Energy efficiency evaluation method based on deep learning model

2016 ◽  
Author(s):  
Meng Fansheng ◽  
Li Bin ◽  
Yue Zenglei ◽  
Cheng Jiangnan ◽  
Liu Zhi ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Deep Learning ◽  
Evaluation Method ◽  
Learning Model ◽  
Efficiency Evaluation ◽  
Energy Efficiency Evaluation ◽  
Deep Learning Model

scholarly journals Energy efficiency evaluation method for submersible pushingflow agitator for sewage treatment

2018 ◽  
Vol 53 ◽  
pp. 04012
Author(s):  
Zhang Xiaoxin ◽  
Huang Jin ◽  
Lin Ling ◽  
Wang Yueping ◽  
Zhang Xinheng
Keyword(s):  
Energy Efficiency ◽  
Evaluation Method ◽  
Sewage Treatment ◽  
Efficiency Evaluation ◽  
Calculation Formula ◽  
Factors Affecting ◽  
Research Advance ◽  
Energy Efficiency Evaluation ◽  
At Home

This paper introduced the development and research advance of the submersible pushing-flow agitator for sewage treatment at home and abroad. By analyzing four factors affecting the energy efficiency of the submersible pushing-flow agitator, the energy efficiency evaluation method and calculation formula of the submersible pushing-flow agitator for sewage treatment in China are analyzed and discussed in this paper.

scholarly journals A Novel Interference Avoidance Based on a Distributed Deep Learning Model for 5G-enabled IoT

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6555
Author(s):  
Radwa Ahmed Osman ◽  
Sherine Nagy Saleh ◽  
Yasmine N. M. Saleh
Keyword(s):  
Energy Efficiency ◽  
Deep Learning ◽  
Optimization Technique ◽  
Data Communication ◽  
Learning Model ◽  
Base Station ◽  
System Throughput ◽  
Interference Avoidance ◽  
5G Networks ◽  
Deep Learning Model

The co-existence of fifth-generation (5G) and Internet-of-Things (IoT) has become inevitable in many applications since 5G networks have created steadier connections and operate more reliably, which is extremely important for IoT communication. During transmission, IoT devices (IoTDs) communicate with IoT Gateway (IoTG), whereas in 5G networks, cellular users equipment (CUE) may communicate with any destination (D) whether it is a base station (BS) or other CUE, which is known as device-to-device (D2D) communication. One of the challenges that face 5G and IoT is interference. Interference may exist at BSs, CUE receivers, and IoTGs due to the sharing of the same spectrum. This paper proposes an interference avoidance distributed deep learning model for IoT and device to any destination communication by learning from data generated by the Lagrange optimization technique to predict the optimum IoTD-D, CUE-IoTG, BS-IoTD and IoTG-CUE distances for uplink and downlink data communication, thus achieving higher overall system throughput and energy efficiency. The proposed model was compared to state-of-the-art regression benchmarks, which provided a huge improvement in terms of mean absolute error and root mean squared error. Both analytical and deep learning models reached the optimal throughput and energy efficiency while suppressing interference to any destination and IoTG.

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