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Détails du magazine
Format
Magazine
eISSN
2300-3405
Première publication
24 Oct 2012
Période de publication
4 fois par an
Langues
Anglais

Chercher

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

Détails du magazine
Format
Magazine
eISSN
2300-3405
Première publication
24 Oct 2012
Période de publication
4 fois par an
Langues
Anglais

Chercher

4 Articles
access type Accès libre

Chessboard and Chess Piece Recognition With the Support of Neural Networks

Publié en ligne: 16 Dec 2020
Pages: 257 - 280

Résumé

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).

Mots clés

  • chessboard detection
  • chess pieces recognition
  • pattern recognition
  • chess-board recognition
  • chess
  • neural networks
access type Accès libre

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

Publié en ligne: 16 Dec 2020
Pages: 281 - 304

Résumé

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.

Mots clés

  • applied machine learning
  • machine learning teaching
  • machine learning engineering
access type Accès libre

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

Publié en ligne: 16 Dec 2020
Pages: 305 - 337

Résumé

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.

Mots clés

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

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

Publié en ligne: 16 Dec 2020
Pages: 339 - 357

Résumé

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.

Mots clés

  • Laser Beam Machining
  • Polymers
  • DEA
  • AHP
  • TOPSIS
  • MCDM
4 Articles
access type Accès libre

Chessboard and Chess Piece Recognition With the Support of Neural Networks

Publié en ligne: 16 Dec 2020
Pages: 257 - 280

Résumé

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).

Mots clés

  • chessboard detection
  • chess pieces recognition
  • pattern recognition
  • chess-board recognition
  • chess
  • neural networks
access type Accès libre

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

Publié en ligne: 16 Dec 2020
Pages: 281 - 304

Résumé

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.

Mots clés

  • applied machine learning
  • machine learning teaching
  • machine learning engineering
access type Accès libre

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

Publié en ligne: 16 Dec 2020
Pages: 305 - 337

Résumé

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.

Mots clés

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

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

Publié en ligne: 16 Dec 2020
Pages: 339 - 357

Résumé

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.

Mots clés

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

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