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A novel approach for feature extraction from a gamma‑ray energy spectrum based on image descriptor transferring for radionuclide identification

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Nuclear Science and Techniques 2022年第12期33卷 88-104页

作者： Hao‑Lin Liu Hai‑Bo Ji Jiang‑Mei Zhang Cao‑Lin Zhang Jing Lu Xing‑Hua Feng Department of Automation University of Science and Technology of ChinaHefei 230026China School of Information Engineering Southwest University of Science and TechnologyMianyang 621010China Fundamental Science on Nuclear Wastes and Environment Safety Laboratory Southwest University of Science and TechnologyMianyang 621010China

This study proposes a novel feature extraction approach for radionuclide identification to increase the precision of identification of the gamma-ray energy spectrum set.For easier utilization of the information contained in the spectra,the vectors of the gamma-ray energy spectra from Euclidean space,which are fingerprints of the different types of radionuclides,were mapped to matrices in the Banach space.Subsequently,to make the spectra in matrix form easier to apply to image-based deep learning frameworks,the matrices of the gamma-ray energy spectra were mapped to images in the RGB color space.A deep convolutional neural network(DCNN)model was constructed and trained on the ImageNet dataset.The mapped gamma-ray energy spectrum images were applied as inputs to the DCNN model,and the corresponding outputs of the convolution layers and fully connected layers were transferred as descriptors of the images to construct a new classification model for radionuclide identification.The transferred image descriptors consist of global and local features,where the activation vectors of fully connected layers are global features,and activations from convolution layers are local features.A series of comparative experiments between the transferred image descriptors,peak information,features extracted by the histogram of the oriented gradients(HOG),and scale-invariant feature transform(SIFT)using both synthetic and measured data were applied to 11 classical classifiers.The results demonstrate that although the gamma-ray energy spectrum images are completely unfamiliar to the DCNN model and have not been used in the pre-training process,the transferred image descriptors achieved good classification results.The global features have strong semantic information,which achieves an average accuracy of 92.76%and 94.86%on the synthetic dataset and measured dataset,respectively.The results of the statistical comparison of features demonstrate that the proposed approach outperforms the peak-searching-based method,HOG,and SIFT on the synthet

关键词： Radionuclide identification feature extraction Transfer learning Gamma energy spectrum analysis Image descriptor

A novel feature extraction method for ship-radiated noise

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Defence Technology（防务技术） 2022年第4期18卷 604-617页

作者： Hong Yang Lu-lu Li Guo-hui Li Qian-ru Guan School of Electronic Engineering Xi’an University of Posts and TelecommunicationsXi’anShaanxi710121China

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.

关键词： Complete ensemble empirical mode decomposition with adaptive noise Ship-radiated noise feature extraction Classification and recognition

Test-Driven feature extraction of Web Components

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Journal of Computer Science & Technology 2022年第2期37卷 389-404页

作者：龙永浩陈彦呈陈湘萍石晓红周凡 School of Computer Science and Engineering National Engineering Research Center of Digital Life Sun Yat-sen UniversityGuangzhou 510006China School of Design The Hong Kong Polytechnic UniversityHong KongChina Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion The School of Communication and DesignSun Yat-sen UniversityGuangzhou 510006China School of Information Technology and Engineering Guangzhou College of CommerceGuangzhou 511363China

With the growing requirements of web applications,web components are developed to package the implementation of commonly-used features for reuse.In some cases,the developer may want to reuse some features which cannot be customized by the component’s APIs.He/she has to extract the implementation by hand.It is labor-intensive and error-prone.Considering the widely-used test cases which can be useful to specify the software features,a test-driven approach is proposed to extract the implementation of the desired features in web components.The satisfaction of the user^requirements is transformed into the passing rate of user-specified test cases.In this way,the quality of the extraction result can be evaluated automatically.Meanwhile,a record/replay-based GUI test generation method is proposed to ensure that the extraction result has the correct GUI appearance.To extract the feature implementation,a hierarchical genetic algorithm is proposed to find the code snippet that can pass all the tests and has the approximate smallest size.We compare our method with two existing feature extraction methods.The result shows that our method can extract the correct implementation with the minimum size.A human-subject study is conducted to show the effectiveness and weaknesses of our method in helping users extract the features.

关键词： feature extraction genetic algorithm graphical user interface software reuse

Towards real-time ML-based DDoS detection via cost-efficient window-based feature extraction

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Science China(Information Sciences) 2023年第5期66卷 114-129页

作者： Haibin LI Yi ZHAO Wenbing YAO Ke XU Qi LI Department of Computer Science and Technology Tsinghua University Beijing National Research Center for Information Science and Technology (BNRist) Institute for Network Sciences and Cyberspace Tsinghua University

Distributed denial of service(DDoS) detection is still an open and challenging problem. In particular, sophisticated attacks, e.g., attacks that disguise attack packets as benign traffic always appear, which can easily evade traditional signature-based methods. Due to the low requirements for computing resources compared to deep learning, many machine learning(ML)-based methods have been realistically deployed to address this issue. However, most existing ML-based DDo S detection methods are highly dependent on the features extracted from each flow, which incur remarkable detection delay and computation overhead. This article investigates the limitations of typical ML-based DDo S detection methods caused by the extraction of flow-level features. Moreover, we develop a cost-efficient window-based method that extracts features from a fixed number of packets periodically, instead of per flow, aiming to reduce the detection delay and computation overhead. The newly proposed window-based method has the advantages of well-controlled overhead and wide support of common routers due to its simplicity and high efficiency by design. Through extensive experiments on real datasets, we evaluate the performance of flow-based and window-based methods.The experimental results demonstrate that our proposed window-based method can significantly reduce the detection delay and computation overhead while ensuring detection accuracy.

关键词： DDoS attack machine learning feature extraction detection delay cost-efficiency

New supervised learning classifiers for structural damage diagnosis using time series features from a new feature extraction technique

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Earthquake Engineering and Engineering Vibration 2022年第1期21卷 169-191页

作者： Masoud Haghani Chegeni Mohammad Kazem Sharbatdar Reza Mahjoub Mahdi Raftari Department of Civil Engineering Khorramabad BranchIslamic Azad UniversityKhorramabadIran*** Faculty of Civil Engineering Semnan UniversitySemnanIran***

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.

关键词： structural damage diagnosis statistical pattern recognition feature extraction time series analysis supervised learning classification

Micro-Doppler feature extraction of micro-rotor UAV under the background of low SNR

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Journal of Systems Engineering and Electronics 2022年第6期33卷 1127-1139页

作者： HE Weikun SUN Jingbo ZHANG Xinyun LIU Zhenming College of Electronic Information and Automation Civil Aviation University of ChinaTianjin 300300China Cyber Intelligent Technology Co. LtdJi’nan 250100China

Micro-Doppler feature extraction of unmanned aerial vehicles(UAVs)is important for their identification and classification.Noise and the motion state of the UAV are the main factors that may affect feature extraction and estimation precision of the micro-motion parameters.The spectrum of UAV echoes is reconstructed to strengthen the micro-motion feature and reduce the influence of the noise on the condition of low signal to noise ratio(SNR).Then considering the rotor rate variance of UAV in the complex motion state,the cepstrum method is improved to extract the rotation rate of the UAV,and the blade length can be intensively estimated.The experiment results for the simulation data and measured data show that the reconstruction of the spectrum for the UAV echoes is helpful and the relative mean square root error of the rotating speed and blade length estimated by the proposed method can be improved.However,the computation complexity is higher and the heavier computation burden is required.

关键词： micro-rotor unmanned aerial vehicle(UAV) low signal to noise ratio(SNR) micro-Doppler feature extraction parameter estimation

Survey of feature selection and extraction techniques for stock market prediction

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Financial Innovation 2023年第1期9卷 667-691页

作者： Htet Htet Htun Michael Biehl Nicolai Petkov Bernoulli Institute for Mathematics Computer ScienceArtificial IntelligenceUniversity of GroningenGroningenThe Netherlands

In stock market forecasting,the identification of critical features that affect the performance of machine learning(ML)models is crucial to achieve accurate stock price predictions.Several review papers in the literature have focused on various ML,statistical,and deep learning-based methods used in stock market forecasting.However,no survey study has explored feature selection and extraction techniques for stock market forecasting.This survey presents a detailed analysis of 32 research works that use a combination of feature study and ML approaches in various stock market applications.We conduct a systematic search for articles in the Scopus and Web of Science databases for the years 2011–2022.We review a variety of feature selection and feature extraction approaches that have been successfully applied in the stock market analyses presented in the articles.We also describe the combination of feature analysis techniques and ML methods and evaluate their performance.Moreover,we present other survey articles,stock market input and output data,and analyses based on various factors.We find that correlation criteria,random forest,principal component analysis,and autoencoder are the most widely used feature selection and extraction techniques with the best prediction accuracy for various stock market applications.

关键词： feature selection feature extraction Dimensionality reduction Stock market forecasting Machine learning

A Template Matching Based feature extraction for Activity Recognition

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Computers, Materials & Continua 2022年第7期72卷 611-634页

作者： Muhammad Hameed Siddiqi Helal Alshammari Amjad Ali Madallah Alruwaili Yousef Alhwaiti Saad Alanazi M.M.Kamruzzaman College of Computer and Information Sciences Jouf UniversitySakakaAljouf2014Kingdom of Saudi Arabia Department of Computer Science COMSATS University IslamabadLahore CampusPakistan

Human activity recognition(HAR)can play a vital role in the monitoring of human activities,particularly for healthcare conscious individuals.The accuracy of HAR systems is completely reliant on the extraction of prominent features.Existing methods find it very challenging to extract optimal features due to the dynamic nature of activities,thereby reducing recognition performance.In this paper,we propose a robust feature extraction method for HAR systems based on template matching.Essentially,in this method,we want to associate a template of an activity frame or sub-frame comprising the corresponding silhouette.In this regard,the template is placed on the frame pixels to calculate the equivalent number of pixels in the template correspondent those in the frame.This process is replicated for the whole frame,and the pixel is directed to the optimum match.The best count is estimated to be the pixel where the silhouette(provided via the template)presented inside the frame.In this way,the feature vector is generated.After feature vector generation,the hiddenMarkovmodel(HMM)has been utilized to label the incoming activity.We utilized different publicly available standard datasets for experiments.The proposed method achieved the best accuracy against existing state-of-the-art systems.

关键词： Activity recognition feature extraction template matching video surveillance

Pixel-Level feature extraction Model for Breast Cancer Detection

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Computers, Materials & Continua 2023年第2期74卷 3371-3389页

作者： Nishant Behar Manish Shrivastava Guru Ghasidas Vishvavidyalaya Bilaspur45009India

Breast cancer is the most prevalent cancer among women,and diagnosing it early is vital for successful treatment.The examination of images captured during biopsies plays an important role in determining whether a patient has cancer or not.However,the stochastic patterns,varying intensities of colors,and the large sizes of these images make it challenging to identify and mark malignant regions in them.Against this backdrop,this study proposes an approach to the pixel categorization based on the genetic algorithm(GA)and principal component analysis(PCA).The spatial features of the images were extracted using various filters,and the most prevalent ones are selected using the GA and fed into the classifiers for pixel-level categorization.Three classifiers—random forest(RF),decision tree(DT),and extra tree(ET)—were used in the proposed model.The parameters of all modelswere separately tuned,and their performance was tested.The results show that the features extracted by using the GA+PCA in the proposed model are influential and reliable for pixel-level classification in service of the image annotation and tumor identification.Further,an image from benign,malignant,and normal classes was randomly selected and used to test the proposed model.The proposed modelGA-PCA-DT has delivered accuracies between 0.99 to 1.0 on a reduced feature set.The predicted pixel sets were also compared with their respective ground-truth values to assess the overall performance of the method on two metrics—the universal image quality index(UIQI)and the structural similarity index(SSI).Both quality measures delivered excellent results.

关键词： Breast cancer machine learning classification feature extraction feature selection