Joint use of eddy current imaging and fuzzy similarities to assess the integrity of steel plates

Versaci, Mario; Angiulli, Giovanni; Barba, Paolo Di; Morabito, Francesco Carlo

doi:10.1515/phys-2020-0159

Cited by 37 publications

(24 citation statements)

References 22 publications

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“…Consequently, it limits its usability. However, by utilizing a fuzzy classifier, we can avoid these issues [ 36 ]. The experiment results demonstrate that our proposed method outperforms the other models in terms of accuracy in classifying cardiovascular disease.…”

Section: Results and Comparisonmentioning

confidence: 99%

Computational Learning Model for Prediction of Heart Disease Using Machine Learning Based on a New Regularizer

Albahr

Albahar

Thanoon

et al. 2021

Computational Intelligence and Neuroscience

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Heart diseases are characterized as heterogeneous diseases comprising multiple subtypes. Early diagnosis and prognosis of heart disease are essential to facilitate the clinical management of patients. In this research, a new computational model for predicting early heart disease is proposed. The predictive model is embedded in a new regularization based on decaying the weights according to the weight matrices’ standard deviation and comparing the results against its parents (RSD-ANN). The performance of RSD-ANN is far better than that of the existing methods. Based on our experiments, the average validation accuracy computed was 96.30% using either the tenfold cross-validation or holdout method.

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Section: Results and Comparisonmentioning

confidence: 99%

Computational Learning Model for Prediction of Heart Disease Using Machine Learning Based on a New Regularizer

Albahr

Albahar

Thanoon

et al. 2021

Computational Intelligence and Neuroscience

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“…In this study, we introduced a basic formula of entropy theory to explain the cognitive balance and established a primary model to prove the effectiveness of the method. To further improve the accuracy of the model, we will focus on the exploitation of a new generation of computing approaches based on neuro-fuzzy approaches and fuzzy entropy principle [53,54].…”

Section: Discussionmentioning

confidence: 99%

Fusion of Cognitive Information: Evaluation and Evolution Method of Product Image Form

Zhang

Liu

2021

Computational Intelligence and Neuroscience

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In order to realize the stability and inheritance of image characteristics in the development process of a series of products, we comprehensively analyzed the cognitive differences among users, designers, and engineers and propose a multicriteria decision system for an intelligent design method of product forms based on a logistic regression model, relative entropy theory, and preference mapping (PREFMAP). First, from the perspective of the role characteristics of the design subjects, an equilibrium evaluation model was constructed using the logistic regression model and relative entropy theory. Second, combining the multidimensional perception space and the characteristics measurement of the product form, the fitness function of the image form was constructed based on PREFMAP. Third, a genetic algorithm was applied to establish the intelligent image-style-oriented design method, which could guide the image form development of a product series through innovative design. Lastly, the method was verified by taking Audi A4L series headlights as an example. And the image evaluation of the two new schemes was greater than that of the previous seven generations of headlights. The results verify the effectiveness and feasibility of the method. In this paper, we structured a relatively preliminary model to explain the fusion of cognitive information. More subjective and objective factors, algorithms, and image recognition technology need to be further studied to improve the model in our future work.

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“…On the other hand, the imaging results may be affected by uncertainties because of the difference in the coupling gel between the LF array transducer and concrete specimens. To overcome such a problem, a fuzzy processing technique would be promising [42].…”

Section: Discussionmentioning

confidence: 99%

Development of Low-Frequency Phased Array for Imaging Defects in Concrete Structures

Ohara

Kikuchi

Tsuji

et al. 2021

Sensors

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The nondestructive inspection of concrete structures is indispensable for ensuring the safety and reliability of aging infrastructures. Ultrasonic waves having a frequency of tens of kHz are frequently used to reduce the scattering attenuation due to coarse aggregates. Such low frequencies enable the measurement of the thickness of concrete structures and detection of layer-type defects, such as delamination, whereas it causes a lack of sensitivity to crack-type defects. In this paper, to realize the ultrasonic phased array (PA) imaging of crack-type defects, we fabricated a low-frequency (LF) array transducer with a center frequency of hundreds of kHz. To avoid the crosstalk between piezoelectric elements and dampen the vibration of each element, we adopted soft lead zirconate titanate (soft PZT) with a low mechanical quality factor. Subsequently, we optimized the geometry of each piezoelectric element using a finite element method to generate a short pulse. After validating the design in a fundamental experiment using a single-element transducer, we fabricated a 32-element array transducer with a center frequency of 350 kHz. To show the imaging capability of the LF array transducer, we applied it to a concrete specimen with a delamination. As a result, the PA with the LF array transducer clearly visualized the delamination, which could not be visualized using the PA with a 2.5 MHz array transducer. Furthermore, we applied it to a more challenging defect, a slit, which is sometimes used to simulate crack-type defects. As a result, the PA with the LF array transducer clearly visualized a slit of 1 mm width and 40 mm height in a concrete specimen. Thus, we demonstrated the usefulness of the LF array transducer for inspecting crack-type defects.

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Joint use of eddy current imaging and fuzzy similarities to assess the integrity of steel plates

Cited by 37 publications

References 22 publications

Computational Learning Model for Prediction of Heart Disease Using Machine Learning Based on a New Regularizer

Computational Learning Model for Prediction of Heart Disease Using Machine Learning Based on a New Regularizer

Fusion of Cognitive Information: Evaluation and Evolution Method of Product Image Form

Development of Low-Frequency Phased Array for Imaging Defects in Concrete Structures

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