A machine learning approach for real‐time selection of preventive actions improving power network resilience

The metabolic engineering of pathways has been used extensively to produce molecules of interest on an industrial scale. Methods like gene regulation or substrate channeling helped to improve the desired product yield. Cell-free systems are used to overcome the weaknesses of engineered strains. One of the challenges in a cell-free system is selecting the optimized enzyme concentration for optimal yield. Here, a machine learning approach is used to select the enzyme concentration for the upper part of glycolysis. The artificial neural network approach (ANN) is known to be inefficient in extrapolating predictions outside the box: high predicted values will bump into a sort of “glass ceiling”. In order to explore this “glass ceiling” space, we developed a new methodology named glass ceiling ANN (GC-ANN). Principal component analysis (PCA) and data classification methods are used to derive a rule for a high flux, and ANN to predict the flux through the pathway using the input data of 121 balances of four enzymes in the upper part of glycolysis. The outcomes of this study are i. in silico selection of optimum enzyme concentrations for a maximum flux through the pathway and ii. experimental in vitro validation of the “out-of-the-box” fluxes predicted using this new approach. Surprisingly, flux improvements of up to 63% were obtained. Gratifyingly, these improvements are coupled with a cost decrease of up to 25% for the assay.

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Vibration based real time brake health monitoring system – A machine learning approach

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/624/1/012027 ◽

2019 ◽

Vol 624 ◽

pp. 012027 ◽

Cited By ~ 1

Author(s):

T M Alamelu Manghai ◽

R Jegadeeshwaran

Keyword(s):

Machine Learning ◽

Real Time ◽

Monitoring System ◽

Health Monitoring ◽

Learning Approach ◽

Health Monitoring System ◽

System A ◽

Machine Learning Approach

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Machine learning approach for multi-perspective volcanic eruption recognition using thermal infrared images

10.5194/egusphere-egu2020-8600 ◽

2020 ◽

Author(s):

Claudia Corradino ◽

Gaetana Ganci ◽

Giuseppe Bilotta ◽

Annalisa Cappello ◽

Ciro Del Negro

Keyword(s):

Machine Learning ◽

Decision Tree ◽

Real Time ◽

Mount Etna ◽

Learning Approach ◽

Bag Of Words ◽

Infrared Images ◽

Machine Learning Approach ◽

Thermal Infrared Images ◽

Thermal Cameras

<p>Detect, locate and characterize eruptions in real-time is fundamental to monitor volcanic activity. Here we present an automatic system able to discover and identify the main types of eruptive activities by exploiting infrared images acquired by the thermal cameras installed around Mount Etna volcano. The system, which employs the machine learning approach, is based on a decision tree tool and a bag of words-based classifier. The decision tree provides information on the visibility level of the monitored area, while the bag of words-based classifiers detects the onset of the eruptive activity and recognize the eruption type among either explosion and/or lava flow or plume. Thus, applied to each image of all thermal cameras over Etna in real-time, the proposed system provides two outputs, namely the visibility level and the recognized activity status. By merging the outcomes coming from each thermal camera, the monitored phenomena can be fully described from different perspectives getting deeper information in real-time and in an automatic way.&#160; &#160;</p>

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