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The impact of physicochemical features of carbon electrodes on the capacitive performance of supercapacitors: A machine learning approach [article]

Sachit Mishra, Rajat Srivastava, Atta Muhammad, Amit Amit, Eliodoro Chiavazzo, Matteo Fasano, Pietro Asinari
2022 arXiv   pre-print
In the current study, Machine Learning (ML) algorithms were used to determine the impact of various physicochemical properties of carbon-based materials on the capacitive performance of electric double-layer  ...  In perspective, this work introduces a new wide dataset of carbon electrodes for supercapacitors extracted from the experimental literature, also giving an instance of how electrochemical technology can  ...  SVM is a well-established ML model used for classification and other learning activities. An optimal hyperplane defines SVM as a discriminative classifier.  ... 
arXiv:2208.04172v1 fatcat:527wvwkndndbfdqea5w6vf2ori

Online hybrid modeling method with application for predicting Bauxite production indicators

Cao Binfang, Xie Yongfang, Yang Chunhua
2015 Rem : Revista Escola de Minas  
Then, a neutral network model of the regular extreme learning machine (RELM), which is based on wavelet function, is presented to predict these two indicators.  ...  It lays the foundation for optimal control of the operation parameters based on mineral grade in the flotation process.  ...  It has been demonstrated that the extreme learning machine has the same global approach property as the neural network (NN).  ... 
doi:10.1590/0370-44672014680245 fatcat:xox26yfh3bhetjisas4v6leeia

Applications of Machine Learning Algorithms to Predictive Manufacturing

Ji-Hyeong Han, Rockwon Kim, Su-Young Chi
2015 Proceedings of the 2015 International Conference on Big Data Applications and Services - BigDAS '15  
Machine learning offers a promising way for manufacturers to address both these problems as they are in an excellent position to employ learning techniques with their massive resource of historical production  ...  However, choosing modelling a strategy in this setting is far from trivial and this is the objective of this article.  ...  In machine learning the approach is flipped, and one lets the model be governed by the data.  ... 
doi:10.1145/2837060.2837066 fatcat:gyhzzpa4hrdxdhxuxagarq6n6q

Nutritive quality prediction of peaches during storage

Yuming Zhong, Yao Bao, Yumin Chen, Dequan Zhai, Jianliang Liu, Huifan Liu
2021 Food Science & Nutrition  
Thus, Fourier transform-near infrared spectroscopy could predict the two clusters based on five nutritive qualities.  ...  We evaluated the nutritive qualities of peach fruit during storage.  ...  A supervised machine learning model was used to predict the quality.  ... 
doi:10.1002/fsn3.2287 fatcat:2p2snuyo35dxrfrj5sokt3vb4e

Degrader Analysis for Diagnostic and Predictive Capabilities: A Demonstration of Progress in DoD CBM+ Initiatives

William Baker, Steven Nixon, Jeffrey Banks, Karl Reichard, Kaitlynn Castelle
2020 Procedia Computer Science  
Sensor data collected by the PHM system can be used with machine learning applications to develop failure mode predictive algorithms with greatest benefit in terms of performance, sustainment costs, and  ...  Sensor data collected by the PHM system can be used with machine learning applications to develop failure mode predictive algorithms with greatest benefit in terms of performance, sustainment costs, and  ...  Creating machine learning models that are accurate and reliable enough to provide useful predictions of machine health condition at any given point in time is non-trivial work.  ... 
doi:10.1016/j.procs.2020.02.253 fatcat:27xbejrgyjdwhmetfca67aqvv4

Prediction of paperboard properties and their variability based on raw material and process data

Rosario Othen, Samuel Schabel, Hannes Vomhoff
2021 Zenodo  
., bending stiffness and curl in machine direction (MD) and cross-machine direction (CD) so as the twist. The feature selection is crucial for the quality of the prediction.  ...  This selection improves the model and ensures its reliability, comprehensibility, and interpretability.  ...  In Neural Prediction of Product Quality Based on Pilot Paper Machine Process Measurements published in 2011 Nieminen et al. used a multi-layer perceptron (MLP) model to predict the laboratory measurements  ... 
doi:10.5281/zenodo.4672842 fatcat:xv2jburb5jg25m2nrme6sfpzxu

Predicting the effluent quality of an industrial wastewater treatment plant by way of optical monitoring

Jani Tomperi, Elisa Koivuranta, Kauko Leiviskä
2017 Journal of Water Process Engineering  
Thus, predictive modelling based on the optical monitoring variables is a potential tool to be used assistance in a process control, keeping the process in a stable operating condition and avoiding environmental  ...  Five variable selection methods were utilized to find the optimal subsets of input variables to develop predictive models for the important parameters of the wastewater treatment process efficiency and  ...  .), is greatly acknowledged for assisting in variable selection issues.  ... 
doi:10.1016/j.jwpe.2017.02.004 fatcat:35ule7xklzfiriqnhhloynnfqi

An Enhanced Wavelet Neural Network Model with Metaheuristic Harmony Search Algorithm for Epileptic Seizure Prediction

Zarita Zainuddin, Kee Huong Lai, Pauline Ong
2015 International Journal of Modeling and Optimization  
First, a binary version of the HS algorithm is employed in the stage of feature selection, which aims at selecting the most optimal subset of input features for the WNN model during the preprocessing stage  ...  In this paper, an enhanced wavelet neural network (WNN) model is proposed by incorporating the metaheuristic harmony search (HS) algorithm.  ...  In the survey reported in [23] , the optimal time frame for a prediction time window, called seizure occurrence period (SOP) was investigated.  ... 
doi:10.7763/ijmo.2015.v5.442 fatcat:ruttsqwwkza53bcuinb33mzibi

Convolutional Neural Network for Water Quality Prediction in WSN

2019 Journal of Networking and Communication Systems (JNACS)  
Here, the Convolutional Neural Network (CNN) is exploited to predict the water quality of in Wireless Sensor Network (WSN).  ...  For that reason, a diversity of sensors can be positioned in the ponds to gather the need parameters and water quality detection can be performed by exploiting the data classification methods.  ...  Hence, in the current society, it is of enormous consequence to discover the approaches of water quality prediction [2] .  ... 
doi:10.46253/jnacs.v2i3.a5 fatcat:4lha4jgiajh3bjwhmqs4xiflby

Coarse Return Prediction in a Cement Industry's Closed Grinding Circuit System through a Fully Connected Deep Neural Network (FCDNN) Model

Morad Danishvar, Sebelan Danishvar, Francisco Souza, Pedro Sousa, Alireza Mousavi
2021 Applied Sciences  
A combined event modeling for feature extraction and the fully connected deep neural network model to predict the coarseness of cement particles is proposed.  ...  A novel data-driven approach utilizing real-time monitoring control technology is proposed for the purpose of optimizing the grinding of cement processing.  ...  The single layer NN has the worst performance in all scenarios, which is comprehensible, due to the more complex and extensive architecture of the proposed approach and the LSTM.  ... 
doi:10.3390/app11041361 fatcat:lnt577js3bdo5p6pqqycp2hj5q

Model predictive control: past, present and future

Manfred Morari, Jay H. Lee
1999 Computers and Chemical Engineering  
More than 15 years after model predictive control (MPC) appeared in industry as an effective means to deal with multivariable constrained control problems, a theoretical basis for this technique has started  ...  The issues of feasibility of the on-line optimization, stability and performance are largely understood for systems described by linear models.  ...  We wish to thank Alberto Bemporad for his assistance in preparing the paper, and Tom Badgwell and Alex Zheng for their helpful reviews.  ... 
doi:10.1016/s0098-1354(98)00301-9 fatcat:4cri3rcjobdjpd4zmfgfcxmcci

Improved Ensemble Feature Selection Based on DT for KPI Prediction

Fulin Gao, Shuai Tan, Hongbo Shi, Yang Tao, Bing Song
2021 IEEE Access  
Finally, a realistic shield tunnel case in China is used to evaluate the feasibility and effectiveness of proposed approach.  ...  After re-evaluating the variable scores, more highquality variables can be selected to build a more accurate and robust KPI prediction model.  ...  The variables that stand out in the comprehensive variable rankings will be used to build KPI prediction models. C.  ... 
doi:10.1109/access.2021.3116201 fatcat:dwnnjbtzdjaj7ou2z6jqgb4i7q

Operationalizing Heterogeneous Data-Driven Process Models for Various Industrial Sectors through Microservice-Oriented Cloud-Based Architecture [chapter]

Valdemar Lipenko, Sebastian Nigl, Andreas Roither-Voigt, Zelenay David
2021 AI and Learning Systems - Industrial Applications and Future Directions  
In this context, modern machine learning capabilities to predict future production quality outcomes, model predictive control to better account for complex multivariable environments of process industry  ...  Tieto successfully applied the outlined approach during the participation in FUture DIrections for Process industry Optimization (FUDIPO), a project funded by the European Commission under the H2020 program  ...  Architecture for SAP ERP integration of predictive models.  ... 
doi:10.5772/intechopen.92896 fatcat:2joa5flqezahfiqfcorwc63xe4

Neural-based Predictive Control Applied to FACTS Devices

Ruben Tapia O., Pavel Zuniga H., Juan M. Ramirez
2006 2006 38th North American Power Symposium  
Key words: Neuro-controller % Power electronic % Predictive control Plant Model Optimization Objective function u Control algorithm ref Plant Model Optimization Objective function u y Control algorithm  ...  In this paper applications are presented employing the neural-based predictive control (NPC) for controlling Flexible AC Transmission Systems (FACTS) devices with the purpose of regulating: bus voltage  ...  Model Based Predictive Control: The model based predictive control (MPC) has been used in many industrial applications related with the refinement, petrochemistry, pulp and paper, as well as in alimentary  ... 
doi:10.1109/naps.2006.359592 fatcat:hce2xe2b2rgszim2tjmu322vau

Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm

Jae-Hong Lee, Do-hyung Kim, Seong-Nyum Jeong, Seong-Ho Choi
2018 Journal of Periodontal & Implant Science  
Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and  ...  matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python.  ...  Nevertheless, maintaining and securing a high-quality dataset is still important for the deep learning approach.  ... 
doi:10.5051/jpis.2018.48.2.114 pmid:29770240 pmcid:PMC5944222 fatcat:a27t37igmncj7kau5w6p7flla4
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