|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Soft measurement of wood defects based on LDA feature fusion and compressed sensor images显示文摘We proposed a detection method for wood defects based on linear discriminant analysis(LDA) and the use of compressed sensor images. Wood surface images were captured, using a camera Oscar F810C IRF camera,and then the image segmentation was performed, and the defect features were extracted from wood board images. To reduce the processing time, LDA algorithm was used to integrate these features and reduce their dimensions. Features after fusion were used to construct a data dictionary and a compressed sensor was designed to recognize the wood defects types. Of the three major defect types, 50 images live knots, dead knots, and cracks were used to test the effects of this method. The average time for feature fusion and classification was 0.446 ms with the classification accuracy of 94%. | Chao Li Yizhuo Zhang Wenjun Tu Cao Jun Hao Liang Huiling Yu | 2017 | Journal of Forestry Research2017，28，6： | 6 |
| 2 | Automated defect analysis in electron microscopic images显示文摘Electron microscopy and defect analysis are a cornerstone of materials science,as they offer detailed insights on the microstructure and performance of a wide range of materials and material systems.Building a robust and flexible platform for automated defect recognition and classification in electron microscopy will result in the completion of analysis orders of magnitude faster after images are recorded,or even online during image acquisition.Automated analysis has the potential to be significantly more efficient,accurate,and repeatable than human analysis,and it can scale with the increasingly important methods of automated data generation.Herein,an automated recognition tool is developed based on a computer vison–based approach;it sequentially applies a cascade object detector,convolutional neural network,and local image analysis methods.We demonstrate that the automated tool performs as well as or better than manual human detection in terms of recall and precision and achieves quantitative image/defect analysis metrics close to the human average.The proposed approach works for images of varying contrast,brightness,and magnification.These promising results suggest that this and similar approaches are worth exploring for detecting multiple defect types and have the potential to locate,classify,and measure quantitative features for a range of defect types,materials,and electron microscopic techniques. | Wei Li Kevin G.Field Dane Morgan | 2018 | npj Computational Materials2018，，1： | 5 |
| 3 | Mechanical defect identification for gas‐insulated switchgear equipment based on time‐frequency vibration signal analysis显示文摘Mechanical defect is an important reason for the failure of gas‐insulated switchgear(GIS)equipment.Based on the time‐frequency characteristic vibration signal analysis on five kinds of mechanical defects,a novel intelligent algorithm model combining complementary ensemble empirical mode decomposition(CEEMD)and genetic al-gorithm improved kernel fuzzy mean clustering(GAKFCM)was proposed to identify the mechanical defect type.First,the mechanical defect platform and detection sys-tem were built.Then CEEMD and IMF sensitivity factors were used to analyse the time‐frequency signal of five kinds of vibration defects,and the feature extraction was performed on the processed vibration signals.Finally,the mechanical vibration defect recognition model was established based on the GAKFCM algorithm and its validity was verified.Results show that the developed detection system can detect mechanical vibration signals sensitively.Singular values,frequency band lines and entropy can reflect the energy attenuation and distribution differences for different type of me-chanical defect vibration signals.The proposed GAKFCM clustering model combining the above vibration feature parameters can effectively find and diagnose the mechanical defect of GIS equipment.Its recognition accuracy reaches 96.74%,especially for the loose contact seat bolts and poor contact failures of the disconnector. | Yao Zhong Jian Hao Ruijin Liao Xupeng Wang Xiping Jiang Feng Wang | 2021 | High Voltage2021，6，3： | 3 |
| 4 | FEATURE ANALYSIS AND EXTRACTION OF INNER DEFECTS FOR SPOT WELD NUGGET OF ALUMINUM ALLOY显示文摘1INTRODUCTIONSpotweldinghasbeenwidelyusedinaluminumaloystructuresofspacecraftandaircraftduetoitscharacteristicsoftechnology.... | Gang, Tie Shen, Chunlong Gong, Runli | 1998 | 中国有色金属学会会刊:英文版1998，8，3： | 1 |
| 5 | Description and Classification of Leather Defects Based on Principal Component Analysis显示文摘The accurate extraction and classification of leather defects is an important guarantee for the automation and quality evaluation of leather industry. Aiming at the problem of data classification of leather defects,a hierarchical classification for defects is proposed.Firstly,samples are collected according to the method of minimum rectangle,and defects are extracted by image processing method.According to the geometric features of representation, they are divided into dot,line and surface for rough classification. From analysing the data which extracting the defects of geometry,gray and texture,the dominating characteristics can be acquired. Each type of defect by choosing different and representative characteristics,reducing the dimension of the data,and through these characteristics of clustering to achieve convergence effectively,realize extracted accurately,and digitized the defect characteristics,eventually establish the database. The results showthat this method can achieve more than 90% accuracy and greatly improve the accuracy of classification. | 丁彩红 黄浩 杨延竹 | 2018 | Journal of Donghua University(English Edition)2018，35，6： | 0 |
| 6 | Multivariate Image Analysis in Gaussian Multi-Scale Space for Defect Detection显示文摘Inspired by the coarse-to-fine visual perception process of human vision system,a new approach based on Gaussianmulti-scale space for defect detection of industrial products was proposed.By selecting different scale parameters of theGaussian kernel,the multi-scale representation of the original image data could be obtained and used to constitute the multi-variate image,in which each channel could represent a perceptual observation of the original image from different scales.TheMultivariate Image Analysis (MIA) techniques were used to extract defect features information.The MIA combined PrincipalComponent Analysis (PCA) to obtain the principal component scores of the multivariate test image.The Q-statistic image,derived from the residuals after the extraction of the first principal component score and noise,could be used to efficiently revealthe surface defects with an appropriate threshold value decided by training images.Experimental results show that the proposedmethod performs better than the gray histogram-based method.It has less sensitivity to the inhomogeneous of illumination,andhas more robustness and reliability of defect detection with lower pseudo reject rate. | Dong-tai Liang~1 Wei-yan Deng~2 Xuan-yin Wang~1 Yang Zhang~11.State Key Laboratory of Fluid Power Transmission and Control Zhejiang University,Hangzhou 310027,P.R.China2.College of Mechanical and Electrical Engineering,China Jiliang University,Hangzhou 310018,P.R.China | 2009 | Journal of Bionic Engineering2009，6，3： | 0 |
| 7 | PARALLEL ALGORITHMS OF ONE-LEG METHOD AND ITERATED DEFECT CORRECTION METHODS显示文摘The parallel algorithms of iterated defect correction methods (PIDeCM’s) are constructed, which are of efficiency and high order B-convergence for general nonlinear stiff systems in ODE’S. As the basis of constructing and discussing PIDeCM’s. a class of parallel one-leg methods is also investigated, which are of particular efficiency for linear systems. | 李寿佛 陈丽容 | 1994 | Numerical Mathematics A Journal of Chinese Universities(English Series)1994，3，1： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员