Adaptive Focused Ion Beam Milling through Machine Learning Algorithm Integration

Turnquist, Max; Lewis, Peter H.; Lau, Troy; Brundage, E.; Magyar, Andrew P.

doi:10.1017/s1431927621005985

Cited by 1 publication

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“…Because the value range of linear regression is continuous, it cannot be used to fit the discrete variable Yi, but it can be used to fit the conditional probability. Therefore, the logistic function is used to map the output range to (0,1) to obtain the probability of each category in the case of two categories, so there is a probability prediction function constructed by the formula [13].…”

Section: Machine Learning Algorithmmentioning

confidence: 99%

Design and Implementation of Distributed System Based on Machine Learning Algorithm and Numerical Simulation

2021

DPS

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In recent years, the Internet industry has entered a period of rapid development, and smart life has brought people a lot of experiences they have never had before. Rich application scenarios not only bring convenience to people, but also expose more and more network security problems. Network traffic types are more diversified, network security monitoring is becoming more and more difficult, and network communication quality and user host security are constantly facing the threat of network intrusion. This paper studies the current network traffic anomaly detection methods. Aiming at the problems of low accuracy and difficult real-time monitoring caused by the limitations of data scale and processing capacity in the previous methods, combined with machine learning and data simulation technology, a multi model fusion streaming parallel anomaly detection method is proposed, which enables distributed processing of massive streaming data on the basis of ensuring algorithm accuracy, At the same time, a visualization system based on network traffic anomaly detection is developed. The system can monitor the flow, and the reliability experiment can also meet the daily needs.

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Section: Machine Learning Algorithmmentioning

confidence: 99%

Design and Implementation of Distributed System Based on Machine Learning Algorithm and Numerical Simulation

2021

DPS

View full text Add to dashboard Cite

show abstract

Adaptive Focused Ion Beam Milling through Machine Learning Algorithm Integration

Cited by 1 publication

References 0 publications

Design and Implementation of Distributed System Based on Machine Learning Algorithm and Numerical Simulation

Design and Implementation of Distributed System Based on Machine Learning Algorithm and Numerical Simulation

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