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Electronics
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Trustworthy Artificial Intelligence in Cyber-Physical Systems
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Trustworthy Artificial Intelligence in Cyber-Physical Systems

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

Deadline for manuscript submissions: 15 November 2024 | Viewed by 7042

Special Issue Editors

Prof. Dr. Tai-Won Um

E-Mail Website
Guest Editor
Graduate School of Data Science, Chonnam National University, Gwangju 61186, Korea
Interests: artificial intelligence of things (AIoT); digital twin; metaverse; cyber-physical system; web 3.0; trustworthy AI
Dr. Jinsul Kim

E-Mail Website
Guest Editor
Department of ICT Convergence System Engineering, Chonnam National University, Gwangju 61186, Korea
Interests: cloud networking; multimedia QoS/QoE; metaverse platform; intelligent network design; immersive media processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gyu Myoung Lee

E-Mail Website
Guest Editor
School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool L3 3AF, UK
Interests: data processing and management; data sharing and analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is very important for autonomous systems and applications interating physical and cyber worlds with information and communication technology in our daily life. This Special Issue focuses on the adoption of AI in cyber-physical systems. Recently, digital twin technologies that model objects, people, and spaces in the physical world as Internet-based virtual objects and predict and prepare for future problems through AI-based simulation are developing. In addition, people and sensors in the physical world can also be linked to the cyber world of the Metaverse to perform social and economic activities, such as games and commerce. Although AI provides tremendous benefits to humans, there are a lot of negative effects due to the lack of trust in the proper use of AI. Therefore, buidling trust between cyber-physical systems and humans is essential in terms of explainablity, robustness, and bias. To address these trust related issues, this Special Issue on “Trustworthy Artificial Intelligence in Cyber-Physical Systems” aims to cover key concepts, archiectural approaches, methodology and technical solutions, including policy, regulatory, and ethics issues for trustworthy AI in intelligent and advanced cyber-physical systems, taking into account emerging technologies, such as digital twin, metaverse, and web 3.0.

Prof. Dr. Tai-Won Um
Dr. Jinsul Kim
Prof. Dr. Gyu Myoung Lee
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

  • trustworthy AI in cyber-physical systems
  • trustworthy AI in metaverse
  • trustworthy AI in web 3.0
  • autonomous digital twins
  • autonomous internet of things (IoT)
  • autonomous system of systems
  • artificial intelligence of things (AIoT)

Published Papers (4 papers)

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Research

15 pages, 4595 KiB  
Article
Lightweight Three-Dimensional Pose and Joint Center Estimation Model for Rehabilitation Therapy
by Yeonggwang Kim, Giwon Ku, Chulseung Yang, Jeonggi Lee and Jinsul Kim
Electronics 2023, 12(20), 4273; https://doi.org/10.3390/electronics12204273 - 16 Oct 2023
Viewed by 908
Abstract
In this study, we proposed a novel transformer-based model with independent tokens for estimating three-dimensional (3D) human pose and shape from monocular videos, specifically focusing on its application in rehabilitation therapy. The main objective is to recover pixel-aligned rehabilitation-customized 3D human poses and [...] Read more.
In this study, we proposed a novel transformer-based model with independent tokens for estimating three-dimensional (3D) human pose and shape from monocular videos, specifically focusing on its application in rehabilitation therapy. The main objective is to recover pixel-aligned rehabilitation-customized 3D human poses and body shapes directly from monocular images or videos, which is a challenging task owing to inherent ambiguity. Existing human pose estimation methods heavily rely on the initialized mean pose and shape as prior estimates and employ parameter regression with iterative error feedback. However, video-based approaches face difficulties capturing joint-level rotational motion and ensuring local temporal consistency despite enhancing single-frame features by modeling the overall changes in the image-level features. To address these limitations, we introduce two types of characterization tokens specifically designed for rehabilitation therapy: joint rotation and camera tokens. These tokens progressively interact with the image features through the transformer layers and encode prior knowledge of human 3D joint rotations (i.e., position information derived from large-scale data). By updating these tokens, we can estimate the SMPL parameters for a given image. Furthermore, we incorporate a temporal model that effectively captures the rotational temporal information of each joint, thereby reducing jitters in local parts. The performance of our method is comparable with those of the current best-performing models. In addition, we present the structural differences among the models to create a pose classification model for rehabilitation. We leveraged ResNet-50 and transformer architectures to achieve a remarkable PA-MPJPE of 49.0 mm for the 3DPW dataset. Full article
(This article belongs to the Special Issue Trustworthy Artificial Intelligence in Cyber-Physical Systems)
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15 pages, 3101 KiB  
Article
Digital Twins Temporal Dependencies-Based on Time Series Using Multivariate Long Short-Term Memory
by Abubakar Isah, Hyeju Shin, Seungmin Oh, Sangwon Oh, Ibrahim Aliyu, Tai-won Um and Jinsul Kim
Electronics 2023, 12(19), 4187; https://doi.org/10.3390/electronics12194187 - 09 Oct 2023
Viewed by 2006
Abstract
Digital Twins, which are virtual representations of physical systems mirroring their behavior, enable real-time monitoring, analysis, and optimization. Understanding and identifying the temporal dependencies included in the multivariate time series data that characterize the behavior of the system are crucial for improving the [...] Read more.
Digital Twins, which are virtual representations of physical systems mirroring their behavior, enable real-time monitoring, analysis, and optimization. Understanding and identifying the temporal dependencies included in the multivariate time series data that characterize the behavior of the system are crucial for improving the effectiveness of Digital Twins. Long Short-Term Memory (LSTM) networks have been used to represent complex temporal dependencies and identify long-term links in the Industrial Internet of Things (IIoT). This paper proposed a Digital Twin temporal dependency technique using LSTM to capture the long-term dependencies in IIoT time series data, estimate the lag between the input and intended output, and handle missing data. Autocorrelation analysis showed the lagged links between variables, aiding in the discovery of temporal dependencies. The system evaluated the LSTM model by providing it with a set of previous observations and asking it to forecast the value at future time steps. We conducted a comparison between our model and six baseline models, utilizing both the Smart Water Treatment (SWaT) and Building Automation Transaction (BATADAL) datasets. Our model’s effectiveness in capturing temporal dependencies was assessed through the analysis of the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF). The results of our experiments demonstrate that our enhanced model achieved a better long-term prediction performance. Full article
(This article belongs to the Special Issue Trustworthy Artificial Intelligence in Cyber-Physical Systems)
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15 pages, 7169 KiB  
Article
Network Traffic Prediction Model in a Data-Driven Digital Twin Network Architecture
by Hyeju Shin, Seungmin Oh, Abubakar Isah, Ibrahim Aliyu, Jaehyung Park and Jinsul Kim
Electronics 2023, 12(18), 3957; https://doi.org/10.3390/electronics12183957 - 20 Sep 2023
Cited by 3 | Viewed by 2295
Abstract
The proliferation of immersive services, including virtual reality/augmented reality, holographic content, and the metaverse, has led to an increase in the complexity of communication networks, and consequently, the complexity of network management. Recently, digital twin network technology, which applies digital twin technology to [...] Read more.
The proliferation of immersive services, including virtual reality/augmented reality, holographic content, and the metaverse, has led to an increase in the complexity of communication networks, and consequently, the complexity of network management. Recently, digital twin network technology, which applies digital twin technology to the field of communication networks, has been predicted to be an effective means of managing complex modern networks. In this paper, a digital twin network data pipeline architecture is proposed that demonstrates an integrated structure for flow within the digital twin network and network modeling from a data perspective. In addition, a network traffic modeling technique using data feature extraction techniques is proposed to realize the digital twin network, which requires the use of massive streaming data. The proposed method utilizes the data generated in the OMNeT++ environment and verifies that the learning time is reduced by approximately 25% depending on the feature extraction interval, while the accuracy remains similar. Full article
(This article belongs to the Special Issue Trustworthy Artificial Intelligence in Cyber-Physical Systems)
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14 pages, 1831 KiB  
Article
Deep Learning Model Performance and Optimal Model Study for Hourly Fine Power Consumption Prediction
by Seungmin Oh, Sangwon Oh, Hyeju Shin, Tai-won Um and Jinsul Kim
Electronics 2023, 12(16), 3528; https://doi.org/10.3390/electronics12163528 - 21 Aug 2023
Viewed by 1089
Abstract
Electricity consumption has been increasing steadily owing to technological developments since the Industrial Revolution. Technologies that can predict power usage and management for improved efficiency are thus emerging. Detailed energy management requires precise power consumption forecasting. Deep learning technologies have been widely used [...] Read more.
Electricity consumption has been increasing steadily owing to technological developments since the Industrial Revolution. Technologies that can predict power usage and management for improved efficiency are thus emerging. Detailed energy management requires precise power consumption forecasting. Deep learning technologies have been widely used recently to achieve high performance. Many deep learning technologies are focused on accuracy, but they do not involve detailed time-based usage prediction research. In addition, detailed power prediction models should consider computing power, such as that of end Internet of Things devices and end home AMIs. In this work, we conducted experiments to predict hourly demands for the temporal neural network (TCN) and transformer models, as well as artificial neural network, long short-term memory (LSTM), and gated recurrent unit models. The study covered detailed time intervals from 1 to 24 h with 1 h increments. The experimental results were analyzed, and the optimal models for different time intervals and datasets were derived. The LSTM model showed superior performance for datasets with characteristics similar to those of schools, while the TCN model performed better for average or industrial power consumption datasets. Full article
(This article belongs to the Special Issue Trustworthy Artificial Intelligence in Cyber-Physical Systems)
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