Rivista e Edizione

Volume 13 (2023): Edizione 1 (January 2023)

Volume 12 (2022): Edizione 4 (October 2022)

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 12 (2022): Edizione 3 (July 2022)

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

Noise Robust Illumination Invariant Face Recognition Via Bivariate Wavelet Shrinkage in Logarithm Domain

Pubblicato online: 23 Jul 2022
Pagine: 169 - 180

Astratto

Abstract

Recognizing faces under various lighting conditions is a challenging problem in artificial intelligence and applications. In this paper we describe a new face recognition algorithm which is invariant to illumination. We first convert image files to the logarithm domain and then we implement them using the dual-tree complex wavelet transform (DTCWT) which yields images approximately invariant to changes in illumination change. We classify the images by the collaborative representation-based classifier (CRC). We also perform the following sub-band transformations: (i) we set the approximation sub-band to zero if the noise standard deviation is greater than 5; (ii) we then threshold the two highest frequency wavelet sub-bands using bivariate wavelet shrinkage. (iii) otherwise, we set these two highest frequency wavelet sub-bands to zero. On obtained images we perform the inverse DTCWT which results in illumination invariant face images. The proposed method is strongly robust to Gaussian white noise. Experimental results show that our proposed algorithm outperforms several existing methods on the Extended Yale Face Database B and the CMU-PIE face database.

Parole chiave

  • face recognition
  • dual-tree complex wavelet transforms (DTCWT)
  • collaborative representation-based classifier (CRC)
  • invariant features
  • pattern recognition
  • computer vision
Accesso libero

Towards a Very Fast Feedforward Multilayer Neural Networks Training Algorithm

Pubblicato online: 23 Jul 2022
Pagine: 181 - 195

Astratto

Abstract

**This paper presents a novel fast algorithm for feedforward neural networks training. It is based on the Recursive Least Squares (RLS) method commonly used for designing adaptive filters. Besides, it utilizes two techniques of linear algebra, namely the orthogonal transformation method, called the Givens Rotations (GR), and the QR decomposition, creating the GQR (symbolically we write GR + QR = GQR) procedure for solving the normal equations in the weight update process. In this paper, a novel approach to the GQR algorithm is presented. The main idea revolves around reducing the computational cost of a single rotation by eliminating the square root calculation and reducing the number of multiplications. The proposed modification is based on the scaled version of the Givens rotations, denoted as SGQR. This modification is expected to bring a significant training time reduction comparing to the classic GQR algorithm. The paper begins with the introduction and the classic Givens rotation description. Then, the scaled rotation and its usage in the QR decomposition is discussed. The main section of the article presents the neural network training algorithm which utilizes scaled Givens rotations and QR decomposition in the weight update process. Next, the experiment results of the proposed algorithm are presented and discussed. The experiment utilizes several benchmarks combined with neural networks of various topologies. It is shown that the proposed algorithm outperforms several other commonly used methods, including well known Adam optimizer.

Parole chiave

  • neural network training algorithm
  • QR decomposition
  • scaled Givens rotations
  • approximation
  • classification
Accesso libero

A Novel Approach to Type-Reduction and Design of Interval Type-2 Fuzzy Logic Systems

Pubblicato online: 23 Jul 2022
Pagine: 197 - 206

Astratto

Abstract

Fuzzy logic systems, unlike black-box models, are known as transparent artificial intelligence systems that have explainable rules of reasoning. Type 2 fuzzy systems extend the field of application to tasks that require the introduction of uncertainty in the rules, e.g. for handling corrupted data. Most practical implementations use interval type-2 sets and process interval membership grades. The key role in the design of type-2 interval fuzzy logic systems is played by the type-2 inference defuzzification method. In type-2 systems this generally takes place in two steps: type-reduction first, then standard defuzzification. The only precise type-reduction method is the iterative method known as Karnik-Mendel (KM) algorithm with its enhancement modifications. The known non-iterative methods deliver only an approximation of the boundaries of a type-reduced set and, in special cases, they diminish the profits that result from the use of type-2 fuzzy logic systems. In this paper, we propose a novel type-reduction method based on a smooth approximation of maximum/minimum, and we call this method a smooth type-reduction. Replacing the iterative KM algorithm by the smooth type-reduction, we obtain a structure of an adaptive interval type-2 fuzzy logic which is non-iterative and as close to an approximation of the KM algorithm as we like.

Parole chiave

  • smooth type-reduction
  • interval type-2 fuzzy logic systems
Accesso libero

New Event Based H State Estimation for Discrete-Time Recurrent Delayed Semi-Markov Jump Neural Networks Via a Novel Summation Inequality

Pubblicato online: 23 Jul 2022
Pagine: 207 - 221

Astratto

Abstract

This paper investigates the event-based state estimation for discrete-time recurrent delayed semi-Markovian neural networks. An event-triggering protocol is introduced to find measurement output with a specific triggering condition so as to lower the burden of the data communication. A novel summation inequality is established for the existence of asymptotic stability of the estimation error system. The problem addressed here is to construct an H state estimation that guarantees the asymptotic stability with the novel summation inequality, characterized by event-triggered transmission. By the Lyapunov functional technique, the explicit expressions for the gain are established. Finally, two examples are exploited numerically to illustrate the usefulness of the new methodology.

Parole chiave

  • Discrete-time neural networks
  • Mixed time delays
  • asymptotic stability
  • event-triggered control
Accesso libero

Flow-Capture Location Model with Link Capacity Constraint Over a Mixed Traffic Network

Pubblicato online: 23 Jul 2022
Pagine: 223 - 234

Astratto

Abstract

This paper constructs and settles a charging facility location problem with the link capacity constraint over a mixed traffic network. The reason for studying this problem is that link capacity constraint is mostly insufficient or missing in the studies of traditional user equilibrium models, thereby resulting in the ambiguous of the definition of road traffic network status. Adding capacity constraints to the road network is a compromise to enhance the reality of the traditional equilibrium model. In this paper, we provide a two-layer model for evaluating the efficiency of the charging facilities under the condition of considering the link capacity constraint. The upper level model in the proposed bi-level model is a nonlinear integer programming formulation, which aims to maximize the captured link flows of the battery electric vehicles. Moreover, the lower level model is a typical traffic equilibrium assignment model except that it contains the link capacity constraint and driving distance constraint of the electric vehicles over the mixed road network. Based on the Frank-Wolfe algorithm, a modified algorithm framework is adopted for solving the constructed problem, and finally, a numerical example is presented to verify the proposed model and solution algorithm.

Parole chiave

  • Traffic assignment problem
  • link capacity constraint
  • charging location
  • path distance constraint
5 Articoli
Accesso libero

Noise Robust Illumination Invariant Face Recognition Via Bivariate Wavelet Shrinkage in Logarithm Domain

Pubblicato online: 23 Jul 2022
Pagine: 169 - 180

Astratto

Abstract

Recognizing faces under various lighting conditions is a challenging problem in artificial intelligence and applications. In this paper we describe a new face recognition algorithm which is invariant to illumination. We first convert image files to the logarithm domain and then we implement them using the dual-tree complex wavelet transform (DTCWT) which yields images approximately invariant to changes in illumination change. We classify the images by the collaborative representation-based classifier (CRC). We also perform the following sub-band transformations: (i) we set the approximation sub-band to zero if the noise standard deviation is greater than 5; (ii) we then threshold the two highest frequency wavelet sub-bands using bivariate wavelet shrinkage. (iii) otherwise, we set these two highest frequency wavelet sub-bands to zero. On obtained images we perform the inverse DTCWT which results in illumination invariant face images. The proposed method is strongly robust to Gaussian white noise. Experimental results show that our proposed algorithm outperforms several existing methods on the Extended Yale Face Database B and the CMU-PIE face database.

Parole chiave

  • face recognition
  • dual-tree complex wavelet transforms (DTCWT)
  • collaborative representation-based classifier (CRC)
  • invariant features
  • pattern recognition
  • computer vision
Accesso libero

Towards a Very Fast Feedforward Multilayer Neural Networks Training Algorithm

Pubblicato online: 23 Jul 2022
Pagine: 181 - 195

Astratto

Abstract

**This paper presents a novel fast algorithm for feedforward neural networks training. It is based on the Recursive Least Squares (RLS) method commonly used for designing adaptive filters. Besides, it utilizes two techniques of linear algebra, namely the orthogonal transformation method, called the Givens Rotations (GR), and the QR decomposition, creating the GQR (symbolically we write GR + QR = GQR) procedure for solving the normal equations in the weight update process. In this paper, a novel approach to the GQR algorithm is presented. The main idea revolves around reducing the computational cost of a single rotation by eliminating the square root calculation and reducing the number of multiplications. The proposed modification is based on the scaled version of the Givens rotations, denoted as SGQR. This modification is expected to bring a significant training time reduction comparing to the classic GQR algorithm. The paper begins with the introduction and the classic Givens rotation description. Then, the scaled rotation and its usage in the QR decomposition is discussed. The main section of the article presents the neural network training algorithm which utilizes scaled Givens rotations and QR decomposition in the weight update process. Next, the experiment results of the proposed algorithm are presented and discussed. The experiment utilizes several benchmarks combined with neural networks of various topologies. It is shown that the proposed algorithm outperforms several other commonly used methods, including well known Adam optimizer.

Parole chiave

  • neural network training algorithm
  • QR decomposition
  • scaled Givens rotations
  • approximation
  • classification
Accesso libero

A Novel Approach to Type-Reduction and Design of Interval Type-2 Fuzzy Logic Systems

Pubblicato online: 23 Jul 2022
Pagine: 197 - 206

Astratto

Abstract

Fuzzy logic systems, unlike black-box models, are known as transparent artificial intelligence systems that have explainable rules of reasoning. Type 2 fuzzy systems extend the field of application to tasks that require the introduction of uncertainty in the rules, e.g. for handling corrupted data. Most practical implementations use interval type-2 sets and process interval membership grades. The key role in the design of type-2 interval fuzzy logic systems is played by the type-2 inference defuzzification method. In type-2 systems this generally takes place in two steps: type-reduction first, then standard defuzzification. The only precise type-reduction method is the iterative method known as Karnik-Mendel (KM) algorithm with its enhancement modifications. The known non-iterative methods deliver only an approximation of the boundaries of a type-reduced set and, in special cases, they diminish the profits that result from the use of type-2 fuzzy logic systems. In this paper, we propose a novel type-reduction method based on a smooth approximation of maximum/minimum, and we call this method a smooth type-reduction. Replacing the iterative KM algorithm by the smooth type-reduction, we obtain a structure of an adaptive interval type-2 fuzzy logic which is non-iterative and as close to an approximation of the KM algorithm as we like.

Parole chiave

  • smooth type-reduction
  • interval type-2 fuzzy logic systems
Accesso libero

New Event Based H State Estimation for Discrete-Time Recurrent Delayed Semi-Markov Jump Neural Networks Via a Novel Summation Inequality

Pubblicato online: 23 Jul 2022
Pagine: 207 - 221

Astratto

Abstract

This paper investigates the event-based state estimation for discrete-time recurrent delayed semi-Markovian neural networks. An event-triggering protocol is introduced to find measurement output with a specific triggering condition so as to lower the burden of the data communication. A novel summation inequality is established for the existence of asymptotic stability of the estimation error system. The problem addressed here is to construct an H state estimation that guarantees the asymptotic stability with the novel summation inequality, characterized by event-triggered transmission. By the Lyapunov functional technique, the explicit expressions for the gain are established. Finally, two examples are exploited numerically to illustrate the usefulness of the new methodology.

Parole chiave

  • Discrete-time neural networks
  • Mixed time delays
  • asymptotic stability
  • event-triggered control
Accesso libero

Flow-Capture Location Model with Link Capacity Constraint Over a Mixed Traffic Network

Pubblicato online: 23 Jul 2022
Pagine: 223 - 234

Astratto

Abstract

This paper constructs and settles a charging facility location problem with the link capacity constraint over a mixed traffic network. The reason for studying this problem is that link capacity constraint is mostly insufficient or missing in the studies of traditional user equilibrium models, thereby resulting in the ambiguous of the definition of road traffic network status. Adding capacity constraints to the road network is a compromise to enhance the reality of the traditional equilibrium model. In this paper, we provide a two-layer model for evaluating the efficiency of the charging facilities under the condition of considering the link capacity constraint. The upper level model in the proposed bi-level model is a nonlinear integer programming formulation, which aims to maximize the captured link flows of the battery electric vehicles. Moreover, the lower level model is a typical traffic equilibrium assignment model except that it contains the link capacity constraint and driving distance constraint of the electric vehicles over the mixed road network. Based on the Frank-Wolfe algorithm, a modified algorithm framework is adopted for solving the constructed problem, and finally, a numerical example is presented to verify the proposed model and solution algorithm.

Parole chiave

  • Traffic assignment problem
  • link capacity constraint
  • charging location
  • path distance constraint

Pianifica la tua conferenza remota con Sciendo