Data-Driven Self-Learning Controller Design Approach for Power-Aware IoT Devices based on Double Q-Learning Strategy-Reference-Cited by-同舟云学术

Data-Driven Self-Learning Controller Design Approach for Power-Aware IoT Devices based on Double Q-Learning Strategy

Published:2021-12-05 Issue: Volume: Page:
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Container-title:2021 IEEE Symposium Series on Computational Intelligence (SSCI)
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Author:

Paterova Tereza,Prauzek Michal,Konecny Jaromir

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9659537/9659538/09659989.pdf?arnumber=9659989

Reference27 articles.

1. Efficient, Long-Term Logging of Rich Data Sensors Using Transient Sensor Nodes

2. Design of Low-Power SoC for Wearable Healthcare Device

3. An adaptive transmission power control algorithm for wearable healthcare systems based on variations in the body conditions;lee;Journal of Information Processing Systems,2019

4. An Overview of Machine Learning within Embedded and Mobile Devices–Optimizations and Applications

5. Machine Learning in Resource-Scarce Embedded Systems, FPGAs, and End-Devices: A Survey

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