Cross-evaluation of a parallel operating SVM – CNN classifier for reliable internal decision-making processes in composite inspection

Meister, Sebastian; Wermes, Mahdieu A. M.; Stüve, Jan; Groves, Roger M.

doi:10.1016/j.jmsy.2021.07.022

Cited by 20 publications

(3 citation statements)

References 59 publications

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“…The use of the confusion matrix is justifiable as it has been extensively employed in artificial intelligence research, including the evaluation of CNN models, to assess the reliability of model performance. [58], [59], [60], [61].…”

Section: E Reliability Of Classification Accuracy Results Estimated B...mentioning

confidence: 99%

Memristor-Based CNNs for Detecting Stress Using Brain Imaging Signals

Bak,

Park,

Lee

et al. 2024

IEEE Trans. Emerg. Top. Comput. Intell.

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Typical convolutional neural networks (CNNs) are widely used to recognize a user's stress state using the functional near-infrared spectroscopy (fNIRS), which is the latest brain imaging technology. fNIRS signals are usually fed into CNN models in the form of high-dimensional image data. However, this approach is not easy to achieve high classification accuracy because of physiological noises in brain signals. It is also likely to overlook the process of evaluating the reliability of calculated classification accuracy. To solve these problems, we proposed a memristor-based CNN (M-CNNs) This model's weight update process involves using stochastic gradient descent with momentum (SGDM), where the normalized conductances of memristors are used as weight substitutes. These conductances are then adjusted to classify stress states. We calculated the classification accuracies between the control and stress groups by using the M-CNNs, and then compared them with those of the CNNs. We used DenseNet, the most recent CNN model, to simulate accuracy under the same conditions. To ensure a fair comparison, we divided the DenseNet into the memristorbased DenseNet (M-DenseNet) and the conventional DenseNet (C-DenseNet). As a result, we discovered that the accuracy of M-CNNs (93.33%) exceeded that of CNNs (87.50%), and is reliable by precision, recall, and F-Score calculated from a confusion matrix. Likewise, M-DenseNet (92.38%) has higher accuracy than C-DenseNet (90.00%), but shows lower accuracy than M-CNNs. Moreover, we observed the reproducibility of M-CNN/DenseNet in various datasets. Therefore, our study suggests a promising application of CNN by combining conductances of memristor for classifying stress states.

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Section: E Reliability Of Classification Accuracy Results Estimated B...mentioning

confidence: 99%

Memristor-Based CNNs for Detecting Stress Using Brain Imaging Signals

Bak,

Park,

Lee

et al. 2024

IEEE Trans. Emerg. Top. Comput. Intell.

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“…These tasks may include detection of resin race-tracking in molds 135 , flow disturbances 136 , and unfilled zones formation 137 during the filling stage of an LCM process as well as inspection of broken-filaments during fiber production 138 . Novel AI-based methods for the inspection of the Automated Fiber Placement (AFP) process have also been presented by several researchers [139][140][141][142][143] . As part of health monitoring of structures, machine/deep learning models have been used for defect/damage detection [144][145][146][147][148][149][150] , characterization of cracks/delamination [151][152][153] and classification of impact levels 154 .…”

Section: The Meta-verse Of Composites Manufacturingmentioning

confidence: 99%

Graphene nanoparticles as data generating digital materials in industry 4.0

Ali

Irfan

Khan

et al. 2023

Sci Rep

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One of the potential applications of 2D materials is to enhance multi-functionality of structures and components used in aerospace, automotive, civil and defense industries. These multi-functional attributes include sensing, energy storage, EMI shielding and property enhancement. In this article, we have explored the potential of using graphene and its variants as data generating sensory elements in Industry 4.0. We have presented a complete roadmap to cover three emerging technologies i.e. advance materials, artificial intelligence and block-chain technology. The utility of 2D materials such as graphene nanoparticles is yet to be explored as an interface for digitalization of a modern smart factory i.e. “factory-of-the-future”. In this article, we have explored how 2D material enhanced composites can act as an interface between physical and cyber spaces. An overview of employing graphene-based smart embedded sensors at various stages of composites manufacturing processes and their application in real-time structural health monitoring is presented. The technical challenges associated with interfacing graphene-based sensing networks with digital space are discussed. Additionally, an overview of the integration of associated tools such as artificial intelligence, machine learning and block-chain technology with graphene-based devices and structures is also presented.

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“…The objectivity of the sustainability review process and the outcomes of PV projects cannot be entirely guaranteed by these methodologies, which are mostly impacted by subjective variables [12]. The objective evaluation methods do not assign different weights to various assessment items; rather, they only take into account how much the various indicators vary from one another in the total index and how much they influence other indicators [13]. Defined as the use of artificial intelligence algorithms in evaluation, the intelligent evaluation approach is thus introduced in this study [14].…”

Section: Introductionmentioning

confidence: 99%

Smart Evaluation of Sustainability of Photovoltaic Projects in the Context of Carbon Neutrality Target

et al. 2022

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To support the sustainable development of photovoltaic (PV) projects in the context of the carbon neutrality aim, a scientific and reliable evaluation technique is crucial. In this research, an AdaBoost-LS-WSVM intelligent evaluation model built on the Spark platform is suggested to increase evaluation accuracy and timeliness. Firstly, the sustainability evaluation index system of PV projects is constructed from five levels: geographic resource sustainability, technical sustainability, economic sustainability, social sustainability, and environmental sustainability in the context of the carbon neutrality target. Then, the AdaBoost-LS-WSVM intelligent evaluation model with Spark as the platform is constructed, and the wavelet kernel function is applied to the LSSVM model to form the LS-WSVM regression model with stronger nonlinear fitting ability. The learning and training of training samples are completed by the AdaBoost model, and multiple weak LS-WSVM regressors are weighted to get a strong LS-WSVM regressor. The regression model is used for assessing the sustainability of PV projects on Spark Big Data runtime platform. Lastly, the scientific accuracy and reliability of the proposed model is confirmed by a case study, which facilitates a timely and effective assessment of the sustainability of PV projects in the context of carbon neutrality target and can provide scientific and reasonable decision support for the construction of a sustainable development model of PV projects.

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Cross-evaluation of a parallel operating SVM – CNN classifier for reliable internal decision-making processes in composite inspection

Cited by 20 publications

References 59 publications

Memristor-Based CNNs for Detecting Stress Using Brain Imaging Signals

Memristor-Based CNNs for Detecting Stress Using Brain Imaging Signals

Graphene nanoparticles as data generating digital materials in industry 4.0

Smart Evaluation of Sustainability of Photovoltaic Projects in the Context of Carbon Neutrality Target

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