An artificial neural network (ANN) solution to the prediction of age-hardening and corrosion behavior of an Al/TiC functional gradient material (FGM)

Dikici, Burak; Tuntaş, Remzi

doi:10.1177/0021998320948945

Cited by 11 publications

(5 citation statements)

References 57 publications

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“…In different scales, appropriate feature registration functions are selected to describe the feature points of multipose face images, and the target pixel parallax analysis and key feature detection of multipose face images are carried out. According to the feature extraction results, the artificial neural network is used for face classification [ 15 ]. The artificial neural network is a three-layer network structure, and the input-output iteration equation of the artificial neural network classifier is as follows:

…”

Section: Methodsmentioning

confidence: 99%

[Retracted] Application of Artificial Neural Network Algorithm in Facial Biological Image Information Scanning and Recognition

Zhang

2022

Contrast Media & Molecular Imaging

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In order to solve the problem that the accuracy of face recognition is not good due to the influence of jitter and environmental factors in the mobile shooting environment, this paper proposes the application of an artificial neural network algorithm in the information scanning and recognition of face biological images. The spatial neighborhood information is integrated into the amplitude detection of multipose face images, and the dynamic corner features of multipose face images are extracted. The structural texture information of multipose face images is compared to a global moving RGB three-dimensional bit plane random field, and the multipose face images are detected and fused. At different scales, appropriate feature registration functions are selected to describe the feature points of multipose face images. The parallax analysis of target pixels and key feature detection of multipose face images are carried out. Image stabilization and automatic recognition are realized by combining artificial neural network learning and feature registration methods. The experimental results show that the experiment is designed with MATLAB, the frame frequency of dynamic face image acquisition is 1200 kHz, the number of collected samples of the multipose face image is 2000, the training sample set is 200, the noise coefficient = 0.24, the number of multipose face image blocks is 120, and the structural similarity is 0.12. It is found that the output signal-to-noise ratio of multipose face image recognition using the method in this paper is high. Conclusion. This method has good performance in feature point registration and high recognition accuracy for multipose face image recognition.

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…”

Section: Methodsmentioning

confidence: 99%

[Retracted] Application of Artificial Neural Network Algorithm in Facial Biological Image Information Scanning and Recognition

Zhang

2022

Contrast Media & Molecular Imaging

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“…The development and analysis of the ANN model was made using JMP Pro (USA) statistical software. The model was design using a feedforward backpropagation model [25], which is widely used and proven to give better results in similar cases [25]. In this model, faults discovered during the training phase are corrected by distributing information backward [25], [26].…”

Section: Artificial Neural Network (Ann) Analysismentioning

confidence: 99%

“…As for the output layer consisted of polarization current (A) which represented the corrosion process happening to Aluiminium 6061. Meanwhile, numbers of neurons in hidden layers were determined through a series of trial and error [25], which resulted in six hidden layers, to obtain the highest determinant coefficient (R 2 ) results.…”

Section: Artificial Neural Network (Ann) Analysismentioning

confidence: 99%

Analysis of The Corrosion Inhibition Efficacy of Camellia Sinensis Extract on Aluminium 6061 Alloys using Artificial Neural Network (ANN)

Andrean,

Harmayanti,

Wahjudi

et al. 2024

memi

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Aluminum alloys, well known for their corrosion resistance, could encounter corrosion issues in acidic environments. These environments induced pitting and exfoliation corrosion due to the absence of oxide layers from such alloys. Camellia sinensis extract, one of the organic extract, had inhibition compounds, including tannins and cathecins, that could inhibit corrosion and reduce the corrosion rate. Hence, this study investigated the corrosion inhibition efficacy of Camellia sinensis extract on Aluminum 6061 alloys when exposed to an acidic environment (1M HCl). Different concentrations of the extract were prepared to obtain the optimum concentration and achieve the highest inhibitor efficiency. In addition, an artificial neural network (ANN) model, was employed to predict polarization current in both inhibited and unhibited solutions. The model was designed using a configuration of one input, six hidden and one output ;ayers. The study discovered that the efficiency reached a remarkable level of 82.68% when using a concentration of 3000 ppm of Camellia sinensis extract. Furthermore, the ANN model demonstrated excellent performance in predicting polarization current across all variations, with determinant coefficient (R2) values of 0.995 for 0 ppm, 0.990 for 1000 ppm, 0.997 for 2000 ppm, 0.9996 for 3000 ppm, and 0.996 for 4000 ppm. These results indicated that the model was reliable in simulating the electrochemical analysis of corrosion behavior which could be used to develop a corrosion rate predictor in the future.

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“…Its theoretical basis is the knowledge of network topology. ANN simulates the processing mechanism of complex information by the nervous system of the human brain [13]. Like the human nervous system, the NN is a complex network structure composed of many simple neurons connected and transmitted, as shown in Fig.…”

Section: A Ai and Nnsmentioning

confidence: 99%

Damage Security Intelligent Identification of Wharf Concrete Structures under Deep Learning and Digital Image Technology

Zhu¹,

Li²,

Zhu³

2023

IJACSA

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Intelligence (AI) technology has quickly developed under the mighty computing power of computers. At this stage, there are many mature non-destructive testing methods in civil engineering, but they are generally only suitable for simple structures and evident damage characteristics. Therefore, it's necessary for us to investigate the damage identification of wharf concrete structures under deep learning and digital image technology. The article propose a damage detection and localization method based on Neural Network (NN) technology in deep learning and Digital Image Correlation (DIC) to identify internal damage to concrete used for wharf construction. Firstly, the identification model of concrete structure is constructed using NN technology. Then, structural damage identification of concrete is further investigated using DIC. Finally, relevant experiments are designed to verify the effect of the model. The results show that: (1) The damage model of concrete structure constructed by NN technology has high convergence and stability and can control the test error well. (2) The image output by the DIC equipment is processed and input into the NN. The errors of the various parameters of different concretes can be within the acceptable range. This paper aims to provide good ideas and references for follow-up structural health monitoring and other topics and has significant engineering application value.

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An artificial neural network (ANN) solution to the prediction of age-hardening and corrosion behavior of an Al/TiC functional gradient material (FGM)

Cited by 11 publications

References 57 publications

[Retracted] Application of Artificial Neural Network Algorithm in Facial Biological Image Information Scanning and Recognition

[Retracted] Application of Artificial Neural Network Algorithm in Facial Biological Image Information Scanning and Recognition

Analysis of The Corrosion Inhibition Efficacy of Camellia Sinensis Extract on Aluminium 6061 Alloys using Artificial Neural Network (ANN)

Damage Security Intelligent Identification of Wharf Concrete Structures under Deep Learning and Digital Image Technology

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