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Machine learning techniques for monitoring the sludge profile in a secondary settler tank

Jesús Zambrano, Oscar Samuelsson, Bengt Carlsson
2019 Applied Water Science  
The aim of this paper is to evaluate and compare the performance of two machine learning methods, Gaussian process regression (GPR) and Gaussian mixture models (GMMs), as two possible methods for monitoring  ...  Results suggest that GMM gives a faster response but is also more sensitive than GPR to changes during normal conditions.  ...  Compliance with ethical standards Conflict of interest On behalf of all authors, the corresponding author states that there is no conflict of interest.  ... 
doi:10.1007/s13201-019-1018-5 fatcat:27i4wrqe25bqpc2pf7cvmmcs2u

CNC machine tool health assessment using Dynamic Bayesian Networks

D.A. Tobon-Mejia, K. Medjaher, N. Zerhouni
2011 IFAC Proceedings Volumes  
In this paper, a contribution on the assessment of the health condition of the cutting tool from a Computer Numerical Control (CNC) machine tool and the prediction of its remaining useful life before its  ...  The proposed method is based on the use of monitoring data and relies on two main phases: an off-line phase and an on-line phase.  ...  The degradation is modeled using a Mixture of Gaussians Hidden Markov Model (MoG-HMM) represented by a Dynamic Bayesian Network (DBN).  ... 
doi:10.3182/20110828-6-it-1002.02741 fatcat:nr2fpdz3ozeglhvgc47ldbtwpq

CNC machine tool's wear diagnostic and prognostic by using dynamic Bayesian networks

D.A. Tobon-Mejia, K. Medjaher, N. Zerhouni
2012 Mechanical systems and signal processing  
The present paper is a contribution on the assessment of the health condition of a Computer Numerical Control (CNC) tool machine and the estimation of its Remaining Useful Life (RUL).  ...  The proposed method is applied on a benchmark of condition monitoring data gathered during several cuts of a CNC tool. Simulation results are obtained and discussed at the end of the paper.  ...  Figure 5 : A Mixture of Gaussians HMM [11] . 1 S 2 S 3 S 1 S 2 S 3 S 1 M 2 M 3 M 2 O 1 O 3 O MoG-HMMs and Dynamic Bayesian Networks In the last decade, a new tool namely the Dynamic Bayesian Networks  ... 
doi:10.1016/j.ymssp.2011.10.018 fatcat:vekikyzzjbgqtefo3o2zyopuwm

Clustering of Data Streams with Dynamic Gaussian Mixture Models. An IoT Application in Industrial Processes

Javier Diaz-Rozo, Concha Bielza, Pedro Larranaga
2018 IEEE Internet of Things Journal  
To cope with this problem, in this paper we propose a new unsupervised learning algorithm based on Gaussian mixture models called Gaussian-based dynamic probabilistic clustering (GDPC) mainly based on  ...  A key problem concerns processing a large amount of data, while the underlying dynamic phenomena related to the machine is possibly evolving over time due to factors, such as degradation.  ...  ACKNOWLEDGMENT The authors would like to thank Etxe-Tar for the machine hardware advice and Ikergune for the support during data stream acquisition.  ... 
doi:10.1109/jiot.2018.2840129 fatcat:j2owm2pyijf4bijxxcrrg6hoym

A mel-frequency cepstral coefficient-based approach for surface roughness diagnosis in hard turning using acoustic signals and gaussian mixture models

Edielson P. Frigieri, Paulo H.S. Campos, Anderson P. Paiva, Pedro P. Balestrassi, João Roberto Ferreira, Carlos A. Ynoguti
2016 Applied Acoustics  
correlation, a new quality monitoring method is proposed using Gaussian Mixture Models (GMM).  ...  During the last years, notable efforts have been made to develop reliable and industrially applicable machining monitoring systems based on different types of sensors, especially indirect methods that  ...  Table 4 4 Gaussian mixture model for surface roughness cluster S1. Gaussian mixture model for surface roughness cluster S3.  ... 
doi:10.1016/j.apacoust.2016.06.027 fatcat:7amjpwdrtjdcneauhmrapewfau

Semi-supervised classification for dynamic Android malware detection [article]

Li Chen, Mingwei Zhang, Chih-Yuan Yang, Ravi Sahita
2017 arXiv   pre-print
The semi-supervised approach efficiently uses the labeled and unlabeled APKs to estimate a finite mixture model of Gaussian distributions via conditional expectation-maximization and efficiently detects  ...  In this paper, we propose a framework that uses model-based semi-supervised (MBSS) classification scheme on the dynamic Android API call logs.  ...  Model-based mixture modeling uses a mixture of Gaussian distributions to develop clustering, classification and semi-supervised learning methods [14] , [27] , [26] , [23] .  ... 
arXiv:1704.05948v1 fatcat:iskjsyl7dzdpnbhwxybovjjoom

On-line estimation of concentration parameters in fermentation processes

Zhi-hua Xiong, Guo-hong Huang, Hui-he Shao
2005 JZUS-A - Journal of Zhejiang University. Science  
The proposed method is based on mixtures of Gaussian processes (GP) with expectation maximization (EM) algorithm employed for parameter estimation of mixture of models.  ...  The mixture model can alleviate computational complexity of GP and also accord with changes of operating condition in fermentation processes, i.e., it would certainly be able to examine what types of process-knowledge  ...  And the conditional density p(y|x) is also a mixture of Gaussian models.  ... 
doi:10.1631/jzus.2005.b0530 pmid:15909338 pmcid:PMC1389884 fatcat:mwsd5jlucbdtvbdfwvxx4b4tqi

Context-Aware Abnormality Monitoring Service for Care-Needing Persons Using a Probabilistic Model

Sol Ji Kang, Kyung Mi Lee, Keon Myung Lee
2016 Indian Journal of Science and Technology  
The model makes probabilistic inference for the latent random variables on the fly with respect to the arriving captured sensor data.  ...  Probabilistic models are useful tools to capture the inherent patterns for care-needing persons from a collection of data and to make inference for uncertain situation.  ...  Many probabilistic models, including Kalman filter, hidden Markov models, and mixture of Gaussian, can be interpreted in the framework of probabilistic graphical models [17] [18] [19] .  ... 
doi:10.17485/ijst/2016/v9i24/96112 fatcat:ywvk3nnv7beizgbka6zexlpk7u

Build, Compute, Critique, Repeat: Data Analysis with Latent Variable Models

David M. Blei
2014 Annual Review of Statistics and Its Application  
analysis problems through model-based probabilistic computations.  ...  With this view, we describe how new research in statistics and machine learning has transformed each of these essential activities.  ...  Continuing with mixtures, we can use any common prior/likelihood pair (e.g., Gamma-Poisson, Gaussian-Gaussian, Dirichlet-Multinomial) to build an appropriate conditionally conjugate mixture model.  ... 
doi:10.1146/annurev-statistics-022513-115657 fatcat:5fe3gpwya5hqzgnolp3iaxczxa

Modern Soft-Sensing Modeling Methods for Fermentation Processes

Xianglin Zhu, Khalil Ur Rehman, Bo Wang, Muhammad Shahzad
2020 Sensors  
The data-driven methods used for the soft-sensing modeling such as support vector machine, multiple least square support vector machine, neural network, deep learning, fuzzy logic, probabilistic latent  ...  Constructing soft sensors with outstanding performance and robustness has become a core issue in industrial procedures.  ...  In [110] , a soft-sensing modeling method based on semi-supervised probabilistic mixture of extreme learning machine (SSPMELM) was developed for monitoring and control of crucial variables.  ... 
doi:10.3390/s20061771 pmid:32210053 fatcat:cicbldmt7jh3jcsomemjqbhrvi

Rapid design of tool-wear condition monitoring systems for turning processes using novelty detection

Abdulrahman F. Al Azmi, Amin Al Habaibeh, John Redgate
2009 International Journal of Manufacturing Technology and Management (IJMTM)  
In this paper, force signals are used for monitoring tool wear in a feature fusion model.  ...  The results prove that the developed system can be used for rapid design of condition monitoring systems for turning operations to predict tool-wear.  ...  A Gaussian Mixture Model (GMM) is used in this paper to estimate the PDF.  ... 
doi:10.1504/ijmtm.2009.023931 fatcat:dgj7areyvbggroeohsspnsjlqq

Degradation assessment of bearing based on machine learning classification matrix

Satish Kumar, Paras Kumar, Girish Kumar
2021 Eksploatacja i Niezawodnosc  
Machine learning classification matrices have been used to train models based on health data and real time feedback.  ...  A classification model which is based on machine learning classification matrix to assess the degradation of bearing is proposed to improve the accuracy of classification model.  ...  using the Gaussian mixture model.  ... 
doi:10.17531/ein.2021.2.20 fatcat:wcrzvh33m5aqroytctuw76xsne

A Tunnel Gaussian Process Model for Learning Interpretable Flight's Landing Parameters [article]

Sim Kuan Goh, Narendra Pratap Singh, Zhi Jun Lim, Sameer Alam
2021 arXiv   pre-print
Specifically, we develop two variants of tunnel Gaussian process (TGP) models to elucidate aircraft's approach and landing dynamics using advanced surface movement guidance and control system (A-SMGCS)  ...  When applied to operational A-SMGCS data, TGP provides the generative probabilistic description of landing dynamics and interpretable tunnel views of approach and landing parameters.  ...  Gaussian process model learns the probabilistic mapping function ( ): = ( ) + (1) where we model using Gaussian process ( , ) with mean function and covariance function and is Gaussian white noise with  ... 
arXiv:2011.09335v3 fatcat:l7rrl52ss5erfbwef5bk57spmu

Survey on Vehicle Detection and Tracking Techniques in Video Surveillance

Swathy M., Nirmala P., Geethu P.
2017 International Journal of Computer Applications  
From a mixture of a finite number of Gaussian distributions with the unknown parameters,ie the probabilistic model, which assumes all the data points, where Gaussian mixture model is a probabilistic model  ...  It generates k-means clustering to incorporate the covariance structure of data and centers of latent Gaussians. For learning mixture models, Gaussian Mixture Model is the fastest algorithm.  ... 
doi:10.5120/ijca2017913086 fatcat:yq22nc2xqfca7krq5hudfhbrwy

Role of Machine Learning in WSN and VANETs

Maryam Gillani, Hafiz Adnan Niaz, Muhammad Tayyab
2021 International Journal of Electrical and Computer Engineering Research  
For such dynamicity, Machine learning (ML) approaches are considered favourable.  ...  Afterwards, ML based WSN and VANETs application, open issues, challenges of rapidly changing networks and various algorithms in relation to ML models and techniques are discussed.  ...  Moreover, based on variation GMM and Gaussian mixture models (GMM), probabilistic trajectory prediction has proven to be useful in predicting the vehicle's direction using previously monitored mobility  ... 
doi:10.53375/ijecer.2021.24 fatcat:72mtbdb3uvaepj6efg7lfz6x6q
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