The biometric-based module of smart grid system

Engel, Ekaterina A.; Ковалев, И В; Ermoshkina, A

doi:10.1088/1757-899x/94/1/012007

Cited by 3 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a matter of fact, these keys, which assure the possibility of controlling the majority of crucial SDN operating characteristics, are often shared among multiple users, thus the access to the SDN core is exposed to significant security threats. In order to improve the protection level of such core-aspects, biometric technologies were proposed [148,149,151].…”

Section: Data Security For the Future Of Smart Distribution Network:mentioning

confidence: 99%

“…With regard to the SG paradigm, Engel et al [151] describes a possible biometric-based module. Typically, a biometric authentication module is made up of: (1) a capture device able to transduce the biometric trait into a signal that can be treated by signal processing algorithms in order to isolate the basic information from the "background"; (2) a feature extraction module, which takes as input the signal provided by the sensor and extracts from it a set of statistical or structural measurements able to characterize the uniqueness of the signal coming from a certain subject with respect to those of all other possible subjects; (3) the comparator module, which aims at comparing two features sets, and providing a degree of similarity between them, usually called matching score; (4) the decision module, which states, according to the computed matching score, if the compared signals belong to the same subject.…”

Section: Data Security For the Future Of Smart Distribution Network:mentioning

confidence: 99%

See 1 more Smart Citation

A Multidisciplinary Approach for the Development of Smart Distribution Networks

et al. 2018

View full text Add to dashboard Cite

Electric power systems are experiencing relevant changes involving the growing penetration of distributed generation and energy storage systems, the introduction of electric vehicles, the management of responsive loads, the proposals for new energy markets and so on. Such an evolution is pushing a paradigm shift that is one of the most important challenges in power network design: the management must move from traditional planning and manual intervention to full “smartization” of medium and low voltage networks. Peculiarities and criticalities of future power distribution networks originate from the complexity of the system which includes both the physical aspects of electric networks and the cyber aspects, like data elaboration, feature extraction, communication, supervision and control; only fully integrated advanced monitoring systems can foster this transition towards network automation. The design and development of such future networks require distinct kinds of expertise in the industrial and information engineering fields. In this context, this paper provides a comprehensive review of current challenges and multidisciplinary interactions in the development of smart distribution networks. The aim of this paper is to discuss, in an integrated and organized manner, the state of the art while focusing on the need for interaction between different disciplines and highlighting how innovative and future-proof outcomes of both research and practice can only emerge from a coordinated design of all the layers in the smart distribution network architecture.

show abstract

Section: Data Security For the Future Of Smart Distribution Network:mentioning

confidence: 99%

Section: Data Security For the Future Of Smart Distribution Network:mentioning

confidence: 99%

A Multidisciplinary Approach for the Development of Smart Distribution Networks

et al. 2018

View full text Add to dashboard Cite

show abstract

Section: Data Security For the Future Of Smart Distribution Network:mentioning

confidence: 99%

“…With regard to the SG paradigm, [150] describes a possible biometric-based module. Typically, a biometric authentication module is made up of: (1) a capture device able to transduce the biometric trait into a signal that can be treated by signal processing algorithms in order to isolate the basic information from the "background"; (2) a feature extraction module, which takes as input the signal provided by the sensor and extracts from it a set of statistical or structural measurements able to characterize the uniqueness of the signal coming from a certain subject with respect to those of all other possible subjects; (3) the comparator module, which aims at comparing two features sets, and providing a degree of similarity between them, usually called matching score; (4) the decision module, which states, according to the computed matching score, if the compared signals belong to the same subject.…”

Section: Data Security For the Future Of Smart Distribution Network:mentioning

confidence: 99%

Multidisciplinary Approach for the Development of Smart Distribution Networks

Ghiani¹,

Serpi²,

Pilloni³

et al. 2018

Preprint

View full text Add to dashboard Cite

Electric power systems are experiencing relevant changes involving the growing penetration of distributed generation and energy storage systems, the introduction of electric vehicles, the management of responsive loads, the proposals for new energy markets and so on. Such evolution is pushing for a paradigm shift: the management must move from traditional planning and manual intervention to full "smartization" of medium and low voltage networks. Peculiarities and criticalities of future power distribution networks originate from the complexity of the system that includes both the physical aspects of electric networks and the cyber aspects, like data elaboration, feature extraction, communication, supervision and control; only fully integrated advanced monitoring systems can foster this transition towards network automation. The design and development of such future networks require distinct kinds of expertise in the industrial and information engineering fields. In this context, this paper provides a comprehensive review of current challenges and multidisciplinary interactions in the development of smart distribution networks.

show abstract

“…As a result of the sensitivity shortcoming common to uni-biometric systems, Reference [24] proposed a multi-model technique by adopting fused faces and fingerprints, i.e., making use of a collaboration-based classifier to identify the faces of a different person. A selective neural network was jointly used with “Viola–Jones method-based PCA” by Reference [26] for a gridding system. Here, over 100 faces from a face-based database were distinguishable, which proves the success of the method.…”

Section: Introductionmentioning

confidence: 99%

Multi-Biometric System Based on Cutting-Edge Equipment for Experimental Contactless Verification

Kolda

Krejcar

Selamat

et al. 2019

Sensors

View full text Add to dashboard Cite

Biometric verification methods have gained significant popularity in recent times, which has brought about their extensive usage. In light of theoretical evidence surrounding the development of biometric verification, we proposed an experimental multi-biometric system for laboratory testing. First, the proposed system was designed such that it was able to identify and verify a user through the hand contour, and blood flow (blood stream) at the upper part of the hand. Next, we detailed the hard and software solutions for the system. A total of 40 subjects agreed to be a part of data generation team, which produced 280 hand images. The core of this paper lies in evaluating individual metrics, which are functions of frequency comparison of the double type faults with the EER (Equal Error Rate) values. The lowest value was measured for the case of the modified Hausdorff distance metric - Maximally Helicity Violating (MHV). Furthermore, for the verified biometric characteristics (Hamming distance and MHV), appropriate and suitable metrics have been proposed and experimented to optimize system precision. Thus, the EER value for the designed multi-biometric system in the context of this work was found to be 5%, which proves that metrics consolidation increases the precision of the multi-biometric system. Algorithms used for the proposed multi-biometric device shows that the individual metrics exhibit significant accuracy but perform better on consolidation, with a few shortcomings.

show abstract

The biometric-based module of smart grid system

Cited by 3 publications

References 10 publications

A Multidisciplinary Approach for the Development of Smart Distribution Networks

A Multidisciplinary Approach for the Development of Smart Distribution Networks

Multidisciplinary Approach for the Development of Smart Distribution Networks

Multi-Biometric System Based on Cutting-Edge Equipment for Experimental Contactless Verification

Contact Info

Product

Resources

About