A Machine Learning Approach to Cluster Characterization for Atom Probe Tomography

Bennett, Roland A.; Proudian, Andrew P.; Zimmerman, Jeramy D.

doi:10.1017/s1431927621001987

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…e static scheduling method needs to obtain a batch of task information and system status in advance and will not change the scheduling strategy until the task is completed. e emergence of intelligent warehouse logistics dynamic scheduling can make up for the shortcomings brought by static scheduling, but the dynamic scheduling method mentioned in the research of dynamic scheduling method is actually a rescheduling method, which will readjust the scheduling strategy according to the system state [10][11][12][13][14]. However, in complex environments, the calculation and evaluation of intelligent algorithms such as genetic algorithm and particle swarm optimization take a long time and often cannot meet the real-time requirements of the system.…”

Section: Introductionmentioning

confidence: 99%

Research on Intelligent Warehousing and Logistics Management System of Electronic Market Based on Machine Learning

Zhang¹,

Zhou²,

Jin³

et al. 2022

Computational Intelligence and Neuroscience

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This study applies the Internet of things information-aware technology to the process of electronic market warehousing and logistics management, effectively perceives warehouse electronic product logistics information, and improves the real-time perception of electronic product logistics information and the efficiency of electronic product storage logistics management. This study first analyzes the needs of the intelligent electronic market warehouse logistics management system and then builds the IoT architecture of the intelligent warehouse logistics assembly logistics management system for electronic warehouses based on machine learning algorithms, which solves the problems that exist in the current workshop electronic market warehouse logistics management. Then, the principle of RFID technology is introduced. The accuracy of RFID tag estimation is analyzed by the PEPC tag estimation algorithm. It is concluded that the PEPC tag estimation algorithm reduces the tag estimation error and improves the accuracy of tag estimation. Finally, an intelligent warehousing logistics management system based on IoT RFID technology is established. The test results show that the system can meet the requirements of intelligent warehousing function in the electronic market, which will greatly improve the warehousing efficiency of electronic products.

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