scholarly journals Machine Learning Enhanced Spectroscopic Analysis: Towards Autonomous Chemical Mixture Characterization for Rapid Process Optimization

2022 ◽  
Author(s):  
Andrea Angulo ◽  
Lankun Yang ◽  
Eray S Aydil ◽  
Miguel A. Modestino
Keyword(s):  
Machine Learning ◽  
Process Optimization ◽  
Parameter Space ◽  
Chemical Process ◽  
Spectroscopic Analysis ◽  
Process Development ◽  
Operating Parameter ◽  
Optimization Methods ◽  
Chemical Mixture ◽  
Mixture Characterization

Autonomous chemical process development and optimization methods use algorithms to explore the operating parameter space based on feedback from experimentally determined exit stream compositions. Measuring the compositions of multicomponent streams...

scholarly journals Machine Learning Classification and Regression Approaches for Optical Network Traffic Prediction

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1578
Author(s):  
Daniel Szostak ◽  
Adam Włodarczyk ◽  
Krzysztof Walkowiak
Keyword(s):  
Machine Learning ◽  
Optical Networks ◽  
Network Traffic ◽  
Optical Network ◽  
Optimization Methods ◽  
Supervised Machine Learning ◽  
Traffic Prediction ◽  
Machine Learning Classification ◽  
Classification And Regression ◽  
Network Technologies

Rapid growth of network traffic causes the need for the development of new network technologies. Artificial intelligence provides suitable tools to improve currently used network optimization methods. In this paper, we propose a procedure for network traffic prediction. Based on optical networks’ (and other network technologies) characteristics, we focus on the prediction of fixed bitrate levels called traffic levels. We develop and evaluate two approaches based on different supervised machine learning (ML) methods—classification and regression. We examine four different ML models with various selected features. The tested datasets are based on real traffic patterns provided by the Seattle Internet Exchange Point (SIX). Obtained results are analyzed using a new quality metric, which allows researchers to find the best forecasting algorithm in terms of network resources usage and operational costs. Our research shows that regression provides better results than classification in case of all analyzed datasets. Additionally, the final choice of the most appropriate ML algorithm and model should depend on the network operator expectations.

scholarly journals Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

2021 ◽  
Vol 11 (4) ◽  
pp. 1627
Author(s):  
Yanbin Li ◽  
Gang Lei ◽  
Gerd Bramerdorfer ◽  
Sheng Peng ◽  
Xiaodong Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Design Optimization ◽  
Optimization Methods ◽  
Machine Learning Algorithms ◽  
Cloud Services ◽  
Robust Design Optimization ◽  
Support Vector ◽  
Future Directions ◽  
Electromagnetic Devices ◽  
Recent Developments

This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices.

Predicting Differential Ion Mobility Behaviour in silico using Machine Learning

The Analyst ◽  
2021 ◽  
Author(s):  
Christian Ieritano ◽  
J. Larry Campbell ◽  
Scott Hopkins
Keyword(s):  
Machine Learning ◽  
Parameter Space ◽  
Ion Mobility ◽  
In Silico ◽  
Differential Mobility Spectrometry ◽  
Differential Mobility ◽  
Mobility Behaviour ◽  
Separation Conditions

Although there has been a surge in popularity of differential mobility spectrometry (DMS) within analytical workflows, determining separation conditions within the DMS parameter space still requires manual optimization. A means...

Process Optimization for Suction Plastic Forming of In-Mold Decoration Plastic Sheet with CAE

2010 ◽  
Vol 102-104 ◽  
pp. 74-78
Author(s):  
Bin Gao ◽  
Xiu Rong Nan ◽  
Bai Zhong Wu
Keyword(s):  
Process Optimization ◽  
Thin Shell ◽  
Optimization Methods ◽  
Analysis Software ◽  
Optimization Scheme ◽  
Forming Process ◽  
Element Analysis ◽  
Plastic Sheet ◽  
Mesh Model ◽  
Plastic Forming

The suction plastic forming process for in-mold decoration plastic sheet has been the best process for thin-shell plastic exterior decoration parts. But the suction plastic forming products still suffers from the uneven thickness. Based on the general finite element analysis software POLYFLOW for viscoelastic fluid, a set of optimization methods for suction plastic forming process of in-mold decoration plastic sheet is introduced in this paper to reduce the uneven level of thickness. These methods include establishing process optimization scheme, building mesh model, selecting the material constitutive model and determining its parameters, imposing boundary conditions and blowing pressure, and applying the mold movement. Finally, the optimized suction plastic forming process is used to produce the in-mold decoration plastic rear bumper sample of an automobile, and the results show that optimized process is effective and applicable.

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