Autonomous CNN (AutoCNN): A data-driven approach to network architecture determination

Aradhya, Abhay M S; Ashfahani, Andri; Angelina, Fienny; Pratama, Mahardhika; Mello, Rodrigo Fernandes de; Sundaram, Suresh

doi:10.1016/j.ins.2022.05.100

Cited by 9 publications

(2 citation statements)

References 20 publications

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“…A CNN is a reliable deep learning algorithm for solving the problems of image recognition, high-level information reconstruction, automatic pilots and security protection. 29 This model mainly consists of an input layer, convolution layer, activation layer (ReLU function) and fully connected layer. As the core component of the CNN model, the convolution kernel contains the features that need to be captured from the objective area, and the convolution results represent the weight of the features in the objective area.…”

Section: Algorithm For the Spatial Deduction Of Roof Stressmentioning

confidence: 99%

Overall sensing method for the three-dimensional stress of roadway via machine learning on SHM data

Tan

Chen

Gao

et al. 2023

Structural Health Monitoring

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Monitoring roof stress is essential for identifying structural anomalies and preventing disasters during underground construction. However, current sensors mainly focus on monitoring in one dimension, and it is challenging to obtain the mechanical status of the overall roof owing to the limitations of sensor numbers and the working environment. Therefore, we aimed to present an overall sensing method for the three-dimensional stress status of a roadway roof through machine learning (ML) based on limited monitoring points. First, the framework of the overall sensing method was developed, where a three-dimensional stress sensor was created to obtain the mechanical behaviours of some sensitive positions, and an ML model driven by the physical mechanism and limited monitoring data was developed to derive the overall stress situation. The developed sensor was installed in a case study, and the ML model was formulated based on the field-monitoring data. A series of experiments were conducted to derive the stress distribution of the roadway roof in the study case. Furthermore, a numerical simulation was conducted to compare the reasonability of the deduction results. The experimental results indicated that the deduction results of roof stress were reasonable, and thus the proposed sensing method is reliable.

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Section: Algorithm For the Spatial Deduction Of Roof Stressmentioning

confidence: 99%

Overall sensing method for the three-dimensional stress of roadway via machine learning on SHM data

Tan

Chen

Gao

et al. 2023

Structural Health Monitoring

View full text Add to dashboard Cite

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“…CNNs are object-recognitionfocused networks that excel in pattern recognition tasks [10]. Their architecture has been continuously improved to enhance performance [11], and they find applications across various knowledge domains, particularly in image classification [12], [13]. Noteworthy CNN-based architectures include which combines CNNs with long short-term memory networks (CNN-LSTM) for sequential data processing [14], R-CNN region-based networks [15], and the fast R-CNN, which improves detection speed for region-based networks [16].…”

Section: Introductionmentioning

confidence: 99%

Comparison of convolutional neural network models for user’s facial recognition

Pinzón-Arenas,

Jimenez-Moreno,

Martinez Baquero

2024

IJECE

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This paper compares well-known convolutional neural networks (CNN) models for facial recognition. For this, it uses its database created from two registered users and an additional category of unknown persons. Eight different base models of convolutional architectures were compared by transfer of learning, and two additional proposed models called shallow CNN and shallow directed acyclic graph with CNN (DAG-CNN), which are architectures with little depth (six convolution layers). Within the tests with the database, the best results were obtained by the GoogLeNet and ResNet-101 models, managing to classify 100% of the images, even without confusing people outside the two users. However, in an additional real-time test, in which one of the users had his style changed, the models that showed the greatest robustness in this situation were the Inception and the ResNet-101, being able to maintain constant recognition. This demonstrated that the networks of greater depth manage to learn more detailed features of the users' faces, unlike those of shallower ones; their learning of features is more generalized. Declare the full term of an abbreviation/acronym when it is mentioned for the first time.

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