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2020 IEEE Transactions on Automation Science and Engineering  
Chu Bayesian Nonlinear Gaussian Mixture Regression and Its Application to Virtual Sensing for Multimode Industrial Processes ............................................................................  ...  Chen 933 A Minimum-Cost Modeling Method for Nonlinear Industrial Process Based on Multimodel Migration and Bayesian Model Averaging Method .................................................... F.  ... 
doi:10.1109/tase.2020.2974073 fatcat:kngxsiwuhvfyhfv3fyoowl3zkq

2020 Index IEEE Transactions on Automation Science and Engineering Vol. 17

2020 IEEE Transactions on Automation Science and Engineering  
., +, TASE Jan. 2020 88-99 Bayesian Nonlinear Gaussian Mixture Regression and its Application to Virtual Sensing for Multimode Industrial Processes.  ...  ., +, TASE April 2020 1070-1075 Bayesian Nonlinear Gaussian Mixture Regression and its Application to Virtual Sensing for Multimode Industrial Processes.  ...  ., +, TASE July 2020 1237 -1249 Comments and Corrections to "Process Mining to Discover Shoppers' Pathways at a Fashion Retail Store Using a WiFi-Base Indoor Positioning System" [Oct 17 1786 [Oct 17  ... 
doi:10.1109/tase.2020.3037603 fatcat:kyt63444lfc45amrjebyjw34qu

Soft sensor modeling for unobserved multimode nonlinear processes based on modified kernel partial least squares with latent factor clustering

Xiaogang Deng, Yongxuan Chen, Ping Wang, Yuping Cao
2020 IEEE Access  
SOFT SENSING PROCEDURE BASED ON LFC-KPLS The proposed LFC-KPLS soft sensing procedure for unobserved multimode processes involves two stages: offline modeling and online application.  ...  Some typical data-driven soft sensor modeling methods include partial least squares (PLS), Gaussian mixture regression (GMR) and extreme machine learning (ELM) [4] - [7] , etc.  ... 
doi:10.1109/access.2020.2974783 fatcat:nzbgbi3lqvgclljkbzu3dososq

Quality prediction for polypropylene production process based on CLGPR model

Zhiqiang Ge, Tao Chen, Zhihuan Song
2011 Control Engineering Practice  
Feasibility and efficiency of the proposed soft sensor are demonstrated through the application to an industrial polypropylene production process.  ...  Online measurement of the melt index is typically unavailable in industrial polypropylene production processes, soft sensing models are therefore required for estimation and prediction of this important  ...  It is demonstrated that a large class of ANN based Bayesian regression models will finally converge to an approximate Gaussian process.  ... 
doi:10.1016/j.conengprac.2011.01.002 fatcat:wwfk54uhm5hczgex6zcv7rj66q

Multimodal Human Hand Motion Sensing and Analysis -A Review

Yaxu Xue, Zhaojie Ju, Kui Xiang, Jing Chen, Honghai Liu
2018 IEEE Transactions on Cognitive and Developmental Systems  
a particular focus on the multimodal hand motion sensing and analysis; finally, cuttingedge applications of hand motion analysis are reviewed, with further discussion on facing challenges and new future  ...  It provides important information about the gestures, tactile, speed and contact force, captured via multiple sensing technologies.  ...  Moreover, it is difficult to choose an optimal HMM for a given set of training sequences in a larger model. 2) Gaussian Mixture Model: Gaussian Mixture Model (GMM), which measures the Gaussian component  ... 
doi:10.1109/tcds.2018.2800167 fatcat:ojznwvn3gzg7rgnfq7yg3wf4jm

2021 Index IEEE Transactions on Industrial Informatics Vol. 17

2021 IEEE Transactions on Industrial Informatics  
Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name.  ...  The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  Guan, Q., +, TII Aug. 2021 5304-5313 A Novel Feature-Extraction-Based Process Monitoring Method for Multimode Processes With Common Features and Its Applications to a Rolling Process.  ... 
doi:10.1109/tii.2021.3138206 fatcat:ulsazxgmpfdmlivigjqgyl7zre

MMDF2018 Workshop Report [article]

Chun-An Chou, Xiaoning Jin, Amy Mueller, Sarah Ostadabbas
2018 arXiv   pre-print
practitioners across a wide variety of disciplines must still follow a trial-and-error process to identify the optimum procedure for each individual application and data sources.  ...  However, despite research thrusts explored since the late 1990's, to date no standard, generalizable solutions have emerged for effectively integrating and processing multimodal data, and consequently  ...  We would also like to express our sincere gratitude to the members of the Advisory Committee, whose input helped shape this event and bring it into existence, to the Logistics Chairman Gaby Fiorenza for  ... 
arXiv:1808.10721v1 fatcat:w4pmt5vqgvcn7nltkisitjpyam

Augmented reality using artificial neural networks –a review

Sreekumar Narayanan, Srinath Doss
2019 International Journal of Engineering & Technology  
AR with ANN has profound applications in various sectors and has been developed in an extended way but still has some distance to go afore industries, the military and the common public will receive it  ...  AR would modernize the way people animate and the way industries endeavor by effective utilization.  ...  [34] model the background of markers by a mixture of two Gaussian and compute a constriction mask by color segmentation.  ... 
doi:10.14419/ijet.v8i4.29981 fatcat:rsy4yk7wkvbkjdd5dm7qhnsruq

Multimodal Tactile Sensor [chapter]

Nicholas Wettels, Jeremy A. Fishel, Gerald E. Loeb
2014 Springer Tracts in Advanced Robotics  
Tactile sensing and signal processing is necessary for human dexterity and is likely to be required in mechatronic systems such as robotic and prosthetic limbs if they are to achieve similar dexterity.  ...  For example, information about the material composition of an object can be inferred from the rate of heat transfer from a heated finger to the object, but only if the location and force of contact are  ...  Artificial neural networks (ANN) and Gaussian mixture model regression (GMMR) can be used to extract three dimensional force vectors from a moderate number of nonlinear impedance sensing channels.  ... 
doi:10.1007/978-3-319-03017-3_19 fatcat:43hqxl7cwrbcvd56fx4xhaqgpy

Machine Learning Paradigms for Speech Recognition: An Overview

Li Deng, Xiao Li
2013 IEEE Transactions on Audio, Speech, and Language Processing  
On the other hand, even though ASR is available commercially for some applications, it is largely an unsolved problem-for almost all applications, the performance of ASR is not on par with human performance  ...  ; and Bayesian learning.  ...  Appreciations also go to MSR for the encouragement and support of this "mentor-mentee project", to Helen Meng as the previous EIC for handling the white-paper reviews during 2009, and to the reviewers  ... 
doi:10.1109/tasl.2013.2244083 fatcat:fv4qulshnrh4fgzmzb45mkqwmq

Deep Learning and Machine Vision for Food Processing: A Survey [article]

Lili Zhu, Petros Spachos, Erica Pensini, Konstantinos Plataniotis
2021 arXiv   pre-print
The quality and safety of food is an important issue to the whole society, since it is at the basis of human health, social development and stability.  ...  Ensuring food quality and safety is a complex process, and all stages of food processing must be considered, from cultivating, harvesting and storage to preparation and consumption.  ...  It provides researchers and the industry with faster and more efficient working methods and makes it possible for consumers to obtain safer food.  ... 
arXiv:2103.16106v1 fatcat:jr3pw7a6inf2tlpef3fk3p2xma

2020 Index IEEE Transactions on Industrial Informatics Vol. 16

2020 IEEE Transactions on Industrial Informatics  
Gao, F., Data-Driven Two-Dimensional Deep Correlated Representation Learning for Nonlinear Batch Process Monitoring; TII April 2020 2839-2848 Jiang, S., see Li, Y., 1076-1085 Jiang, X., see Gong, K.,  ...  Moulay, E., Online GMM Clustering and Mini-Batch Gradient Descent Based Optimization for Industrial IoT 4.0; 1427-1435 Messina, F., see Fortino, G., TII Sept. 2020 6133-6142 Mi, C., see Zhu, C., TII  ...  ., +, TII July 2020 4703-4713 Bayesian Just-in-Time Learning and Its Application to Industrial Soft Sensing.  ... 
doi:10.1109/tii.2021.3053362 fatcat:blfvdtsc3fdstnk6qoaazskd3i

Deep learning and machine vision for food processing: A survey

Lili Zhu, Petros Spachos, Erica Pensini, Konstantinos N. Plataniotis
2021 Current Research in Food Science  
The quality and safety of food is an important issue to the whole society, since it is at the basis of human health, social development and stability.  ...  Ensuring food quality and safety is a complex process, and all stages of food processing must be considered, from cultivating, harvesting and storage to preparation and consumption.  ...  It provides researchers and the industry with faster and more efficient working methods and makes it possible for consumers to obtain safer food.  ... 
doi:10.1016/j.crfs.2021.03.009 pmid:33937871 pmcid:PMC8079277 fatcat:cqzvzbwwjrdulnve6shf7o2agu

Big Data Processing and Mining for Next Generation Intelligent Transportation Systems

Jelena Fiosina, Maxims Fiosins, Jörg P. Müller
2013 Jurnal Teknologi  
Real-world application scenarios are needed to derive requirements for software architecture and novel features of ITS in the context of the Internet of Things (IoT) and cloud technologies.  ...  This study presents real-world scenarios of ITS applications, and demonstrates the need for next-generation Big Data analysis and optimization strategies.  ...  Acknowledgements The research leading to these results has received funding from the European Union Seventh  ... 
doi:10.11113/jt.v63.1949 fatcat:xtrirhsm3rg4tkw2siwitjoxhu

A sequential distance-based approach for imputing missing data: Forward Imputation

Nadia Solaro, Alessandro Barbiero, Giancarlo Manzi, Pier Alda Ferrari
2016 Advances in Data Analysis and Classification  
of stochastic volatility (SV) based on Gaussian processes, a flexible framework for Bayesian nonlinear regression, is provided.  ...  For example, we may model f t and ε i,t by auto-regressive processes and λ i and ω i by diffuse Gaussian densities.  ... 
doi:10.1007/s11634-016-0243-0 fatcat:yvrqlgllsbesbnvnzzci2egpl4
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