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Applied Sciences
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Innovative Applications of Artificial Intelligence in Multidisciplinary Sciences: Latest Advances...
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Innovative Applications of Artificial Intelligence in Multidisciplinary Sciences: Latest Advances and Prospects

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 May 2024 | Viewed by 5184

Special Issue Editors

Prof. Dr. Zhanlong Zhang

E-Mail Website
Guest Editor
State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing, China
Interests: signal processing; electromagnetic parameter measurement; deep learning and machine learning; fault diagnosis; multi-physical field modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Jianquan Liao

E-Mail Website
Guest Editor
College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Interests: power system protection and control; power quality of the DC distribution network; power system stability and control
Special Issues, Collections and Topics in MDPI journals
Dr. Peiyu Jiang

E-Mail Website
Guest Editor Assistant
Shenzhen International Graduate School, Tsinghua University, Beijing, China
Interests: fault diagnosis and condition recognition; vibration and noise detection and sensing technology; multi-physical field coupling simulation and calculation

Special Issue Information

Dear Colleagues,

With the transformation of the digital age, artificial intelligence has developed rapidly and penetrated into multidisciplinary application fields, such as power systems, the Internet, bioengineering, medical pharmacy, traffic engineering, industrial control, financial engineering and so on. Artificial intelligence brings new technical methods to solve problems in different disciplines, including power equipment condition assessment and fault diagnosis, AC/DC power system operation planning and control, computer image processing, clinical experiments, biological data recognition, traffic flow prediction, financial market stock price prediction and complex multi-physical field modeling. Clearly, this extensive application of artificial intelligence technology represented by deep learning, machine learning and intelligent optimization algorithms brings hopeful solutions to complex problems in different disciplines.

This Special Issue focuses on the latest applications of artificial intelligence in multidisciplinary sciences. Its topics include but are not limited to the following: multi-physical field simulation modeling, the AC/DC power system, intelligent traffic management, equipment fault diagnosis, image processing, medical diagnosis technology, automatic driving, etc. We welcome research articles covering new technologies and methods in various disciplinary fields.

Prof. Dr. Zhanlong Zhang
Dr. Jianquan Liao
Guest Editors

Dr. Peiyu Jiang
Guest Editor Assistant

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. Applied Sciences 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

  • artificial intelligence
  • deep learning
  • machine learning
  • intelligent optimization algorithm
  • engineering application
  • image processing
  • time series data
  • fault diagnosis
  • power system
  • the internet
  • traffic engineering
  • medical pharmaceuticals
  • bioengineering
  • financial engineering

Published Papers (7 papers)

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Research

19 pages, 5545 KiB  
Article
Ensemble Empirical Mode Decomposition Granger Causality Test Dynamic Graph Attention Transformer Network: Integrating Transformer and Graph Neural Network Models for Multi-Sensor Cross-Temporal Granularity Water Demand Forecasting
by Wenhong Wu and Yunkai Kang
Appl. Sci. 2024, 14(8), 3428; https://doi.org/10.3390/app14083428 - 18 Apr 2024
Viewed by 283
Abstract
Accurate water demand forecasting is crucial for optimizing the strategies across multiple water sources. This paper proposes the Ensemble Empirical Mode Decomposition Granger causality test Dynamic Graph Attention Transformer Network (EG-DGATN) for multi-sensor cross-temporal granularity water demand forecasting, which combines the Transformer and [...] Read more.
Accurate water demand forecasting is crucial for optimizing the strategies across multiple water sources. This paper proposes the Ensemble Empirical Mode Decomposition Granger causality test Dynamic Graph Attention Transformer Network (EG-DGATN) for multi-sensor cross-temporal granularity water demand forecasting, which combines the Transformer and Graph Neural Networks. It employs the EEMD–Granger test to delineate the interconnections among sensors and extracts the spatiotemporal features within the causal domain by stacking dynamical graph spatiotemporal attention layers. The experimental results demonstrate that compared to baseline models, the EG-DGATN improves the MAPE metrics by 2.12%, 4.33%, and 6.32% in forecasting intervals of 15 min, 45 min, and 90 min, respectively. The model achieves an R2 score of 0.97, indicating outstanding predictive accuracy and exceptional explanatory power for the target variable. This research highlights significant potential applications in predictive tasks within smart water management systems. Full article
(This article belongs to the Special Issue Innovative Applications of Artificial Intelligence in Multidisciplinary Sciences: Latest Advances and Prospects)
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19 pages, 9752 KiB  
Article
PPA-SAM: Plug-and-Play Adversarial Segment Anything Model for 3D Tooth Segmentation
by Jiahao Liao, Hongyuan Wang, Hanjie Gu and Yinghui Cai
Appl. Sci. 2024, 14(8), 3259; https://doi.org/10.3390/app14083259 - 12 Apr 2024
Viewed by 305
Abstract
In Cone Beam Computed Tomography (CBCT) images, accurate tooth segmentation is crucial for oral health, providing essential guidance for dental procedures such as implant placement and difficult tooth extractions (impactions). However, due to the lack of a substantial amount of dental data and [...] Read more.
In Cone Beam Computed Tomography (CBCT) images, accurate tooth segmentation is crucial for oral health, providing essential guidance for dental procedures such as implant placement and difficult tooth extractions (impactions). However, due to the lack of a substantial amount of dental data and the complexity of tooth morphology in CBCT images, the task of tooth segmentation faces significant challenges. This may lead to issues such as overfitting and training instability in existing algorithms, resulting in poor model generalization. Ultimately, this may impact the accuracy of segmentation results and could even provide incorrect diagnostic and treatment information. In response to these challenges, we introduce PPA-SAM, an innovative dual-encoder segmentation network that merges the currently popular Segment Anything Model (SAM) with the 3D medical segmentation network, VNet. Through the use of adapters, we achieve parameter reuse and fine-tuning, enhancing the model’s adaptability to specific CBCT datasets. Simultaneously, we utilize a three-layer convolutional network as both a discriminator and a generator for adversarial training. The PPA-SAM model seamlessly integrates the high-precision segmentation performance of convolutional networks with the outstanding generalization capabilities of SAM models, achieving more accurate and robust three-dimensional tooth segmentation in CBCT images. Evaluation of a small CBCT dataset demonstrates that PPA-SAM outperforms other networks in terms of accuracy and robustness, providing a reliable and efficient solution for three-dimensional tooth segmentation in CBCT images. This research has a positive impact on the management of dentofacial conditions from oral implantology to orthognathic surgery, offering dependable technological support for future oral diagnostics and treatment planning. Full article
(This article belongs to the Special Issue Innovative Applications of Artificial Intelligence in Multidisciplinary Sciences: Latest Advances and Prospects)
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17 pages, 3312 KiB  
Article
DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction
by Zengyu Cai, Chunchen Tan, Jianwei Zhang, Liang Zhu and Yuan Feng
Appl. Sci. 2024, 14(5), 2173; https://doi.org/10.3390/app14052173 - 05 Mar 2024
Viewed by 661
Abstract
As network technology continues to develop, the popularity of various intelligent terminals has accelerated, leading to a rapid growth in the scale of wireless network traffic. This growth has resulted in significant pressure on resource consumption and network security maintenance. The objective of [...] Read more.
As network technology continues to develop, the popularity of various intelligent terminals has accelerated, leading to a rapid growth in the scale of wireless network traffic. This growth has resulted in significant pressure on resource consumption and network security maintenance. The objective of this paper is to enhance the prediction accuracy of cellular network traffic in order to provide reliable support for the subsequent base station sleep control or the identification of malicious traffic. To achieve this target, a cellular network traffic prediction method based on multi-modal data feature fusion is proposed. Firstly, an attributed K-nearest node (KNN) graph is constructed based on the similarity of data features, and the fused high-dimensional features are incorporated into the graph to provide more information for the model. Subsequently, a dual branch spatio-temporal graph neural network with an attention mechanism (DBSTGNN-Att) is designed for cellular network traffic prediction. Extensive experiments conducted on real-world datasets demonstrate that the proposed method outperforms baseline models, such as temporal graph convolutional networks (T-GCNs) and spatial–temporal self-attention graph convolutional networks (STA-GCNs) with lower mean absolute error (MAE) values of 6.94% and 2.11%, respectively. Additionally, the ablation experimental results show that the MAE of multi-modal feature fusion using the attributed KNN graph is 8.54% lower compared to that of the traditional undirected graphs. Full article
(This article belongs to the Special Issue Innovative Applications of Artificial Intelligence in Multidisciplinary Sciences: Latest Advances and Prospects)
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18 pages, 6514 KiB  
Article
Fault Diagnosis of Inter-Turn Fault in Permanent Magnet-Synchronous Motors Based on Cycle-Generative Adversarial Networks and Deep Autoencoder
by Wenkuan Huang, Hongbin Chen and Qiyang Zhao
Appl. Sci. 2024, 14(5), 2139; https://doi.org/10.3390/app14052139 - 04 Mar 2024
Viewed by 618
Abstract
This paper addresses the issue of the difficulty in obtaining inter-turn fault (ITF) samples in electric motors, specifically in permanent magnet-synchronous motors (PMSMs), where the number of ITF samples in the stator windings is severely lacking compared to healthy samples. To effectively identify [...] Read more.
This paper addresses the issue of the difficulty in obtaining inter-turn fault (ITF) samples in electric motors, specifically in permanent magnet-synchronous motors (PMSMs), where the number of ITF samples in the stator windings is severely lacking compared to healthy samples. To effectively identify these faults, an improved fault diagnosis method based on the combination of a cycle-generative adversarial network (GAN) and a deep autoencoder (DAE) is proposed. In this method, the Cycle GAN is used to expand the collection of fault samples for PMSMs, while the DAE enhances the capability to extract and analyze these fault samples, thus improving the accuracy of fault diagnosis. The experimental results demonstrate that Cycle GAN exhibits an excellent capability to generate ITF fault samples. The proposed method achieves a diagnostic accuracy rate of up to 98.73% for ITF problems. Full article
(This article belongs to the Special Issue Innovative Applications of Artificial Intelligence in Multidisciplinary Sciences: Latest Advances and Prospects)
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13 pages, 2924 KiB  
Article
Matting Algorithm with Improved Portrait Details for Images with Complex Backgrounds
by Rui Li, Dan Zhang, Sheng-Ling Geng and Ming-Quan Zhou
Appl. Sci. 2024, 14(5), 1942; https://doi.org/10.3390/app14051942 - 27 Feb 2024
Viewed by 483
Abstract
With the continuous development of virtual reality, digital image applications, the required complex scene video proliferates. For this reason, portrait matting has become a popular topic. In this paper, a new matting algorithm with improved portrait details for images with complex backgrounds (MORLIPO) [...] Read more.
With the continuous development of virtual reality, digital image applications, the required complex scene video proliferates. For this reason, portrait matting has become a popular topic. In this paper, a new matting algorithm with improved portrait details for images with complex backgrounds (MORLIPO) is proposed. This work combines the background restoration module (BRM) and the fine-grained matting module (FGMatting) to achieve high-detail matting for images with complex backgrounds. We recover the background by inputting a single image or video, which serves as a priori and aids in generating a more accurate alpha matte. The main framework uses the image matting model MODNet, the MobileNetV2 lightweight network, and the background restoration module, which can both preserve the background information of the current image and provide a more accurate prediction of the alpha matte of the current frame for the video image. It also provides the background prior of the previous frame to predict the alpha matte of the current frame more accurately. The fine-grained matting module is designed to extract fine-grained details of the foreground and retain the features, while combining with the semantic module to achieve more accurate matting. Our design allows training on a single NVIDIA 3090 GPU in an end-to-end manner and experiments on publicly available data sets. Experimental validation shows that our method performs well on both visual effects and objective evaluation metrics. Full article
(This article belongs to the Special Issue Innovative Applications of Artificial Intelligence in Multidisciplinary Sciences: Latest Advances and Prospects)
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15 pages, 3912 KiB  
Article
VTG-GPT: Tuning-Free Zero-Shot Video Temporal Grounding with GPT
by Yifang Xu, Yunzhuo Sun, Zien Xie, Benxiang Zhai and Sidan Du
Appl. Sci. 2024, 14(5), 1894; https://doi.org/10.3390/app14051894 - 25 Feb 2024
Viewed by 786
Abstract
Video temporal grounding (VTG) aims to locate specific temporal segments from an untrimmed video based on a linguistic query. Most existing VTG models are trained on extensive annotated video-text pairs, a process that not only introduces human biases from the queries but also [...] Read more.
Video temporal grounding (VTG) aims to locate specific temporal segments from an untrimmed video based on a linguistic query. Most existing VTG models are trained on extensive annotated video-text pairs, a process that not only introduces human biases from the queries but also incurs significant computational costs. To tackle these challenges, we propose VTG-GPT, a GPT-based method for zero-shot VTG without training or fine-tuning. To reduce prejudice in the original query, we employ Baichuan2 to generate debiased queries. To lessen redundant information in videos, we apply MiniGPT-v2 to transform visual content into more precise captions. Finally, we devise the proposal generator and post-processing to produce accurate segments from debiased queries and image captions. Extensive experiments demonstrate that VTG-GPT significantly outperforms SOTA methods in zero-shot settings and surpasses unsupervised approaches. More notably, it achieves competitive performance comparable to supervised methods. The code is available on GitHub. Full article
(This article belongs to the Special Issue Innovative Applications of Artificial Intelligence in Multidisciplinary Sciences: Latest Advances and Prospects)
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14 pages, 1691 KiB  
Article
Stable Low-Rank CP Decomposition for Compression of Convolutional Neural Networks Based on Sensitivity
by Chenbin Yang and Huiyi Liu
Appl. Sci. 2024, 14(4), 1491; https://doi.org/10.3390/app14041491 - 12 Feb 2024
Viewed by 710
Abstract
Modern convolutional neural networks (CNNs) play a crucial role in computer vision applications. The intricacy of the application scenarios and the growing dataset both significantly raise the complexity of CNNs. As a result, they are often overparameterized and have significant computational costs. One [...] Read more.
Modern convolutional neural networks (CNNs) play a crucial role in computer vision applications. The intricacy of the application scenarios and the growing dataset both significantly raise the complexity of CNNs. As a result, they are often overparameterized and have significant computational costs. One potential solution for optimizing and compressing the CNNs is to replace convolutional layers with low-rank tensor decomposition. The most suitable technique for this is Canonical Polyadic (CP) decomposition. However, there are two primary issues with CP decomposition that lead to a significant loss in accuracy. Firstly, the selection of tensor ranks for CP decomposition is an unsolved issue. Secondly, degeneracy and instability are common problems in the CP decomposition of contractional tensors, which makes fine-tuning the compressed model difficult. In this study, a novel approach was proposed for compressing CNNs by using CP decomposition. The first step involves using the sensitivity of convolutional layers to determine the tensor ranks for CP decomposition effectively. Subsequently, to address the degeneracy issue and enhance the stability of the CP decomposition, two novel techniques were incorporated: optimization with sensitivity constraints and iterative fine-tuning based on sensitivity order. Finally, the proposed method was examined on common CNN structures for image classification tasks and demonstrated that it provides stable performance and significantly fewer reductions in classification accuracy. Full article
(This article belongs to the Special Issue Innovative Applications of Artificial Intelligence in Multidisciplinary Sciences: Latest Advances and Prospects)
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