Diagnosis of Unbalance in Lightweight Rotating Machines Using a Recurrent Neural Network Suitable for an Edge-Computing Framework

2022 ◽  
Author(s):  
L. Y. Imamura ◽  
S. L. Avila ◽  
F. S. Pacheco ◽  
M. B. C. Salles ◽  
L. S. Jablon
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Edge Computing ◽  
Rotating Machines ◽  
Computing Framework

scholarly journals Hard Disk Drive Failure Prediction for Mobile Edge Computing Based on an LSTM Recurrent Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jing Shen ◽  
Yongjian Ren ◽  
Jian Wan ◽  
Yunlong Lan
Keyword(s):  
Neural Network ◽  
Hard Disk Drive ◽  
Recurrent Neural Network ◽  
Failure Prediction ◽  
Hard Disk ◽  
Disk Drive ◽  
Edge Computing ◽  
Good Prediction ◽  
Mobile Edge Computing ◽  
Good Prediction Accuracy

With the increase in intelligence applications and services, like real-time video surveillance systems, mobile edge computing, and Internet of things (IoT), technology is greatly involved in our daily life. However, the reliability of these systems cannot be always guaranteed due to the hard disk drive (HDD) failures of edge nodes. Specifically, a lot of read/write operations and hazard edge environments make the maintenance work even harder. HDD failure prediction is one of the scalable and low-overhead proactive fault tolerant approaches to improve device reliability. In this paper, we propose an LSTM recurrent neural network-based HDD failure prediction model, which leverages the long temporal dependence feature of the drive health data to improve prediction efficiency. In addition, we design a new health degree evaluation method, which stores current health details and deterioration. The comprehensive experiments on two real-world hard drive datasets demonstrate that the proposed approach achieves a good prediction accuracy with low overhead.

scholarly journals DRAC: a delta recurrent neural network-based arithmetic coding algorithm for edge computing

2021 ◽  
Author(s):  
Bowei Shan ◽  
Yong Fang
Keyword(s):  
Neural Network ◽  
Power Consumption ◽  
Recurrent Neural Network ◽  
State Of The Art ◽  
The State ◽  
Edge Computing ◽  
Experimental Results ◽  
Arithmetic Coding ◽  
Speedup Ratio ◽  
Consumption Saving

AbstractThis paper develops an arithmetic coding algorithm based on delta recurrent neural network for edge computing devices called DRAC. Our algorithm is implemented on a Xilinx Zynq 7000 Soc board. We evaluate DRAC with four datasets and compare it with the state-of-the-art compressor DeepZip. The experimental results show that DRAC outperforms DeepZip and achieves 5X speedup ratio and 20X power consumption saving.

scholarly journals A collaborative cloud-edge computing framework in distributed neural network

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Shihao Xu ◽  
Zhenjiang Zhang ◽  
Michel Kadoch ◽  
Mohamed Cheriet
Keyword(s):  
Neural Network ◽  
Ant Colony Algorithm ◽  
Scheduling Algorithm ◽  
Task Assignment ◽  
Edge Computing ◽  
Network Training ◽  
Task Scheduling Algorithm ◽  
Current Task ◽  
Computing Framework ◽  
The Internet Of Things

Abstract The emergence of edge computing provides a new solution to big data processing in the Internet of Things (IoT) environment. By combining edge computing with deep neural network, it can make better use of the advantages of multi-layer architecture of the network. However, the current task offloading and scheduling frameworks for edge computing are not well applicable to neural network training tasks. In this paper, we propose a task model offloading algorithm by considering how to optimally deploy neural network model into the edge nodes. An adaptive task scheduling algorithm is also designed to adaptively optimize the task assignment by using the improved ant colony algorithm. Based on them, a collaborative cloud-edge computing framework is proposed, which can be used in the distributed neural network. Moreover, this framework sets up some mechanisms so that the cloud can collaborate with edge computing in the work. The simulation results show that the framework can reduce time delay and energy consumption, and improve task accuracy.

Online Proactive Caching in Mobile Edge Computing Using Bidirectional Deep Recurrent Neural Network

2019 ◽  
Vol 6 (3) ◽  
pp. 5520-5530 ◽  
Author(s):  
Laha Ale ◽  
Ning Zhang ◽  
Huici Wu ◽  
Dajiang Chen ◽  
Tao Han
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Deep Recurrent Neural Network

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

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