scholarly journals Research on anomaly detection and real-time reliability evaluation with the log of cloud platform

2022 ◽  
Author(s):  
Bo Wang ◽  
Qingyi Hua ◽  
Haoming Zhang ◽  
Xin Tan ◽  
Yahui Nan ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Reliability Evaluation ◽  
Cloud Platform

scholarly journals Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4237
Author(s):  
Hoon Ko ◽  
Kwangcheol Rim ◽  
Isabel Praça
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Feature Detection ◽  
Trading System ◽  
Judgment Accuracy ◽  
Energy Trading ◽  
Real Time Processing ◽  
Detection Model ◽  
Time Required ◽  
The Relationship

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

scholarly journals Anomaly Detection and Attack Classification for Train Real-Time Ethernet

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 22528-22541
Author(s):  
Ruifeng Duo ◽  
Xiaobo Nie ◽  
Ning Yang ◽  
Chuan Yue ◽  
Yongxiang Wang
Keyword(s):  
Anomaly Detection ◽  
Real Time

Real-time causal processing of anomaly detection for hyperspectral imagery

2014 ◽  
Vol 50 (2) ◽  
pp. 1511-1534 ◽  
Author(s):  
Shih-Yu Chen ◽  
Yulei Wang ◽  
Chao-Cheng Wu ◽  
Chunhong Liu ◽  
Chein-I Chang
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Hyperspectral Imagery

scholarly journals Multi-Parameter Vital Sign Telemedicine System Using Web Socket for COVID-19 Pandemics

Healthcare ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 285
Author(s):  
Chuchart Pintavirooj ◽  
Tanapon Keatsamarn ◽  
Treesukon Treebupachatsakul
Keyword(s):  
Real Time ◽  
Vital Sign ◽  
Vital Signs ◽  
Mobile Network ◽  
Cloud Platform ◽  
Telemedicine System ◽  
Web Based ◽  
The Real ◽  
Real Time Application ◽  
Commercial Platform

Telemedicine has become an increasingly important part of the modern healthcare infrastructure, especially in the present situation with the COVID-19 pandemics. Many cloud platforms have been used intensively for Telemedicine. The most popular ones include PubNub, Amazon Web Service, Google Cloud Platform and Microsoft Azure. One of the crucial challenges of telemedicine is the real-time application monitoring for the vital sign. The commercial platform is, by far, not suitable for real-time applications. The alternative is to design a web-based application exploiting Web Socket. This research paper concerns the real-time six-parameter vital-sign monitoring using a web-based application. The six vital-sign parameters are electrocardiogram, temperature, plethysmogram, percent saturation oxygen, blood pressure and heart rate. The six vital-sign parameters were encoded in a web server site and sent to a client site upon logging on. The encoded parameters were then decoded into six vital sign signals. Our proposed multi-parameter vital-sign telemedicine system using Web Socket has successfully remotely monitored the six-parameter vital signs on 4G mobile network with a latency of less than 5 milliseconds.

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