Rivista e Edizione

Volume 47 (2022): Edizione 2 (June 2022)

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Volume 46 (2021): Edizione 4 (December 2021)

Volume 46 (2021): Edizione 3 (September 2021)

Volume 46 (2021): Edizione 2 (June 2021)

Volume 46 (2021): Edizione 1 (March 2021)

Volume 45 (2020): Edizione 4 (December 2020)

Volume 45 (2020): Edizione 3 (September 2020)

Volume 45 (2020): Edizione 2 (June 2020)

Volume 45 (2020): Edizione 1 (March 2020)

Volume 44 (2019): Edizione 4 (December 2019)

Volume 44 (2019): Edizione 3 (September 2019)

Volume 44 (2019): Edizione 2 (June 2019)

Volume 44 (2019): Edizione 1 (March 2019)

Volume 43 (2018): Edizione 4 (December 2018)

Volume 43 (2018): Edizione 3 (September 2018)

Volume 43 (2018): Edizione 2 (June 2018)

Volume 43 (2018): Edizione 1 (March 2018)

Volume 42 (2017): Edizione 4 (December 2017)

Volume 42 (2017): Edizione 3 (September 2017)

Volume 42 (2017): Edizione 2 (June 2017)

Volume 42 (2017): Edizione 1 (February 2017)

Volume 41 (2016): Edizione 4 (November 2016)

Volume 41 (2016): Edizione 3 (September 2016)

Volume 41 (2016): Edizione 2 (June 2016)

Volume 41 (2016): Edizione 1 (March 2016)

Volume 40 (2015): Edizione 4 (December 2015)

Volume 40 (2015): Edizione 3 (September 2015)

Volume 40 (2015): Edizione 2 (June 2015)

Volume 40 (2015): Edizione 1 (March 2015)

Volume 39 (2014): Edizione 4 (December 2014)

Volume 39 (2014): Edizione 3 (September 2014)

Volume 39 (2014): Edizione 2 (June 2014)

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Volume 38 (2013): Edizione 4 (December 2013)

Volume 38 (2013): Edizione 3 (September 2013)

Volume 38 (2013): Edizione 2 (June 2013)

Volume 38 (2013): Edizione 1 (March 2013)

Volume 37 (2012): Edizione 4 (December 2012)

Volume 37 (2012): Edizione 3 (September 2012)

Volume 37 (2012): Edizione 2 (June 2012)

Volume 37 (2012): Edizione 1 (March 2012)

Dettagli della rivista
Formato
Rivista
eISSN
2300-3405
Pubblicato per la prima volta
24 Oct 2012
Periodo di pubblicazione
4 volte all'anno
Lingue
Inglese

Cerca

Volume 45 (2020): Edizione 4 (December 2020)

Dettagli della rivista
Formato
Rivista
eISSN
2300-3405
Pubblicato per la prima volta
24 Oct 2012
Periodo di pubblicazione
4 volte all'anno
Lingue
Inglese

Cerca

4 Articoli
access type Accesso libero

Chessboard and Chess Piece Recognition With the Support of Neural Networks

Pubblicato online: 16 Dec 2020
Pagine: 257 - 280

Astratto

Abstract

Chessboard and chess piece recognition is a computer vision problem that has not yet been efficiently solved. Digitization of a chess game state from a picture of a chessboard is a task typically performed by humans or with the aid of specialized chessboards and pieces. However, those solutions are neither easy nor convenient. To solve this problem, we propose a novel algorithm for digitizing chessboard configurations.

We designed a method of chessboard recognition and pieces detection that is resistant to lighting conditions and the angle at which images are captured, and works correctly with numerous chessboard styles. Detecting the board and recognizing chess pieces are crucial steps of board state digitization.

The algorithm achieves 95% accuracy (compared to 60% for the best alternative) for positioning the chessboard in an image, and almost 95% for chess pieces recognition. Furthermore, the sub-process of detecting straight lines and finding lattice points performs extraordinarily well, achieving over 99.5% accuracy (compared to the 74% for the best alternative).

Parole chiave

  • chessboard detection
  • chess pieces recognition
  • pattern recognition
  • chess-board recognition
  • chess
  • neural networks
access type Accesso libero

Return on Investment in Machine Learning: Crossing the Chasm between Academia and Business

Pubblicato online: 16 Dec 2020
Pagine: 281 - 304

Astratto

Abstract

Academia remains the central place of machine learning education. While academic culture is the predominant factor influencing the way we teach machine learning to students, many practitioners question this culture, claiming the lack of alignment between academic and business environments. Drawing on professional experiences from both sides of the chasm, we describe the main points of contention, in the hope that it will help better align academic syllabi with the expectations towards future machine learning practitioners. We also provide recommendations for teaching of the applied aspects of machine learning.

Parole chiave

  • applied machine learning
  • machine learning teaching
  • machine learning engineering
access type Accesso libero

Fusing Multi-Attribute Decision Models for Decision Making to Achieve Optimal Product Design

Pubblicato online: 16 Dec 2020
Pagine: 305 - 337

Astratto

Abstract

Manufacturers need to select the best design from alternative design concepts in order to meet up with the demand of customers and have a larger share of the competitive market that is flooded with multifarious designs. Evaluation of conceptual design alternatives can be modelled as a Multi-Criteria Decision Making (MCDM) process because it includes conflicting design features with different sub features. Hybridization of Multi Attribute Decision Making (MADM) models has been applied in various field of management, science and engineering in order to have a robust decision-making process but the extension of these hybridized MADM models to decision making in engineering design still requires attention. In this article, an integrated MADM model comprising of Fuzzy Analytic Hierarchy Process (FAHP), Fuzzy Pugh Matrix and Fuzzy VIKOR was developed and applied to evaluate conceptual designs of liquid spraying machine. The fuzzy AHP was used to determine weights of the design features and sub features by virtue of its fuzzified comparison matrix and synthetic extent evaluation. The fuzzy Pugh matrix provides a methodical structure for determining performance using all the design alternatives as basis and obtaining aggregates for the designs using the weights of the sub features. The fuzzy VIKOR generates the decision matrix from the aggregates of the fuzzified Pugh matrices and determine the best design concept from the defuzzified performance index. At the end, the optimal design concept is determined for the liquid spraying machine.

Parole chiave

  • Hybridized Multi-Attribute Decision-making
  • Fuzzy AHP
  • Fuzzified Pugh Matrix
  • Fuzzy VIKOR
  • Optimal conceptual design
access type Accesso libero

A Holistic Approach to Polymeric Material Selection for Laser Beam Machining using Methods of DEA and TOPSIS

Pubblicato online: 16 Dec 2020
Pagine: 339 - 357

Astratto

Abstract

Laser Beam machining (LBM) nowadays finds a wide acceptance for cutting various materials and cutting of polymer sheets is no exception. Greater reliability of process coupled with superior quality of finished product makes LBM widely used for cutting polymeric materials. Earlier researchers investigated the carbon dioxide laser cutting to a few thermoplastic polymers in thickness varying from 2mm to 10mm. Here, an approach is being made for grading the suitability of polymeric materials and to answer the problem of selection for LBM cutting as per their weightages obtained by using multi-decision making (MCDM) approach. An attempt has also been made to validate the result thus obtained with the experimental results obtained by previous researchers. The analysis encompasses the use of non-parametric linear-programming method of data envelopment analysis (DEA) for process efficiency assessment combined with technique for order preference by similarity to an ideal solution (TOPSIS) for selection of polymer sheets, which is based on the closeness values. The results of this uniquely blended analysis reflect that for 3mm thick polymer sheet is polypropelene (PP) to be highly preferable over polyethylene (PE) and polycarbonate (PC). While it turns out to be that polycarbonate (PC) to be highly preferable to other two polymers for 5mm thick polymer sheets. Hence the present research analysis fits very good for the polymer sheets of 3mm thickness while it deviates a little bit for the 5mm sheets.

Parole chiave

  • Laser Beam Machining
  • Polymers
  • DEA
  • AHP
  • TOPSIS
  • MCDM
4 Articoli
access type Accesso libero

Chessboard and Chess Piece Recognition With the Support of Neural Networks

Pubblicato online: 16 Dec 2020
Pagine: 257 - 280

Astratto

Abstract

Chessboard and chess piece recognition is a computer vision problem that has not yet been efficiently solved. Digitization of a chess game state from a picture of a chessboard is a task typically performed by humans or with the aid of specialized chessboards and pieces. However, those solutions are neither easy nor convenient. To solve this problem, we propose a novel algorithm for digitizing chessboard configurations.

We designed a method of chessboard recognition and pieces detection that is resistant to lighting conditions and the angle at which images are captured, and works correctly with numerous chessboard styles. Detecting the board and recognizing chess pieces are crucial steps of board state digitization.

The algorithm achieves 95% accuracy (compared to 60% for the best alternative) for positioning the chessboard in an image, and almost 95% for chess pieces recognition. Furthermore, the sub-process of detecting straight lines and finding lattice points performs extraordinarily well, achieving over 99.5% accuracy (compared to the 74% for the best alternative).

Parole chiave

  • chessboard detection
  • chess pieces recognition
  • pattern recognition
  • chess-board recognition
  • chess
  • neural networks
access type Accesso libero

Return on Investment in Machine Learning: Crossing the Chasm between Academia and Business

Pubblicato online: 16 Dec 2020
Pagine: 281 - 304

Astratto

Abstract

Academia remains the central place of machine learning education. While academic culture is the predominant factor influencing the way we teach machine learning to students, many practitioners question this culture, claiming the lack of alignment between academic and business environments. Drawing on professional experiences from both sides of the chasm, we describe the main points of contention, in the hope that it will help better align academic syllabi with the expectations towards future machine learning practitioners. We also provide recommendations for teaching of the applied aspects of machine learning.

Parole chiave

  • applied machine learning
  • machine learning teaching
  • machine learning engineering
access type Accesso libero

Fusing Multi-Attribute Decision Models for Decision Making to Achieve Optimal Product Design

Pubblicato online: 16 Dec 2020
Pagine: 305 - 337

Astratto

Abstract

Manufacturers need to select the best design from alternative design concepts in order to meet up with the demand of customers and have a larger share of the competitive market that is flooded with multifarious designs. Evaluation of conceptual design alternatives can be modelled as a Multi-Criteria Decision Making (MCDM) process because it includes conflicting design features with different sub features. Hybridization of Multi Attribute Decision Making (MADM) models has been applied in various field of management, science and engineering in order to have a robust decision-making process but the extension of these hybridized MADM models to decision making in engineering design still requires attention. In this article, an integrated MADM model comprising of Fuzzy Analytic Hierarchy Process (FAHP), Fuzzy Pugh Matrix and Fuzzy VIKOR was developed and applied to evaluate conceptual designs of liquid spraying machine. The fuzzy AHP was used to determine weights of the design features and sub features by virtue of its fuzzified comparison matrix and synthetic extent evaluation. The fuzzy Pugh matrix provides a methodical structure for determining performance using all the design alternatives as basis and obtaining aggregates for the designs using the weights of the sub features. The fuzzy VIKOR generates the decision matrix from the aggregates of the fuzzified Pugh matrices and determine the best design concept from the defuzzified performance index. At the end, the optimal design concept is determined for the liquid spraying machine.

Parole chiave

  • Hybridized Multi-Attribute Decision-making
  • Fuzzy AHP
  • Fuzzified Pugh Matrix
  • Fuzzy VIKOR
  • Optimal conceptual design
access type Accesso libero

A Holistic Approach to Polymeric Material Selection for Laser Beam Machining using Methods of DEA and TOPSIS

Pubblicato online: 16 Dec 2020
Pagine: 339 - 357

Astratto

Abstract

Laser Beam machining (LBM) nowadays finds a wide acceptance for cutting various materials and cutting of polymer sheets is no exception. Greater reliability of process coupled with superior quality of finished product makes LBM widely used for cutting polymeric materials. Earlier researchers investigated the carbon dioxide laser cutting to a few thermoplastic polymers in thickness varying from 2mm to 10mm. Here, an approach is being made for grading the suitability of polymeric materials and to answer the problem of selection for LBM cutting as per their weightages obtained by using multi-decision making (MCDM) approach. An attempt has also been made to validate the result thus obtained with the experimental results obtained by previous researchers. The analysis encompasses the use of non-parametric linear-programming method of data envelopment analysis (DEA) for process efficiency assessment combined with technique for order preference by similarity to an ideal solution (TOPSIS) for selection of polymer sheets, which is based on the closeness values. The results of this uniquely blended analysis reflect that for 3mm thick polymer sheet is polypropelene (PP) to be highly preferable over polyethylene (PE) and polycarbonate (PC). While it turns out to be that polycarbonate (PC) to be highly preferable to other two polymers for 5mm thick polymer sheets. Hence the present research analysis fits very good for the polymer sheets of 3mm thickness while it deviates a little bit for the 5mm sheets.

Parole chiave

  • Laser Beam Machining
  • Polymers
  • DEA
  • AHP
  • TOPSIS
  • MCDM

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