Rivista e Edizione

Volume 12 (2022): Edizione 3 (July 2022)

Volume 12 (2022): Edizione 2 (April 2022)

Volume 12 (2022): Edizione 1 (January 2022)

Volume 11 (2021): Edizione 4 (October 2021)

Volume 11 (2021): Edizione 3 (July 2021)

Volume 11 (2021): Edizione 2 (April 2021)

Volume 11 (2021): Edizione 1 (January 2021)

Volume 10 (2020): Edizione 4 (October 2020)

Volume 10 (2020): Edizione 3 (July 2020)

Volume 10 (2020): Edizione 2 (April 2020)

Volume 10 (2020): Edizione 1 (January 2020)

Volume 9 (2019): Edizione 4 (October 2019)

Volume 9 (2019): Edizione 3 (July 2019)

Volume 9 (2019): Edizione 2 (April 2019)

Volume 9 (2019): Edizione 1 (January 2019)

Volume 8 (2018): Edizione 4 (October 2018)

Volume 8 (2018): Edizione 3 (July 2018)

Volume 8 (2018): Edizione 2 (April 2018)

Volume 8 (2018): Edizione 1 (January 2018)

Volume 7 (2017): Edizione 4 (October 2017)

Volume 7 (2017): Edizione 3 (July 2017)

Volume 7 (2017): Edizione 2 (April 2017)

Volume 7 (2017): Edizione 1 (January 2017)

Volume 6 (2016): Edizione 4 (October 2016)

Volume 6 (2016): Edizione 3 (July 2016)

Volume 6 (2016): Edizione 2 (April 2016)

Volume 6 (2016): Edizione 1 (January 2016)

Volume 5 (2015): Edizione 4 (October 2015)

Volume 5 (2015): Edizione 3 (July 2015)

Volume 5 (2015): Edizione 2 (April 2015)

Volume 5 (2015): Edizione 1 (January 2015)

Volume 4 (2014): Edizione 4 (October 2014)

Volume 4 (2014): Edizione 3 (July 2014)

Volume 4 (2014): Edizione 2 (April 2014)

Volume 4 (2014): Edizione 1 (January 2014)

Volume 3 (2013): Edizione 4 (October 2013)

Volume 3 (2013): Edizione 3 (July 2013)

Volume 3 (2013): Edizione 2 (April 2013)

Volume 3 (2013): Edizione 1 (January 2013)

Dettagli della rivista
Formato
Rivista
eISSN
2449-6499
Pubblicato per la prima volta
30 Dec 2014
Periodo di pubblicazione
4 volte all'anno
Lingue
Inglese

Cerca

Volume 11 (2021): Edizione 2 (April 2021)

Dettagli della rivista
Formato
Rivista
eISSN
2449-6499
Pubblicato per la prima volta
30 Dec 2014
Periodo di pubblicazione
4 volte all'anno
Lingue
Inglese

Cerca

5 Articoli
Accesso libero

Type-2 Fuzzy Logic Systems in Applications: Managing Data in Selective Catalytic Reduction for Air Pollution Prevention

Pubblicato online: 29 Jan 2021
Pagine: 85 - 97

Astratto

Abstract

The article presents our research on applications of fuzzy logic to reduce air pollution by DeNOx filters. The research aim is to manage data on Selective Catalytic Reduction (SCR) process responsible for reducing the emission of nitrogen oxide (NO) and nitrogen dioxide (NO2). Dedicated traditional Fuzzy Logic Systems (FLS) and Type-2 Fuzzy Logic Systems (T2FLS) are proposed with the use of new methods for learning fuzzy rules and with new types of fuzzy implications (the so-called ”engineering implications”). The obtained results are consistent with the results provided by experts. The main advantage of this paper is that type-2 fuzzy logic systems with ”engineering implications” and new methods of learning fuzzy rules give results closer to expert expectations than those based on traditional fuzzy logic systems. According to the literature review, no T2FLS were applied to manage DeNOx filter prior to the research presented here.

Parole chiave

  • Selective Catalytic Reduction (SCR)
  • fuzzy management of DeNOx filter
  • fuzzy logic systems
  • ”engineering” fuzzy implications
  • learning fuzzy rules
Accesso libero

Hardware Rough Set Processor Parallel Architecture in FPGA for Finding Core in Big Datasets

Pubblicato online: 29 Jan 2021
Pagine: 99 - 110

Astratto

Abstract

This paper presents FPGA and softcore CPU based solution for large datasets parallel core calculation using rough set methods. Architectures shown in this paper have been tested on two real datasets running presented solutions inside FPGA unit. Tested datasets had 1 000 to 10 000 000 objects. The same operations were performed in software implementation. Obtained results show the big acceleration in computation time using hardware supporting core generation in comparison to pure software implementation.

Parole chiave

  • rough sets
  • FPGA
  • core attributes
  • parallel architecture
Accesso libero

A Survey on Multi-Agent Based Collaborative Intrusion Detection Systems

Pubblicato online: 29 Jan 2021
Pagine: 111 - 142

Astratto

Abstract

Multi-Agent Systems (MAS) have been widely used in many areas like modeling and simulation of complex phenomena, and distributed problem solving. Likewise, MAS have been used in cyber-security, to build more efficient Intrusion Detection Systems (IDS), namely Collaborative Intrusion Detection Systems (CIDS). This work presents a taxonomy for classifying the methods used to design intrusion detection systems, and how such methods were used alongside with MAS in order to build IDS that are deployed in distributed environments, resulting in the emergence of CIDS. The proposed taxonomy, consists of three parts: 1) general architecture of CIDS, 2) the used agent technology, and 3) decision techniques, in which used technologies are presented. The proposed taxonomy reviews and classifies the most relevant works in this topic and highlights open research issues in view of recent and emerging threats. Thus, this work provides a good insight regarding past, current, and future solutions for CIDS, and helps both researchers and professionals design more effective solutions.

Parole chiave

  • IDS
  • CIDS
  • MAS
  • Artificial Intelligence
Accesso libero

Monitoring Regenerative Heat Exchanger in Steam Power Plant by Making Use of the Recurrent Neural Network

Pubblicato online: 29 Jan 2021
Pagine: 143 - 155

Astratto

Abstract

Artificial Intelligence algorithms are being increasingly used in industrial applications. Their important function is to support operation of diagnostic systems. This paper presents a new approach to the monitoring of a regenerative heat exchanger in a steam power plant, which is based on a specific use of the Recurrent Neural Network (RNN). The proposed approach was tested using real data. This approach can be easily adapted to similar monitoring applications of other industrial dynamic objects.

Parole chiave

  • recurrent neural network
  • intelligent industrial monitoring
  • Almeida–Pineda recurrent back-propagation
  • regenerative heat exchanger
  • steam power plant
Accesso libero

An Approach to Generalization of the Intuitionistic Fuzzy Topsis Method in the Framework of Evidence Theory

Pubblicato online: 29 Jan 2021
Pagine: 157 - 175

Astratto

Abstract

A generalization of technique for establishing order preference by similarity to the ideal solution (TOPSIS) in the intuitionistic fuzzy setting based on the redefinition of intuitionistic fuzzy sets theory (A IFS) in the framework of Dempster-Shafer theory (DST) of evidence is proposed. The use of DST mathematical tools makes it possible to avoid a set of limitations and drawbacks revealed recently in the conventional Atanassov’s operational laws defined on intuitionistic fuzzy values, which may produce unacceptable results in the solution of multiple criteria decision-making problems. This boosts considerably the quality of aggregating operators used in the intuitionistic fuzzy TOPSIS method. It is pointed out that the conventional TOPSIS method may be naturally treated as a weighted sum of some modified local criteria. Because this aggregating approach does not always reflects well intentions of decision makers, two additional aggregating methods that cannot be defined in the framework of conventional A IFS based on local criteria weights being intuitionistic fuzzy values, are introduced. Having in mind that different aggregating methods generally produce different alternative rankings to obtain the compromise ranking, the method for aggregating of aggregation modes has been applied. Some examples are used to illustrate the validity and features of the proposed approach.

Parole chiave

  • TOPSIS
  • intuitionistic fuzzy sets
  • Dempster-Shafer theory
  • aggregating modes
5 Articoli
Accesso libero

Type-2 Fuzzy Logic Systems in Applications: Managing Data in Selective Catalytic Reduction for Air Pollution Prevention

Pubblicato online: 29 Jan 2021
Pagine: 85 - 97

Astratto

Abstract

The article presents our research on applications of fuzzy logic to reduce air pollution by DeNOx filters. The research aim is to manage data on Selective Catalytic Reduction (SCR) process responsible for reducing the emission of nitrogen oxide (NO) and nitrogen dioxide (NO2). Dedicated traditional Fuzzy Logic Systems (FLS) and Type-2 Fuzzy Logic Systems (T2FLS) are proposed with the use of new methods for learning fuzzy rules and with new types of fuzzy implications (the so-called ”engineering implications”). The obtained results are consistent with the results provided by experts. The main advantage of this paper is that type-2 fuzzy logic systems with ”engineering implications” and new methods of learning fuzzy rules give results closer to expert expectations than those based on traditional fuzzy logic systems. According to the literature review, no T2FLS were applied to manage DeNOx filter prior to the research presented here.

Parole chiave

  • Selective Catalytic Reduction (SCR)
  • fuzzy management of DeNOx filter
  • fuzzy logic systems
  • ”engineering” fuzzy implications
  • learning fuzzy rules
Accesso libero

Hardware Rough Set Processor Parallel Architecture in FPGA for Finding Core in Big Datasets

Pubblicato online: 29 Jan 2021
Pagine: 99 - 110

Astratto

Abstract

This paper presents FPGA and softcore CPU based solution for large datasets parallel core calculation using rough set methods. Architectures shown in this paper have been tested on two real datasets running presented solutions inside FPGA unit. Tested datasets had 1 000 to 10 000 000 objects. The same operations were performed in software implementation. Obtained results show the big acceleration in computation time using hardware supporting core generation in comparison to pure software implementation.

Parole chiave

  • rough sets
  • FPGA
  • core attributes
  • parallel architecture
Accesso libero

A Survey on Multi-Agent Based Collaborative Intrusion Detection Systems

Pubblicato online: 29 Jan 2021
Pagine: 111 - 142

Astratto

Abstract

Multi-Agent Systems (MAS) have been widely used in many areas like modeling and simulation of complex phenomena, and distributed problem solving. Likewise, MAS have been used in cyber-security, to build more efficient Intrusion Detection Systems (IDS), namely Collaborative Intrusion Detection Systems (CIDS). This work presents a taxonomy for classifying the methods used to design intrusion detection systems, and how such methods were used alongside with MAS in order to build IDS that are deployed in distributed environments, resulting in the emergence of CIDS. The proposed taxonomy, consists of three parts: 1) general architecture of CIDS, 2) the used agent technology, and 3) decision techniques, in which used technologies are presented. The proposed taxonomy reviews and classifies the most relevant works in this topic and highlights open research issues in view of recent and emerging threats. Thus, this work provides a good insight regarding past, current, and future solutions for CIDS, and helps both researchers and professionals design more effective solutions.

Parole chiave

  • IDS
  • CIDS
  • MAS
  • Artificial Intelligence
Accesso libero

Monitoring Regenerative Heat Exchanger in Steam Power Plant by Making Use of the Recurrent Neural Network

Pubblicato online: 29 Jan 2021
Pagine: 143 - 155

Astratto

Abstract

Artificial Intelligence algorithms are being increasingly used in industrial applications. Their important function is to support operation of diagnostic systems. This paper presents a new approach to the monitoring of a regenerative heat exchanger in a steam power plant, which is based on a specific use of the Recurrent Neural Network (RNN). The proposed approach was tested using real data. This approach can be easily adapted to similar monitoring applications of other industrial dynamic objects.

Parole chiave

  • recurrent neural network
  • intelligent industrial monitoring
  • Almeida–Pineda recurrent back-propagation
  • regenerative heat exchanger
  • steam power plant
Accesso libero

An Approach to Generalization of the Intuitionistic Fuzzy Topsis Method in the Framework of Evidence Theory

Pubblicato online: 29 Jan 2021
Pagine: 157 - 175

Astratto

Abstract

A generalization of technique for establishing order preference by similarity to the ideal solution (TOPSIS) in the intuitionistic fuzzy setting based on the redefinition of intuitionistic fuzzy sets theory (A IFS) in the framework of Dempster-Shafer theory (DST) of evidence is proposed. The use of DST mathematical tools makes it possible to avoid a set of limitations and drawbacks revealed recently in the conventional Atanassov’s operational laws defined on intuitionistic fuzzy values, which may produce unacceptable results in the solution of multiple criteria decision-making problems. This boosts considerably the quality of aggregating operators used in the intuitionistic fuzzy TOPSIS method. It is pointed out that the conventional TOPSIS method may be naturally treated as a weighted sum of some modified local criteria. Because this aggregating approach does not always reflects well intentions of decision makers, two additional aggregating methods that cannot be defined in the framework of conventional A IFS based on local criteria weights being intuitionistic fuzzy values, are introduced. Having in mind that different aggregating methods generally produce different alternative rankings to obtain the compromise ranking, the method for aggregating of aggregation modes has been applied. Some examples are used to illustrate the validity and features of the proposed approach.

Parole chiave

  • TOPSIS
  • intuitionistic fuzzy sets
  • Dempster-Shafer theory
  • aggregating modes

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