DNA based computing for understanding complex shapes

Ullah, Aman; D’Addona, Doriana M.; Arai, Nobuyuki

doi:10.1016/j.biosystems.2014.01.003

Cited by 19 publications

(26 citation statements)

References 44 publications

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“…8, we reported the best enzyme sequence (see [5,11] for more details about amino acids sequences), found by NACO, that mimics the tertiary structure of the natural enzyme 1AGY. 1AGY is characterized by bilaterally symmetric with a beta-sheet core and a set of alpha helices connected by unstructured loops.…”

Section: Resultsmentioning

confidence: 99%

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Naïve Bayes ant colony optimization for designing high dimensional experiments

Borrotti

Minervini

Lucrezia

et al. 2016

Applied Soft Computing

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a b s t r a c tIn a large number of experimental problems, high dimensionality of the search area and economical constraints can severely limit the number of experimental points that can be tested. Within these constraints, classical optimization techniques perform poorly, in particular, when little a priori knowledge is available. In this work we investigate the possibility of combining approaches from statistical modeling and bio-inspired algorithms to effectively explore a huge search space, sampling only a limited number of experimental points. To this purpose, we introduce a novel approach, combining ant colony optimization (ACO) and naïve Bayes classifier (NBC) that is, the naïve Bayes ant colony optimization (NACO) procedure. We compare NACO with other similar approaches developing a simulation study. We then derive the NACO procedure with the goal to design artificial enzymes with no sequence homology to the extant one. Our final aim is to mimic the natural fold of 200 amino acids 1AGY serine esterase from Fusarium solani.

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Section: Resultsmentioning

confidence: 99%

“…Different optimization approaches have been proposed in the literature to tackle problems with high dimensional and complex search spaces [5][6][7][8][9][10][11]. Some approaches are based on Evolutionary Computation Algorithms [12].…”

Section: Introductionmentioning

confidence: 99%

Naïve Bayes ant colony optimization for designing high dimensional experiments

Borrotti

Minervini

Lucrezia

et al. 2016

Applied Soft Computing

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“…In this study is explained (Section 5.1) a case of tool-wear understanding, obtained by the synthesis of protein by the use of the DNA mapping [16].…”

Section: Analysis Using Dna Based Computingmentioning

confidence: 99%

“…The 2D array takes the role of an mRNA and it is translated into a 2D protein array by the use of a genetic code: this procedure allows to determine the IC features and so to understand the trend of the tool-wear. The IC features of a 2D array protein have the possibility to let understand if a given image is the representation of a specific shape/process/phenomenon [16].…”

Section: Dbc For Tool-wear Understandingmentioning

confidence: 99%

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Tool Wear Control through Cognitive Paradigms

et al. 2015

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In the modern manufacturing systems, machining parameters are fundamental to achieve efficiency for the whole production process. The feed rate, the cutting speed and several other parameters affect significantly the machining efficiency; furthermore, the selection of an appropriate cutting tool results fundamental to set-up, in the possible best way, the other parameters. This problem is one of the most complex in machining processes and it refers directly to the quality of the finished product. The life of the selected cutting tool, under the conditions given by the other parameters, is crucial in term of efficiency and it should be estimated as accurately as possible and permanently kept under control. An optical monitoring process (video camera) can observe the tool wear development. The images give the opportunity to drive the process in achieving the target of zero defects manufacturing but there is the need to firstly elaborate and homogenize them, in order to standardize the control and predict the tool wear. By the use of a DNA Based-Computing method, the influence of user-settings on the elaboration of a set of images will be investigated. In order to supply a direction for the development of methodologies for real-time tool wear recognition and prediction in a complex and high automated environment, this paper proposes an approach for the identification of the tool wear defection. The methodology designs an artificial Neural Network (NN) for automatic tool wear recognition: a set of images are standardized in grayscale and then processed in order to extract features for NN training phase. By the use of a DNA Based Computing method (DBC), the influence of user-settings on the elaboration of a set of images will be then investigated. It will be a crucial point for the development of a new method for the real-time tool wear recognition that will be based on the information provided by the DBC

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What is knowledge in Industry 4.0?

Ullah

2020

Engineering Reports

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Industry 4.0 relevant systems (eg, cyber‐physical systems, digital twins, and alike) need digitized knowledge to function. Before digitizing knowledge, a fundamental question arises: What is knowledge? In order to answer this question, this study first reviews the definitions of knowledge found in the extant literature of epistemology, engineering design, manufacturing, organization science, information science, and education science. Since the definitions reported so far are not succinct and suffer circularity, this study overcomes this by introducing a three‐element‐based definition of knowledge—a piece knowledge consists of three elements defined as claim, provenance, and inference. This results in four types of knowledge defined as definitional, deductive, inductive, and creative knowledge, and each type of knowledge is again divided into some categories. Some real‐life scenarios relevant to engineering design and manufacturing are used to clarify the proposed knowledge types/categories; the relevant pieces of knowledge are represented by knowledge graphs (concept maps) for the sake of digitization. The myriad proximal and distal relationships between knowledge and other relevant entities (human/machine learning, logical inferences, experimental data, analytical results, creative thinking, and cognitive reflections) become succinct and transparent due to the proposed definition of knowledge. Consequently, this study establishes the fundamentals of developing sophisticated methods and tools for the advancement of Industry 4.0.

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DNA based computing for understanding complex shapes

Cited by 19 publications

References 44 publications

Naïve Bayes ant colony optimization for designing high dimensional experiments

Naïve Bayes ant colony optimization for designing high dimensional experiments

Tool Wear Control through Cognitive Paradigms

What is knowledge in Industry 4.0?

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