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Chinese Journal of Electronics 2024年第3期33卷 803-813页

作者： Yongkui SUN Yuan CAO Peng LI Guo XIE Tao WEN Shuai SU National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University School of Electronic and Information Engineering Beijing Jiaotong University Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University

As one of the most important railway signaling equipment,railway point machines undertake the major task of ensuring train operation safety.Thus fault diagnosis for railway point machines becomes a hot topic.Considering the advantage of the anti-interference characteristics of vibration signals,this paper proposes an novel intelligent fault diagnosis method for railway point machines based on vibration signals.A feature extraction method combining variational mode decomposition(VMD) and multiscale fluctuation-based dispersion entropy is developed,which is verified a more effective tool for feature selection.Then,a two-stage feature selection method based on Fisher discrimination and ReliefF is proposed,which is validated more powerful than single feature selection methods.Finally,support vector machine is utilized for fault diagnosis.Experiment comparisons show that the proposed method performs best.The diagnosis accuracies of normal-reverse and reverse-normal switching processes reach 100% and 96.57% respectively.Especially,it is a try to use new means for fault diagnosis on railway point machines,which can also provide references for similar fields.

关键词： Fault diagnosis Vibrations Support vector machines Diversity reception Switches feature extraction Rail transportation

Study on Image Recognition Algorithm for Residual Snow and Ice on Photovoltaic Modules

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Energy Engineering 2024年第4期121卷 895-911页

作者： Yongcan Zhu JiawenWang Ye Zhang Long Zhao Botao Jiang Xinbo Huang College of Electronics and Information Xi’an Polytechnic UniversityXi’an710048China Xi’an Key Laboratory of Interconnected Sensing and Intelligent Diagnosis for Electrical Equipment Xi’an710048China

The accumulation of snow and ice on PV modules can have a detrimental impact on power generation,leading to reduced efficiency for prolonged periods.Thus,it becomes imperative to develop an intelligent system capable of accurately assessing the extent of snow and ice coverage on PV modules.To address this issue,the article proposes an innovative ice and snow recognition algorithm that effectively segments the ice and snow areas within the collected images.Furthermore,the algorithm incorporates an analysis of the morphological characteristics of ice and snow coverage on PV modules,allowing for the establishment of a residual ice and snow recognition process.This process utilizes both the external ellipse method and the pixel statistical method to refine the identification process.The effectiveness of the proposed algorithm is validated through extensive testing with isolated and continuous snow area pictures.The results demonstrate the algorithm’s accuracy and reliability in identifying and quantifying residual snow and ice on PV modules.In conclusion,this research presents a valuable method for accurately detecting and quantifying snow and ice coverage on PV modules.This breakthrough is of utmost significance for PV power plants,as it enables predictions of power generation efficiency and facilitates efficient PV maintenance during the challenging winter conditions characterized by snow and ice.By proactively managing snow and ice coverage,PV power plants can optimize energy production and minimize downtime,ensuring a sustainable and reliable renewable energy supply.

关键词： Photovoltaic(PV)module residual snow and ice snow detection feature extraction image processing

Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual features Extracted from GTD Dataset

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1427-1467页

作者： Mohammed Abdalsalam Chunlin Li Abdelghani Dahou Natalia Kryvinska School of Computer Science and Technology Wuhan University of TechnologyWuhan430070China LDDI Laboratory Faculty of Science and TechnologyUniversity of Ahmed DRAIAAdrar01000Algeria Information Systems Department Faculty of ManagementComenius UniversityBratislava82005Slovakia

One of the biggest dangers to society today is terrorism, where attacks have become one of the most significantrisks to international peace and national security. Big data, information analysis, and artificial intelligence (AI) havebecome the basis for making strategic decisions in many sensitive areas, such as fraud detection, risk management,medical diagnosis, and counter-terrorism. However, there is still a need to assess how terrorist attacks are related,initiated, and detected. For this purpose, we propose a novel framework for classifying and predicting terroristattacks. The proposed framework posits that neglected text attributes included in the Global Terrorism Database(GTD) can influence the accuracy of the model’s classification of terrorist attacks, where each part of the datacan provide vital information to enrich the ability of classifier learning. Each data point in a multiclass taxonomyhas one or more tags attached to it, referred as “related tags.” We applied machine learning classifiers to classifyterrorist attack incidents obtained from the GTD. A transformer-based technique called DistilBERT extracts andlearns contextual features from text attributes to acquiremore information from text data. The extracted contextualfeatures are combined with the “key features” of the dataset and used to perform the final classification. Thestudy explored different experimental setups with various classifiers to evaluate the model’s performance. Theexperimental results show that the proposed framework outperforms the latest techniques for classifying terroristattacks with an accuracy of 98.7% using a combined feature set and extreme gradient boosting classifier.

关键词： Artificial intelligence machine learning natural language processing data analytic DistilBERT feature extraction terrorism classification GTD dataset

feature extraction OF VIBRATION SIGNALS BASED ON WAVELET PACKET TRANSFORM

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Chinese Journal of Mechanical Engineering 2004年第1期17卷 25-27页

作者： ShaoJunpeng JiaHuijuan DepartmentotMechanicalEngineering HarbinUniversityofScienceandTechnologyHarbin150080China

A method is proposed for the analysis of vibration signals from components ofrotating machines, based on the wavelet packet transformation (WPT) and the underlying physicalconcepts of modulation mechanism. The method provides a finer analysis and better time-frequencylocalization capabilities than any other analysis methods. Both details and approximations are splitinto finer components and result in better-localized frequency ranges corresponding to each node ofa wavelet packet tree. For the punpose of feature extraction, a hard threshold is given and theenergy of the coefficients above the threshold is used, as a criterion for the selection of the bestvector. The feature extraction of a vibration signal is accomplished by computing thereconstruction signal and its spectrum. When applied to a rolling bear vibration signal featureextraction, the proposed method can lead to be very effective.

关键词： Wavelet packet transform feature extraction Vibration signal

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extraction of feature Points for Non-Uniform Rational B-Splines(NURBS)-Based Modeling of Human Legs

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Journal of Donghua University(English Edition) 2022年第4期39卷 299-303页

作者：王玺吴宗谦李乔 College of Information Science and Technology Donghua UniversityShanghai 201620China Engineering Research Center of Digitized Textile&Fashion Technology Ministry of EducationShanghai 201620China College of Textiles Donghua UniversityShanghai 201620China

Methods of digital human modeling have been developed and utilized to reflect human shape features.However,most of published works focused on dynamic visualization or fashion design,instead of high-accuracy modeling,which was strongly demanded by medical or rehabilitation scenarios.Prior to a high-accuracy modeling of human legs based on non-uniform rational B-splines(NURBS),the method of extracting the required quasi-grid network of feature points for human legs is presented in this work.Given the 3 D scanned human body,the leg is firstly segmented and put in standardized position.Then re-sampling of the leg is conducted via a set of equidistant cross sections.Through analysis of leg circumferences and circumferential curvature,the characteristic sections of the leg as well as the characteristic points on the sections are then identified according to the human anatomy and shape features.The obtained collection can be arranged to form a grid of data points for knots calculation and high-accuracy shape reconstruction in future work.

关键词： 3D scan digital human modeling non-uniform rational B-splines(NURBS) feature extraction

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feature extraction and Modeling of Welding Quality Monitoring in Pulsed Gas Touch Argon Welding Based on Arc Voltage Signal

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Journal of Shanghai Jiaotong university(Science) 2014年第1期19卷 11-16页

作者：张志芬钟继勇陈玉喜陈善本 Institute of Welding Engineering School of Material Science and Engineering Shanghai Jiaotong University

Arc sensing plays a significant role in the control and monitoring of welding quality for aluminum alloy pulsed gas touch argon welding(GTAW). A method for online quality monitoring based on adaptive boosting algorithm is proposed through the analysis of acquired arc voltage signal. Two feature extraction algorithms were developed in time domain and frequency domain respectively to extract six statistic characteristic parameters before removing the pulse interference using the wavelet packet transform(WPT), based on which the Adaboost classification model is successfully established to evaluate and classify the welding quality into two classes and the classified accuracy of the model is as high as 98.81%. The Adaboost algorithm has been verified to be feasible in the online evaluation of welding quality.

关键词： gas touch argon welding(GTAW) arc voltage signal feature extraction Adaboost algorithm

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feature extraction without phase error for THz reflective spectroscopy

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Science China(Information Sciences) 2012年第1期55卷 127-132页

作者： ZHANG LiangLiang,ZHONG Hua,ZHU DeChong,ZUO Jian&ZHANG CunLin Beijing Key Laboratory for Terahertz Spectroscopy and Imaging,Key Laboratory of Terahertz Optoelectronics,Ministry of Education of China,Capital Normal University,Beijing 100048,China Beijing Key Laboratory for Terahertz Spectroscopy and Imaging Key Laboratory of Terahertz Optoelectronics Ministry of Education of China Capital Normal University Beijing China

We propose two methods for avoiding misplacement phase error in terahertz reflection time-domain spectroscopy(THz-RTDS).The absorption signatures of the materials are extracted directly from the second-order derivative of the phase of the sample beam or the first derivative of the relative reflectance with respect to frequency.The techniques provide straightforward and fast solutions to solve the phase-retrieval problem in RTDS.

关键词： feature extraction phase error reflective spectroscopy

feature extraction of overlapping hevea leaves:A comparative study

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Information Processing in Agriculture 2018年第2期5卷 234-245页

作者： Sule T.Anjomshoae Mohd Shafry Bin Mohd Rahim Department of Software Engineering Faculty of ComputingUniversiti Teknologi Malaysia81310 SkudaiJohor BahruMalaysia

Automation of rubber tree clone classification has inspired research into new methods of leaf feature extraction.In current practice,rubber clone inspectors has been using several leaf features to identify clone types.One of the unique features of rubber tree leaf is palmate leaflets.This characteristic generates different leaflet positions,where the leaves are overlapping or separated.In this research,we propose keypoint extraction and line detection methods to extract shape and axil(angle between petioles)features of leaflet positions.The results of keypoint extraction methods,namely,SIFT,Harris,and FAST,were compared and discussed for shape feature extraction.Next,Hough transformation and boundary-tracing methods were compared to identify the suitable axil detection method.The evaluation result demonstrates the proper keypoint extraction method for shape context and the clear advantages of Hough Transformation in accuracy of angle detection.

关键词： Rubber tree leaves feature extraction SIFT Harris FAST Hough transformation Boundary extraction

feature extraction of jujube fruit wrinkle based on the watershed segmentation

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International Journal of Agricultural and Biological Engineering 2017年第4期10卷 165-172页

作者： Zhang Junxiong Ma Qingqing Li Wei Xiao Tingting College of Engineering China Agricultural UniversityBeijing 100083China

The degree of surface wrinkles on a dried jujube fruit(Ziziphus jujuba Mill.)is an important quality grading criterion.The aim of this research was to propose an image processing method based on the watershed segmentation to extract the wrinkle features of jujube fruits.Original images of jujube fruit taken under cyan light were transformed into grayscale images.The noise in these images was then removed by morphological reconstruction.The H-minima extended transformation was used to label the foreground of jujube fruit images after reconstruction,and the labeled foreground regions were segmented by a distance transform-based watershed algorithm.Then,the grayscale images were filtered with a local range filter.The segmentation function was obtained using the minima imposition method.Finally,a watershed segmentation was used to extract the wrinkle features of jujube fruits.Experiments on 304 images of jujube fruit showed that the accuracy of wrinkle-based grading obtained by the algorithm was 92.11%,which proved that this method could be used to classify jujube wrinkles.

关键词： feature extraction watershed segmentation image processing jujube fruit wrinkle quality grading

Computer vision-based six layered ConvNeural network to recognize sign language for both numeral and alphabet signs

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Biomimetic Intelligence & Robotics 2024年第1期4卷 45-58页

作者： Muhammad Aminur Rahaman Kabiratun Ummi Oyshe Prothoma Khan Chowdhury Tanoy Debnath Anichur Rahman Md.Saikat Islam Khan Department of Computer Science and Engineering Green University of BangladeshDhakaBangladesh Department of Computer Science and Engineering National Institute of Textile Engineering and Research(NITER)Constituent Institute of Dhaka UniversityDhakaBangladesh Department of Computer Science and Engineering Mawlana Bhashani Science and Technology UniversityTangailBangladesh

People who have trouble communicating verbally are often dependent on sign language,which can be difficult for most people to understand,making interaction with them a difficult endeavor.The Sign Language Recognition(SLR)system takes an input expression from a hearing or speaking-impaired person and outputs it in the form of text or voice to a normal person.The existing study related to the Sign Language Recognition system has some drawbacks,such as a lack of large datasets and datasets with a range of backgrounds,skin tones,and ages.This research efficiently focuses on Sign Language Recognition to overcome previous limitations.Most importantly,we use our proposed Convolutional Neural Network(CNN)model,“ConvNeural”,in order to train our dataset.Additionally,we develop our own datasets,“BdSL_OPSA22_STATIC1”and“BdSL_OPSA22_STATIC2”,both of which have ambiguous backgrounds.“BdSL_OPSA22_STATIC1”and“BdSL_OPSA22_STATIC2”both include images of Bangla characters and numerals,a total of 24,615 and 8437 images,respectively.The“ConvNeural”model outperforms the pre-trained models with accuracy of 98.38%for“BdSL_OPSA22_STATIC1”and 92.78%for“BdSL_OPSA22_STATIC2”.For“BdSL_OPSA22_STATIC1”dataset,we get precision,recall,F1-score,sensitivity and specificity of 96%,95%,95%,99.31%,and 95.78%respectively.Moreover,in case of“BdSL_OPSA22_STATIC2”dataset,we achieve precision,recall,F1-score,sensitivity and specificity of 90%,88%,88%,100%,and 100%respectively.

关键词： Conv NeuralSign language CNN Static feature extraction Convolution2D Fully connected layer Dropout