Industrial Fault Detection Based on Discriminant Enhanced Stacking Auto-Encoder Model

Liu, Bowen; Chai, Yi; Jiang, Yutao; Wang, Yiming

doi:10.3390/electronics11233993

Cited by 5 publications

(4 citation statements)

References 34 publications

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“…Here, the classification of distinct kinds of classes takes place using the ESAE model. SAE could efficiently extract the deep feature in the dataset and has the features of faster convergence based on its underlying concept [22]. However, based on the theory of information bottleneck, still, the capability of feature extraction can be optimized.…”

Section: ) Image Classificationmentioning

confidence: 99%

Rider Optimization With Deep Learning Based Image Encryption for Secure Drone Communication

Raja,

Lydia,

Acharya

et al. 2023

IEEE Access

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In recent years, drones or Unmanned Aerial Vehicles (UAVs) got significant attention among researchers because of their extensive application in commercial applications, border surveillance, etc. As the conventional terrestrial communication system does not work effectively on heavy calamities namely floods, landslides, cyclones, earthquakes, etc., UAVs can offer a potential solution for inexpensive, rapid, and wireless communication. Despite the drones' benefits in emergency monitoring, security is been a main factor because of the existence of wireless connections for transmission. Therefore, this article introduces optimal deep learning with image encryption-based secure drone communication (ODLIE-SDC) technique. The major intention of the ODLIE-SDC technique lies in the effectual secure communication and classification process in emergency monitoring scenarios. To accomplish this, the presented ODLIE-SDC technique designs a hyperchaotic map-based image encryption technique and its optimal keys are produced by the use of a rider optimization algorithm (ROA). The image classification process is performed encompassing EfficientNet-B4-CBAM feature extraction and enhanced stacked autoencoder (ESAE) classification. Finally, the hyperparameter tuning of the EfficientNet-B4-CBAM technique takes place using the Bayesian optimization (BO) algorithm. The experimental validation of the ODLIE-SDC technique is tested on the AIDER dataset. The comprehensive comparative analysis reported the enhanced performance of the ODLIE-SDC technique over other existing approaches.

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Section: ) Image Classificationmentioning

confidence: 99%

Rider Optimization With Deep Learning Based Image Encryption for Secure Drone Communication

Raja,

Lydia,

Acharya

et al. 2023

IEEE Access

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“…The networking input to Hidden Layer (HL) is assumed to be the encoder approach. Additionally, the HL to the resultant layer was regarded as the decoder method [34]. The encoder data can be rebuilt to novel data with the decoding approach.…”

Section: Violence Classification Modulementioning

confidence: 99%

Tuna Swarm Algorithm With Deep Learning Enabled Violence Detection in Smart Video Surveillance Systems

Aldehim,

Asiri,

Aljebreen

et al. 2023

IEEE Access

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In smart video surveillance systems, violence detection becomes challenging to ensure public safety and security. With the proliferation of surveillance cameras in public areas, there is an increasing need for automated algorithms that can accurately and efficiently detect violent behavior in real time. This article presents a Tuna Swarm Optimization with Deep Learning Enabled Violence Detection (TSODL-VD) technique to classify violent actions in surveillance videos. The TSODL-VD technique enables the recognition of violence and can be a measure to avoid chaotic situations. In the presented TSODL-VD technique, the residual-DenseNet model is applied for feature vector generation from the input video frames and then passed into the stacked autoencoder (SAE) classifier. The SAE model is enforced to recognize the events into violence and non-violence events. To improve the violence detection effectiveness of the TSODL-VD procedure, the TSO protocol is utilized as a hyperparameter optimizer for the residual-DenseNet model. The performance validation of the TSODL-VD procedure has experimented on a benchmark violence dataset. The experimental results demonstrate that the TSODL-VD technique accomplishes precise and rapid detection outcomes over the recent state-of-the-art approaches.

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“…Support vector machine (SVM) has good generalization performance and can achieve the best balance between learning ability and model complexity. The function of support vector is better to solve the problems of small sample, overlearning, local extreme value and high dimension which are often encountered by machine learning algorithms [10].…”

Section: Convolutional Neural Network Modelmentioning

confidence: 99%

Analysis of bridge foundation pile detection based on convolutional neural network model

Chen

2023

Applied Mathematics and Nonlinear Sciences

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In order to quickly and accurately detect the instability of foundation piles on concrete Bridges, an improved convolutional neural network based image recognition method for concrete bridge pile foundations was proposed. In order to improve the image quality, the entropy threshold method is used to process the image, and the two-channel convolutional neural network is designed to extract the image features fully. The improved traditional Relu activation function avoids model underfitting. Support vector machine (SVM) was used to replace Softmax classifier to improve computing efficiency. The experiment of bridge pile foundation image recognition shows that the improved convolutional neural network has significantly improved the recognition rate of the real bridge pile foundation. No matter how many degrees the image is rotated, the method in this article always maintains a high recognition rate, and the recognition rate does not fluctuate much, indicating that the algorithm in this article has good robustness to rotation and translation. In summary, the average recognition rate of the 5 groups was 96.26%. The feasibility of this method in identifying bridge pile foundation is proved.

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Industrial Fault Detection Based on Discriminant Enhanced Stacking Auto-Encoder Model

Cited by 5 publications

References 34 publications

Rider Optimization With Deep Learning Based Image Encryption for Secure Drone Communication

Rider Optimization With Deep Learning Based Image Encryption for Secure Drone Communication

Tuna Swarm Algorithm With Deep Learning Enabled Violence Detection in Smart Video Surveillance Systems

Analysis of bridge foundation pile detection based on convolutional neural network model

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