walking states can be obtained. In an identifying stage, when the walking states of the samples to be tested are not uniform with walking states of a registered sample collecting base, projection on projection shafts of the different walking states obtained through training are respectively conducted, and classification is conducted by the adoption of a nearest neighbor classifier. By means of the gait identifying method, prediction and estimation can be conducted without the need of changing one walking state to another walking state, and the gait identifying problem of the walking gait changes can be solved by the direct adoption of the machine learning.

Disclosed are a system and method of multi-modality sensor data classification and fusion comprising partitioning data stored in a read only memory unit on a sensor node using a low query complexity boundary-decision classifier, applying an iterative two-dimensional nearest neighbor classifier to the partitioned data, forming a low query complexity classifier from a combination of the low query complexity boundary-decision classifier and the iterative two-dimensional nearest neighbor classifier, using the low query complexity classifier to identify classification parameters of the sensor node, and monitoring a network of spatially distributed sensor nodes based on the classification parameters of the sensor node. The boundary-decision classifier comprises a single low neuron count hidden layer and a single binary-decision output sensor node, or alternatively, the boundary-decision classifier comprises a linear classifier. Moreover, the network is a wireless unattended ground sensor network, and the data comprises signals transmitted by the sensor node.

An implementation method for monitoring the status of a hydraulic turbine set based on HSLNE comprises the following steps of (1) signal detection and acquiring, namely collecting a set of vibration signals which can comprehensively reflect vibration anomalies of different noise sources by utilizing a vibration sensor on the hydraulic turbine set; (2) feature extraction, namely calculating an optional linear projection matrix A by using the HLSNE, and performing feature extraction on the vibration signals according to the linear projection matrix A; (3) status identification, namely identifying whether the status of the vibration signals subjected to feature extraction is normal or abnormal; (4) status analysis, namely adopting a nearest neighbor classifier to judge, classify and analyze a fault source of abnormal vibration signals; (5) result output, namely presenting a diagnosis decision according to results of the status analysis. The beneficial effect of the implementation method is mainly reflected in that a heavy-tailed linear stochastic neighbor embedding analysis method (HLSNE) is applied to the status monitoring of the hydraulic turbine set, so that multi-parameter setting and adjusting are feasible in a gradient optimization process by using a fast descent method in the actual application process, parameters do not need to be adjusted manually, and the efficiency and robustness of the hydraulic turbine set status monitoring process are effectively improved.