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Special Issue "Selected Papers from the 7th Asian Conference on Artificial Intelligence Technology (ACAIT 2023)"

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

Deadline for manuscript submissions: 31 January 2024 | Viewed by 2634

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Special Issue Editors

Prof. Dr. Yixiong Feng

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Guest Editor

State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
Interests: modern mechanical design theory and method; product digital design and manufacture; big data and cloud technology in design and manufacture
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Weihua Xu

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Guest Editor

School of Artificial Intelligence, Southwest University, Chongqing 400100, China
Interests: cognitive computing; data mining; granular computing; information fusion; knowledge engineering
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Dr. Dongdong Li

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Guest Editor

Automatic Target Recognition (ATR) Key Lab, College of Electronic Science and Engineering, National University of Defense Technology (NUDT), Changsha 410073, China
Interests: devleoping air-to-ground sensing algorithms for drones (e.g. classification, detection, tracking, localization and mapping)
Special Issues, Collections and Topics in MDPI journals

Dr. Shanhe Lou

E-Mail Website
Guest Editor

School of Mechanical and Aerospace Engineering, Singapore Nanyang Technological University, Singapore 637616, Singapore
Interests: autonomous vehicle; mechanical design theory; human-machine interaction

Special Issue Information

Dear Colleagues,

The 7th Asian Conference on Artificial Intelligence Technology (ACAIT 2023) will be held in Jiaxing, China from 3 to 5 November 2023 (https://www.acaitconf.com/). Under the theme of “Intelligence Creation, Welcome Future”, we are preparing plenary and keynote presentations and special sessions for scientists and engineers to present their latest research findings in this rapidly changing field. We would like you to enjoy not only the research side of the conference, but also the hospitality of Jiaxing and its citizens and we are looking forward to seeing you at ACAIT 2023.

This Special Issue will be composed of selected papers from the ACAIT 2023 Conference. High-quality original submissions that focus on research results and applications of all areas of AI are welcome. Potential topics of this Special Issue include the following keywords, but are not limited to these presented topics.

Prof. Dr. Yixiong Feng
Prof. Dr. Weihua Xu
Dr. Dongdong Li
Dr. Shanhe Lou
Guest Editors

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 submissions that pass pre-check are 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 semimonthly 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 2200 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

image analysis
video analysis
medical image processing
intelligent vehicles
machine learning
data mining
knowledge representation and reasoning
multimodality information fusion
natural language processing
swarm intelligence
intelligent robot and systems
intelligent control
smart medical systems
autonomous systems
other AI-enabled applications

Published Papers (6 papers)

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Research

Open AccessArticle

Activeness: A Novel Neural Coding Scheme Integrating the Spike Rate and Temporal Information in the Spiking Neural Network

Zongxia Wang

Naigong Yu

and

Yishen Liao

Electronics 2023, 12(19), 3992; https://doi.org/10.3390/electronics12193992 - 22 Sep 2023

Viewed by 182

Abstract

In neuromorphic computing, the coding method of spiking neurons serves as the foundation and is crucial for various aspects of network operation. Existing mainstream coding methods, such as rate coding and temporal coding, have different focuses, and each has its own advantages and limitations. This paper proposes a novel coding scheme called activeness coding that integrates the strengths of both rate and temporal coding methods. It encompasses precise timing information of the most recent neuronal spike as well as the historical firing rate information. The results of basic characteristic tests demonstrate that this encoding method accurately expresses input information and exhibits robustness. Furthermore, an unsupervised learning method based on activeness-coding triplet spike-timing dependent plasticity (STDP) is introduced, with the MNIST classification task used as an example to assess the performance of this encoding method in solving cognitive tasks. Test results show an improvement in accuracy of approximately 4.5%. Additionally, activeness coding also exhibits potential advantages in terms of resource conservation. Overall, activeness offers a promising approach for spiking neural network encoding with implications for various applications in the field of neural computation. Full article

(This article belongs to the Special Issue Selected Papers from the 7th Asian Conference on Artificial Intelligence Technology (ACAIT 2023))

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Open AccessArticle

DFFA-Net: A Differential Convolutional Neural Network for Underwater Optical Image Dehazing

and

Electronics 2023, 12(18), 3876; https://doi.org/10.3390/electronics12183876 - 14 Sep 2023

Viewed by 225

Abstract

This paper proposes DFFA-Net, a novel differential convolutional neural network designed for underwater optical image dehazing. DFFA-Net is obtained by deeply analyzing the factors that affect the quality of underwater images and combining the underwater light propagation characteristics. DFFA-Net introduces a channel differential module that captures the mutual information between the green and blue channels with respect to the red channel. Additionally, a loss function sensitive to RGB color channels is introduced. Experimental results demonstrate that DFFA-Net achieves state-of-the-art performance in terms of quantitative metrics for single-image dehazing within convolutional neural network-based dehazing models. On the widely-used underwater Underwater Image Enhancement Benchmark (UIEB) image dehazing dataset, DFFA-Net achieves a peak signal-to-noise ratio (PSNR) of 24.2631 and a structural similarity index (SSIM) score of 0.9153. Further, we have deployed DFFA-Net on a self-developed Remotely Operated Vehicle (ROV). In a swimming pool environment, DFFA-Net can process hazy images in real time, providing better visual feedback to the operator. The source code has been open sourced. Full article

(This article belongs to the Special Issue Selected Papers from the 7th Asian Conference on Artificial Intelligence Technology (ACAIT 2023))

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Open AccessArticle

A Reinforcement Learning List Recommendation Model Fused with Graph Neural Networks

Zhongming Lv

and

Xiangrong Tong

Electronics 2023, 12(18), 3748; https://doi.org/10.3390/electronics12183748 - 05 Sep 2023

Viewed by 249

Abstract

Existing list recommendation methods present a list consisting of multiple items for feedback recommendation to user requests, which has the advantages of high flexibility and direct user feedback. However, the structured representation of state data limits the embedding of users and items, making them isolated from each other, missing some useful infomation for recommendation. In addition, the traditional non-end-to-end learning series takes a long time and accumulates errors. During the model training process, the results of each task can easily affect the next calculation, thus affecting the entire training effect. Aiming at the above problems, this paper proposes a Reinforcement Learning List Recommendation Model Fused with a Graph Neural Network, GNLR. The goal of this model is to maximize the recommendation effect while ensuring that the list recommendation system accurately analyzes user preferences to improve user experience. To this end, firstly, we use an user–item bipartite graph and Graph Neural Network to aggregate neighborhood information for users and items to generate graph structured representation; secondly, we adopt an attention mechanism to assign corresponding weights to neighborhood information to reduce the influence of noise nodes in heterogeneous information networks; finally, we alleviate the problems of traditional non-end-to-end methods through end-to-end training methods. The experimental results show that the method proposed in this paper can alleviate the above problems, and the recommendation hit rate and accuracy rate increase by about 10%. Full article

(This article belongs to the Special Issue Selected Papers from the 7th Asian Conference on Artificial Intelligence Technology (ACAIT 2023))

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Open AccessArticle

Sub-Pixel Convolutional Neural Network for Image Super-Resolution Reconstruction

and

Electronics 2023, 12(17), 3572; https://doi.org/10.3390/electronics12173572 - 24 Aug 2023

Viewed by 338

Abstract

Image super-resolution (SR) reconstruction technology can improve the quality of low-resolution (LR) images. There are many available deep learning networks different from traditional machine learning algorithms. However, these networks are usually prone to poor performance on complex computation, vanishing gradients, and loss of useful information. In this work, we propose a sub-pixel convolutional neural network (SPCNN) for image SR reconstruction. First, to reduce the strong correlation, the RGB mode was translated into YC_bC_r mode, and the Y channel data was chosen as the input LR image. Meanwhile, the LR image was chosen as the network input to reduce computation instead of the interpolation reconstructed image as used in the super-resolution convolutional neural network (SRCNN). Then, two convolution layers were built to obtain more features, and four non-linear mapping layers were used to achieve different level features. Furthermore, the residual network was introduced to transfer the feature information from the lower layer to the higher layer to avoid the gradient explosion or vanishing gradient phenomenon. Finally, the sub-pixel convolution layer based on up-sampling was designed to reduce the reconstruction time. Experiments on three different data sets proved that the proposed SPCNN performs superiorly to the Bicubic, sparsity constraint super-resolution (SCSR), anchored neighborhood regression (ANR), and SRCNN methods on reconstruction precision and time consumption. Full article

(This article belongs to the Special Issue Selected Papers from the 7th Asian Conference on Artificial Intelligence Technology (ACAIT 2023))

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Open AccessArticle

VSLAM Optimization Method in Dynamic Scenes Based on YOLO-Fastest

and

Electronics 2023, 12(17), 3538; https://doi.org/10.3390/electronics12173538 - 22 Aug 2023

Viewed by 384

Abstract

Simultaneous localization and mapping (SLAM) is one of the core technologies for intelligent mobile robots. However, when robots perform VSLAM in dynamic scenes, dynamic objects can reduce the accuracy of mapping and localization. If deep learning-based semantic information is introduced into the SLAM system to eliminate the influence of dynamic objects, it will require high computing costs. To address this issue, this paper proposes a method called YF-SLAM, which is based on a lightweight object detection network called YOLO-Fastest and tightly coupled with depth geometry to remove dynamic feature points. This method can quickly identify the dynamic target area in a dynamic scene and then use depth geometry constraints to filter out dynamic feature points, thereby optimizing the VSLAM positioning performance while ensuring real-time and efficient operation of the system. This paper evaluates the proposed method on the publicly available TUM dataset and a self-made indoor dataset. Compared with ORB-SLAM2, the root-mean-square error of the Absolute Trajectory Error (ATE) can be reduced by 98.27%. The system successfully locates and constructs an accurate environmental map in a real indoor dynamic environment using a mobile robot. It is a VSLAM system that can run in real-time on low-power embedded platforms. Full article

(This article belongs to the Special Issue Selected Papers from the 7th Asian Conference on Artificial Intelligence Technology (ACAIT 2023))

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Open AccessArticle

Maritime Target Recognition and Location System Based on Lightweight Neural Network

and

Electronics 2023, 12(15), 3292; https://doi.org/10.3390/electronics12153292 - 31 Jul 2023

Viewed by 357

Abstract

China’s sea surface area is vast, the need to monitor the area is too large, and the traditional human monitoring method consumes a lot of manpower. Additionally, the monitoring period is too long; the monitoring efficiency is too low; and long-term human monitoring can easily cause visual fatigue, as well as missed detection and error detection. At present, the detection of sea surface targets generally includes infrared, visible light and other different means, which can obtain the image information of sea surface targets in different ways. The infrared target detection of the sea surface can be processed in the spatial domain and frequency domain, respectively, but the image resolution is not high in general, and the detection effect is not good because it is easily affected by weather. In this paper, we propose a maritime target detection method based on embedded vision. Based on visible video images, this paper realizes the rapid detection and recognition of sea surface targets. Clouds and waves in ocean images are filtered by adding an image preprocessing module. Compared with the traditional two-frame difference method, this algorithm has better detection capability for sea surface targets. Experiments were carried out in different weather conditions to detect moving ships at sea. By comparing the number of detection boxes and the detection accuracy, the accuracy of this method reaches 90.2 percent. By designing a single camera location algorithm for the marine environment, the world coordinate location of the marine target is realized. On this basis, the communication function is added to realize the intelligent monitoring of the sea surface without human intervention. Full article

(This article belongs to the Special Issue Selected Papers from the 7th Asian Conference on Artificial Intelligence Technology (ACAIT 2023))

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