Recent Advances in Applied Deep Neural Network

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 4564

Special Issue Editors

Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: machine learning; machine olfaction; olfactory EEG recognition; affective computing
College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
Interests: intelligent detection and recognition; application of deep networks in airport energy conservation and emission reduction; airport intelligent equipment; application of deep networks in airport operation; autonomous robots
Hebei Provincial Key Laboratory of Green Chemical Technology and High Efficient Energy Saving, Hebei University of Technology, Tianjin 300401, China
Interests: intelligent algorithm; deep learning; application of pattern recognition in chemical safety; application of machine learning in high-efficiency energy saving
Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: intelligent robots; machine olfaction; signal processing; image recognition; deep learning

Special Issue Information

Dear Colleagues,

Deep neural networks have been applied in various fields, such as unmanned vehicles, intelligent answers, intelligent translation, smart grids, weather forecast, stock forecast, face comparison, voice print ratios, etc. Many other interesting applications, such as intelligent illustration, automatic poetry and automatic composition, can be achieved through deep neural networks. However, with the rapid development of deep networks, some new problems and challenges inevitably arise, which require the continuous development of original and more powerful technologies, while paying attention to their practical applications.

The goal of this Special Issue is to cover the significant advances in applied deep neural networks, including, but not limited to, novel method design concepts, online real-time computing technology based on deep learning, hardware implementation of deep networks, simulation of information processes of human consciousness and thinking using deep networks, and novel application of deep neural networks.

Dr. Huirang Hou
Dr. Yang Wang
Dr. Bo Zhang
Prof. Dr. Qing-Hao Meng
Guest Editors

Manuscript Submission Information

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Keywords

  • applied deep neural network
  • machine learning
  • pattern recognition
  • neuromorphic technologies
  • machine intelligence
  • image or signal processing
  • applications of deep neural network

Published Papers (3 papers)

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Research

14 pages, 3399 KiB  
Article
Quick Identification of Open/Closed State of GIS Switch Based on Vibration Detection and Deep Learning
by Kun Zhang, Yong Zhang, Junjie Wu and Zhizhong Li
Electronics 2023, 12(14), 3204; https://doi.org/10.3390/electronics12143204 - 24 Jul 2023
Viewed by 974
Abstract
The rapid and accurate identification of the opening and closing state of the knife switch in a gas insulated switchgear (GIS) is very important for the timely detection of equipment faults and for the reduction of related accidents. However, existing technologies, such as [...] Read more.
The rapid and accurate identification of the opening and closing state of the knife switch in a gas insulated switchgear (GIS) is very important for the timely detection of equipment faults and for the reduction of related accidents. However, existing technologies, such as image recognition, are vulnerable to weather or light intensity, while microswitch, attitude sensing and other methods are unable to induce equipment power failure with sufficient speed, which brings many new challenges to the operation and maintenance of a GIS. Therefore, this research designs a GIS shell vibration detection system for knife switch state discrimination, introduces a deep learning algorithm for knife switch vibration signal analysis, and proposes a fast convolutional neural network (FCNN) to identify the knife switch state. For the designed FCNN, a normalization layer and a nonlinear activation layer are used after each convolution layer to obviously reduce feature quantity and increase algorithm efficiency. In order to test the recognition performance based on the vibration detection system, this study carried out two kinds of knife switch opening and closing experiments. One group with artificial noise was added, the other group did not include artifical noise, and a corresponding data set was constructed. The experimental results show that the recognition accuracy for both datasets reaches 100%, and the FCNN algorithm is better than the five classical algorithms in terms of prediction efficiency. This study shows that the vibration detection technology based on deep learning can be used to effectively identify the opening and closing state of a GIS knife switch, and is expected to be promoted and applied. Full article
(This article belongs to the Special Issue Recent Advances in Applied Deep Neural Network)
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17 pages, 5283 KiB  
Article
Fault Identification of U-Net Based on Enhanced Feature Fusion and Attention Mechanism
by Qifeng Sun, Xin Wang, Hongsheng Ni, Faming Gong and Qizhen Du
Electronics 2023, 12(12), 2562; https://doi.org/10.3390/electronics12122562 - 06 Jun 2023
Viewed by 1144
Abstract
Accurate fault identification is essential for geological interpretation and reservoir exploitation. However, the unclear and noisy composition of seismic data makes it difficult to identify the complete fault structure using conventional methods. Thus, we have developed an attentional U-shaped network (EAResU-net) based on [...] Read more.
Accurate fault identification is essential for geological interpretation and reservoir exploitation. However, the unclear and noisy composition of seismic data makes it difficult to identify the complete fault structure using conventional methods. Thus, we have developed an attentional U-shaped network (EAResU-net) based on enhanced feature fusion for automated end-to-end fault interpretation of 3D seismic data. EAResU-net uses an enhanced feature fusion mechanism to reduce the semantic gap between the encoder and decoder and improve the representation of fault features in combination with residual structures. In addition, EAResU-net introduces an attention mechanism, which effectively suppresses seismic data noise and improves model accuracy. The experimental results on synthetic and field data demonstrate that, compared with traditional deep learning methods for fault detection, our EAResU-net can achieve more accurate and continuous fault recognition results. Full article
(This article belongs to the Special Issue Recent Advances in Applied Deep Neural Network)
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20 pages, 26970 KiB  
Article
AMFF-YOLOX: Towards an Attention Mechanism and Multiple Feature Fusion Based on YOLOX for Industrial Defect Detection
by Yu Chen, Yongwei Tang, Huijuan Hao, Jun Zhou, Huimiao Yuan, Yu Zhang and Yuanyuan Zhao
Electronics 2023, 12(7), 1662; https://doi.org/10.3390/electronics12071662 - 31 Mar 2023
Cited by 1 | Viewed by 2022
Abstract
Industrial defect detection has great significance in product quality improvement, and deep learning methods are now the dominant approach. However, the volume of industrial products is enormous and mainstream detectors are unable to maintain a high accuracy rate during rapid detection. To address [...] Read more.
Industrial defect detection has great significance in product quality improvement, and deep learning methods are now the dominant approach. However, the volume of industrial products is enormous and mainstream detectors are unable to maintain a high accuracy rate during rapid detection. To address the above issues, this paper proposes AMFF-YOLOX, an improved industrial defect detector based on YOLOX. The proposed method can reduce the activation function and normalization operation of the bottleneck in the backbone network, and add an attention mechanism and adaptive spatial feature fusion within the feature extraction network to enable the network to better focus on the object. Ultimately, the accuracy of the prediction is enhanced without excessive loss of speed in network prediction, with competitive performance compared to mainstream detectors. Experiments show that the proposed method in this paper achieves 61.06% (85.00%) mAP@0.5:0.95 (mAP@0.5) in the NRSD-MN dataset, 51.58% (91.09%) is achieved in the PCB dataset, and 49.08% (80.48%) is achieved in the NEU-DET dataset. A large number of comparison and ablation experiments validate the effectiveness and competitiveness of the model in industrial defect detection scenarios. Full article
(This article belongs to the Special Issue Recent Advances in Applied Deep Neural Network)
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