A data-driven implementation of telecommunication network systems

2002 ◽  
Author(s):  
H. Nishikawa ◽  
S. Miyata ◽  
S. Yoshida ◽  
T. Muramatsu ◽  
H. Ishii ◽  
...  
Keyword(s):  
Telecommunication Network ◽  
Data Driven ◽  
Network Systems

scholarly journals Data-driven control of complex networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Giacomo Baggio ◽  
Danielle S. Bassett ◽  
Fabio Pasqualetti
Keyword(s):  
Complex Networks ◽  
Network Dynamics ◽  
Noisy Data ◽  
Data Driven ◽  
Control Algorithms ◽  
Optimal Controls ◽  
Network Systems ◽  
The Past ◽  
Efficient Control ◽  
Finite Set

AbstractOur ability to manipulate the behavior of complex networks depends on the design of efficient control algorithms and, critically, on the availability of an accurate and tractable model of the network dynamics. While the design of control algorithms for network systems has seen notable advances in the past few years, knowledge of the network dynamics is a ubiquitous assumption that is difficult to satisfy in practice. In this paper we overcome this limitation, and develop a data-driven framework to control a complex network optimally and without any knowledge of the network dynamics. Our optimal controls are constructed using a finite set of data, where the unknown network is stimulated with arbitrary and possibly random inputs. Although our controls are provably correct for networks with linear dynamics, we also characterize their performance against noisy data and in the presence of nonlinear dynamics, as they arise in power grid and brain networks.

The State-of-the-Art in Structural Integrity Management: A Review and Proposed Data-Driven Approach

2019 ◽  
Author(s):  
YeongAe Heo
Keyword(s):  
Risk Management ◽  
Environmental Conditions ◽  
Structural Integrity ◽  
State Of The Art ◽  
Cost Effective ◽  
Offshore Structures ◽  
Data Driven ◽  
Future Research ◽  
Network Systems ◽  
Management Approaches

Abstract Probabilistic risk-based approaches have been used for cost-effective structural design and maintenance guidelines. The effectiveness of these provisions, however, has yet to be adequately validated. Also, current risk management approaches are hardly adaptable to changes in operational and environmental conditions as well as structural properties over the service life of structures. As the need and demand of real-time monitoring systems have increased dramatically for high-value and high-risk facilities such as offshore structures particularly, it is important to discuss directions for future research to advance the risk-based management approaches by utilizing the invaluable “big-scale” field data obtained from sensor network systems. Therefore, knowledge gaps in the current state-of-the-art structural risk management approaches are discussed in this paper. Subsequently, a novel risk management framework is presented in this paper integrating physics-based data into a data-driven decision model. The proposed decision framework will improve system adaptability to future performance requirements due to changing operational and environmental conditions and will be applicable to any structural systems instrumented by sophisticated SHM systems such as complex naval and marine systems.

A Novel Hybrid Strategy for Multimode Operation Mapping and Feature Extraction on Data-Driven Statistical Fault Detection Methods

2018 ◽  
Author(s):  
Horacio Pinzón ◽  
Cinthia Audivet ◽  
Javier Alexander ◽  
Melitsa Torres ◽  
Marlon Consuegra ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Extraction Procedure ◽  
Operating Conditions ◽  
Data Driven ◽  
Superior Performance ◽  
Normal Operation ◽  
Monitoring Strategy ◽  
Network Systems ◽  
Multimode Operation

Fault detection and diagnosis schemes based on data-driven statistical modelling are highly dependent on an accurate and exhaustive feature extraction procedure to deliver a superior performance as a monitoring strategy. Otherwise conducted, a deficient feature extraction procedure leads to a monitoring structure widely deviated from normal operating conditions. If an operating state is not identified as it, an increment in false alarm rate would be evidenced whenever the process shifts towards that condition and the monitoring scheme triggers the abnormal condition warning. On the other hand, if two similar operating conditions could not be individualized i.e. to be identified as a single operating state, a lack of sensitivity for minor — yet typical — deviations would render a monitoring strategy with prominent misdetection rates. Although Multimode Operational Mapping requires the proper identification of a finite set of normal process states, it is a challenging task especially for large-scale systems. Its complexity derives from a broad universe of monitoring variables, highly interactuating process units integrated over very dynamic network systems, among others. This is the case of natural gas transmission infrastructure, as it deals with variable upstream production rates, diverse consumption trends from customers, internal processes constrains, merged in a stringent operating scheme. This paper proposes a novel strategy to address the identification and feature extraction of normal conditions on multimode operation systems. The proposed framework uses a segmentation approach based on operator’s knowledge, the Takagi-Sugeno-Kang fuzzy engine and k-means algorithm to characterize the normal operation states of the system. The results show an improvement in the performance of Principal Component Analysis during abnormal conditions detection, in addition an increase on the sensitivity of Hotelling and Q statistics.

scholarly journals Blockchain-empowered Data-driven Networks

2021 ◽  
Vol 54 (3) ◽  
pp. 1-38
Author(s):  
Xi Li ◽  
Zehua Wang ◽  
Victor C. M. Leung ◽  
Hong Ji ◽  
Yiming Liu ◽  
...  
Keyword(s):  
Data Storage ◽  
Data Privacy ◽  
Computer Network ◽  
Network Control ◽  
Data Driven ◽  
Network Services ◽  
Network Systems ◽  
Secure Storage ◽  
Future Data ◽  
Promising Solution

The paths leading to future networks are pointing towards a data-driven paradigm to better cater to the explosive growth of mobile services as well as the increasing heterogeneity of mobile devices, many of which generate and consume large volumes and variety of data. These paths are also hampered by significant challenges in terms of security, privacy, services provisioning, and network management. Blockchain, which is a technology for building distributed ledgers that provide an immutable log of transactions recorded in a distributed network, has become prominent recently as the underlying technology of cryptocurrencies and is revolutionizing data storage and processing in computer network systems. For future data-driven networks (DDNs), blockchain is considered as a promising solution to enable the secure storage, sharing, and analytics of data, privacy protection for users, robust, trustworthy network control, and decentralized routing and resource managements. However, many important challenges and open issues remain to be addressed before blockchain can be deployed widely to enable future DDNs. In this article, we present a survey on the existing research works on the application of blockchain technologies in computer networks and identify challenges and potential solutions in the applications of blockchains in future DDNs. We identify application scenarios in which future blockchain-empowered DDNs could improve the efficiency and security, and generally the effectiveness of network services.

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