feature extraction

摘要： Traditional named entity recognition methods need professional domain knowl-edge and a large amount of human participation to extract features,as well as the Chinese named entity recognition method based on a neural network model,which brings the prob-lem that vector representation is too singular in the process of character vector representa-tion.To solve the above problem,we propose a Chinese named entity recognition method based on the BERT-BiLSTM-ATT-CRF model.Firstly,we use the bidirectional encoder representations from transformers(BERT)pre-training language model to obtain the se-mantic vector of the word according to the context information of the word;Secondly,the word vectors trained by BERT are input into the bidirectional long-term and short-term memory network embedded with attention mechanism(BiLSTM-ATT)to capture the most important semantic information in the sentence;Finally,the conditional random field(CRF)is used to learn the dependence between adjacent tags to obtain the global optimal sentence level tag sequence.The experimental results show that the proposed model achieves state-of-the-art performance on both Microsoft Research Asia(MSRA)corpus and people’s daily corpus,with F1 values of 94.77% and 95.97% respectively.

摘要： Plant recognition has great potential in forestry research and management.A new method combined back propagation neural network and radial basis function neural network to identify tree species using a few features and samples.The process was carried out in three steps:image pretreatment,feature extraction,and leaf recognition.In the image pretreatment processing,an image segmentation method based on hue,saturation and value color space and connected component labeling was presented,which can obtain the complete leaf image without veins and back-ground.The BP-RBF hybrid neural network was used to test the influence of shape and texture on species recogni-tion.The recognition accuracy of different classifiers was used to compare classification performance.The accuracy of the BP-RBF hybrid neural network using nine dimensional features was 96.2%,highest among all the classifiers.

摘要： Low-temperature composite insulation is commonly applied in high-temperature super-conducting apparatus while partial discharge(PD)is found to be an important indicator to reveal insulation statues.In order to extract feature parameters of PD signals more effectively,a method combined variational mode decomposition with multi-scale entropy and image feature is proposed.Based on the simulated test platform,original and noisy signals of three typical PD defects were obtained and decomposed.Accordingly,relative moments and grayscale co-occurrence matrix were employed for feature extraction by K-modal component diagram.Afterwards,new PD feature vectors were obtained by dimension reduction.Finally,effectiveness of different feature extraction methods was evaluated by pattern recognition based on support vector machine and K-nearest neighbour.Result shows that the proposed feature extraction method has a higher recognition rate by comparison and is robust in processing noisy signals.

摘要： To improve the feature extraction of ship-radiated noise in a complex ocean environment,a novel feature extraction method for ship-radiated noise based on complete ensemble empirical mode decomposition with adaptive selective noise(CEEMDASN) and refined composite multiscale fluctuation-based dispersion entropy(RCMFDE) is proposed.CEEMDASN is proposed in this paper which takes into account the high frequency intermittent components when decomposing the signal.In addition,RCMFDE is also proposed in this paper which refines the preprocessing process of the original signal based on composite multi-scale theory.Firstly,the original signal is decomposed into several intrinsic mode functions(IMFs)by CEEMDASN.Energy distribution ratio(EDR) and average energy distribution ratio(AEDR) of all IMF components are calculated.Then,the IMF with the minimum difference between EDR and AEDR(MEDR)is selected as characteristic IMF.The RCMFDE of characteristic IMF is estimated as the feature vectors of ship-radiated noise.Finally,these feature vectors are sent to self-organizing map(SOM) for classifying and identifying.The proposed method is applied to the feature extraction of ship-radiated noise.The result shows its effectiveness and universality.

摘要： The motivation for this article is to propose new damage classifiers based on a supervised learning problem for locating and quantifying damage.A new feature extraction approach using time series analysis is introduced to extract damage-sensitive features from auto-regressive models.This approach sets out to improve current feature extraction techniques in the context of time series modeling.The coefficients and residuals of the AR model obtained from the proposed approach are selected as the main features and are applied to the proposed supervised learning classifiers that are categorized as coefficient-based and residual-based classifiers.These classifiers compute the relative errors in the extracted features between the undamaged and damaged states.Eventually,the abilities of the proposed methods to localize and quantify single and multiple damage scenarios are verified by applying experimental data for a laboratory frame and a four-story steel structure.Comparative analyses are performed to validate the superiority of the proposed methods over some existing techniques.Results show that the proposed classifiers,with the aid of extracted features from the proposed feature extraction approach,are able to locate and quantify damage;however,the residual-based classifiers yield better results than the coefficient-based classifiers.Moreover,these methods are superior to some classical techniques.

摘要： Single-cell RNA-sequencing(scRNA-seq)is a rapidly increasing research area in biomed-ical signal processing.However,the high complexity of single-cell data makes efficient and accurate analysis difficult.To improve the performance of single-cell RNA data processing,two single-cell features calculation method and corresponding dual-input neural network structures are proposed.In this feature extraction and fusion scheme,the features at the cluster level are extracted by hier-archical clustering and differential gene analysis,and the features at the cell level are extracted by the calculation of gene frequency and cross cell frequency.Our experiments on COVID-19 data demonstrate that the combined use of these two feature achieves great results and high robustness for classification tasks.

摘要： In this paper,a novel hybrid texture feature set and fractional derivative filter-based breast cancer detection model is introduced.This paper also introduces the application of a histogram of linear bipolar pattern features(HLBP)for breast thermogram classification.Initially,breast tissues are separated by masking operation and filtered by Gr¨umwald–Letnikov fractional derivative-based Sobel mask to enhance the texture and rectify the noise.A novel hybrid feature set usingHLBP and other statistical feature sets is derived and reduced by principal component analysis.Radial basis function kernel-based support vector machine is employed for detecting the abnormality in the thermogram.The performance parameters are calculated using five-fold cross-validation scheme using MATLAB 2015a simulation software.The proposedmodel achieves the classification accuracy,sensitivity,specificity,and area under the curve of 94.44%,95.55%,92.22%,96.11%,respectively.A comparative investigation of different texture features with respect to fractional orderαto classify the breast malignancy is also presented.The proposed model is also compared with a few existing state-of-art schemes which verifies the efficacy of the model.Fractional orderαoffers extra adaptability in overcoming the limitations of thermal imaging techniques and assists radiologists in prior breast cancer detection.The proposed model is more generalized which can be used with different thermal image acquisition protocols and IoT based applications.

摘要： A comprehensive study was conducted to differentiate cardiovascular disease (CVD) subjects from non-CVD subjects using short recording electrocardiogram (ECG) of 244 Malaysian adults in The MalaysianCohort project. An automated peak detection algorithm to detect nine fiducialpoints of electrocardiogram (ECG) was developed. Forty-eight features wereextracted in both time and frequency domains, including statistical featuresobtained from heart rate variability and Poincare plot analysis. These includefive new features derived from spectrum counts of five different frequencyranges. Feature selection was then made based on p-value and correlationmatrix. Selected features were used as input for five classifiers of artificialneural network (ANN), k-nearest neighbors (kNN), support vector machine(SVM), discriminant analysis (DA), and decision tree (DT). Results showedthat six features related to T wave were statistically significant in distinguishingCVD and non-CVD groups. ANN had performed the best with 94.44% specificity and 86.3% accuracy, followed by kNN with 80.56% specificity, 86.49%sensitivity and 83.56% accuracy. The novelties of this study were in providingalternative solutions to detect P-onset, P-offset, T-offset as well as QRS-onsetpoints using discrete wavelet transform method. Additionally, two out of thefive newly proposed spectral features were significant in differentiating bothgroups, at frequency ranges of 1–10 Hz and 5–10 Hz. The prediction outcomeswere also comparable to previous related studies and significantly importantin using ECG to predict cardiac-related events among CVD and non-CVDsubjects in the Malaysian population.

摘要： Hand veins can be used effectively in biometric recognition since they are internal organs that,in contrast to fingerprints,are robust under external environment effects such as dirt and paper cuts.Moreover,they form a complex rich shape that is unique,even in identical twins,and allows a high degree of freedom.However,most currently employed hand-based biometric systems rely on hand-touch devices to capture images with the desired quality.Since the start of the COVID-19 pandemic,most handbased biometric systems have become undesirable due to their possible impact on the spread of the pandemic.Consequently,new contactless hand-based biometric recognition systems and databases are desired to keep up with the rising hygiene awareness.One contribution of this research is the creation of a database for hand dorsal veins images obtained contact-free with a variation in capturing distance and rotation angle.This database consists of 1548 images collected from 86 participants whose ages ranged from 19 to 84 years.For the other research contribution,a novel geometrical feature extraction method has been developed based on the Curvelet Transform.This method is useful for extracting robust rotation invariance features from vein images.The database attributes and the veins recognition results are analyzed to demonstrate their efficacy.

摘要： Skin cancer(melanoma)is one of the most aggressive of the cancers and the prevalence has significantly increased due to increased exposure to ultraviolet radiation.Therefore,timely detection and management of the lesion is a critical consideration in order to improve lifestyle and reduce mortality.To this end,we have designed,implemented and analyzed a hybrid approach entailing convolutional neural networks(CNN)and local binary patterns(LBP).The experiments have been performed on publicly accessible datasets ISIC 2017,2018 and 2019(HAM10000)with data augmentation for in-distribution generalization.As a novel contribution,the CNN architecture is enhanced with an intelligible layer,LBP,that extracts the pertinent visual patterns.Classification of Basal Cell Carcinoma,Actinic Keratosis,Melanoma and Squamous Cell Carcinoma has been evaluated on 8035 and 3494 cases for training and testing,respectively.Experimental outcomes with cross-validation depict a plausible performance with an average accuracy of 97.29%,sensitivity of 95.63%and specificity of 97.90%.Hence,the proposed approach can be used in research and clinical settings to provide second opinions,closely approximating experts’intuition.