Predicting the Operating States of Grinding Circuits by Use of Recurrence Texture Analysis of Time Series Data
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[1] M. Ausloos,et al. RECURRENCE PLOT AND RECURRENCE QUANTIFICATION ANALYSIS TECHNIQUES FOR DETECTING A CRITICAL REGIME. EXAMPLES FROM FINANCIAL MARKET INIDICES , 2004, cond-mat/0412765.
[2] Yigen Zeng,et al. Monitoring grinding parameters by vibration signal measurement - a primary application , 1994 .
[3] Manuel A. Duarte-Mermoud,et al. Control of a grinding mill circuit using fractional order controllers , 2017 .
[4] Chris Aldrich,et al. Froth image analysis by use of transfer learning and convolutional neural networks , 2018 .
[5] Cordelia Schmid,et al. Constructing models for content-based image retrieval , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.
[6] Yong Li Cheng Shao. APPLICATION OF GREY RELATION ANALYSIS AND RBF NETWORK ON GRINDING-CONCENTRATION'S SOFT SENSING , 2005 .
[7] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[8] Lars Lundberg,et al. Classifying environmental sounds using image recognition networks , 2017, KES.
[9] Mostafa Mehdipour-Ghazi,et al. Plant identification using deep neural networks via optimization of transfer learning parameters , 2017, Neurocomputing.
[10] B. Julesz. A brief outline of the texton theory of human vision , 1984, Trends in Neurosciences.
[11] Chris Aldrich,et al. Monitoring of mineral processing systems by using textural image analysis , 2013 .
[12] Simone Marinai,et al. Deep neural networks for record counting in historical handwritten documents , 2019, Pattern Recognit. Lett..
[13] Chris Aldrich,et al. Identification of dynamic process systems with surrogate data methods , 2001 .
[14] Andrew Zisserman,et al. A Statistical Approach to Texture Classification from Single Images , 2004, International Journal of Computer Vision.
[15] Jefersson Alex dos Santos,et al. Towards better exploiting convolutional neural networks for remote sensing scene classification , 2016, Pattern Recognit..
[16] Chris Aldrich,et al. Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods , 2013, Advances in Computer Vision and Pattern Recognition.
[17] Vibration signal emission from mono-size particle breakage , 1996 .
[18] Jian Tang,et al. Feature extraction and selection based on vibration spectrum with application to estimating the load parameters of ball mill in grinding process , 2012 .
[19] Ali Işın,et al. Cardiac arrhythmia detection using deep learning , 2017 .
[20] Ying Wah Teh,et al. Time-series clustering - A decade review , 2015, Inf. Syst..
[21] Minho Lee,et al. Deep learning with support vector data description , 2015, Neurocomputing.
[22] Wei Yu,et al. Visualizing and Comparing AlexNet and VGG using Deconvolutional Layers , 2016 .
[23] Chris Aldrich,et al. Acoustic estimation of the particle size distributions of sulphide ores in a laboratory ball mill , 2000 .
[24] Jian Tang,et al. Experimental analysis of wet mill load based on vibration signals of laboratory-scale ball mill shell , 2010 .
[25] Chris Aldrich,et al. Estimating size fraction categories of coal particles on conveyor belts using image texture modeling methods , 2012, Expert Syst. Appl..
[26] Stan Sclaroff,et al. Do less and achieve more: Training CNNs for action recognition utilizing action images from the Web , 2015, Pattern Recognit..
[27] Tianyou Chai,et al. Multi-frequency signal modeling using empirical mode decomposition and PCA with application to mill load estimation , 2015, Neurocomputing.
[28] Jitendra Malik,et al. Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons , 2001, International Journal of Computer Vision.
[29] Yigen Zeng,et al. Application of vibration signals to monitoring crushing parameters , 1993 .
[30] Robert P. W. Duin,et al. Support Vector Data Description , 2004, Machine Learning.
[31] Katherine R. Storrs,et al. Deep Convolutional Neural Networks Outperform Feature-Based But Not Categorical Models in Explaining Object Similarity Judgments , 2017, Front. Psychol..
[32] Olaf Hellwich,et al. Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology , 2017, Comput. Medical Imaging Graph..
[33] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[34] Chris Aldrich,et al. Visualization of the controller states of an autogenous mill from time series data , 2014 .
[35] B. Julesz. Textons, the elements of texture perception, and their interactions , 1981, Nature.
[36] Douglas W. Fuerstenau,et al. The energy efficiency of ball milling in comminution , 2002 .