scholarly journals Automation and control of laser wakefield accelerators using Bayesian optimization

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
R. J. Shalloo ◽  
S. J. D. Dann ◽  
J.-N. Gruse ◽  
C. I. D. Underwood ◽  
A. F. Antoine ◽  
...  
Keyword(s):  
Pulse Length ◽  
Bayesian Optimization ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Laser Wakefield ◽  
Accelerating Structure ◽  
Automation And Control ◽  
Laser Pulse Shape ◽  
And Control ◽  
Wakefield Accelerators

AbstractLaser wakefield accelerators promise to revolutionize many areas of accelerator science. However, one of the greatest challenges to their widespread adoption is the difficulty in control and optimization of the accelerator outputs due to coupling between input parameters and the dynamic evolution of the accelerating structure. Here, we use machine learning techniques to automate a 100 MeV-scale accelerator, which optimized its outputs by simultaneously varying up to six parameters including the spectral and spatial phase of the laser and the plasma density and length. Most notably, the model built by the algorithm enabled optimization of the laser evolution that might otherwise have been missed in single-variable scans. Subtle tuning of the laser pulse shape caused an 80% increase in electron beam charge, despite the pulse length changing by just 1%.

Locally Recurrent Neural Networks and Their Applications

2010 ◽  
pp. 195-222
Author(s):  
Todor D. Ganchev
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Computational Models ◽  
Machine Learning Techniques ◽  
Spatial Correlations ◽  
Temporal Correlations ◽  
Learning Techniques ◽  
Locally Recurrent ◽  
The One ◽  
And Control

In this chapter we review various computational models of locally recurrent neurons and deliberate the architecture of some archetypal locally recurrent neural networks (LRNNs) that are based on them. Generalizations of these structures are discussed as well. Furthermore, we point at a number of realworld applications of LRNNs that have been reported in past and recent publications. These applications involve classification or prediction of temporal sequences, discovering and modeling of spatial and temporal correlations, process identification and control, etc. Validation experiments reported in these developments provide evidence that locally recurrent architectures are capable of identifying and exploiting temporal and spatial correlations (i.e., the context in which events occur), which is the main reason for their advantageous performance when compared with the one of their non-recurrent counterparts or other reasonable machine learning techniques.

scholarly journals Machine Learning for Benchmarking Models of Heating Energy Demand of Houses in Northern Canada

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1158
Author(s):  
Behrad Bezyan ◽  
Radu Zmeureanu
Keyword(s):  
Machine Learning ◽  
Energy Demand ◽  
Energy Use ◽  
Machine Learning Techniques ◽  
Northern Canada ◽  
Automation And Control ◽  
And Performance ◽  
Energy Signature ◽  
Heating Energy Demand ◽  
And Control

In most cases, the benchmarking models of energy use in houses are developed based on current and past data, and they continue to be used without any update. This paper proposes the method of retraining of benchmarking models by applying machine learning techniques when new measurements are made available. The method uses as a case study the measurements of heating energy demand from two semi-detached houses of Northern Canada. The results of the prediction of heating energy demand using static or augmented window techniques are compared with measurements. The daily energy signature is used as a benchmarking model due to its simplicity and performance. However, the proposed retraining method can be applied to any form of benchmarking model. The method should be applied in all possible situations, and be an integral part of intelligent building automation and control systems (BACS) for the ongoing commissioning for building energy-related applications.

Application of Machine Learning Techniques in Additive Manufacturing: A Review

2022 ◽  
pp. 1-24
Author(s):  
Amithkumar Gajakosh ◽  
R. Suresh Kumar ◽  
V. Mohanavel ◽  
Ragavanantham Shanmugam ◽  
Monsuru Ramoni
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Manufacturing Process ◽  
Control Process ◽  
Machine Learning Techniques ◽  
Review Of The Literature ◽  
Learning Techniques ◽  
Manufactured Products ◽  
And Control ◽  
Monitoring Quality

This chapter provides an analysis of the state-of-the-art in ML applications for optimizing the additive manufacturing process. This chapter primarily presents a review of the literature on the use of machine learning (ML) in optimizing the additive manufacturing process at various stages. The chapter identifies ML-researched areas in which ML can be used to optimize processes such as process design, process plan and control, process monitoring, quality enhancement of additively manufactured products, and so on. In addition, general literature on the intersection of additive manufacturing and machine learning will be presented. The benefits and drawbacks of ML for additive manufacturing will be discussed, as well as existing obstacles that are currently limiting applications.

scholarly journals Data Caching at Fog Nodes Under IoT Networks: Review of Machine Learning Approaches

2020 ◽  
Author(s):  
Riya Tapwal ◽  
Nitin Gupta ◽  
Qin Xin
Keyword(s):  
Machine Learning ◽  
Fog Computing ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Bandwidth Utilization ◽  
Data Caching ◽  
Learning Techniques ◽  
Future Demands ◽  
Iot Devices ◽  
And Control

<div>IoT devices (wireless sensors, actuators, computer devices) produce large volume and variety of data and the data</div><div>produced by the IoT devices are transient. In order to overcome the problem of traditional IoT architecture where</div><div>data is sent to the cloud for processing, an emerging technology known as fog computing is proposed recently.</div><div>Fog computing brings storage, computing and control near to the end devices. Fog computing complements the</div><div>cloud and provide services to the IoT devices. Hence, data used by the IoT devices must be cached at the fog nodes</div><div>in order to reduce the bandwidth utilization and latency. This chapter discusses the utility of data caching at the</div><div>fog nodes. Further, various machine learning techniques can be used to reduce the latency by caching the data</div><div>near to the IoT devices by predicting their future demands. Therefore, this chapter also discusses various machine</div><div>learning techniques that can be used to extract the accurate data and predict future requests of IoT devices.</div>

A Bayesian optimized discriminant analysis model for condition monitoring of face milling cutter using vibration datasets

2021 ◽  
pp. 1-12
Author(s):  
Naman S. Bajaj ◽  
Abhishek D. Patange ◽  
R. Jegadeeshwaran ◽  
Kaushal A. Kulkarni ◽  
Rohan S. Ghatpande ◽  
...  
Keyword(s):  
Machine Learning ◽  
Discriminant Analysis ◽  
Condition Monitoring ◽  
Machining Process ◽  
Bayesian Optimization ◽  
Machine Learning Techniques ◽  
Analysis Model ◽  
Tool Condition ◽  
Learning Techniques ◽  
Optimization Search

Abstract With the advent of Industry 4.0, which conceptualizes self-monitoring of rotating machine parts by adopting techniques like Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), data analytics, cloud computing, etc. The significant research area in predictive maintenance is Tool Condition Monitoring (TCM) as the tool condition affects the overall machining process and its economics. Lately, machine learning techniques are being used to classify the tool's condition in operation. These techniques are cost-saving and help industries with adopting future-proof solutions for their operations. One such technique called Discriminant analysis (DA) must be examined particularly for TCM. Owing to its less expensive computation and shorter run times, using them in TCM will ensure effective use of the cutting tool and reduce maintenance times. This paper presents a Bayesian optimized discriminant analysis model to classify and monitor the tool condition into three user-defined classes. The data is collected using an in-house designed and developed Data Acquisition (DAQ) module set up on a Vertical Machining Center (VMC). The hyperparameter tuning has been incorporated using Bayesian optimization search, and the parameter which gives the best model was found out to be ‘Linear’, achieving an accuracy of 93.3%. This work confirms the feasibility of machine learning techniques like DA in the field of TCM and using Bayesian optimization algorithms to fine-tune the model, making it industry-ready.

scholarly journals Data Caching at Fog Nodes Under IoT Networks: Review of Machine Learning Approaches

2020 ◽  
Author(s):  
Riya Tapwal ◽  
Nitin Gupta ◽  
Qin Xin
Keyword(s):  
Machine Learning ◽  
Fog Computing ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Bandwidth Utilization ◽  
Data Caching ◽  
Learning Techniques ◽  
Future Demands ◽  
Iot Devices ◽  
And Control

<div>IoT devices (wireless sensors, actuators, computer devices) produce large volume and variety of data and the data</div><div>produced by the IoT devices are transient. In order to overcome the problem of traditional IoT architecture where</div><div>data is sent to the cloud for processing, an emerging technology known as fog computing is proposed recently.</div><div>Fog computing brings storage, computing and control near to the end devices. Fog computing complements the</div><div>cloud and provide services to the IoT devices. Hence, data used by the IoT devices must be cached at the fog nodes</div><div>in order to reduce the bandwidth utilization and latency. This chapter discusses the utility of data caching at the</div><div>fog nodes. Further, various machine learning techniques can be used to reduce the latency by caching the data</div><div>near to the IoT devices by predicting their future demands. Therefore, this chapter also discusses various machine</div><div>learning techniques that can be used to extract the accurate data and predict future requests of IoT devices.</div>

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