Status Monitoring and Fault Diagnosis Network System Based on UPnP

2007 ◽  
Vol 353-358 ◽  
pp. 2632-2635
Author(s):  
Pei Yu Li ◽  
Da Peng Tan ◽  
Tao Qing Zhou ◽  
Bo Yu Lin
Keyword(s):  
Fault Diagnosis ◽  
Embedded System ◽  
Real Time ◽  
Data Storage ◽  
Network Architecture ◽  
Network System ◽  
Time Data ◽  
Status Monitoring ◽  
Self Recognition ◽  
Real Time Data

Aiming at some problems in the fields of industry monitoring technology (IMT) such as bad dynamic ability and poor versatility, this paper brought forward a kind of intelligent Status monitoring and Fault diagnosis Network System (SFNS) based on UPnP-Universal Plug and Play. The model for fault diagnosis network system was established according to characteristics and requirements of IMT network, and system network architecture was designed and realized by UPnP. Using embedded system technology, real-time data collection node, monitoring center node and data storage server were designed, and that supplies powerful real-time data support for SFNS. Industry fields experiments proved that this system can realize self recognition, seamless linkage and other self adapting ability, and can break through the limitation of real IP address to achieve real-time remote monitoring on line.

scholarly journals Analisa Real-Time Data log honeypot menggunakan Algoritma K-Means pada serangan Distributed Denial of Service

Repositor ◽  
2020 ◽  
Vol 2 (5) ◽  
pp. 541
Author(s):  
Denni Septian Hermawan ◽  
Syaifuddin Syaifuddin ◽  
Diah Risqiwati
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Denial Of Service ◽  
The Internet ◽  
Distributed Denial Of Service ◽  
Time Data ◽  
Local Networks ◽  
Real Time Data ◽  
Internet Networks ◽  
Data Log

AbstrakJaringan internet yang saat ini di gunakan untuk penyimpanan data atau halaman informasi pada website menjadi rentan terhadap serangan, untuk meninkatkan keamanan website dan jaringannya, di butuhkan honeypot yang mampu menangkap serangan yang di lakukan pada jaringan lokal dan internet. Untuk memudahkan administrator mengatasi serangan digunakanlah pengelompokan serangan dengan metode K-Means untuk mengambil ip penyerang. Pembagian kelompok pada titik cluster akan menghasilkan output ip penyerang.serangan di ambil sercara realtime dari log yang di miliki honeypot dengan memanfaatkan MHN.Abstract The number of internet networks used for data storage or information pages on the website is vulnerable to attacks, to secure the security of their websites and networks, requiring honeypots that are capable of capturing attacks on local networks and the internet. To make it easier for administrators to tackle attacks in the use of attacking groupings with the K-Means method to retrieve the attacker ip. Group divisions at the cluster point will generate the ip output of the attacker. The strike is taken as realtime from the logs that have honeypot by utilizing the MHN.

scholarly journals Drilling Efficiency Improvement and Rate of Penetration Optimization by Machine Learning and Data Analytics

2020 ◽  
Vol 5 (3) ◽  
pp. 381-394
Author(s):  
Sridharan Chandrasekaran ◽  
G. Suresh Kumar
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Data Storage ◽  
Predictive Accuracy ◽  
Empirical Models ◽  
Operational Parameters ◽  
Time Data ◽  
Bottom Line ◽  
Rate Of Penetration ◽  
Real Time Data

Rate of Penetration (ROP) is one of the important factors influencing the drilling efficiency. Since cost recovery is an important bottom line in the drilling industry, optimizing ROP is essential to minimize the drilling operational cost and capital cost. Traditional the empirical models are not adaptive to new lithology changes and hence the predictive accuracy is low and subjective. With advancement in big data technologies, real- time data storage cost is lowered, and the availability of real-time data is enhanced. In this study, it is shown that optimization methods together with data models has immense potential in predicting ROP based on real time measurements on the rig. A machine learning based data model is developed by utilizing the offset vertical wells’ real time operational parameters while drilling. Data pre-processing methods and feature engineering methods modify the raw data into a processed data so that the model learns effectively from the inputs. A multi – layer back propagation neural network is developed, cross-validated and compared with field measurements and empirical models.

scholarly journals Design of Real-Time Database for the Vacuum Cold-Black Environment Simulator

2018 ◽  
Vol 232 ◽  
pp. 02034
Author(s):  
Jingyi Shao ◽  
Juan Ning ◽  
Yangyang Liu
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Network Architecture ◽  
Large Data ◽  
Space Environment ◽  
Time Data ◽  
Data Volume ◽  
Environment Simulation ◽  
Real Time Database ◽  
Interaction Efficiency

There is an urgent need to develop a real-time database (RTDB) to handle large amounts of real-time data, to realize process monitoring of vacuum cold-black environment simulation, cold-black environment data acquisition, fault processing, data storage, etc. This paper conducts real-time database design through network architecture, functional modules, communication interfaces, data management, and transaction scheduling. Simulation test and associated debugging test show that the real-time database can efficiently process large data volume, and the data interaction efficiency is greatly improved to meet the needs of space environment simulation equipment, laying a solid data foundation for subsequent applications.

The Real-Time Optimization Strategy on the Mission Database

2014 ◽  
Vol 1049-1050 ◽  
pp. 2001-2005
Author(s):  
Hua Wang ◽  
Bing Liu ◽  
Huan Ming Liu ◽  
Hui Fen Duan ◽  
Jun Lei Bao
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Relational Database ◽  
Optimization Strategy ◽  
Time Data ◽  
The Real ◽  
Storage Mechanism ◽  
Synchronization Mechanism ◽  
Real Time Data ◽  
And Control

In order to make up the real-time performance of tracking and control information database, this paper design a kind of two-layer’s real-time data storage model based on memory database and relational database. In this article, the two-layer’s real-time data storage mechanism and life cycle are expounded in detail, analyzing and inducing the real-time data characteristic and storage strategy, putting forward the memory database’s self-adaptive index algorithm of T-tree index and hash index, and introducing the database synchronization mechanism between the memory database and relational database and so on. In this way, so as to improve and optimize the real-time, reliability and security of database, provides a reliable data guarantee for future expansion of the real-time application.

Continuous and Real-Time Data Acquisition Embedded System for EAST

2010 ◽  
Vol 57 (2) ◽  
pp. 696-699 ◽  
Author(s):  
S. Li ◽  
Jiarong R. Luo ◽  
Yichun C. Wu ◽  
Guiming M. Li ◽  
Feng Wang ◽  
...  
Keyword(s):  
Embedded System ◽  
Data Acquisition ◽  
Real Time ◽  
Time Data ◽  
Real Time Data

scholarly journals High Performance Predictive Analytics in IoT

2019 ◽  
Vol 8 (4) ◽  
pp. 9266-9270
Keyword(s):  
Real Time ◽  
Network Architecture ◽  
High Performance ◽  
Predictive Analytics ◽  
Time Data ◽  
New Techniques ◽  
Real Time Data ◽  
Iot Devices ◽  
Indexing Technique ◽  
To Receive

Internet of things (IoT) is a quick-moving gathering of web associated sensors implanted in a wide-extending assortment of physical articles. While things can be any physical item (energize or lifeless) on the planet, to which you could associate or implant a sensor. Sensors can take countless potential estimations. Sensors produce gigantic measures of new, organized, unstructured, ongoing information, and structures enormous information. IoT information is exceptionally huge and confused, which can give genuine-time setting and supposition data about genuine articles or nature. Among the different challenges that the present IoT is facing, the three prime areas of concern are, need of efficient framework to receive IoT data, a need of a new scalable parallel indexing technique for efficiently storing IoT data and securing IoT generated data at all the stages i.e. from the edge devices to the cloud. A new efficient framework is introduced, which can retrieve meaningful information from these IoT devices and efficiently index it. For processing such enormous real time data generated from IoT devices, new techniques are introducing which are scalable and secure. The research proposes a general IoT network architecture. It describes the interconnectivity among the different things such as sensors, receivers and cloud. The proposed architecture efficiently receives real time data from all the sensors. The prime focus is on the elimination of the existing issues in IoT. Along with this, the provision has to make for standard future proofing against these new proposed schemes.

scholarly journals Prognostics and Health Management System for Electric Vehicles with a Hierarchy Fusion Framework: Concepts, Architectures, and Methods

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Cheng Wang ◽  
Tongtong Ji ◽  
Feng Mao ◽  
Zhenpo Wang ◽  
Zhiheng Li
Keyword(s):  
Life Cycle ◽  
Fault Diagnosis ◽  
Real Time ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Health Management ◽  
Research Progress ◽  
Prognostics And Health Management ◽  
Time Data ◽  
Real Time Data

The prognostics and health management (PHM) of electric vehicles is an important guarantee for their safety and long-term development. At present, there are few studies researching about life cycle PHM system of electric vehicles. In this paper, we first summarize the research progress and key methods of PHM. Then, we propose a three-level PHM system with a hierarchy fusion architecture for electric vehicles based on the structure, data source of them. In the PHM system, we introduce a database consisting of the factory data, real-time data, and detection data. The electric vehicle's factory parameters are used for determining the life curve of the electric vehicle and its components, the real-time data are used for predicting the remaining useful lifetime (RUL) of the electric vehicle and its components, and the detection data are used for fault diagnosis. This health management database is established to help make condition-based maintenance decisions for electric vehicles. In this way, a complete electric vehicle PHM system is formed, which can realize the whole-life-cycle life prediction and fault diagnosis of electric vehicles.

Sign in / Sign up

Export Citation Format

Share Document