Special Issue "Deep Neural Networks and Their Applications"

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

Deadline for manuscript submissions: 31 March 2020.

Special Issue Editor

Prof. Hyongsuk Kim
E-Mail Website
Guest Editor
Division of Electronics Engineering and Intelligent Robot Research Center of Chonbuk National University, Chonbuk National University, Jeonju 567-54896, Korea
Interests: neural networks; deep learning; memristors; neuromorphics; intelligent robotics

Special Issue Information

Dear Colleagues,

By virtue of the success of recent deep neural network technologies, Artificial Intelligence has recently received great attention from almost all fields of academia and industries. Though the current success of Artificial Intelligence arose with the software version of neural networks, it is gradually extending to hardware implementations and human–computer interfaces. This Special Issue aims to provide a platform to researchers from both software and hardware of Artificial Intelligence to share cutting-edge developments in the field. The scope of this Special Issue is deep learning, neuromorphics, and brain–computer interfaces.

We solicit original research papers as well as review articles, including but not limited to the following key words:

  • Artificial Intelligence
  • Brain–computer interface (BCI)
  • Brain signal processing for BCI
  • Deep learning (AI) algorithm
  • Deep learning (AI) architecture
  • Deep learning applications
  • Intelligent bioinformatics
  • Intelligent robots
  • Intelligent systems
  • Machine learning
  • Memristors
  • Neural networks
  • Neural rehabilitation engineering
  • Neuromorphics
  • Parallel processing
  • Web intelligence applications and search

Prof. Hyongsuk Kim
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Artificial Intelligence
  • Brain–computer interface (BCI)
  • Brain signal processing for BCI
  • Deep learning (AI) algorithm
  • Deep learning (AI) architecture
  • Deep learning applications
  • Intelligent bioinformatics
  • Intelligent robots
  • Intelligent systems
  • Machine learning
  • Memristors
  • Neural networks
  • Neural rehabilitation engineering
  • Neuromorphics
  • Parallel processing
  • Web intelligence applications and search

Published Papers (8 papers)

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Research

Open AccessArticle
Fully Convolutional Single-Crop Siamese Networks for Real-Time Visual Object Tracking
Electronics 2019, 8(10), 1084; https://doi.org/10.3390/electronics8101084 - 24 Sep 2019
Abstract
The visual object tracking problem seeks to track an arbitrary object in a video, and many deep convolutional neural network-based algorithms have achieved significant performance improvements in recent years. However, most of them do not guarantee real-time operation due to the large computation [...] Read more.
The visual object tracking problem seeks to track an arbitrary object in a video, and many deep convolutional neural network-based algorithms have achieved significant performance improvements in recent years. However, most of them do not guarantee real-time operation due to the large computation overhead for deep feature extraction. This paper presents a single-crop visual object tracking algorithm based on a fully convolutional Siamese network (SiamFC). The proposed algorithm significantly reduces the computation burden by extracting multiple scale feature maps from a single image crop. Experimental results show that the proposed algorithm demonstrates superior speed performance in comparison with that of SiamFC. Full article
(This article belongs to the Special Issue Deep Neural Networks and Their Applications)
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Open AccessArticle
Dynamic Deep Forest: An Ensemble Classification Method for Network Intrusion Detection
Electronics 2019, 8(9), 968; https://doi.org/10.3390/electronics8090968 - 30 Aug 2019
Abstract
Network Intrusion Detection System (NIDS) is one of the key technologies to prevent network attacks and data leakage. In combination with machine learning, intrusion detection has achieved great progress in recent years. However, due to the diversity of intrusion types, the representation learning [...] Read more.
Network Intrusion Detection System (NIDS) is one of the key technologies to prevent network attacks and data leakage. In combination with machine learning, intrusion detection has achieved great progress in recent years. However, due to the diversity of intrusion types, the representation learning ability of the existing models is still deficient, which limits the further improvement of the detection performance. Meanwhile, with the increasing of model complexity, the training time becomes longer and longer. In this paper, we propose a Dynamic Deep Forest method for network intrusion detection. It uses cascade tree structure to strengthen the representation learning ability. At the same time, the training process is accelerated due to small-scale parameter fitting and dynamic level-growing strategy. The proposed Dynamic Deep Forest is a tree-based ensemble approach and consists of two parts. The first part, Multi-Grained Traversing, uses selectors to pick up features as complete as possible. The selectors are constructed dynamically so that the training process will stop as soon as the optimal feature combination is found. The second part, Cascade Forest, introduces level-by-level tree structures. It has fewer hyper-parameters and follows a dynamic level-growing strategy to reduce model complexity. In experiments, we evaluate our model on network intrusion dataset KDD’99. The results show that the Dynamic Deep Forest method obtains higher recall and precision through a short time of model training. Moreover, the Dynamic Deep Forest method has lower risk of misclassification, which is more stable and reliable in a real network environment. Full article
(This article belongs to the Special Issue Deep Neural Networks and Their Applications)
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Open AccessArticle
Ship Target Detection Algorithm Based on Improved Faster R-CNN
Electronics 2019, 8(9), 959; https://doi.org/10.3390/electronics8090959 - 29 Aug 2019
Abstract
Ship target detection has urgent needs and broad application prospects in military and marine transportation. In order to improve the accuracy and efficiency of the ship target detection, an improved Faster R-CNN (Faster Region-based Convolutional Neural Network) algorithm of ship target detection is [...] Read more.
Ship target detection has urgent needs and broad application prospects in military and marine transportation. In order to improve the accuracy and efficiency of the ship target detection, an improved Faster R-CNN (Faster Region-based Convolutional Neural Network) algorithm of ship target detection is proposed. In the proposed method, the image downscaling method is used to enhance the useful information of the ship image. The scene narrowing technique is used to construct the target regional positioning network and the Faster R-CNN convolutional neural network into a hierarchical narrowing network, aiming at reducing the target detection search scale and improving the computational speed of Faster R-CNN. Furthermore, deep cooperation between main network and subnet is realized to optimize network parameters after researching Faster R-CNN with subject narrowing function and selecting texture features and spatial difference features as narrowed sub-networks. The experimental results show that the proposed method can significantly shorten the detection time of the algorithm while improving the detection accuracy of Faster R-CNN algorithm. Full article
(This article belongs to the Special Issue Deep Neural Networks and Their Applications)
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Open AccessArticle
A Deep Learning-Based Scatter Correction of Simulated X-ray Images
Electronics 2019, 8(9), 944; https://doi.org/10.3390/electronics8090944 - 27 Aug 2019
Abstract
X-ray scattering significantly limits image quality. Conventional strategies for scatter reduction based on physical equipment or measurements inevitably increase the dose to improve the image quality. In addition, scatter reduction based on a computational algorithm could take a large amount of time. We [...] Read more.
X-ray scattering significantly limits image quality. Conventional strategies for scatter reduction based on physical equipment or measurements inevitably increase the dose to improve the image quality. In addition, scatter reduction based on a computational algorithm could take a large amount of time. We propose a deep learning-based scatter correction method, which adopts a convolutional neural network (CNN) for restoration of degraded images. Because it is hard to obtain real data from an X-ray imaging system for training the network, Monte Carlo (MC) simulation was performed to generate the training data. For simulating X-ray images of a human chest, a cone beam CT (CBCT) was designed and modeled as an example. Then, pairs of simulated images, which correspond to scattered and scatter-free images, respectively, were obtained from the model with different doses. The scatter components, calculated by taking the differences of the pairs, were used as targets to train the weight parameters of the CNN. Compared with the MC-based iterative method, the proposed one shows better results in projected images, with as much as 58.5% reduction in root-mean-square error (RMSE), and 18.1% and 3.4% increases in peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), on average, respectively. Full article
(This article belongs to the Special Issue Deep Neural Networks and Their Applications)
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Open AccessArticle
A Rapid Recognition Method for Electronic Components Based on the Improved YOLO-V3 Network
Electronics 2019, 8(8), 825; https://doi.org/10.3390/electronics8080825 - 25 Jul 2019
Cited by 1
Abstract
Rapid object recognition in the industrial field is the key to intelligent manufacturing. The research on fast recognition methods based on deep learning was the focus of researchers in recent years, but the balance between detection speed and accuracy was not well solved. [...] Read more.
Rapid object recognition in the industrial field is the key to intelligent manufacturing. The research on fast recognition methods based on deep learning was the focus of researchers in recent years, but the balance between detection speed and accuracy was not well solved. In this paper, a fast recognition method for electronic components in a complex background is presented. Firstly, we built the image dataset, including image acquisition, image augmentation, and image labeling. Secondly, a fast recognition method based on deep learning was proposed. The balance between detection accuracy and detection speed was solved through the lightweight improvement of YOLO (You Only Look Once)-V3 network model. Finally, the experiment was completed, and the proposed method was compared with several popular detection methods. The results showed that the accuracy reached 95.21% and the speed was 0.0794 s, which proved the superiority of this method for electronic component detection. Full article
(This article belongs to the Special Issue Deep Neural Networks and Their Applications)
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Open AccessArticle
Predicting Image Aesthetics for Intelligent Tourism Information Systems
Electronics 2019, 8(6), 671; https://doi.org/10.3390/electronics8060671 - 13 Jun 2019
Cited by 1
Abstract
Image perception can vary considerably between subjects, yet some sights are regarded as aesthetically pleasant more often than others due to their specific visual content, this being particularly true in tourism-related applications. We introduce the ESITUR project, oriented towards the development of ’smart [...] Read more.
Image perception can vary considerably between subjects, yet some sights are regarded as aesthetically pleasant more often than others due to their specific visual content, this being particularly true in tourism-related applications. We introduce the ESITUR project, oriented towards the development of ’smart tourism’ solutions aimed at improving the touristic experience. The idea is to convert conventional tourist showcases into fully interactive information points accessible from any smartphone, enriched with automatically-extracted contents from the analysis of public photos uploaded to social networks by other visitors. Our baseline, knowledge-driven system reaches a classification accuracy of 64.84 ± 4.22% telling suitable images from unsuitable ones for a tourism guide application. As an alternative we adopt a data-driven Mixture of Experts (MEX) approach, in which multiple learners specialize in partitions of the problem space. In our case, a location tag is attached to every picture providing a criterion to segment the data by, and the MEX model accordingly defined achieves an accuracy of 85.08 ± 2.23%. We conclude ours is a successful approach in environments in which some kind of data segmentation can be applied, such as touristic photographs. Full article
(This article belongs to the Special Issue Deep Neural Networks and Their Applications)
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Open AccessArticle
Automatic Tool for Fast Generation of Custom Convolutional Neural Networks Accelerators for FPGA
Electronics 2019, 8(6), 641; https://doi.org/10.3390/electronics8060641 - 06 Jun 2019
Cited by 2
Abstract
This paper presents a platform that automatically generates custom hardware accelerators for convolutional neural networks (CNNs) implemented in field-programmable gate array (FPGA) devices. It includes a user interface for configuring and managing these accelerators. The herein-presented platform can perform all the processes necessary [...] Read more.
This paper presents a platform that automatically generates custom hardware accelerators for convolutional neural networks (CNNs) implemented in field-programmable gate array (FPGA) devices. It includes a user interface for configuring and managing these accelerators. The herein-presented platform can perform all the processes necessary to design and test CNN accelerators from the CNN architecture description at both layer and internal parameter levels, training the desired architecture with any dataset and generating the configuration files required by the platform. With these files, it can synthesize the register-transfer level (RTL) and program the customized CNN accelerator into the FPGA device for testing, making it possible to generate custom CNN accelerators quickly and easily. All processes save the CNN architecture description are fully automatized and carried out by the platform, which manages third-party software to train the CNN and synthesize and program the generated RTL. The platform has been tested with the implementation of some of the CNN architectures found in the state-of-the-art for freely available datasets such as MNIST, CIFAR-10, and STL-10. Full article
(This article belongs to the Special Issue Deep Neural Networks and Their Applications)
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Open AccessArticle
Surface Defects Recognition of Wheel Hub Based on Improved Faster R-CNN
Electronics 2019, 8(5), 481; https://doi.org/10.3390/electronics8050481 - 29 Apr 2019
Abstract
Machine vision is one of the key technologies used to perform intelligent manufacturing. In order to improve the recognition rate of multi-class defects in wheel hubs, an improved Faster R-CNN method was proposed. A data set for wheel hub defects was built. This [...] Read more.
Machine vision is one of the key technologies used to perform intelligent manufacturing. In order to improve the recognition rate of multi-class defects in wheel hubs, an improved Faster R-CNN method was proposed. A data set for wheel hub defects was built. This data set consisted of four types of defects in 2,412 1080 × 1440 pixels images. Faster R-CNN was modified, trained, verified and tested based on this database. The recognition rate for this proposed method was excellent. The proposed method was compared with the popular R-CNN and YOLOv3 methods showing simpler, faster, and more accurate defect detection, which demonstrates the superiority of the improved Faster R-CNN for wheel hub defects. Full article
(This article belongs to the Special Issue Deep Neural Networks and Their Applications)
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