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Electronics
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Digital Intelligence Technology and Applications, 2nd Edition
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Digital Intelligence Technology and Applications, 2nd Edition

A special issue of Electronics (ISSN 2079-9292).

Deadline for manuscript submissions: 15 May 2026 | Viewed by 3013

Special Issue Editors

Dr. Xinyu Zhang

E-Mail Website
Guest Editor
Xiangjiang Laboratory, Hunan University of Technology and Business, Changsha 410205, China
Interests: pattern recognition; artificial intelligence; data processing and analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Dan He

E-Mail Website
Guest Editor
School of Civil Engineering, University of Queensland, St Lucia, QLD 4072, Australia
Interests: intelligent transportation systems; spatial-temporal data management and data mining
Special Issues, Collections and Topics in MDPI journals
Dr. Yang-Tao Wang

E-Mail Website
Guest Editor
School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China
Interests: deep learning; image recognition; graph neural network and multimedia content analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancements in digital intelligence technology have revolutionized various fields, driving significant progress and innovation. Digital intelligence encompasses a spectrum of technologies, including advanced algorithms, machine learning, data analytics, and artificial intelligence, which enhance our ability to process, analyze, and leverage digital data effectively. These technologies are becoming indispensable in domains such as healthcare, smart cities, smart transportation, industrial informatization, and security.

This Special Issue, titled “Digital Intelligence Technology and Applications, 2nd Edition”, aims to promote high-quality research that addresses critical issues, emerging challenges, and innovative applications in the development, analysis, and implementation of digital intelligence solutions across various sectors. We invite original research articles and comprehensive reviews on topics including, but not limited to, the following:

  1. Advances in digital data preprocessing and cleaning;
  2. Representation learning for digital data;
  3. Multimodal data fusion and analysis;
  4. Applications of digital intelligence in intelligent transportation and smart cities;
  5. Advancements in deep learning and machine learning for digital intelligence;
  6. Digital intelligence in healthcare and biomedical applications;
  7. AI-driven decision making and automation;
  8. Digital intelligence in natural environment monitoring and disaster management;
  9. Human–computer interaction and user experience in digital intelligence systems;
  10. Industrial applications of digital intelligence.

Dr. Xinyu Zhang
Dr. Dan He
Dr. Yang-Tao Wang
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

  • digital intelligence
  • artificial intelligence
  • machine learning
  • data analysis
  • digital intelligence technology and applications

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Related Special Issue

Published Papers (9 papers)

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Research

20 pages, 2659 KB  
Article
Twin-Space Decoupling and Interaction for Efficient Vision-Language Transfer
by Wei Liang, Junqiang Li, Zhengkai Guo, Zhiwei Peng, Xiaocui Li, Junfeng Yang, Chuang Li and Wei Long
Electronics 2025, 14(21), 4314; https://doi.org/10.3390/electronics14214314 - 3 Nov 2025
Abstract
Pre-trained visual language models have become excellent basic models for many downstream tasks in transfer learning. However, due to the serious gap between the data scale of downstream tasks and the large-scale data used by pre-trained models, migration to downstream tasks will face [...] Read more.
Pre-trained visual language models have become excellent basic models for many downstream tasks in transfer learning. However, due to the serious gap between the data scale of downstream tasks and the large-scale data used by pre-trained models, migration to downstream tasks will face the dilemma of discriminability and generalization. Therefore, it is necessary to learn task-specific knowledge while retaining general knowledge. How to accurately identify and distinguish these two types of representations remains a challenge. This paper proposes a dual-subspace driven cross-modal semantic interaction and dynamic feature fusion framework, which uses a decentralized covariance dual-subspace decomposition method to decouple visual and text features by constructing task subspaces and general knowledge subspaces, and performs refined modal interactions on the decoupled general features and task features through a cross-modal semantic interaction adapter module. Finally, a cross-level semantic fusion module based on a gating mechanism is used to achieve dynamic fusion of different semantics from shallow to deep. We verify the effectiveness of this method on three tasks: generalization to novel classes, novel target datasets, and domain generalization. Compared with a variety of advanced methods, the proposed method has achieved excellent performance in all evaluation tasks. Full article
(This article belongs to the Special Issue Digital Intelligence Technology and Applications, 2nd Edition)
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19 pages, 3823 KB  
Article
Full Process Dynamics and HIL Simulation of Precise Airdrop System
by Wen Zou, Zhanxin Cui, Jiaoyan Li and Qingbin Zhang
Electronics 2025, 14(21), 4285; https://doi.org/10.3390/electronics14214285 - 31 Oct 2025
Viewed by 64
Abstract
Amid intensifying competition in airdrop equipment development, there is a growing demand for large-load, high-precision, maneuverable, and low-cost airdrop systems. However, Precision Aerial Delivery Systems (PADS) exhibit structural complexity and immature dynamics theory for flexible-body parachute/parafoil systems. Flight testing proves prohibitively expensive, while [...] Read more.
Amid intensifying competition in airdrop equipment development, there is a growing demand for large-load, high-precision, maneuverable, and low-cost airdrop systems. However, Precision Aerial Delivery Systems (PADS) exhibit structural complexity and immature dynamics theory for flexible-body parachute/parafoil systems. Flight testing proves prohibitively expensive, while random environmental interference hinders data consistency. To address these challenges, this paper integrates navigation control systems and actuators with dynamics models through a Hardware-in-the-Loop (HIL) simulation system for comprehensive performance evaluation. Full article
(This article belongs to the Special Issue Digital Intelligence Technology and Applications, 2nd Edition)
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17 pages, 611 KB  
Article
HGNN-AS: Enhancing Hypergraph Neural Network for Node Classification Accuracy with Attention and Self-Attention
by Chuang Li, Lanfang Huang, Ruihai Liu, Dian He, Minghui Chen and Qian Wu
Electronics 2025, 14(21), 4282; https://doi.org/10.3390/electronics14214282 - 31 Oct 2025
Viewed by 178
Abstract
The incorporation of attention mechanisms into hypergraphs is an effective method for obtaining appropriate node and hyperedge representations. However, the ability of attention mechanisms to aggregate features on a hypergraph can be improved, particularly for noisy hypergraphs with connections between unrelated nodes that [...] Read more.
The incorporation of attention mechanisms into hypergraphs is an effective method for obtaining appropriate node and hyperedge representations. However, the ability of attention mechanisms to aggregate features on a hypergraph can be improved, particularly for noisy hypergraphs with connections between unrelated nodes that were constructed in a KNN-like manner. In this paper, we propose HGNN-AS, an enhanced hypergraph neural network model that achieves good accuracy on node classification tasks by combining an attention mechanism and a self-attention mechanism. Specifically, we introduce two self-attention mechanisms to improve how the HGNN-AS model expresses attention when distinguishing mislinked neighbours. Moreover, we add multihead attention mechanisms to our model to stabilize the training effect. The proposed model is evaluated on benchmark node classification tasks, including citation network classification and visual object recognition. Our experimental results demonstrate that our model outperforms most advanced methods on both tasks; among them, the accuracy improvement on the Cora dataset is the most noticeable, with an accuracy of 83.9%. Full article
(This article belongs to the Special Issue Digital Intelligence Technology and Applications, 2nd Edition)
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22 pages, 662 KB  
Article
Multi-Chain Fusion Reasoning for Knowledge Graph Link Prediction
by Shaonian Huang, Peilin Li, Huanran Wang and Zhixin Chen
Electronics 2025, 14(20), 4127; https://doi.org/10.3390/electronics14204127 - 21 Oct 2025
Viewed by 332
Abstract
The knowledge graph link prediction task currently faces challenges such as insufficient semantic fusion of structured knowledge and unstructured text, limited representation learning of long-tailed entities, and insufficient interpretability of the reasoning process. Aiming at the above problems, this paper proposes a multi-chain [...] Read more.
The knowledge graph link prediction task currently faces challenges such as insufficient semantic fusion of structured knowledge and unstructured text, limited representation learning of long-tailed entities, and insufficient interpretability of the reasoning process. Aiming at the above problems, this paper proposes a multi-chain fusion reasoning framework to realize accurate link prediction. First, a dual retrieval mechanism based on semantic similarity metrics and embedded feature matching is designed to construct a high-confidence candidate entity set; second, entity-attribute chains, entity-relationship chains, and historical context chains are established by integrating context information from external knowledge bases to generate a candidate entity set. Finally, a self-consistency scoring method fusing type constraints and semantic space alignment is proposed to realize the joint validation of structural rationality and semantic relevance of candidate entities. Experiments on two public datasets show that the method in this paper fully utilizes the ability of multi-chain reasoning and significantly improves the accuracy of knowledge graph link prediction. Full article
(This article belongs to the Special Issue Digital Intelligence Technology and Applications, 2nd Edition)
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25 pages, 5852 KB  
Article
ADEmono-SLAM: Absolute Depth Estimation for Monocular Visual Simultaneous Localization and Mapping in Complex Environments
by Kaijun Zhou, Zifei Yu, Xiancheng Zhou, Ping Tan, Yunpeng Yin and Huanxin Luo
Electronics 2025, 14(20), 4126; https://doi.org/10.3390/electronics14204126 - 21 Oct 2025
Viewed by 468
Abstract
Aiming to address the problems of scale uncertainty and dynamic object interference in monocular visual simultaneous localization and mapping (SLAM), this paper proposes an absolute depth estimation network-based monocular visual SLAM method, namely, ADEmono-SLAM. Firstly, some detail features including oriented fast and rotated [...] Read more.
Aiming to address the problems of scale uncertainty and dynamic object interference in monocular visual simultaneous localization and mapping (SLAM), this paper proposes an absolute depth estimation network-based monocular visual SLAM method, namely, ADEmono-SLAM. Firstly, some detail features including oriented fast and rotated brief (ORB) features of input image are extracted. An object depth map is obtained through an absolute depth estimation network, and some reliable feature points are obtained by a dynamic interference filtering algorithm. Through these operations, the potential dynamic interference points are eliminated. Secondly, the absolute depth image is obtained by using the monocular depth estimation network, in which a dynamic point elimination algorithm using target detection is designed to eliminate dynamic interference points. Finally, the camera poses and map information are obtained by static feature point matching optimization. Thus, the remote points are randomly filtered by combining the depth values of the feature points. Experiments on the karlsruhe institute of technology and toyota technological institute (KITTI) dataset, technical university of munich (TUM) dataset, and mobile robot platform show that the proposed method can obtain sparse maps with absolute scale and improve the pose estimation accuracy of monocular SLAM in various scenarios. Compared with existing methods, the maximum error is reduced by about 80%, which provides an effective method or idea for the application of monocular SLAM in the complex environment. Full article
(This article belongs to the Special Issue Digital Intelligence Technology and Applications, 2nd Edition)
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19 pages, 4123 KB  
Article
A Feature-Enhancement 6D Pose Estimation Method for Weakly Textured and Occluded Targets
by Xiaoqing Liu, Kaijun Zhou, Qingyuan Zeng and Peng Li
Electronics 2025, 14(20), 4125; https://doi.org/10.3390/electronics14204125 - 21 Oct 2025
Viewed by 341
Abstract
To achieve real-time and accurate pose estimation for weakly textured or occluded targets, this study proposes a feature-enhancement 6D pose estimation method based on DenseFusion. Firstly, in the image feature extraction stage, skip connections and attention modules, which could effectively fuse deep and [...] Read more.
To achieve real-time and accurate pose estimation for weakly textured or occluded targets, this study proposes a feature-enhancement 6D pose estimation method based on DenseFusion. Firstly, in the image feature extraction stage, skip connections and attention modules, which could effectively fuse deep and shallow features, are introduced to enhance the richness and effectiveness of image features. Secondly, in the point cloud feature extraction stage, PointNet is applied to the initial feature extraction of the point cloud. Then, the K-nearest neighbor method and the Pool globalization method are applied to obtain richer point cloud features. Subsequently, in the dense feature fusion stage, an adaptive feature selection module is introduced to further preserve and enhance effective features. Finally, we add a supervision network to the original pose estimation network to enhance the training results. The results of the experiment show that the improved method performs significantly better than classic methods in both the LineMOD dataset and Occlusion LineMOD dataset, and all enhancements improve the real-time performance and accuracy of pose estimation. Full article
(This article belongs to the Special Issue Digital Intelligence Technology and Applications, 2nd Edition)
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18 pages, 3873 KB  
Article
An Adaptive JPEG Steganography Algorithm Based on the UT-GAN Model
by Lina Tan, Yi Li, Yan Zeng and Peng Chen
Electronics 2025, 14(20), 4046; https://doi.org/10.3390/electronics14204046 - 15 Oct 2025
Viewed by 483
Abstract
Adversarial examples pose severe challenges to information security, as their impacts directly extend to steganography and steganalysis technologies. This scenario, in turn, has further spurred the research and application of adversarial steganography. In response, we propose a novel adversarial embedding scheme rooted in [...] Read more.
Adversarial examples pose severe challenges to information security, as their impacts directly extend to steganography and steganalysis technologies. This scenario, in turn, has further spurred the research and application of adversarial steganography. In response, we propose a novel adversarial embedding scheme rooted in a hybrid, partially data-driven approach. The proposed scheme first leverages an adversarial neural network (UT-GAN, Universal Transform Generative Adversarial Network) to generate stego images as a preprocessing step. Subsequently, it dynamically adjusts the cost function with the aid of a DCTR (Discrete Cosine Transform Residual)-based gradient calculator to optimize the images, ensuring that the final adversarial images can resist detection by steganalysis tools. The encoder in this scheme adopts a unique architecture, where its internal parameters are determined by a partially data-driven mechanism. This design not only enhances the capability of traditional steganography schemes to counter advanced steganalysis technologies but also effectively reduces the computational overhead during stego image generation. Full article
(This article belongs to the Special Issue Digital Intelligence Technology and Applications, 2nd Edition)
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24 pages, 3661 KB  
Article
Real-Time Occluded Target Detection and Collaborative Tracking Method for UAVs
by Yandi Ai, Ruolong Li, Chaoqian Xiang and Xin Liang
Electronics 2025, 14(20), 4034; https://doi.org/10.3390/electronics14204034 - 14 Oct 2025
Viewed by 596
Abstract
To address the failure of unmanned aerial vehicle (UAV) target tracking caused by occlusion and limited field of view in dense low-altitude obstacle environments, this paper proposes a novel framework integrating occlusion-aware modeling and multi-UAV collaboration. A lightweight tracking model based on the [...] Read more.
To address the failure of unmanned aerial vehicle (UAV) target tracking caused by occlusion and limited field of view in dense low-altitude obstacle environments, this paper proposes a novel framework integrating occlusion-aware modeling and multi-UAV collaboration. A lightweight tracking model based on the Mamba backbone is developed, incorporating a Dilated Wavelet Receptive Field Enhancement Module (DWRFEM) to fuse multi-scale contextual features, significantly mitigating contour fragmentation and feature degradation under severe occlusion. A dual-branch feature optimization architecture is designed, combining the Distilled Tanh Activation with Context (DiTAC) activation function and Kolmogorov–Arnold Network (KAN) bottleneck layers to enhance discriminative feature representation. To overcome the limitations of single-UAV perception, a multi-UAV cooperative system is established. Ray intersection is employed to reduce localization uncertainty, while spherical sampling viewpoints are dynamically generated based on obstacle density. Safe trajectory planning is achieved using a Crested Porcupine Optimizer (CPO). Experiments on the Multi-Drone Multi-Target Tracking (MDMT) dataset demonstrate that the model achieves 84.1% average precision (AP) at 95 Frames Per Second (FPS), striking a favorable balance between speed and accuracy, making it suitable for edge deployment. Field tests with three collaborative UAVs show sustained target coverage in complex environments, outperforming traditional single-UAV approaches. This study provides a systematic solution for robust tracking in challenging low-altitude scenarios. Full article
(This article belongs to the Special Issue Digital Intelligence Technology and Applications, 2nd Edition)
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16 pages, 2037 KB  
Article
Risk Assessment of New Distribution Network Dispatching Operations Considering Multiple Uncertain Factors
by Lianrong Pan, Xiao Yang, Shangbing Yuan, Jiaan Li and Haowen Xue
Electronics 2025, 14(20), 4012; https://doi.org/10.3390/electronics14204012 - 13 Oct 2025
Viewed by 298
Abstract
In traditional scheduling operations, dispatchers mainly rely on SCADA/EMS systems or personal experience. However, with access to a large number of new energy sources, the scale of the distribution network continues to expand, and its topology becomes increasingly complex, leading to potential security [...] Read more.
In traditional scheduling operations, dispatchers mainly rely on SCADA/EMS systems or personal experience. However, with access to a large number of new energy sources, the scale of the distribution network continues to expand, and its topology becomes increasingly complex, leading to potential security risks in scheduling operations. Therefore, it is very important to carry out risk assessments before scheduling operations. In this paper, risk theory is introduced into the field of distribution network scheduling operations, and a new risk assessment method is proposed considering various uncertain factors in the distribution network. In order to comprehensively analyze the influence of uncertainty factors in the operational process of a new distribution network, the output probability models of wind power, photovoltaic power, and load are first constructed in this study. Then, the improved Latin hypercube sampling method is used to extract the operating state of the distribution network system from the probability model, and the node voltage over-limit and line power flow overload are used as indicators to measure the severity of the consequences so as to establish a quantitative scheduling operation risk assessment system and analyze its framework in detail. Finally, simulation analysis is carried out in the improved IEEE-RTS79 test system: taking 15–25 lines from the operation state to the maintenance state as an example, this paper analyzes the influence of different locations and capacities of wind and solar access on the scheduling operation risk of distribution networks. The results can provide a reference for dispatchers to prevent risks before operation. Full article
(This article belongs to the Special Issue Digital Intelligence Technology and Applications, 2nd Edition)
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