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

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

Deadline for manuscript submissions: 15 September 2024 | Viewed by 9263

Special Issue Editors

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
School of Artificial Intelligence, Southwest University, Chongqing 400100, China
Interests: cognitive computing; data mining; granular computing; information fusion; knowledge engineering
Special Issues, Collections and Topics in MDPI journals
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
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 2400 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

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.
  • 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 (9 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

15 pages, 1059 KiB  
Article
Fault-Tolerant Safety-Critical Control for Nonlinear Affine System by Using High-Order Control Barrier Function
by Zhe Dong, Jianning Li and Hailun Wang
Electronics 2023, 12(21), 4549; https://doi.org/10.3390/electronics12214549 - 06 Nov 2023
Viewed by 726
Abstract
A class of fault-tolerant safety-critical controller design methods based on high-order control barrier function (HOCBF) is proposed to address the problem of safety and stability of system affected by actuator faults in safety-constrained nonlinear affine system. Firstly, the fault information is incorporated into [...] Read more.
A class of fault-tolerant safety-critical controller design methods based on high-order control barrier function (HOCBF) is proposed to address the problem of safety and stability of system affected by actuator faults in safety-constrained nonlinear affine system. Firstly, the fault information is incorporated into the conventional HOCBF to obtain a new type of HOCBF with faults. Secondly, in the case of a strictly required range of control inputs, a sufficient condition is proposed to satisfy the existing constraints, where the control inputs are always within the restricted range and the sufficient condition is expressed as feasibility constraints. Next, based on the quadratic programming (QP) method, the control Lyapunov function, fault HOCBF, and feasibility constraints are incorporated together to ensure that the overall feasibility, stabilization, and safety are guaranteed of the considered closed-loop system. Finally, the adaptive cruise control system is taken as an example to verify the effectiveness of the proposed method. Full article
Show Figures

Figure 1

20 pages, 7504 KiB  
Article
Research on Automatic Vertical Parking Path-Planning Algorithms for Narrow Parking Spaces
by Yanfeng Wu, Xuan Li, Jianping Gao and Xinling Yang
Electronics 2023, 12(20), 4203; https://doi.org/10.3390/electronics12204203 - 10 Oct 2023
Viewed by 1000
Abstract
Narrow parking spaces pose difficulties in path planning and spot turning caused by sudden changes and discontinuities in path curvature. To address these problems, this paper investigates the performance of three path-planning algorithms and proposes a path-optimization algorithm. First, a narrow parking space [...] Read more.
Narrow parking spaces pose difficulties in path planning and spot turning caused by sudden changes and discontinuities in path curvature. To address these problems, this paper investigates the performance of three path-planning algorithms and proposes a path-optimization algorithm. First, a narrow parking space is defined based on single-step parking using the arc-line combination parking algorithm. Second, to compare the arc-line combination algorithm, Hybrid A* algorithm, and particle swarm optimization parking algorithm with respect to different narrow parking spaces, a multi-objective evaluation function is proposed, including three evaluation indicators, namely, the path length, the number of positive and negative conversions of vehicle speed, and the smoothness of the path. Their performance is compared using a simulation conducted in MATLAB. With the same starting point and different parking space widths, the three algorithms are simulated to generate different planned paths. Then, the evaluation indices are obtained to compare the performance of the algorithms based on the multi-objective function, the values of which indicate the fitness of the algorithm in a narrow parking environment. The results show that the Hybrid A* algorithm is better than the others for narrow parking spaces. Third, to smooth the planning path, a path optimization algorithm based on the cubic B-spline curve and gradient descent is proposed. Finally, the results of a simulation conducted on the proposed algorithm and the Hybrid A* algorithm are provided: the average minimum curvature of the path was reduced by 0.005 m−1, and the path meets the requirements of the minimum turning radius constraint of the analyzed vehicle. The results show that the proposed algorithm can effectively eliminate the curvature mutation point and constrain the path curvature to meet the requirements of a smooth path. Full article
Show Figures

Figure 1

23 pages, 11932 KiB  
Article
Graph U-Shaped Network with Mapping-Aware Local Enhancement for Single-Frame 3D Human Pose Estimation
by Bing Yu, Yan Huang, Guang Cheng, Dongjin Huang and Youdong Ding
Electronics 2023, 12(19), 4120; https://doi.org/10.3390/electronics12194120 - 02 Oct 2023
Viewed by 661
Abstract
The development of 2D-to-3D approaches for 3D monocular single-frame human pose estimation faces challenges related to noisy input and failure to capture long-range joint correlations, leading to unreasonable predictions. To this end, we propose a straightforward, but effective U-shaped network called the mapping-aware [...] Read more.
The development of 2D-to-3D approaches for 3D monocular single-frame human pose estimation faces challenges related to noisy input and failure to capture long-range joint correlations, leading to unreasonable predictions. To this end, we propose a straightforward, but effective U-shaped network called the mapping-aware U-shaped graph convolutional network (M-UGCN) for single-frame applications. This network applies skeletal pooling/unpooling operations to expand the limited convolutional receptive field. For noisy inputs, as local nodes have direct access to the subtle discrepancies between poses, we define an additional mapping-aware local-enhancement mechanism to focus on local node interactions across multiple scales. We evaluated our proposed method on the benchmark datasets Human3.6M and MPI-INF-3DHP, and the experimental results demonstrated the robustness of the M-UGCN against noisy inputs. Notably, the average error in the proposed method was found to be 4.1% lower when compared to state-of-the-art methods adopting similar multi-scale learning approaches. Full article
Show Figures

Figure 1

11 pages, 3803 KiB  
Article
Activeness: A Novel Neural Coding Scheme Integrating the Spike Rate and Temporal Information in the Spiking Neural Network
by Zongxia Wang, Naigong Yu and Yishen Liao
Electronics 2023, 12(19), 3992; https://doi.org/10.3390/electronics12193992 - 22 Sep 2023
Viewed by 725
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 [...] Read more.
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
Show Figures

Figure 1

15 pages, 4711 KiB  
Article
DFFA-Net: A Differential Convolutional Neural Network for Underwater Optical Image Dehazing
by Xujia Hou, Feihu Zhang, Zewen Wang, Guanglei Song, Zijun Huang and Jinpeng Wang
Electronics 2023, 12(18), 3876; https://doi.org/10.3390/electronics12183876 - 14 Sep 2023
Viewed by 705
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 [...] Read more.
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
Show Figures

Figure 1

15 pages, 1944 KiB  
Article
A Reinforcement Learning List Recommendation Model Fused with Graph Neural Networks
by Zhongming Lv and Xiangrong Tong
Electronics 2023, 12(18), 3748; https://doi.org/10.3390/electronics12183748 - 05 Sep 2023
Cited by 2 | Viewed by 651
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 [...] Read more.
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
Show Figures

Figure 1

15 pages, 13788 KiB  
Article
Sub-Pixel Convolutional Neural Network for Image Super-Resolution Reconstruction
by Guifang Shao, Qiao Sun, Yunlong Gao, Qingyuan Zhu, Fengqiang Gao and Junfa Zhang
Electronics 2023, 12(17), 3572; https://doi.org/10.3390/electronics12173572 - 24 Aug 2023
Cited by 2 | Viewed by 1437
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 [...] Read more.
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 YCbCr 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
Show Figures

Figure 1

13 pages, 2441 KiB  
Article
VSLAM Optimization Method in Dynamic Scenes Based on YOLO-Fastest
by Zijing Song, Weihua Su, Haiyong Chen, Mianshi Feng, Jiahe Peng and Aifang Zhang
Electronics 2023, 12(17), 3538; https://doi.org/10.3390/electronics12173538 - 22 Aug 2023
Cited by 2 | Viewed by 1268
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 [...] Read more.
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
Show Figures

Figure 1

15 pages, 12356 KiB  
Article
Maritime Target Recognition and Location System Based on Lightweight Neural Network
by Xiao Zhao, Zhenjia Chen, Min Wang and Jingbo Wang
Electronics 2023, 12(15), 3292; https://doi.org/10.3390/electronics12153292 - 31 Jul 2023
Viewed by 692
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 [...] Read more.
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
Show Figures

Figure 1

Back to TopTop